Extraction Optimization

Preface

Extraction is an important separation process that is extensively used in various applications in food engineering. Extraction is used either to recover important food components, being a main processing stage for the production of certain food products (sugar, oils, proteins), or to isolate desired components (antioxidants, flavors). It also removes contaminants and other undesirable components (alkaloids, cholesterol) from food sources. Extraction, as a major operation of the manufacturing process in many food industries, requires a food engineer to have knowledge of the thermodynamics, theory, methods, and systems of extraction, while the feasible design and efficient operation of an extraction process requires knowledge of the technological parameters of the process and of optimization methods respectively. Solvent or water extraction, liquid–liquid extraction, and leaching have already been incorporated in basic food processing, while supercritical fluid extraction (SFE) is a promising alternative method for new or improved applications in food engineering; SFE is recommended for the production of high-value food components as natural food products. Despite the great variety of extraction applications already in use, it is difficult to interpret and transfer theory to application at the industrial scale. Even in recent applications of extraction, optimal extraction or separation conditions are obtained empirically. One primary goal of food process engineering is finding out how an extraction process works best—achieving the optimum in respect of yield, quality, or cost—and consequently finding the adjustable processing factor values that produce the optimum.

iii

iv

Preface

Optimization of extraction processes is needed either for extraction processes that are already in use or for future process designs for certain applications in food engineering. Consequently, it is important for the food industry to optimize food extraction processes; that is, to attain the optimum operating conditions (yield, quality, operation time, and cost) using appropriate methods and techniques. In this book, we review the extraction methods used in food processing from fundamental theory to optimum practical application using the relevant equipment and the appropriate methods of optimization. All of the contributors are experts in the field of extraction, in research as well as in applications in industrial food processes. This book will be useful for food process engineers who employ extraction processes. In order to introduce and apply an extraction process in the food industry, the knowledge of thermodynamic principles and the existing extractive systems is necessary. Irrespective of the extraction method used, the feasibility of the process or the system is affected by the operation parameters of the process or system. Consequently, the best result in respect of yield or quality characteristics of the final product should be achieved. That is the objective of the optimization of a process or a system. An elementary review of thermodynamics is presented in Chapter 1, containing the basic laws, glossary, concepts, and relations of thermodynamics, particularly of chemical thermodynamics covering mixtures, solutions, phase equalibria, chemical reactions, etc. Chapter 1 presents fundamental knowledge useful for the understanding of extraction theory, operation, and applications in food processing. Solid–liquid extraction or the leaching proces is described in Chapter 2. The characteristics and steps, as well as the basic variables affecting the extraction operation in relation to the solvent and the microstructure of the solid being extracted, are described. The relationship between the solid microstructure and the extraction rate based on the mass transfer mechanism due to liquid diffusion inside the solid is also explained. Supercritical fluid extraction (SFE) using supercritical fluids (SCFs), a novel method providing the possibility for “green” processing of foods, is reviewed in Chapter 3. The basic (physical and chemical) properties of SCFs, particularly of the commonly used SC-CO2, and the key factors that play significant roles in the success of the SFE operation (as solubility of solutes in SCFs, phase equilibria, mass transfer) are discussed; the effective use of SCFs in extraction and fractionation of food-related materials is also discussed. The methods and equipment used in conventional solvent extraction and the relative extraction systems that have been applied in food industrial processes are presented in Chapter 4 with the equipment required for solid pretreat-

Preface

v

ment and solvent recovery. Similarly, for the alternative SFE method the selection and design of the required equipment are discussed. Optimization theory and appropriate methods involved in design and process operation having economical and technological interest for food engineers are discussed in Chapter 5. An extraction process should operate efficiently by determining the operation time in order to achieve the best yield. The applications of optimization in food engineering and especially in food extraction (conventional or SFE) processing are reviewed. In the following chapters, the most important applications of extraction processes in food production industry are presented under the aspect of optimization of the already operating extraction systems; novel and alternative operations and case studies are also presented. Vegetable oil extraction of oil-bearing materials is covered in Chapter 6. The most efficient method of solvent extraction and the factors affecting the operation such as the solvent characteristics, extraction temperature, solid pretreatment, modes of operation, and equipment are discussed. Commercial units used for oil extraction and extraction technology for oilseeds with the pre- and post-treatment are presented; extraction of essential oils is also discussed. Extraction processes for protein isolation from various edible protein sources (oilseeds, cereals, etc.) and production of protein products with many applications in food systems are covered in Chapter 7. Commercial processing and advanced methods of vegetable protein isolates are discussed from the viewpoint of yield and quality (nutritional, organoleptic, functional properties) of protein products. Sugar extraction from sugar beet and sugar cane is presented in Chapter 8, as extraction is one of the major stages in the sugar manufacturing industry. Characteristics of sugar beet and cane, parameters affecting the extraction processes, and the optimization of the industrial process of beet sugar are discussed. Techniques of extraction, extractors, and other equipment required for industrial sugar production are presented. Extraction of starch and carbohydrates from corn is also discussed. The extraction processes for isolation of desired components such as flavor and aroma substances or compounds with antioxidant activity (natural antioxidants) from plant sources are reported in Chapters 9 and 10, respectively. The main sources of natural antioxidants and synergists, the relative extraction processes, and the effect of processing parameters, as well as the conventional or SFE extraction procedures and purification of extracts, are discussed. Solvent extraction is one of the most important methods for producing natural extracts of aroma and flavor compounds; extraction solvents used, SFE processes, and applications of solvent extraction of flavor and aroma compounds are discussed. The extraction processes for removal of undesirable or toxic constituents,

vi

Preface

such as alkaloids from natural products, or compounds with adverse effect on human health (e.g., cholesterol) from various food products with emphasis on SFE processes are covered in Chapters 11 and 12, respectively. The physiological effects and applications of alkaloids, the commercial-scale operations for extraction and isolation of the most valuable alkaloids, especially the decaffeination of coffee and black tea using conventional extraction or SFE, are described. Similarly, the physiological effects of cholesterol on health and the extraction and fractionation processes of cholesterol from milk, fat, eggs, and meats are described. Both for alkaloids and cholesterol removal, future prospects using SFE are offered. The safety, health, and environmental issues of solvent extraction, concerning mainly the edible oil and fat extraction industry, are described in Chapter 13, along with regulatory concerns and toxicity of solvents (hexane or alternative solvents) in commercial processes. Constantina Tzia George Liadakis

Contents

Preface Contributors 1. Introduction: Fundamental Notes on Chemical Thermodynamics Petros Tzias

iii ix 1

2. Solid–Liquid Extraction Jose´ Miguel Aguilera

35

3. Supercritical Fluid Extraction in Food Engineering Nurhan Turgut Dunford, Jerry W. King, and Gary R. List

57

4. Extraction Systems Rudolf Eggers and Philip T. Jaeger

95

5. Optimization Constantina Tzia

137

6. Fats and Oils from Plant Materials Lei Xu and Levente L. Diosady

181

7. Proteins from Plant Materials Lei Xu and Levente L. Diosady

217

vii

viii

Contents

8. Sugars and Carbohydrates Pascal Christodoulou

235

9. Flavor and Aroma Substances Youn-Woo Lee and Youn Yong Lee

305

10. Extraction of Natural Antioxidants Vassiliki Oreopoulou

329

11. Extraction of Alkaloids from Natural Plants Using Supercritical Fluids Marleny D. A. Saldan˜a and Rahoma S. Mohamed 12. Removal of Cholesterol from Food Products Using Supercritical Fluids Rahoma S. Mohamed, Marleny D. A. Saldan˜a, Alvaro B. A. de Azevedo, and Uiram Kopcak

347

369

13. Solvent Extraction: Safety, Health, and Environmental Issues Phillip J. Wakelyn and Peter J. Wan

391

Index

429

Contributors

Jose´ Miguel Aguilera Universidad Cato´lica de Chile, Santiago, Chile Pascal Christodoulou

Hellenic Sugar Industry S.A., Thessaloniki, Greece

Alvaro B. A. de Azevedo The State University of Campinas–Unicamp, Campinas, Sa˜o Paulo, Brazil Levente L. Diosady Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada Nurhan Turgut Dunford Food and Agriculture Products Research and Technology Center, and Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, Oklahoma, U.S.A. Rudolf Eggers Technische Germany

Universita¨t

Hamburg-Harburg,

Hamburg,

Philip T. Jaeger Technische Universita¨t Hamburg-Harburg, Hamburg, Germany Jerry W. King National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture, Peoria, Illinois, U.S.A.

ix

x

Contributors

Uiram Kopcak The State University of Campinas–Unicamp, Campinas, Sa˜o Paulo, Brazil Youn-Woo Lee Supercritical Fluid Research Lab, Korea Institute of Science and Technology, Seoul, Korea Youn Yong Lee Korea Institute of Science and Technology, Seoul, Korea Gary R. List U.S. Department of Agriculture, Peoria, Illinois, U.S.A. Rahoma S. Mohamed School of Chemical Engineering, The State University of Campinas–Unicamp, Campinas, Sa˜o Paulo, Brazil Vassiliki Oreopoulou National Technical University of Athens, Athens, Greece Marleny D. A. Saldan˜a The State University of Campinas–Unicamp, Campinas, Sa˜o Paulo, Brazil Constantina Tzia National Technical University of Athens, Athens, Greece Petros Tzias Athena S.A., Athens, Greece Phillip J. Wakelyn National Cotton Council, Washington, D.C., U.S.A. Peter J. Wan U.S. Department of Agriculture, New Orleans, Louisiana, U.S.A. Lei Xu University of Toronto, Toronto, Ontario, Canada

1 Introduction Fundamental Notes on Chemical Thermodynamics Petros Tzias Athena S.A. Athens, Greece

I. INTRODUCTION Thermodynamics is a very useful tool for many scientists and engineers, including geologists, geophysicists, and mechanical engineers, and is considered the scientific cornerstone for chemical engineers. When we study a handbook containing papers from a certain field of chemical engineering, it is very useful to have some notes on thermodynamics on hand to help us understand what we’re reading. This is the purpose of this chapter; to give a brief, elementary review of thermodynamics, reminding to the reader the basic laws, glossary, concepts, and relations of this important branch of science so as to make the study of the present volume much easier and pleasant. This chapter is intended for chemical engineers and chemists, and for this reason we devote most of this chapter to dealing with chemical thermodynamics, covering mixtures, solutions, phase equilibria, chemical reactions, etc. We are going to cite simple definitions of the different thermodynamic functions and quantities, and we will not enter to any analytical description or philosophical discussion about them; similarly we will not prove the mentioned thermodynamic relations, as these are beyond the scope of this chapter. We will deal mainly with reversible processes, so far as an interest is in chemical thermodynamics, and we will not refer to statistical or nuclear and relativistic phenomena. 1

2

Tzias

II. SCOPE Thermodynamics is a branch of physical sciences concerned with the study of the transformation of heat, work, and other kinds of energy (electrical, light energy, etc.) from one form to another, in the different physicochemical phenomena and determines the laws and relations governing and describing these energy transformations. Historically (1), many scientists studied the interconversion of heat and work, from N.L.S. Carnot with his famous ideal gas cycle, to Clausius, who laid the foundations of the classical thermodynamics with the expression of the first and second laws of thermodynamics. Later, J. Willard Gibbs extended the application of the thermodynamic postulates and relations to chemical reaction and phase equilibria putting the foundations for the development of the field, which we call today chemical thermodynamics. Thermodynamics consists of a collection of equalities and inequalities which interrelate physical and chemical properties of substances as well as some physical or chemical phenomena. These relations are deduced in a mathematical way from some laws, the thermodynamic laws, which are derived directly from experience. The physical quantities used are taken either from physics, or they are introduced in thermodynamics. Using these relations, we can predict the possible direction of chemical reactions or the final result of a physical process, as well as the quantities of the different kinds of energy involved in these transformations. Thermodynamics is an experimental science (2). All the physical or chemical quantities used in its relations are independently measurable, but some of them are easier to be measured than others. Another feature of thermodynamics that makes it very important and useful is the capability of calculation, through its relations, quantities measured with difficulty or low accuracy by others measured easier and more accurately. Another advantage is that very often from existed data of some physical quantities we can calculate through thermodynamic relations the values of the physical quantities we are interested in, in this way avoiding long and difficult experiments and also saving time and money. Therefore, it becomes obvious that thermodynamics can be a very useful tool to a chemist or a chemical engineer.

III. GLOSSARY OF BASIC THERMODYNAMIC TERMS (2–6) There are a variety of thermodynamic terms, the most common of which are defined below, together with their SI units (3).

Introduction

3

The amount of substance (2) nb of an entity b is a physical quantity proportional to the number nb of entities b in the system. The SI unit of the amount of substance is the mol. A. Pressure, Volume, Temperature Pressure and volume are concepts taken from physics. Pressure is defined as the ratio of a perpendicular force to a surface by the area of this surface. The SI unit of pressure is the Pa. Volume (m3) is the three-dimensional space occupied by a substance. Specific volume (m3/kg) is the volume per unit mass. Molar volume is the volume divided by the amount of substance. Density is the reciprocal of the specific volume. Temperature is a fundamental concept used in thermodynamics, and its definition is a very difficult matter. However we could simply describe it as the degree of hotness of a substance or as the property of matter which has equal magnitude in systems, connected by diathermic walls and where thermal flow does not exist (3). B. Temperature Scales (4) The Celcius scale (°C) is established by assigning the value 0.01°C to the triple point of water and the value 100°C to the boiling point of water at an atmospheric pressure of 1 standard atm (760 torr). The absolute temperature scale (T) is a scale which is related to the Celcius scale by the relation: T = t (°C) + 273.15 T is given in degrees Kelvin. In this scale it is enough to define just one point and this is the triple of water equal to 273.16 K. The freezing point of water at 1 atm is 273.15 K. The ideal gas temperature scale defined as: T = 273.16K lim

P273.16→0

冉 冊 PT

P273.16

where PT and P273,16 are the pressures of a gas trapped in a gas thermometer at the temperatures T and 273.16 K. The ideal gas temperature scale is the same with the absolute scale. Constant volume gas thermometers are used to determine the thermodynamic absolute temperatures. The International Practical Temperature Scale (IPTS-68). This scale gives the temperatures at some reproducible fixed points together with some interpo-

4

Tzias

lating instruments and functions, by which we find the temperatures between these points. The fixed points as well as the functions have been determined by constant volume gas thermometers. The IPTS-68 gives the possibility by the above mentioned instruments and functions to measure in an easy and accurate way the absolute temperature. In the literature (4), we can find temperatures at different fixed points as well as the instruments used to measure the absolute temperature between these points. In the SI system for the absolute temperature we use the degree K. C. System In thermodynamics, we call any part of the real world we are choosing to study a system. All the rest of the parts of the world are the surroundings of the system. Practically by surroundings we consider the part of the world around the system which can interact with it. A system is closed if no matter enters or leaves it during any process we study. Otherwise it is open. A system is called isolated if neither matter nor energy enters or leaves it. The state of a system is defined by the values of its properties. Properties are physical quantities like temperature, volume, pressure, etc., which are related with a system and they have fixed values at any given state of the system. In order to define the state of a system, it is not necessary to know the values of all its properties, but only a certain number of them, which are called independent and all the rest (dependent) can be calculated by the values of the independent ones. Since a property is fixed by the state of a system it is also called a state function. A mathematical relationship between thermodynamic state functions is called an “equation of state.” D. Extensive and Intensive Properties Considering that we divide a system in different parts, if for one property its value for the whole system is equal to the sum of its values for the different parts, then this property is called extensive. Extensive properties are the volume, the mass, the internal energy, etc. If the value of a property for the whole system is equal to the values for its different parts, then the property is called intensive. Intensive properties are the temperature, the pressure, the molar volume, the density, etc. E. Phase If for a system, throughout all its parts, all its intensive properties have the same value, then the system is a homogeneous one and is called a phase. One system

Introduction

5

can consist of more than one phases. In that case it is heterogeneous and some of its intensive properties have not the same value at all its parts. One phase can be open when it exchanges matter either with its surroundings or with another phase within the system. In the opposite case it is called closed. F. Process Process is the pathway through which one system passes from one state to another. If during a process the temperature of the system remains constant, the process is called isothermic, if the volume of the system remains constant, the process is called isometric or isochoric, and if the pressure remains constant, the process is called isobaric. If no heat enters or leaves a system undergoing one process the system and the process are called adiabatic. A process is called reversible if it takes place slowly and in such a way that at any stage of the process the properties of the system differ from equilibrium by infinitesimal amounts. Otherwise the process is called irreversible. All natural processes are irreversible. G. Molar Quantities From any extensive quantity X of a phase, it is defined an intensive quantity Xm by the relation: X ∑ ni

Xm =

(1)

i

where

∑n

is the sum of the amounts of the different substances contained in

i

i

this phase. H. Mole Fraction The mole fraction Xa of a substance a in a phase is defined by the ratio: Xa =

na ∑ ni

(2)

i

where na the amount of substance of a and

∑n

i

the sum of the amounts of all

i

substances in this phase. It follows immediately from the above definition that

6

Tzias

∑X =1

(3)

i

i

I. Molality Molality m is the number of moles of a substance in 1 kg of solvent. J. Molarity Molarity c is the number of moles of a substance in 1 L of solvent. K. Partial Molar Quantities From any extensive quantity X of a phase we define an intensive quantity called the partial molar quantity Xa of the substance a in the phase by the relation: Xa =

冉 冊 ∂X ∂na

(4)

T,P,ni≠na

where ni ≠ na means all n’s except na in this phase.

IV.

THE CONCEPTS OF W, PE, KE, U, Q AND S (6–8)

W (work), PE (potential energy), and KE (kinetic energy) are concepts borrowed from physics. Work (W) is produced by a force (F) acting on a system and replacing it by a distance (ds) in the direction of the force and equals to: dW = F ⴢ ds In the case of a uniform pressure P acting on a system’s wall of surface S and replacing it by a distance x dW = −P ⴢ s ⴢ dx

or

dW = −P ⴢ dV

(5)

By energy we mean the ability of a system to produce work. Potential energy (PE) is the energy possessed by a system because of its position. PE = m ⴢ g ⴢ h

(6)

where m is the mass of the system, g the gravity acceleration and h the height of the system from zero level. Kinetic energy (KE) is the energy possessed by a system of mass m, because of its velocity (ν) and is equal to:

Introduction

KE =

mE2 2

7

(7)

Internal energy (U) is the total energy, except potential and kinetic, contained within a system at a certain state. It is a magnitude determined by the state of a system. We can’t measure the absolute value of U, but only differences ∆U = U2 − U1 between two states (1) and (2). ∆U depends only on the states (1) and (2) and not on the path followed to pass from one to the other. Heat (Q) is the amount of energy, transferred from one system to another because of the difference in the temperature of the two systems. The amount of heat depends on the followed path and not on the original and final states of a procedure. For a system receiving heat Q, Q = C ⴢ (T2 − T1)

(8)

where C is the heat capacity of the system and T2, T1 its final and original temperatures. The entropy (S) (6) is an extensive property, depending on the state of a system. It can be defined as: dS =

dQ T

(9)

where dS is the heat received by it in a reversible way at a temperature T. In natural processes S always increases and dQ ≤ T ⴢ dS

(10)

The SI unit for W, PE, KE, and Q is the Joule. Joule . The SI unit for S is K

V. THERMODYNAMIC LAWS (2, 5, 6, 7) Thermodynamic laws are laws formed from experience, and there is no exception to them. There are several expressions for them all equivalent with each other. Below are given the most common ones. A. First Law of Thermodynamics In any process the total energy is conserved. In other words, there is no device which can create or eliminate energy. Considering the transformation of heat to work or vice-versa in a system, the first law can be expressed as:

8

Tzias

dU = dW + dQ

(11)

where dU is the variation of the internal energy of the system, dW is the work produced, and dQ is the heat transferred. dU is an exact differential depending only upon the original and final state of the system, but dQ and dW are not exact differentials and depend upon the pathway of the process. By substituting Eqs. (5) and (9) to (11), the first law can be expressed as: dU = −P ⴢ dV + T ⴢ dS

(12)

B. Second Law of Thermodynamics It is not possible to transfer heat from a lower temperature to a higher temperature without the expenditure of work. In other words, in any process the total entropy of an isolated system increases. C. Third Law of Thermodynamics The expression of the third law is not possible without reference to statistical mechanics (2). As expression for the third law we could give Plank’s postulate (7): “At 0K the entropy of a pure crystalline is zero.” D. Law of Thermal Equilibrium If two systems are in thermal equilibrium with a third one, then they are also in thermal equilibrium between them. This law is not an independent one but it is derived from the first and second thermodynamic laws (2).

VI. THERMODYNAMIC FUNCTIONS AND SOME RELATIONS FOR ONE-PHASE CLOSED SYSTEM (6, 7) We have already met the properties P, V, T and the thermodynamic functions U, Q, W, and S and for the last two functions: W = −P ⴢ dV and dS =

dQ T

Furthermore, there are the following very important thermodynamic functions: the enthalpy H = U + P ⴢ V the Helmholtz free energy A = U − T ⴢ S

(13) (14)

Introduction

9

and the Gibbs energy or Gibbs function G=H−TⴢS=A+PⴢV=U−TⴢS+PⴢV

(15)

All the three above functions are state functions and they have the dimensions of energy. Since they are state functions their differentials are exact differentials and for reversible processes we have: dH = T ⴢ dS + V ⴢ dP dA = −S ⴢ dT − P ⴢ dV dG = −S ⴢ dT + V ⴢ dP

(16) (17) (18)

By applying the properties of exact differentials we can obtain the following very useful relations:

冉 冉 冉 冉

冊 冊 冊 冊

冉 冊 冉 冊 冉 冊 冉 冊

∂U = −P, ∂V S

∂U =T ∂S

(19)

∂A = −P ∂V

(20)

V

∂A = −S, ∂T V

T

∂G = V, ∂P T

∂G = −S ∂T

(21)

∂H =V ∂P

(22)

P

∂H = T, ∂S P

S

and also the following:

冉 冉 冉 冉 冉

冊 冉冊 冊 冉 冊 冊 冉冊 冊 冉 冊 冊冉 冊冉 冊

∂T ∂P =− ∂V ∂S S

∂T ∂V = ∂P ∂S S

∂S ∂P = ∂V ∂T T

∂P ∂T

V

∂T ∂V

P

(24)

P

(25)

V

∂S ∂V =− ∂P ∂T T

(23)

V

(26)

P

∂V = −1 ∂P T

(27)

10

Tzias

a=

冉 冊 冉 冊 冉 冊 冉 冊 冉 冊

1 ∂V is the coefficient of thermal expansion V ∂T

(28)

P

1 ∂V is the isothermal compressibility V ∂P

(29)

1 ∂V KS = − is the adiabatic compressibility V ∂P

(30)

K=−

T

S

CV =

∂U is the heat capacity at constant volume ∂T

(31)

∂H is the heat capacity at constant pressure ∂T

(32)

V

CP =

P

VII. FUNDAMENTAL INEQUALITIES We have already seen (Sec. IV.) that although for a reversible process dQ = T ⴢ dS and for natural processes dQ ≤ T ⴢ dS. Similarly for natural processes from Eqs. (12), (16), (17), and (18) we derive: dU ≤ T ⴢ dS − P ⴢ dV dH ≤ T ⴢ dS + V ⴢ dP dA ≥ −S ⴢ dT − P ⴢ dV dG ≥ −S ⴢ dT + V ⴢ dP

(33) (34) (35) (36)

This means that for a natural process at equilibrium the above functions get the lowest value, except the entropy which gets the maximum. In other words for any closed isolated system (2):

冉冊 ∂S ∂t

>0

(37)

U,V,ni

which means, that if anything happens in that system then S is increasing, and if

冉冊 ∂S ∂t

=0

U,V,ni

then the system is in equilibrium. Similarly, from:

(38)

Introduction

冉 冊 ∂U ∂t

11

0.06), plots of log X vs. t are straight lines with a slope equal to −Dq12/2.303L2, from which D can be obtained as an overall diffusion coefficient (effective or apparent diffusion coefficient). The dimensionless number Dt/L2 can be used as a criterion of closeness to the steady state. If it is much larger than unity (e.g., Dt >> L2) an equilibrium or steady-state condition may be assumed. Also, two parameters may be established for un-steady-state conditions: a “velocity of diffusion,” √D/πt and a “penetration distance,” √4Dt (3). Note that both parameters involve the square root of time. Solid–liquid extraction of various food materials is controlled by internal diffusion except in the case of very small particles, poor agitation, or presence of a skin (8–11). The extent of control between external and internal diffusion is indicated by the Sherwood number NSh = kcL/D, where kc is the liquid-phase mass transfer coefficient, L a characteristic dimension of the solid (e.g., particle size), and D the internal diffusion coefficient. If NSh > 200, internal control can be safely assumed (7). B. Determination of Diffusion Coefficients Data for diffusion coefficients are necessary to make calculations using the Fickean approach. Diffusivities may be determined experimentally or predicted. Experimental methods used for liquids are, among others, the diaphragm cell, the

Solid–Liquid Extraction

41

rotating disk (used for drug dissolution), nuclear magnetic resonance (NMR) spin-echo techniques, and interferometer methods (Gouy interferometer). For details of these methods, see Cussler (3). Orders of magnitude of these coefficients are important to remember (all units in cm2/s): for gases, 10−1; for liquids, around 10−5; and for solids, between 10−8 and 10−30 (if m2/s is used as unit, values must be divided by a factor of 104). Consequently, perfume in air diffuses 10,000 times faster than the tea extract in a cup of hot water! Diffusion coefficients of polymers in solvents under dilute conditions may be as low as 10−6 to 10−8 (cm2/s) and of gases through synthetic membranes vary widely between 10−8 and 10−11 (cm2/s). Numerous attempts have been made to predict diffusivities in liquids. According to the Stokes-Einstein equation, the diffusion coefficient of large spherical solute species in a pure solvent of viscosity η is given by: D=

kT 6πηrs

(5)

where k is the Boltzmann constant [gas constant divided by Avogadro’s number (R/N0)], T the absolute temperature, and rs the effective radius of the diffusing molecule. This expression is misleading in the sense that in practice viscosity and solute radius effects are more important than those of temperature. Also, adaptations of Eq. (5) for dilute solutions of macromolecules must encompass consideration of their size and shape. Nevertheless, the Stokes-Einstein equation has provided the foundation for several useful semiempirical equations. Most data for diffusion coefficients of liquids or gases (vapors) in solid foods come from solutions to the diffusion equation for standard geometries combined with experimental results, so they are “effective diffusion coefficients.” The main problem is that all steps involved in the diffusion of a solute through a solid matrix are ignored and the process is characterized by a single coefficient that is highly dependent on experimental conditions, solid geometry, and microstructural arrangement. It is not surprising, then, that “diffusion coefficients” for solute extraction vary by several orders of magnitude, even for the same solute. As explained before, effective or apparent diffusivities appear as a calculated value of D obtained from a transient diffusion experiment. Defining M as the total amount of solute that has diffused in (impregnation) or out (extraction) of a solid of regular geometry (slab, cylinder, or sphere) at any time and M∞ as the amount transferred after equilibrium is reached, the ratio M/M∞ may be calculated by integrating Fick’s second law under appropriate boundary conditions. For a slab of thickness L when 1 − M/M∞ is plotted against t, the slope is −π2Dapp/4L2 (the expression changes for other geometries) from which a value of Dapp can be determined. The term “apparent” confirms that we do not know the exact mechanism of transport, which in most cases may be complex.

42

C.

Aguilera

Diffusion Through the Solid Matrix

It should be evident from the previous analysis that microstructure influences molecular diffusion through its effect on the diffusion coefficient. Solute diffusivities can be defined in the liquid phase (DL) or in the wet solid (DS), and expressions for Fick’s laws can be written accordingly. Differences between DL and DS can be attributed to factors such as membrane resistance, complexity of the diffusion path, sorption of the solute by the inert solid, etc. If all of these factors were accounted for, DS could be related to DL by an expression of the form: DS = Fm × DL

(6)

where Fm is a correction factor including all phenomena related to microstructure. Schwartzberg and Chao (7) have reviewed extensively the subject of solute diffusivities in foods. DL values for various food solutes at infinite dilution in water at 25°C vary from 5.4 × 10−6 for sucrose to 1–7 × 10−7 cm2/s for some proteins. Selected DS values listed in the literature where microstructural effects appear important are presented in Table 1. Extraction experiments from which these data were obtained do not lead to easy determination of a single, constant diffusion coefficient. Typical curves for hexane extraction of oil from soybean grits and flakes of different particle size and a theoretical curve are shown in Fig. 2 (12). The slope of the curves becomes less steep as extraction proceeds, meaning a decrease in the value of DS so that usually an initial as well as a final diffusion coefficient has to be determined. The initial DS may be influenced by the previously mentioned release of surface solute (washing); thus, it is not truly Table 1 Diffusion Coefficients in Foods and Reference Values

Food material

Solute

Solvent

Temp. (°C)

Dilute solution Sugar cane (across grain) Sugar cane (with grain) Sugar beets Gelatin gel Dilute solution Pickled cucumbers Dilute solution Small curds Peanut slices Tungseed slices Dry solid matrix

Sucrose Sucrose Sucrose Sucrose Sucrose NaCl NaCl Lactose Lactose Oil Oil Glyceride

Water Water Water Water Water Water Water Water Water Hexane Hexane —

25 75 75 24 5 25 25 25 25 25 30 50

Source: Ref. 1.

DAB × 106 cm2/s 5.4 5.1 3.0 1.6–2.5 0.1–0.2 16.1 5.3–11.0 4.9 3.0 0.007 0.006 5 × 10−4

Solid–Liquid Extraction

43

Figure 2 Kinetics of oil extraction from soybean flakes (------) and ground particles ) and theoretical behavior. Numbers on curves are the thickness of flakes or ( particle size (in microns).

representative of diffusion of solute inside the solid. Consequently, data on solid diffusivities listed in Table 1 should be analyzed with caution, always referring to the original work if further conclusions are to be inferred. DL values from the literature and diffusivities calculated for extraction of the same component may be used to determine the order of magnitude of the correction factor Fm in Eq. (6). The DL of caffeine in water is 6.9 × 10−6 cm2/s whereas the diffusivity of coffee solubles during extraction from grinds is of the order of 1.1 × 10−6 cm2/s (13). In sucrose extraction from beets the calculated diffusivities are one-half to one-third the DL at the same temperature. Diffusivities of pure linoleic and oleic acid in hexane are 3.6 × 10−7 and 2.6 × 10−7 cm2/s, respectively, while inside the seed they are reduced to 2.6–6.2 × 10−8 cm2/s (14). Hence, the correction factor in Eq. (6) appears to be of the order of 0.1–0.9 for small solutes, with higher values occurring when membranes have been denatured, as in sugar beet processing. For macromolecules Fm is obviously larger. In fact, Schwartzberg and Chao (7) report that solutes exceeding a molecular weight of 2000 cannot diffuse into intact cells of coffee grounds. The reduction of the diffusion coefficient

44

Aguilera

for zein (corn protein) in dilute solution and inside the endosperm is about 1000fold (15). Sorption of solvent and/or solutes by the solid also retards diffusion. Microstructural entrapment seems to play a role at least as important as physicochemical sorption. Lignocellulosic materials are known to sorb water that is no longer available as solvent, particularly if ground into a fine powder. Sorption is critical when organic solvents must be removed from spent solids after extraction, in an operation known as desolventizing. Residual hexane left in rapeseed meals concentrates preferentially in the hulls, dissolved in the residual oil, entrapped inside thick-walled cells (16), or simply adsorbed. In this latter case, internal diffusion coefficients for hexane during adsorption into the solid matrix are very small (about 10−10 cm2/s) and increase with hexane content, probably due to swelling of the structure.

IV. THE MICROSTRUCTURAL APPROACH A. Simple Correction Factors On examination of Eq. (3) and data previously presented it can be concluded that the influence of food microstructure on extraction rate is predominantly by its effect on the diffusion coefficient. Chemical engineers use the effective (or apparent) diffusion coefficient Deff when dealing with impermeable porous solids with fluid-filled pores: Deff = D

ε τ

(7)

where D is the diffusion coefficient of the solute in the fluid filling the pores, ε is the void fraction or porosity of the solid, and τ is the tortuosity of the pores, which attempts to account for the longer distance traversed by the solute along a sinuous path. Porosity may be very low in potato tissue (⬃2%) or high as in apples (⬃20%). For solid materials used in chemical engineering (adsorbents, porous catalysts) tortuosity varies between 2–6, and porosity between 0.3–0.8, thus Deff may be 6 to 15 times lower than D. When the size of the pore and the solute are of comparable magnitude (e.g., in some membranes), Eq. (7) is corrected by a factor λ that depends on the ratio of solute radius to pore radius. The restricted diffusion of spherical molecules within cylindrical pores (Dp) is usually modeled by the so-called Renkin equation (17): Dp = D(1 − λ)2f(λ)

(8)

The squared term of the equation is a partition coefficient and accounts for steric hindrance at the pore entrance. The factor f(λ) is a polynomial function

Solid–Liquid Extraction

45

of λ and corrects for the friction between the diffusing molecule and the walls of the pore. The previous results may be expressed in a more general form. For a twophase composite where spherical particles of a material 1 are dispersed in a continuous phase 2, an effective diffusion coefficient may be obtained from the expression:

冉

冊

Deff − D1 D − D1 =φ 2 Deff + 2D1 D2 + 2D1

(9)

where D1 is the diffusion coefficient through the interstitial pores and D2 is the diffusion coefficient through the particles. So far we have assumed that pores are quite large. When the size of pores is of the order of magnitude of the distance between molecular collisions, Fick’s laws no longer applies and the so-called Knudsen diffusion takes place in which molecules collide with the pore walls. We leave this section with the impression that a much better work in using Fick’s laws could be made to predict extraction rates if we had an idea of the microstructural architecture of a solid food particle. Microscopy can assist in developing such structural models and in finding parameters such as tortuosity, porosity, and pore size. B. Introducing Architectural Effects Other correction factors where the diffusivity is corrected taking into account the architecture of the structure have been discussed by Cussler (3). A flake with uniformly distributed platelets impermeable to the solute, parallel or perpendicular to the diffusing stream where the volume fraction of the platelets is φF and their aspect ratio is α, results in an effective diffusivity given by the expression:

冉

Deff 1 = 2 2 D 1 + α φF /(1 − φF)

冊

(10)

A plot of Deff/D vs. φF having α (long dimension divided by short dimension of platelets) as parameter is shown in Fig. 3. When platelets are arranged parallel to the diffusion (segmented line), tortuosity is 1 and the effective diffusivity varies linearly with φF, underscoring the unidirectional nature of the diffusion. When platelets are arranged perpendicular to the diffusion Deff/D depends strongly on α and a nonlinear dependence on φF is evident. For a constant value of α, Deff/D becomes smaller as φF increases. It is also interesting to note that when α is very large, say α = 30, even at low values of φF the ratio Deff/D can become very small. Thus, this model shows an important effect of microstructure and architecture on the rate of trans-

46

Aguilera

Figure 3 Curves showing the correlation between Deff/D and volume fraction φF of impermeable platelets in a slab (φF) for different aspect ratios α of platelets. The effective diffusion coefficient varies sharply with φF when platelets are placed perpendicular to the diffusional flow.

fer of solute. However, it does not consider the possibility of nonuniform spatial distribution of the impermeable elements. Another model that considers the situation of impermeable spheres occupying a volume fraction φS and uniformly distributed within the matrix giving rise to the expression of Deff/D given by Cussler (3) is:

冋

2(1 − φs) Deff + D 2 + φs

册

(11)

Evidently the size and distribution of the spheres is not considered in the model. In summary, although some expressions like (10) and (11) are available for

Solid–Liquid Extraction

47

biphasic systems with uniformly distributed elements, they do not account for nonuniform architectural arrangements.

V. MATHEMATICAL MODEL FOR EXTRACTION FROM A TWO-DIMENSIONAL MATRIX Fick’s first law for the steady state and second law for the transient or unsteady state in two dimensions of a homogeneous and isotropic media are described by the following equations:

冉

冊

∂C ∂C + ∂X ∂Y

(12)

∂ ∂C ∂ ∂C ∂C = D + D ∂t ∂X ∂X ∂Y ∂Y

(13)

J = −D

冉 冊 冉 冊

Applying the precedent equations to a structure with two different phases A and B, continuity of mass flow must be observed at every boundary between the phases. Crank (6) proposed a discrete form (finite difference) for the unidirectional diffusional case of a two-phase compound: 1 C(i,n+1) − C(i,n) DB DA {∆XA + ∆XB} = (C(i+1,n) − C(i,n)) − (C(i,n) − C(i−1,n)) (14) 2 ∆t ∆XB ∆XA where i is the position of the element or node and n the iteration index. Based on this equation and applying explicit finite difference discretization to the Fick’s second law in two dimensions using ∆X = ∆Y = ∆L while taking into account the continuity between phases, equal distance increment in the phases (i.e., ∆XA = ∆XB = ∆X) and constant diffusion coefficients at each point throughout the extraction process, the following expression is obtained: C(i,j,n+1) = C(i,j,n) =

∆t (∆L)2

× [D(i+1,j,n)(C(i+1,j,n) − C(i,j,n)) − D(i−1,j,n)(C(i,j,n) − C(i−1,j,n)) + D(i,j+1,n)(C(i,j+1,n) − C(i,j,n)) − Di,j−1,n)(C(i,j,n) − C(i,j−1,n))]

(15)

which is based on the application of the Schmidt method of the finite difference approach to heterogeneous compounds (e.g., different diffusion coefficients at each specific spatial locations) as presented in Crank (6). The subindex (i,j,n) refers to the position (i,j) of an element in the matrix at the iteration step n, equivalent to a time t = n × ∆t.

48

Aguilera

Modeling extraction with Eq. (15) starts by defining a matrix of M × N elements representing the object being extracted bidirectionally and introducing the specific architectural elements in the form of pores, walls, or platelets. Geometrical arrangements of the structure are modeled assuming different diffusion coefficients for each phase or element. The boundary condition for the model assumed that the object was surrounded by pure solvent at all times (bulk concentration of solute in solvent equal to zero or infinite volume of solvent). This is equivalent to saying that the internal resistance is controlling or the Biot number for mass transfer is infinite. The concentration at each point of the matrix and the corresponding diffusion coefficients at time 0 were the initial conditions for this model. Extraction was simulated for different architectures of the matrix. Extraction curves are semilog plots depicting variations in time of the ratio of the instant mass of solute remaining in the solid to the total initial mass (q/q0). A. Modeling Extraction from Cellular Material An example of the previous approach has been applied to extraction from a cellular structure modified by various pretreatments (18). Figure 4 shows the architectural arrangements associated with four pretreatments. The basic structure considers that all cells are intact and completely surrounded by a thick cell wall (A). Enzymatic treatment with a cocktail of cell wall–degrading enzymes may pierce the wall of outer cells at different places, leaving openings that connect the cytoplasm directly to the solvent (B). A third situation may be that induced by blanching, resulting in degradation of the cell wall membrane (and possibly the cell wall itself), diminishing the overall internal resistance for transport of solute as most cells become interconnected (C). Another conceivable architecture is that of a totally connected cytoplasm and broken outer cells exposed to the solvent due to extensive physical destruction of the tissue (D). In all cases, the proportion of cell wall material to cytoplasm is the same. The simulation used data obtained from Schwartzberg and Chao (7) for infusion of salt (NaCl) in pickled cucumbers and water. Data for the cell wall complex were Dcw = 53 µm2/s and Ccw = 1 g/L, and for the cytoplasm D = 219 µm2/s and C = 3 g/L. ∆L was 20 µm and ∆t was 0.2 s. The overall size of the matrix was 380 × 380 µm in a grid of 19 × 19 elements. The resulting extraction curves for the simulations are presented in Fig. 5. The fastest extraction rate corresponded to pieces having complete breakage of the cell wall and a layer of cytoplasm exposed directly to the solvent. Washing of solute from the outermost layers removes 20% of the solute almost instantly. Since it was assumed that the cell wall complex also contained solute and had a finite diffusivity, even the arrangement containing intact external cells showed some extraction. However, if the cell wall complex had been assumed

Solid–Liquid Extraction

49

Figure 4 Architectural arrangement of cells of pickled cucumbers subject to different treatments (theoretical).

50

Aguilera

Figure 5 Resulting curves of the extent of extraction vs. time simulating salt diffusion in pickles with and without pretreatments to modify the microstructure.

impermeable, extraction would have been zero in this case for all times. Actual extraction of triglycerides from oilseeds having intact cells by organic solvents is extremely slow (1). The fastest extraction is achieved in the situation of full rupture of the cell complex, and the slowest for intact cells. In the latter case, the curves start from zero concentration (no washing) and the time to achieve 90% removal of solute is 40% longer than in the previous case. Maintenance of intact cell membranes may be beneficial to selectively extract small solutes from the cytoplasm keeping large molecules and debris within the piece to facilitate downstream separation and purification (as in sucrose extraction from sugar beets). The use of microscopy techniques to correlate the state of the microstructure and its effect on extraction is presented in Aguilera and Garcı´a (19) for aqueous extraction of protein from lupins; Aguilera and Lusas (20) to assess the

Solid–Liquid Extraction

51

extractability of oil from flakes, grits, and extrudates of high-oil corn using hexane; Fan et al. (21) for the extraction of peanut oil; and Rastogi and Niranjan (22) for osmotic dehydration of pineapple tissue, among others. With the advent of nonintrusive microscopy techniques and real-time video microscopy, structural features prior and during extraction may be observed and different architectural arrangements (presence of elements, phase ratios, geometrical parameters, etc.) characterized by image processing and analysis. Combination of microstructural information, better data on diffusivities of individual phases, and advanced computer techniques should lead to a more fundamental approach to the study of mass transfer phenomena in foods and other biological materials (23).

VI. USE OF MASS TRANSFER COEFFICIENTS The problem with Eq. (1) is that the distance over which concentration changes occur (δ) must be known to determine the gradient. This is not easy task for processes that occur inside process equipment. Moreover, we are in the presence of interfacial mass transfer or transfer between two different phases (solid and liquid). Chemical engineers prefer to study interfacial mass transfer using mass transfer coefficients (individual or overall), which multiplied by a measurable driving force give the rate of mass transfer. Thus, the distance problem becomes hidden in the coefficient, which also contains implicitly the diffusivity. A practical expression for the rate of solute extraction takes the form: Rate = mass transfer coefficient × driving force

(16)

The driving force in extraction should be the difference between the chemical activity of the solute inside the solid and that in the bulk of the solution. For practical reasons, the rate is expressed as the product of the difference between an external solute concentration (cout) and that in the interior of the solid (cin), and a mass transfer coefficient based on concentration. For this difference to be meaningful it must be expressed in the same base; thus, cin is usually taken as that concentration of solute in the liquid phase that would be in equilibrium with the concentration inside the solid c*. Then the driving force for extraction becomes (c* − cs), where cs could be measured in the bulk of the solution and Eq. (16) takes the form: N1 = Kc(c* − cs)

(17)

This simple equation states that the rate of extraction N1 depends on the difference in a thermodynamic variable (expressed as concentration) and a global mass transfer coefficient Kc that includes all physical and microstructural parameters of the process. If the interfacial area for transfer (a) is unknown, the coeffi-

52

Aguilera

cient becomes Kca. If each phase is taken separately, individual mass transfer coefficients ki can be defined for transport between the interface and the bulk of the respective phase [see Cussler (3) for details]. If a solid is modeled as series of structures (i.e., a plant cell with protoplasm, plasmalemma, cell walls, etc.), the observed global mass transfer coefficient may be related to individual mass transfer coefficients inside each of the structures as a sum of resistances in series: 1 1 =Σ Kc kci

(18)

and a rate-limiting step having the highest resistance (or the slowest flow) may be identified. This structure then controls the mass transfer process and efforts should be made to increase the rate within this phase. This approach can also be used when studying extraction as a series of sequential steps as shown in Fig. 1. Theoretically, there is a relationship between D and Kc (e.g., Kc is proportional to D/δ, where δ is a thin film over which diffusion occurs) and, as previously concluded, study of the influence of microstructure on extraction rate is reduced to the analysis of its effects on the mass transfer coefficient. Mass transfer coefficients are often correlated to dimensionless numbers, allowing predictions to be made for other experimental conditions. Dimensionless numbers are ratios of transport parameters and have physical meaning. For example, the most relevant of these numbers for solid–liquid extraction is the Sherwood number (NSh), which is the ratio of mass transfer velocity (kL) to diffusion velocity (D). A large NSh in solvent extraction means that the controlling step is diffusion inside the solid, and reduction of particle size or restructuring into a porous material may prove effective in speeding extraction. NSh is in turn related to several other dimensionless numbers, e.g., to the Schmidt number representing flow effects (momentum transfer) by the expression: NSh =

冉冊

ν kcL =f D D

1/3

(19)

where ν is the kinematic viscosity.

VII. CONCLUSIONS Food engineers can make a significant contribution to the study of extraction or leaching by introducing the microstructural variable into the problem. A good architectural description of the solid being extracted together with fundamental data of diffusion coefficients in different structures or phases can be combined with modern computational methods to predict extraction rates from food solids.

Solid–Liquid Extraction

ACKNOWLEDGMENTS This work was partly funded by project FONDEF D9712026.

NOMENCLATURE a A Bn c C D Deff , Dapp E/R Fm J j k kc Kc L M N0 NSh q qn R r t T X

interfacial area of transfer (m2/m3) area for diffusion (m2) parameter in eq. 4 molar concentration (moles/m3) concentration (g/L) diffusion coefficient or diffusivity (m2/s) effective or apparent diffusion coefficient (m2/s) extract-to-solid volume ratio correction factor in eq. 6 mass flux (moles/s) flux (moles/s m2) Boltzmann constant liquid phase mass transfer coefficient global mass transfer coefficient characteristic length or slab thickness (m) total amount of solute extracted (g) Avogadro’s number Sherwood number mass of solute in the solid (g) parameter in eq. 4 gas constant direction of flow or position inside the solid time (s) absolute temperature (K) extent of extraction of solute (dimensionless)

Symbols α α δ ε φ η τ

aspect ratio (length/width) m.E/R film thickness porosity or void fraction volume fraction of platelets or spheres viscosity tortuosity

53

54

λ ν υ

Aguilera

correction factor index (1 for infinite slabs, 2 for infinite cylinders, 3 for spheres) kinematic viscosity

REFERENCES 1. JM Aguilera, DW Stanley. Microstructural Principles of Food Processing and Engineering. Gaithersburg, MD: Aspen Publishers, 1999. 2. V Gekkas. Transport Phenomena of Foods and Biological Materials. Boca Raton, FL: CRC Press, 1992. 3. EL Cussler. Diffusion: Mass Transfer in Fluid Systems, 2nd ed. Cambridge, UK: Cambridge University Press, 1997. 4. RB Bird, WE Stewart, EN Lightfoot. Transport Phenomena. New York: Wiley, 1960. 5. CJ King. Separation Processes, 2nd ed. New York: McGraw-Hill, 1981. 6. J Crank. The Mathematics of Diffusion. London: Oxford University Press, 1975. 7. HG Schwartzberg, RY Chao. Solute diffusivities in leaching processes. Food Technol 2(2):73–86, 1982. 8. U Cogan, A Yaron, Z Berk, S Mizrahi. Isolation of soybean protein: Processing conditions on yields and purity. JAOCS 44:321–324, 1967. 9. HA Leninger, WA Beverloo. Food Process Engineering. Dordrecht, Netherlands: D. Reidel, 1975, p. 502. 10. M Moo-Young, G Andre. An appraisal of mass transfer phenomena in food processing operations. In: P Linko, Y Malkki, J Olkku J Larinkari eds. Food Engineering Progress. London: Applied Science Publishers, 1980, Vol. 1, pp. 342–353. 11. M Spiro, CM Page. The kinetics and mechanism of caffeine infusion from coffee: Hydrodynamic aspects. J Sci Food Agr 35:925–930, 1984. 12. G Karnofsky. The theory of solvent extraction. JAOCS 26:564–570, 1949. 13. A Voilley, D Simatos. Modeling the solubilization process during coffee brewing. J Food Proces Eng 3:185–198, 1980. 14. R Anderson, R Osredkar, C Zagar. Diffusion properties of lipids: pure and in seeds. JAOCS 54:487–489, 1977. 15. MK Russell, GT Tsao. Protein removal from corn endosperm by solvent extraction. AIChE Symp Series No. 218, 78:83–89, 1982. 16. FH Schneider, U Rutte. Zur bindung der resthexans in raps-extraktionsschroten. Fette Seifen Anstrichmittel 86:331–339, 1984. 17. L Lebrun, GA Junter. Diffusion of sucrose and dextran through agar membranes. Enz Microb Techn 15:1057–1062, 1993. 18. J Crossley, JM Aguilera. Modelling the effect of microstructure on food extraction. J Food Process Eng, 24:161–177, 2001. 19. JM Aguilera, HD Garcı´a. Protein extraction from lupin seeds: a mathematical model. Int J Food Sci Technol 24:17–27, 1989. 20. JM Aguilera, EW Lusas. Laboratory and pilot solvent extraction of extruded high oil corn. JAOCS 63: 239–243, 1986.

Solid–Liquid Extraction

55

21. HP Fan, JC Morris, H Wakeham. Diffusion phenomena in solvent extraction of peanut oil: Effect of cellular structure. Ind Eng Chem 40(2):195–199, 1948. 22. NK Rastogi, K Niranjan. Enhanced mass transfer during osmotic dehydration of high pressure treated pineapple. J Food Sci 63:508–511, 1998. 23. JM Aguilera, PJ Lillford. Microstructure as applied to food engineering. In: P Fito, E Ortega-Rodrı´guez, GV Barbosa-Ca´novas eds. Food Engineering 2000. London: Chapman and Hall, 1996, pp. 20–36.

3 Supercritical Fluid Extraction in Food Engineering Nurhan Turgut Dunford Oklahoma State University Stillwater, Oklahoma, U.S.A.

Jerry W. King and Gary R. List U.S. Department of Agriculture Peoria, Illinois, U.S.A.

I. INTRODUCTION The origins of supercritical fluid extraction (SFE) are firmly rooted in the phenomenon of solute solubility in gases as typified by the classic studies of Hannay and Hogarth in the 1880s (1). However, this curious and somewhat unexpected phenomenon, i.e., the dissolution of solids in such fluids as supercritical ethanol and water, was not exploited for any useful purpose until the midpart of the next century. From a modern perspective, SFE was first exploited in Russia in the 1950s (2) and later in Germany (3) for the extraction and fractionation of components in natural products. However, it was the appearance of the Zosel patent (4), issued in Germany in the late 1960s, that firmly documented the myriad of possibilities for using supercritical fluids (SCFs) as processing agents. The U.S. version of this patent, entitled, “Process for the Separation of Mixtures of Substances,” provided more than 60 examples of applications of super- and subcritical ethylene or carbon dioxide to food processing and other applications. HowNames are necessary to report factually on available data. However, the USDA neither guarantees nor warrants the standard of the product, and use of the name by USDA implies no approval of the products and the exclusion of other that may also be suitable.

57

58

Dunford et al.

ever, this broad coverage did not deter other parties from similar patent filings over the next 30 years or the industrial exploitation of critical fluids as versatile processing agents. The classic, often cited applications of SCFs, especially supercritical carbon dioxide (SC-CO2) or liquefied carbon dioxide, are for the decaffeination of coffee (5) and the processing of hops (6). Thus, there is a historical precedent for using critical fluid media in food engineering, which continues up to this present day. Since the above-cited applications, there has been a plethora of additional applications of SFE to foodstuffs, agricultural raw materials, and associated natural products (7). This has resulted in the development of 32 processing plants worldwide employing SCFs. Currently, most of these facilities are centered in Germany, the United States, France, and Japan. Nations such as Great Britain, Australia, Canada, India, and Italy continue to develop more plant capacity for critical fluid processing; and other nations with a rich litany of natural products will undoubtedly enter the marketplace as users of SCF technology in the future. It should be noted that not all of the new processing schemes employing SCFs will be wholly based on extraction, but may include fractionation schemes or reactions conducted in critical fluid media. Table 1 shows a list, which is not inclusive, of many users of critical fluid extraction for food and natural product processing throughout the world. Key players in the various market segments are as follows: decaffeination—General Foods, SKW Trostberg, Kaffee HAG, Hermsen; hops processing—HVG Barth (NATECO2), John Haas, Yakima Chief, Carlton United Breweries, Steiner Hops, English Hops, SKW Trostberg; flavors/spices—Cultor, Quest, Flavex,

Table 1 Organizations Processing or Offering Critical Fluid–Derived Products Flavex (Germany) Hermsen (Germany) HVG Barth (Germany) Kaffe HAG (Germany) SKW Trostberg (Germany) KD-Pharma-IQA (Germany–Spain) General Foods (United States) John Haas (United States) Praxair (United States) Yakima Chief (United States) Carlton United Breweries (United Kingdom) English Hops (United Kingdom) Steiner Hops (Germany–United Kingdom– United States)

Fuji Flavor (Japan) Kobe (Japan) Mori Oil Mills (Japan) Ogawa (Japan) Takasago (Japan) Takeda (Japan) Cultor (France) HITEX (France) Norac (Canada) Aroma Tech OY (Finland) Quest (Holland) Wells Investment Ltd. (New Zealand)

Supercritical Fluid Extraction

59

Norac, Ogawa, Fuji Flavor, Kobe, Mori Oil Mills, Takeda. Many of the processors listed under the generic heading of flavors/spices also produce a variety of other natural products such as specialty oils, natural pigments, and antioxidants. For example, Flavex is also involved in the processing of ginseng among other moieties, and Norac in Canada processes rosemary antioxidants, saw palmetto, kava kava, and other botanicals (8). Since the early 1990s, an awareness of the potential of critical fluid processing as a viable component for “green” processing has arisen (7). This coupled with an increasing consumer awareness of the identity and use of chemical solvents in food and natural products processing has provided further impetus for the use of benign solvents as SC-CO2, ethanol, and water. Recently, the use of SC-CO2 for the processing of certain nutraceutical products (9) has provided additional possibilities for exploiting SCFs. As governments worldwide are also currently making regulations on the use of organic solvents even more strict (10), including the banning of some traditional solvents, environmentally benign SCFs such as SC-CO2 or non-ozone-depleting fluorocarbons will become even more attractive. In this chapter, we have reviewed some of the basic fundamentals of SCFs and their use for the extraction and fractionation of food-related materials. The relevant physical and chemical properties of SCFs, particularly SCCO2, are initially discussed, followed by the key factors that contribute to successful SFE, namely, the solubility of solutes in SCFs, phase equilibria, and mass transfer considerations. A discussion of the fundamentals required in effectively using SCFs, such as the type of extraction or fractionation, along with choice of solvents, entrainers, and condition of the matrix to be extracted, follows. The various application areas of SCF extraction are discussed at length in following chapters.

II. PROPERTIES OF SUPERCRITICAL FLUIDS The mathematical modeling, simulation, and design of an SFE process require knowledge of physicochemical properties of compounds of interest. Further interpretation of physicochemical property values can be obtained by an understanding of molecular behavior. For example, the ideal gas law [Eq. (1)] is a prime example of the correlation of properties. PV = NRT

(1)

Critical temperature and pressure, compressibility, density, heat capacity, dielectric constant, viscosity, diffusivity, and thermal conductivity are important properties, which are needed for the characterization of both solvent and solutes in an extraction process. Pure-component properties as well as mixture proper-

60

Dunford et al.

ties are used to describe individual solute and solvent characteristics and how the solute and solvent behave as constituents of mixtures. A. Critical Constants The critical point of a pure substance is usually defined as the temperature and pressure at which the gas and liquid phases become indistinguishable. However, in the case of a solid and a liquid, these two phases do not become identical when their densities are equal. Therefore, solid–liquid, solid–gas, and solid–solid equilibrium lines do not end by critical points, as do gas–liquid lines (Fig. 1). When a substance is compressed and heated to its critical point it enters a phase referred to as “supercritical phase.” The matter that is in the supercritical region is called a supercritical fluid (SCF). Temperature, pressure, and molar volume of a substance at the critical point are defined as the critical temperature (Tc), critical pressure (Pc), and critical molar volume (Vc), respectively. These parameters are collectively referred to as critical constants. Each substance has a unique set of critical constants (Table 2). Applications of SFE in food engineering involve processing of many materials, which contain a number of components. Identification and characterization of natural material components are usually very difficult. Therefore, in the literature SFE often characterizes processes above the critical point of the solvent. In this case, mixture of solvent and solute usually exists as two distinct, partially miscible phases below the critical point of the mixture. Similarly, extraction processes just below the critical point of the solvent are usually referred to as near-critical extraction based on the pure solvent properties rather than that of the mixture. Critical properties of small molecular weight compounds can be found in standard handbooks (12, 13). Critical properties of many biological materials cannot be determined experimentally due to the fact that these materials will be decomposed before their critical point is reached. Usually, semiempirical methods are used to estimate critical properties of natural products. The group contribution method is also widely used to estimate critical properties of biological materials. Reid et al. (13) reviewed Ambrose, Fedors, and Joback modification of Lydersen’s method in detail. The Ambrose group contribution method is widely used to estimate critical properties of organic materials. In this method, the critical properties are estimated using the following relations: Tc = Tb[1 + (1.242 + Σ∆T)−1]; Pc = M(0.339 + Σ∆P)−2; Vc = 40 + Σ∆ ν

(2)

The ∆ values for a number of compounds are given in Reid et al. (13). When the compound of interest contains functional groups for which ∆ parameters are not available, use of group contribution methods becomes difficult. In such cases, more empirical models are used (14, 15).

Supercritical Fluid Extraction

61

Figure 1 Phase diagram for a pure substance.

B. Physicochemical Properties A unique feature of the SCF is the adjustability of their density in the supercritical region by regulating the temperature and pressure of the system. An increase in temperature leads to a decrease in density in all cases. Density of a fluid is extremely sensitive to temperature and pressure near the critical point (Pr = 1, Tr = 1). Reduced density (ρr = ρ/ρc) of a pure compound at reduced pressure 1.0

62

Dunford et al.

Table 2 Critical Pressure and Temperature of Common Fluids for SCFE

Fluid Carbon dioxide Chlorotrifluoromethane Ethylene Ethane Propane Propylene Cyclohexane Isopropanol Benzene Toluene p-Xylene Trichlorofluoromethane Ammonia Water

Crit. temp., Tc (°C)

Crit. pressure, Pc (× 105 Pa)

31.1 28.9 9.3 32.3 96.7 91.9 280.3 235.2 289.0 318.6 343.1 198.1 132.5 374.2

73.7 39.2 50.3 48.8 42.4 46.2 40.7 47.6 48.9 41.1 35.2 44.1 112.7 220.4

can be changed from a value of about 0.1, a gas-like density, to about 2.0, a liquid-like density, by regulating reduced temperature in the range of 0.9–1.2 (Fig. 2, Table 3). As the reduced densities become liquid-like, SCF begins to act like a liquid solvent. However, as the reduced temperature is raised to a value of about 1.6, the fluid becomes gas-like due to the decreased density (expansion of fluid) with increasing temperature. Data on the physical properties of mixtures is scarcer than for the pure compounds. An interesting publication of Magee (16) includes a list of references, which report density calculations for CO2-rich mixtures from various predictive models or theories. At high pressures, gases deviate from ideal behavior due to enhanced physical forces between ions, dipoles, induced dipoles, and higher poles, which contribute to the molecular interactions in the system. The potential energy of interaction, Ep, of point charges q1 and q2 can be defined as a function of the permittivity of the medium, ε, and the distance between the charges, r [Eq. (3)]. Ep =

q1q2 4πεr

(3)

The relative permittivity, εr, also known as dielectric constant of the medium, is defined as follows:

Supercritical Fluid Extraction

63

Figure 2 Variation of reduced density of a pure substance in the near-critical region (20).

Table 3 Comparison of Physical Properties of Gas, Liquid, and SCF (98)

Physical property

Gas (1 atm, 15–30°C)

Supercritical fluid Tc, Pc

Tc, 4Pc

Liquid 15–30°C

Diffusion coefficienta (cm2/s) 0.1–0.4 0.7 × 10−3 0.2 × 10−3 (0.2–2) × 10−5 −4 −4 −4 Viscosity (g/cm.s) (1–3) × 10 (1–3) × 10 (3–9) × 10 (0.2–3) × 10−2 −3 Density (g/mL) (0.6–2) × 10 0.2–0.5 0.4–0.9 0.6–1.6 a

Self-diffusion for gas and dense gas; binary mixture for liquid.

64

Dunford et al.

εr =

ε ε0

(4)

where, ε0 is the permittivity in a vacuum. The static dielectric constant (DC) is a useful property to estimate the solvent properties of the relatively polar fluids such as ethanol, methanol, and water. DC is also a density-dependent property and can be changed significantly by modest changes in temperature and pressure of the system. The DCs for SCFs become important because they are a measure of intermolecular force enhancement through dipole–dipole interactions. For example, DC values for CO2 rise rapidly between 70 and 200 × 105 Pa at 40°C and reach liquid-like values around 200 × 105 Pa (Fig. 3) (17). This partially explains the greater solvent power of SC-CO2 for low-volatility compounds at elevated pressures. C. Transport Properties 1. Viscosity Viscosity is an important property, which is needed for the characterization of momentum, mass, and energy transport within a system. The viscosity of a gas increases with increasing temperature at a moderate pressure whereas that of an SCF decreases (18). This phenomenon is attributed to the higher molecular velocity and increasingly hindered “collision transfer” of momentum at high temperature and pressures. Viscosity of a substance changes by a factor of 3–5 in the critical region rather than orders of magnitude (Table 3). Thus, when pressure is increased, gas viscosity approaches liquid-like value more slowly than does density (19). The viscosity of CO2 as a function of pressure is shown in Fig. 4. The viscosity of CO2 is only 0.09 cP even at pressures as high as 300– 400 atm, which is an order of magnitude lower than the typical viscosities of liquid solvents (20). As another example, viscosity of supercritical water (Fig. 5) is less than one-tenth of liquid water, and as a consequence its diffusion coefficient and ion mobilities are one order of magnitude greater than those of the liquid water. Viscosity measurement and relevant estimation techniques for pure and SCF mixtures have been discussed by Vesovic and Wakeham (21) in detail. Magee (16) also reviews the literature related to the viscosity for gas mixtures; however, this kind of information is virtually nonexistent for the SCF-natural product mixtures. 2. Diffusivity Knowledge of diffusion coefficients in SCF is important to the design and efficient operation of SFE processes. The diffusion coefficient is the proportionality

Supercritical Fluid Extraction

65

Figure 3 Density and dielectric constant of carbon dioxide as a function of pressure at 50°C (17).

constant between the molecular flux of a compound and its composition gradient. Fick’s law defines it as follows: J1 = −D12

∂C1 ∂Z

J2 = −D21

∂C2 ∂Z

(5)

where C1, C2, and J1, J2 are the molar concentrations and fluxes of two components, respectively. Fick’s law holds for binary, isobaric mixtures and assumes presence of no external force field. The diffusivity of a pure SCF is orders of magnitude greater than that of a liquid, i.e., liquid CO2, which results in im-

66

Dunford et al.

Figure 4 Viscosity behavior of carbon dioxide (20).

Figure 5 Viscosity of water as a function of density and temperature (18).

Supercritical Fluid Extraction

67

proved mass transfer rates during SFE. In general, the binary diffusion coefficient in SCF increases with temperatures and decreases with pressure at constant pressure and temperature, respectively (Fig. 6). Although the effect of composition on diffusion coefficient is quite small at low pressures, it becomes significant at higher densities. Solute diffusion coefficients decrease with increasing solute molecular mass. Furthermore, molecular mobility is hindered if the solute and solvent molecules are significantly different in size. The experimental methods used to determine diffusion coefficients in SCF

Figure 6 Diffusivity behavior of carbon dioxide (20).

68

Dunford et al.

systems, such as solid dissolution, capillary peak broadening, photon correlation spectroscopy, nuclear magnetic resonance, and radioactive tracer response techniques, have been reviewed by Giddings and Seager (22) and Liong et al. (23). Examples of measured diffusion coefficients reported in the literature are for C16 fatty acid esters (24), caffeine (25), and C18 unsaturated fatty acid methyl esters (26). 3. Heat Capacity and Thermal Conductivity Heat capacity and thermal conductivity data are needed for the characterization of heat transfer behavior in a system. In the critical region, heat capacity at constant pressure is large and goes through a maximum (Fig. 7) (27). However, heat capacity at constant volume shows only small changes in the critical region (Fig. 7). Angus et al. (28) have reviewed the heat capacity data published up to 1976. Magee and Ely (29) have measured the heat capacity of CO2 at constant volume, at a larger density range, 0.2–2.5 times the critical density. They also compared their experimental results with those calculated using an extended Benedict-Webb-Rubin equation of state. In general, the measured and calculated heat capacity values agreed well, except that relative errors were higher at the near-critical region. Magee (16) also reviewed the heat capacity data for pure CO2 and CO2-rich mixtures.

Figure 7 Heat capacities for carbon dioxide at 320 K (27).

Supercritical Fluid Extraction

69

Thermal conductivity of fluids has been recognized as an important transport property, which is needed for the design of the SFE process equipment. Thermal conductivity “λ” is a proportionality constant between the heat flux, Q, and the temperature gradient that exists in the fluid. The Fourier’s law defines it as follows: Q = λ 䉮T

(6)

For most SCFs thermal conductivity increases with increasing temperature and density of the system. Table 4 shows the thermal conductivity data for water and CO2 as a function of temperature at several pressures. The Transient Hot-Wire Technique and The Coaxial Cylinder Method are two techniques that are used to measure thermal conductivity of SCFs over a wide range of conditions (21). Although the Euken expression and correlations based on the work of Dymond had been used to estimate thermal conductivity of dense fluids, the calculated values did not agree well with the experimental data (21). Nieto de Castro (30) has reviewed the literature on the thermal conductivity in SCF.

III. EQUILIBRIUM PROPERTIES A. Phase Equilibrium Process modeling and operating conditions, as well as economic feasibility for an extraction process, require information on phase equilibria between the solute(s) and solvent(s) involved. This information can be obtained experimentally or via phase equilibrium calculations. Phase equilibrium calculations require an equation of state, which describes the mathematical relation between volume, pressure, temperature, and composition in the system. Deviations from ideal gas law are expressed in the equations of state such as van der Waals [Eq. (7)] and Redlich-Kwong (31) or Peng-Robinson equations (32).

Table 4 Thermal Conductivity [λ(mW/mK)] of Some Fluids Used in SCF Technology (21) T = Tc + 20 K

CO2 H2O

T = Tc + 100 K

P = 0.1 MPa

P = 10 MPa

P = 0.1 MPa

P = 10 MPa

Tc (K)

18.8 54.0

51.1 67.8

25.5 63.7

31.9 73.0

304.1 647.1

70

Dunford et al.

冉 冊 P+

a (V − b) = RT V2

(7)

where a and b are the constants specific for each gas. The van der Waals equation of state assumes that molecules restricted to a volume (V − b) and repulsive interactions cause molecules to behave as small but impenetrable spheres. Attractive forces between molecules reduce the pressure exerted by a real gas. The critical constants can be calculated from the van der Waals equation by setting the first and second derivatives of Eq. (7) with respect to zero to obtain the following relations: Vc = 3b; Pc =

a 8a ; Tc = 2 27Rb 27b

(8)

Through algebraic manipulation, then, the critical compression factor, Zc, can be given as: Zc =

PcVc 3 = RTc 8

(9)

In a similar fashion, dimensionless reduced properties of a gas can be expressed as ratios of the respective critical properties, as: Pr =

P V T ; Vr = ; Tr = Pc Vc Tc

(10)

The “law of corresponding states” (33) states that if reduced variables are used to define the state of gases, all gases show the same PVT behavior; in another words, two different gases, each at the same Pr and Tr, will have the same Vr. Using this analogy, van der Waals’ equation can also be written in terms of reduced variables as: Pr =

3 8Tr − 3Vr − 1 V 2r

(11)

This equation eliminates the constants a and b and provides a more direct method of estimating physical properties. Other equations of state, their mixing rules (13, 34–36) along with PVT surface measurements, and their correlations for a number of fluids (37), are available in the literature. Models describing equilibria between SCF and condensed phases are classified in two groups: fluid–liquid and fluid–solid equilibria (31, 38–41). Van Konyenburg (42, 43) developed a system for classification of phase behavior for binary fluid mixtures. This system groups the phase behavior of fluid mixtures into six general types, which are distinguished by the behavior of their critical lines (20, 32, 44). Rizvi et al. (32) has reviewed the phase diagrams of

Supercritical Fluid Extraction

71

class I and class III mixtures for representative of typical biomaterials of interest to the food industry. Modeling of such phase equilibria shows highly nonideal behavior due to the complexity of these natural products, which vary enormously in molecular size and chemical nature. Despite these complexities, equations of state have been developed to predict phase behavior of natural substances such as fatty acid esters (45), vegetable oils (46), rapeseed oil (47), and limonene (48) in SCFs. The equations of state calculations require the knowledge of acentric factor, ω, which is defined as follows: ω = −logPvpr − 1

(12)

where Pvpr is the reduced vapor pressure at Tr = 0.7. The acentric factor is a macroscopic property that measures the extent to which the force field around a molecule deviates from spherical symmetry. The acentric factor is practically zero for small, spherical, or highly symmetrical and nonpolar molecules. Several experimental and predictive methods for the determination of the acentric factor of compounds have been reported in the literature (13). Araujo and Meireles (45) used Tu’s (49) indirect method and correlation of Vetere (50) to estimate the acentric factor for high and low molecular weight fatty acids, respectively. The study showed that acentric factor of the compounds increased with increasing carbon number and linearly decreased with increasing double bond in the chemical structure. B. Solute Solubility The development of mathematical models describing the solubility behavior of solutes in SCFs requires an understanding of intermolecular interaction among solute and solvent, as well as solute and solute. Thus, the vapor pressure and the solute–solvent intermolecular interactions are key properties that determine the solubility of a solute in a supercritical solvent. The effect of temperature on solubility manifests itself in the solute’s vapor pressure, the SCF’s density, and molecular interactions in the supercritical phase. At low pressures, solubility decreases with increasing temperature due to the lower SCF density. However, at higher pressures, the SCF’s density is only slightly affected by temperature, so that solute solubility increases with temperature through its effect on solute vapor pressure. In the highly compressible near-critical region, it is also known that the solvent “clusters” around the solute due to the attractive intermolecular interactions (23). This effect is especially important in entrainer systems because abnormally high local solvent density around the solute may preferentially attract entrainer to the solute. Unfortunately, many solubility models assume infinite

72

Dunford et al.

dilution of a solute in a solvent (SCF) and no solute–solute interactions. However, there is experimental and computational evidence that strength of solute– solute interaction may be strong in very dilute solutions (14). One measure of solvent power (capability of a solvent to dissolve various solutes) of an SCF can be described qualitatively by the solubility parameter approach (51). The concept of solubility parameter was first defined for liquids in the condensed phase by Hildebrand and Scott (51) as follows: δ=

冉 冊 −Ev Vl

1/2

(13)

where Ev and Vl are heat of vaporization and molar liquid volume, respectively. A liquid’s solubility parameter is theoretically calculated knowing the heat of vaporization and molar volume of the liquid. Relating the solubility parameter to the energy of vaporization for dense gases is conceptually difficult because the heat of vaporization becomes zero at a solvent’s critical temperature and the vaporization process cannot occur under supercritical conditions. However, Giddings (52) has combined the van der Waals equation with the theory of corresponding states, to yield and empirical relationship, where the solubility parameter is defined by the following formula: δ = 1.25P1/2 c

冋册 ρ ρliq

(14)

where Pc, ρ, and ρliq are critical pressure and density of gas and liquid, respectively. When the solubility parameter defined by Eq. (14) is plotted against pressure, the relationship depicted in Fig. 8 resembles that of density vs. pressure (Fig. 2). At infinite compression, the SCF’s solubility parameter in Eq. (14) reduces to: δliq = 1.25P1/2 c

(15)

where ρ = ρliq, approximating the solvent power of a gas in its liquid state. Returning to Eq. (14), the solubility parameter is a function of density, which is a state variable. The contribution of δliq to δ is known as the “chemical effect” and is dependent on the choice of SCF, while the contribution of ρ/ρliq is attributed to the “state effect” (53). Under supercritical conditions, a large δ is obtained when pressure is high and temperature is slightly over the Tc. The solubility parameter of the fluid may vary from zero at low pressures up to liquid-like values ⬃10 (cal/cm3)1/2 at ultrahigh pressures. SCFs have solubility parameter values in the range of 0–10 (cal/cm3)1/2 (53). King and Friedrich (54) utilized the concept of reduced solubility parameter as a measure of the solute–

Supercritical Fluid Extraction

73

Figure 8 Solubility parameter vs. pressure plot for carbon dioxide (97).

solvent interactions to correlate solubility and distribution coefficients in supercritical and near-critical fluids. The molecular structure of a solute influences its solubility in an SCF (54, 55). Stahl and Quirin (56) were the first to report that the presence of polar functional groups such as OH, C=O, and COOH in a molecule significantly reduced the solubility of a compound in SC-CO2. Solubility studies utilizing sterols indicated that increasing the number of carbon–carbon double bonds decreased the solute’s solubility in SCF (55). Substantial work has been carried on the measurement and prediction of solubility of lipid components in SCF, specifically SC-CO2. The literature on solubility of seed oils in SC-CO2 has been reviewed by King (56). For example, Eisler and Friedrich (57) used the Fujishiro and Hildebrand equation (58) to

74

Dunford et al.

estimate vegetable oil solubility parameters in SC-CO2 and reported that calculated values compared reasonably well with the experimental literature data. Similarly, Chastril (14) determined solubilities of a number of lipid components such as stearic and oleic acid, tripalmitin, butyrin, and olein. Solubilities of αtocopherol, cafestol, cholesterol, and water were also measured in pure CO2 in the range of 40–80°C and 8–25 × 106 Pa (14). Chastril’s study (14) has related solute solubilities directly to density of SCF rather than using the equations of state for the prediction. The solubility of relatively polar compounds such as free amino acids, e.g., glycine, phenylalanine, tryptophan, and leucine, and sugars such as D-glucose and D-xylose in pure CO2 at 40°C and 5–20 × 107 Pa were also reported, and solubilities of these compounds were very low, of the order of 10−7 –10−8 mole fraction (59). C. Solvent Selectivity The selectivity can be defined as the ability of a solvent to dissolve the desired compound to a greater extent than the other constituents of the mixture. Selectivity of an SCF for component 2 vs. component 1 is defined as: S=

y2 y1

(16)

where y1 and y2 are the binary solute solubility of components 1 and 2, respectively. The selectivity of an SCF can be tuned by regulating temperature and pressure of the fluid. This phenomenon allows the use of SCFs for selective extraction and fractionation processes. Selectivity and solubility of a compound are primarily related to the solute vapor pressure and only secondarily to the intermolecular forces in the supercritical phase (55). For example, although the vapor pressure of both cholesterol and ergosterol is negligibly small, selectivity of SC-CO2 for cholesterol is almost two orders of magnitude higher than that of the ergosterol due to the fact that vapor pressure of ergosterol is lower (10−5 –10−6 Pa) than that of cholesterol (10−3 –10−4 Pa), showing that selectivity follows the ratio of vapor pressures (55). D. Enhancement Factor Frequently in SCF technology, a term called the enhancement factor, E, is utilized to describe the propensity of a solute to partition into a SCF. The enhancement factor normalizes the effect of vapor pressure, allowing an estimate to be made of solute–SCF interactions in the supercritical phase, and is defined as follows (44):

Supercritical Fluid Extraction

E=

75

yactual yideal

(17)

Ps P

(18)

and yideal =

where yactual = actual solute solubility in SCF, yideal = solubility in ideal gas, Ps = vapor pressure of the solid, and P = pressure of the system. The solubility of a solute in a solvent changes rapidly near the critical region, and this is reflected in the enhancement factor. Enhancement factors of the order of 105 –107 are common for solubilization of solutes in SCF. For example, enhancement factors are the same (⬃107) for cholesterol, stigmasterol, and ergosterol in CO2 at 35°C and 200 atm, which indicates that the sterol-CO2 attractive forces are similar for these sterols (55). Therefore, as noted above, fractionation of the sterols via SFE is basically dependent on the vapor pressure of the components.

IV. MASS TRANSFER A. Mass Transfer Principles The economical scale-up and design of commercial SFE processes requires mass transfer data for the system. Even though SFEs have excellent mass transfer properties, such as gas-like diffusivity and viscosity, practically no surface tension contribution at interface, liquid-like density, or pressure-dependent solvent power; SFE processes still have mass transfer limitations. For example, if the rate-limiting step or the largest mass transfer resistance of a separation process is the transfer of the solute from the surface of a solid to the SCF phase, the gas-like diffusivity of a SCF will enhance diffusion and consequently speed up the extraction. However, if the extraction is from a liquid phase into a SCF phase, mass transfer in the liquid phase will be the rate-determining step. Similarly, if the extraction is occurring from within a solid, then the internal solid phase diffusion will control the rate of mass transfer. It has been established by the experimental measurements that initially at high solute concentrations in the solid matrix, the mass transfer rate from a solid matrix into a SCF is constant. This region of the extraction curve is referred to as steady-state or solubilitycontrolled mass transfer region. However, after a certain amount of material is extracted, this rate starts to decline and the region is called diffusion-controlled mass transfer region (56). A typical SFE curve for oil production from a solid matrix is shown in Fig. 9 (60). Interfacial tension (IFT) is an important parameter that influences mass

76

Dunford et al.

Figure 9 A typical curve for an SCFE process (60).

transfer and solubility of compounds in SCFs. Packed columns have been used for fractionation of liquid mixtures utilizing SCF technology. The design of such systems requires mass transfer data, including the wetting characteristics of the packing surface by the fluid. Literature on the study of IFT in SCF is scarce and usually deals with model systems. However, Simoes et al. (61) reported the data on the IFT of crude and refined corn, coffee, and citrus oils in SC-CO2. It was shown that IFT of the oils decreased with increasing pressure (61). This phenomenon was explained by the breakup and eventually disappearance of the oil droplets on the packing material surface, with increasing pressure. Experiments carried out with corn oil demonstrated that the decrease in IFT was larger above 20 MPa, and at lower pressures only slight changes in IFT were recorded (61). B. Mass Transfer Models Fundamentals of SFE processes for natural materials and food systems are not well defined due to the complex nature of the matrix and difficulty in obtaining

Supercritical Fluid Extraction

77

mass transfer data under high pressure. The majority of the SFE mass transfer models published in the literature are based on extraction of oil (60, 62, 63). A number of researchers have attempted to simulate mass transfer rates in SCFs by using different approaches, such as empirical kinetic models, linear driving force approximations, the shrinking core leaching model, and local adsorptiondesorption technique. For example, Brunner (64) used dimensionless numbers such as Sherwood, Schmidt, and Reynolds numbers to derive correlations for mass transfer coefficients in various solid fixed-bed SFE processes, i.e., coffee beans and oil seeds. It was shown that extraction rate of whole coffee beans was limited by the diffusion rate of caffeine within the solid matrix. Peker et al. (65) also studied a similar system and developed a mathematical model based on a linear driving force approximation of mass transfer and partitioning of caffeine between the water and the SC-CO2. A similar approach was applied to spice extraction by Goto et al. (66). In this study, the SFE of peppermint oil was described by a mathematical model that accounted for both local adsorption of essential oil on lipids in the peppermint leaves and mass transfer from a solid matrix to SCF. Bulley et al. (67) and Lee et al. (68) used a one-dimensional, un-steadystate mathematical model to describe the SFE of a fixed bed of crushed canola flakes. This method assumed plug flow within the seed bed and that axial dispersion was negligible. They calculated canola oil concentration profiles in both SC-CO2 and solid phases and determined overall volumetric mass transfer coefficients. The model adequately described the constant-rate period of the extraction process. Cygnarowicz-Provost (60) used a similar model but included mass transfer coefficients that represented both constant-rate and diffusion-controlled regimes for the determination of caffeine extraction rates in SC-CO2. Likewise, the SFE of grape oil has been simulated using a plug flow model, allowing mass transfer coefficients in both solid and supercritical phases to be calculated (69). Another mass transfer model, which was applied to SFE of oils from herbaceous matrices (70), assumed that diffusivity of the solute in the solid matrix was the only variable in the system. The shrinking core leaching model may be used when the solute to be extracted constitutes a large portion of the solid matrix and is held in the macropores by mechanical or capillary forces as a condensed phase. This model assumes that a sharp boundary exists between a core of material to be extracted, and an outer region, in which only partially saturated SCF exists in the pores. The shrinking core leaching model is valid where the diffusivity of the solute in SCF is much larger than that of the solvent in the solid. Simulation of such a system requires mathematical description of a moving core boundary, which shrinks nonlinearly due to the depleting solute in the pores during the extraction process. Therefore, the following assumptions may be made to simplify the calculations; a pseudo-steady-state diffusion mechanism for extraction from the outer porous layer, negligible fluid velocity at the interface, and the radius of

78

Dunford et al.

the core can be determined by a simple mass balance (62). The shrinking core leaching model has applied to ginger oil (71) and freeze-dried mackerel powder (72). Another example of the mass transfer simulation efforts in food-related systems was published by Dunford et al. (73). They developed a mathematical model to describe the dynamic extraction behavior of oil and water from Atlantic mackerel at different moisture levels. Their model incorporated the interactions between oil and water in the SC-CO2. This model accurately simulated the experimental data in the solubility-controlled region for the above extraction.

V. ENTRAINER EFFECT Addition of an entrainer, which is also referred to as cosolvent, or modifier to a mixture containing a supercritical component can modify the extraction selectivity markedly. Entrainers, which are mostly liquids usually having solubility parameters larger than the SCF component, can be used to increase solvent power of an SCF in addition to density adjustment of the extraction fluid. Hexane, benzene, chloroform, isopropanol, methanol, ethanol, acetone, and water have all been used as entrainers. Ethanol is the preferred entrainer for food applications because it is not toxic and is approved as GRAS (generally recognized as safe) status component. Entrainers are usually chosen to interact specifically with targeted solute, through hydrogen bonding, acid–base interactions, or strong dipole–dipole interactions. Entrainers also increase yields, then, by decreasing the pressure and solvent requirements in SFE. Entrainers can affect the extraction process in many ways: by increasing the volatility of the solutes, by increasing the density of an SCF, by potentially dilating a liquid condensed phase, or by enhancing the miscibility of the components. An entrainer normally increases the solubility of nonvolatile compounds in an SCF by shifting the equilibrium of the binary mixture and/or by shifting the critical point of the solvent phase. Entrainers, which as noted previously are more polar compounds than SC-CO2, can form electron donor–acceptor complexes, i.e., hydrogen bonds, with polar solutes to increase solubilities and selectivities beyond what would be expected based on solubility in the neat SCF. Supporting evidence for the above trend, particularly increasing solute solubility in SCF, was recorded for the addition of a modest amount of cosolvent (less than 10%). For example, Dobbs et al. (74) showed that the solubility of 2-aminobenzoic acid was increased by 600% by the addition of 3.5% methanol into SC-CO2. Several thermodynamic models have been developed to correlate and in some cases to predict the effects of entrainer on solute solubilities in SCF (3). In a related phenomenon, when dry almonds are extracted with SC-CO2 at 40°C and 6 × 107 Pa, an oily extract containing the flavor components is

Supercritical Fluid Extraction

79

achieved (75). However, when the extraction is carried out in the presence of ethanol, two phases—an oil phase free of flavors and an ethanol phase containing flavor components with a small amount of oil—were obtained (75). Influence of an entrainer has also been shown for the extraction of caffeine from coffee beans, where neat SC-CO2 extraction of dry coffee beans cannot efficiently extract the caffeine as a moist coffee bean (5). Another study showed that the pleasant flavor of tea could be extracted with SC-CO2 in the presence of water; however, isolation of flavors by evaporating the water was not possible since most of the flavor components were removed azeotropically during evaporation (75). An excellent example of where entrainer-based SCF separations are necessary is in the fractionation of polar solutes from nonpolar moieties using SCCO2. Thus, substantial differences in the polarity of molecules, such as neutral oil components (triglycerides) and polar lipids (phospholipids), can be exploited for fractionation purposes with the aid of an entrainer such as a GRAS-approved ethanol. For example, Dunford and Temelli (76) and Montanari et al. (77) studied the extraction of phospholipids from canola and soybean lipids, respectively, using SC-CO2 and ethanol mixtures. Initially, neutral lipids were extracted with SC-CO2 and then ethanol was added to SC-CO2 as an entrainer to enhance the extraction of the relatively polar phospholipids.

VI. CRITERIA FOR SOLVENT SELECTION The critical temperature and pressure of a compound are two very important physical properties, when selecting an appropriate solvent for an SFE application. If fluids with high Pc are chosen as solvents, the extraction process will be potentially more expensive due to the higher cost of equipment, which will need to be designed and manufactured for use at high pressures. If Tc of the solvent is exceedingly high, then heat-sensitive materials could be affected adversely during the SFE process. The chemical stability of the solvent under the processing conditions is another important criterion that should be considered in solvent selection. The chemical decomposition of a number of SCF–entrainer mixtures has been reported in literature (37). As an example, methanol can be very unstable in the presence of stainless steel (37). SFCs should also be inert with respect to the processed raw material to avoid reactions during the extraction process. Lowboiling fluids should be preferred as supercritical solvents for ease of removal after processing. The ideal solvent of choice should also be inexpensive, nonflammable, nonexplosive, noncorrosive, nontoxic, and readily available in high purity. Carbon dioxide fulfills those requirements as a fluid for applications of SFE technology for food processing, and it is preferentially used.

80

Dunford et al.

VII. EFFECT OF MATRIX COMPOSITION AND STRUCTURE ON SFE YIELDS AND RATES Extraction yield is an important parameter, which greatly affects feasibility of a production process. Stahl et al. (78) and Friedrich et al. (79) concurrently reported that the yield of an SFE process is influenced by the size and physical structure of the oilseeds. They showed that oilseeds had to be ground to assure complete extraction of the oil. It was also noted that the shape of the ground seeds as well as their size affected the SFE of oil. Similarly, Snyder et al. (80) illustrated that oil yields were quite low with cracked soybeans; however, theoretical yields were obtained from ground or thinly flaked seeds. Similar results were reported with rapeseed (80) (Fig. 10) and canola (81). Water is present in all biological materials at varying concentrations and plays an important role in the SFE and mass transfer kinetics. In some cases, a matrix’s moisture content determines the surface structure and/or activity of the components, such as enzymes. High moisture content may provide an aqueous barrier inhibiting diffusion of SC-CO2 into the matrix as well as diffusion of oil out of the matrix. An example of this phenomenon has been demonstrated by King et al. (82). Here it was reported that the yield of fat extraction with SCCO2 decreased with increasing moisture content of the beef samples. The presence of water in the matrix may act as an entrainer and improve the selectivity of SCF for a solute, i.e., caffeine extraction from coffee beans in the presence of water, which was mentioned earlier in this chapter (5). During

Figure 10 SFE yields as affected by the size of the oilseeds (78).

Supercritical Fluid Extraction

81

essential oil extraction, polar component solubilities in SC-CO2 were improved at higher moisture content in the sample matrix (83, 84). Swelling of plant materials in the presence of water may also be quite significant for extraction because it widens plant cell capillaries, thereby increasing the matrix porosity with subsequent improvement in solute diffusion (85). This phenomenon reduces the extraction time or may even make an extraction possible. It is important to note that water present in the feed material may be coextracted along with the compounds of interest, affecting the quality and purity of the final product. This effect was demonstrated for oil extraction from canola flakes and fish muscle using SC-CO2 by Dunford and Temelli (86, 87).

VIII. SCF PROCESSING SCHEMES A. Extraction A schematic diagram of a basic SFE process is shown in Fig. 11. As noted in the figure, the major equipment components are the extractor, separators, heat exchangers, pumps, and compressors. If desired, more than one extractor can be connected to the same system. The selection of equipment for a food processing system must ensure a sanitary and safe process. All surfaces, including seals and gaskets, in contact with process fluids and solids must be suitable for food processing and sterilization. Construction materials and fittings must be suitable for the pressure and temperature conditions required by the process. The solvent, i.e., CO2, is pumped into the system as a liquid or gas. If the solvent is pumped as a liquid, it will be cooled in a reservoir or it will be kept in liquid phase by cooling the pump head. Depending on the process requirements a system for entrainer addition can be incorporated into the system. Methods for entrainer addition to an SCF system were reviewed by Dunford (88). The fluid is then pressurized and heated to the desired processing conditions. A solvent preheater can be installed into the system to avoid temperature fluctuations due to the solvent being pumped into the system at a lower temperature. In the case of solid feed material processing, the matrix to be extracted will be packed into the extraction cell in a mesh basket or between frits to prevent its being carried from the cell during extraction. Following extraction, the pressure is reduced to precipitate the extract through a control valve. A high-pressure metering valve can control the flow rate of the fluid. The pressure of the system is controlled by the rate of pumping and a back-pressure valve setting. A computer system can be used to control the extraction conditions. Choosing a continuous process rather than a batch or semicontinuous process may significantly improve the economics of an industrial scale process. Continuous transport of a large volume of solid feed, such as oilseeds, into and out of a high-pressure extractor is costly and difficult. However, advances in

82

Dunford et al.

Figure 11 Typical flow diagram for a SFE process.

high-pressure technology may allow continuous feed of solid materials. Today an extractor design that allows intermittent loading and unloading of solid material through the lock-hopper vessels fitted below and above the extractor while it is pressurized is used in the coffee-decaffeinating plant in Houston, Texas (20). A portion of the solid is discharged to the bottom hopper, while fresh feed is simultaneously charged to the extractor from the top hopper. In the meantime, SCF continuously passes through the extractor countercurrently relative to feed. Such an extractor design has several advantages. Sequencing these operations minimizes raw material feeding and vessel unloading times. The compression costs are lowered since feed loading and unloading are carried out simultaneously while maintaining pressure. Simulated moving-bed technique has been developed (89) in an effort to approach a countercurrent solid–fluid extraction process. In this method, a single fixed-bed column is subdivided into several zones. The apparent movement of the solid is achieved by switching between valves connected at the junctions

Supercritical Fluid Extraction

83

between zones. The solid and extract withdrawal ports are periodically shifted in the direction of the solid movement and opposite to the fluid flow, thus simulating a countercurrent process. This technique has been applied to extraction and fractionation of tocopherol from oleic acid (89) and fragrance and essential oil extraction (90). B. Fractional Extraction Fractionation of components of a mixture can be achieved by various modes of fractional extraction. One fractional extraction method is to collect fractions as a function of time throughout a semicontinuous process. In such a case, the extract composition changes over time due to the changes in the composition of residual material in the extractor. Components that have higher solubility under the initial processing conditions will be selectively extracted in the beginning. Then, as these components are depleted from the matrix, the extraction selectivity will shift to other components. A good example of this behavior would be during SFE of oilseeds. Friedrich and Pryde (91) showed that the amount of phospholipids extracted from full-fat soybean flakes with SC-CO2 increased in the fractions collected toward the end of the extraction process. Fractional extraction can also be achieved by increasing solvent density over time. This mode of extraction using incremental pressure programming has been applied to fish oil ethyl esters (92), rice bran oil (93, 94), and phytosterol recovery from vegetable oil deodorizer distillates (95). Fractional extraction using an entrainer in the supercritical phase is another approach, which was discussed earlier in this chapter (76, 77). C. Column Fractionation SFE from a liquid phase can be performed utilizing a vertical column. This process is also frequently referred to as supercritical fluid fractionation (SFF). The feed may be a liquid mixture or a solution containing solutes that are solids under extraction conditions or at slurry of solid particles. The column is usually filled with a packing material or has an internal structure such as trays or baffles to increase the contact area between fluid solvent and feed material and to enhance mass transfer. The process can be carried out in semicontinuous or continuous mode. The feed and solvent could be in the batch and continuous modes during the semicontinuous and continuous processes, respectively. Continuous column operations tend to be more efficient and the feasibility of the operation can be improved. Countercurrent operations are inherently more efficient than concurrent operations due to the larger concentration difference between solvent and feed stream, which results in better mass transfer rates.

84

Dunford et al.

A schematic diagram of a typical countercurrent SFF process is shown in Fig. 12. The feed is pumped into the column near the top, or at a point between the column top and base, or from the bottom of the column. Because of their relative densities, the liquid phase descends while SCF phase rises through the column during a countercurrent operation. The stream leaving the top of the column contains the extract and the liquid leaving the bottom of the column is the raffinate. The column can be operated at constant temperature, or a temperature gradient can be imposed above the feed entry point. Thermal gradient imposed on the column causes condensation of larger molecules in the extract

Figure 12 Flow diagram for a SFF process.

Supercritical Fluid Extraction

85

stream upon their encountering the lower solvent density at higher temperatures upward in the column under isobaric conditions, thus creating an internal reflux in the column. This process is similar to liquid–liquid extraction with reflux and fractional distillation. Refluxing can improve the extract purity if the process is designed properly. Analogous to terms used in distillation and liquid–liquid extraction process with reflux, the lower and upper parts of the SFF column are referred to as stripping and enrichment sections, respectively. The book by Clifford (27) is an excellent reference for a discussion of the fundamentals and mathematical modeling of SFE and SFF processes.

IX. EXTRACT AND SOLVENT RECOVERY Recovery of extracted compounds is usually done by lowering the pressure. Hence, high molecular weight solutes in an SCF can usually be precipitated or phase separated at higher collection pressures than more volatile substances. Fractional separation of extracted solute mixtures can also be achieved in a series of collection vessels by successively lowering pressure on each vessel. This method has been successfully applied to essential oils and spice extracts by Reverchon (96) and Nguyen et al. (8), respectively. Increasing the extract temperature at constant pressure may be another alternative for extract recovery. This method can be used if the solubility of a compound decreases with increasing temperature. This is what occurs mostly with nonvolatile compounds. This method also improves the feasibility of an SFE process by eliminating recompression of the solvent for recycling. Solute recovery by partial depressurization might not be very successful if the solute is a very volatile compound. In such a case, adsorptive recovery methods might be more effective. For example, the solute may be adsorbed on silica, alumina, or active carbon and then separated from the adsorbent at a higher pressure or by thermal desorption, among other options. Use of molecular filters such as zeolites is another method for extract recovery from SCF. In such a case, pore size of the filter media is chosen such that only fluid passes through the filter (permeate) and extracted solute is recovered as retentate (27). Solvent separated from the solute by means of one of the methods mentioned above can be either vented out to atmosphere or recycled to the system depending on the size of the operation. For small-scale equipment such as laboratory scale SFE units, solute-free gas is vented to the atmosphere due to the higher cost of installing a recycling system as compared with the cheaper gas cost. If the solvent is to be recycled, which is the case for pilot and industrial scale operations, it is usually passed through an adsorbent bed before it is pumped back into the extractor.

86

Dunford et al.

X. CONCLUDING REMARKS As was noted in this chapter, SCF extraction offers considerable versatility for the extraction of food-related products and raw materials. In general, the overall SFE process is simple, embodying many of the principles inherent in other extraction processes. New approaches for the continuous handling of solid and liquid materials, in and out of extraction vessels, continues to evolve, and improved equipment design and operational dynamics are making SFE processes much more efficient and competitive. However, in some product applications, SCF technology faces competition from conventional techniques such as molecular (vacuum) distillation, a time-honored technique, although greater selectivity is potentially available from SFE using the described methods. In closing, we would like to offer the following short list of “critical” references that will supplement the material discussed in this chapter. Although this list is not inclusive of all available literature resources, it should assist the reader wishing to learn more about this novel technology and abets the formal references cited in this chapter.

NOMENCLATURE P V N R T Tc Pc Vc ∆T ∆P ∆V Pr Tr Tb ρ ρc ρr a b δ Ev

pressure volume number of moles gas constant temperature critical temperature critical pressure critical volume group contribution constant at constant temperature group contribution constant at constant pressure group contribution constant at constant volume reduced pressure reduced temperature boiling temperature density critical density reduced density van der Waals constant van de Waals constant solubility parameter heat of vaporization

Supercritical Fluid Extraction

Ep ε εr ε0 qi r Ji Ci Dij Vl ρliq δliq S yi E Ps M S Q ω Pvpr Zc λ 䉮T

87

potential energy of interaction permittivity of the medium relative permittivity, or dielectric constant permittivity in a vacuum point charge for compound i distance between the charges molar flux for compound i molar concentration for compound i binary diffusion coefficient for component i molar liquid volume liquid density liquid solubility parameter selectivity binary solubility of component i in the supercritical phase enhancement factor saturation pressure of solvent molecular weight solubility heat flux acentric factor reduced vapor pressure critical compression factor thermal conductivity temperature gradient

BIBLIOGRAPHY G Brunner. Gas Extraction. Berlin: Springer-Verlag, 1997. BA Charpentier, MR Sevenants (eds.). Supercritical Fluid Extraction and Chromatography: Techniques and Applications. Washington, DC: American Chemical Society, 1988. T Clifford. Fundamentals of Supercritical Fluids. New York: Oxford University Press, 1999. I.S.A.S.F. Proceedings of the 5th Meeting on Supercritical Fluids-Materials and Natural Products Processing. Nancy, France: Institut National Polytechnique de Lorraine, 1998. I.S.A.S.F. Proceedings of the 6th Meeting on Supercritical Fluids-Chemistry and Materials. Nancy, France: Institut National Polytechnique de Lorraine, 1999. KP Johnston, JML Penninger (eds.). Supercritical Fluid Science and Technology. Washington, DC: American Chemical Society, 1989. JW King, GR List (eds.). Supercritical Fluid Technology in Oil and Lipid Chemistry. Champaign, IL: AOCS Press, 1996. MB King, TR Bott (eds.). Extraction of Natural Products Using Near-Critical Solvents. Glasgow: Blackie Academic, 1993.

88

Dunford et al.

E Kiran, JF Brennecke (eds.). Supercritical Fluid Engineering: Fundamentals and Applications. Washington, DC: American Chemical Society, 1993. E Kiran, JMH Levelt Sengers (eds.). Supercritical Fluids. Amsterdam: Kluwer, 1994. MA McHugh, VJ Krukonis. Supercritical Fluid Extraction, 2nd ed. Boston, MA: Butterworth-Heinemann, 1994. M Mukhopadhyay. Natural Extracts Using Supercritical Carbon Dioxide. Boca Raton, FL: CRC Press, 2000. SH Rizvi (ed.). Supercritical Fluid Processing of Food and Biomaterials. London: Blackie Academic, 1994. E Stahl, K-W Quirin, D Gerard. Dense Gases for Extraction and Refining. Berlin: Springer-Verlag, 1987.

REFERENCES 1. JB Hannay, J Hogarth. On the solubility of solids in gases. Proc R Soc London 29: 324, 1879. 2. PMF Paul, WS Wise. The Principles of Gas Extraction. London: Mills & Boon Ltd., 1971. 3. GM Schneider, E Stahl, G Wilke. Extraction with Supercritical Gases. Deerfield Beach, FL: Verlag-Chemie, 1980. 4. K Zosel. Process for the separation of mixtures of substances. U.S. Patent 3,969,196 (1976). 5. K Zosel. Process for the decaffeination of coffee. U.S. Patent 4,260,639 (1981). 6. DRJ Laws. Hop extracts-a review. J Inst Brew 87:24–29, 1981. 7. MB King, TR. Bott. Extraction of Natural Products Using Near Critical Solvents. Glasgow: Blackie Academic, 1993. 8. U Nguyen. Supercritical fluid extraction applications in the nutraceutical industry. Presented at the Annual Meeting of the Canadian Institute of Food Science and Technology, June 6–9, Kelowna, BC, 1999. 9. PT Anastas, TC Williamson (eds.). Green Chemistry. Washington, DC: American Chemical Society, 1996. 10. F Temelli, JW King. Application of critical fluid technology to nutraceutical extraction and refining. Presented at the Short Course on Nutraceuticals and Functional Foods. February 20–24, College Station, TX, 2000. 11. M McCoy. Industry intrigued by CO2 as solvent. Chem Eng News 77(24):11–13, 1999. 12. RH Perry, CH Chilton (eds). Chemical Engineers’ Handbook. 5th ed. Japan: McGraw-Hill Kogakushu, Ltd., 1973. 13. RC Reid, JM, Prausnitz, BE Poling. The Properties of Gasses and Liquids, 4th ed. New York: McGraw-Hill, 1987. 14. J Chrastil. Solubility of solids and liquids in supercritical gases. J Phys Chem 86: 3016–3021, 1982. 15. JM del Valle, JM Aguilera. An improved equation for predicting the solubility of vegetable oils in supercritical CO2. Ind Eng Chem Res 27:1551–1553, 1988. 16. JW Magee. Properties of carbon dioxide rich mixture. In: TJ Bruno, JF Ely (eds.).

Supercritical Fluid Extraction

17. 18. 19. 20. 21.

22. 23. 24. 25.

26.

27. 28. 29. 30.

31. 32. 33. 34. 35. 36. 37.

89

Supercritical Fluid Technology: Reviews in Modern Theory and Applications. Boca Raton, FL: CRC Press, 1991, pp. 293–324. P Hubert, OG Vitzthum. Fluid extraction of hops and tobacco with supercritical gases. Agnew Chem Int Ed Engl 17:710–715, 1978. EU Franck. Physicochemical properties of supercritical solvents. Ber Bunsenges Phys Chem 88:820–825, 1984. G Brunner, S Peter. State of art of extraction with compressed gases (gas extraction). Ger Chem Eng 5:181–195, 1982. MA McHugh, VJ Krukonis. Supercritical Fluid Extraction Principles and Practice, 2nd ed. Boston, MA: Butterworth-Heinemann, 1994. V Vesovic, WA Wakeham. Transport properties of supercritical fluids and fluid mixtures. In: TJ Bruno, JF Ely (eds.). Supercritical Fluid Technology: Reviews in Modern Theory and Applications. Boca Raton, FL: CRC Press, 1991, pp. 245–289. JC Giddings, SL Seagar. Method for rapid determination of diffusion coeffients. I&EC Fund 1:277–283, 1962. KK Liong, PA Wells, NR Foster. Diffusion in supercritical fluids. J Supercrit Fluids 4(2):91–108, 1991. KK Liong, PA Wells, NRJ Foster. Solubility of fatty acid esters in supercritical carbon dioxide. Ind Eng Chem Res 31:390–399, 1992. R Feist, G Schneider. Determination of binary diffusion coefficients of benzene, phenol, naphthalene and caffeine in supercritical carbon dioxide between 308 and 333 K in the pressure range 80-160 bar with supercritical fluid chromatography (SFC). Sep Sci Tech 17(1):261–270, 1982. T Funazukuri, S Hachisu, N Wakao. Measurement of diffusion coefficients of C18 unsaturated fatty acid methyl esters, naphthalene, and benzene in supercritical carbon dioxide by a tracer response technique. Anal Chem 61:118–122, 1989. T Clifford. Fundamentals of Supercritical Fluids. London: Oxford Press, 1999. S Angus, B Armstrong, KM de Reuck. Carbon Dioxide International Tables of the Fluid State, Volume 3, Carbon Dioxide. Oxford: Pergamon Press, 1976. JW Magee, JF Ely. Specific heats (Cv) of saturated and compressed liquid and vapor carbon dioxide. Int J Thermophys 7:1163–1182, 1986. Nieto de Castro. Thermal conductivity and diffusivity in supercritical fluids. In: TJ Bruno, JF Ely, eds. Supercritical Fluid Technology: Reviews in Modern Theory and Applications. Boca Raton, FL: CRC Press, 1991, pp. 335–363. JM Praustnitz. Molecular Thermodynamics of Fluid-Phase Equilibria. Englewood Cliffs, New Jersey: Prentice-Hall, 1969. SSH Rizvi, AL Benado, JA Zollweg, JA Daniels. Supercritical fluid extraction: fundamental principles and modeling methods. Food Techn 6:55–61, 1986. KSJ Pitzer. Corresponding states for perfect liquids. Chem Phys 7:583–590, 1939. J Vidal. Equation of state-reworking the old forms. Fluid Phase Equil 13:15–33, 1983. J Vidal. Phase equilibria and density calculations for mixtures in the critical range with simple equations of state. Ber Bunsenges Phys Chem 88(9):784–791, 1984. KE Gubbins. Equations of state. New theories. Fluid Phase Equil 13:35, 1983. TJ Bruno. Thermophysical property data for supercritical extraction design. In: TJ Bruno, JF Ely (eds.). Supercritical Fluid Technology: Reviews in Modern Theory and Applications. Boca Raton, FL: CRC Press, 1991, pp. 293-334.

90

Dunford et al.

38. M Gitterman, I Procaccia. Quantitative theory of solubility in supercritical fluids. J Chem Phys 78(5):2648–2654, 1983. 39. I Procaccia, M Gitterman. Quantitative analysis of supercritical extraction. AIChE J 29(4):686–687, 1983. 40. RT Kurnik, RC Reid. Solubility extrema in solid-fluid equlibria. AIChE J 27(5): 861–863, 1981. 41. RT Kurnik, RC Reid. Solubility of solid mixtures in supercritical fluids. Fluid Phase Equil 8(1):93–105, 1982. 42. PH van Konyenburg, R Scott. Critical lines and phase equilibria in van der Waals mixtures. Phil Trans R Soc (London) A. 298:495–540, 1980. 43. R Scott, PH van Konyenburg. Static properties of solutions. Part 2. Van der Waals and related models for hydrocarbon mixtures. Discuss Faraday Soc 49:76–87, 1970. 44. JM Prausnitz, RN Lichtenthaler, EG de Azevedo. Molecular Thermodynamics of Fluid-Phase Equilibrium, 3rd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1999. 45. ME Araujo, MAA Meireles. Improving phase equilibrium calculation with PengRobinson EOS for fats and oils related compounds/supercritical CO2 systems. Fluid Phase Equil 169:49–64, 2000. 46. E Batista, S Monnerat, L Stragevitch, CG Pina, CB Goncalves, AJA Meirelles. Prediction of liquid–liquid equilibrium for systems of vegetable oils, fatty acids and ethanol. J Chem Eng Data. 44:1365–1369, 1999. 47. L Coniglio, Kobayashi, T, R Gani. Model prediction of supercritical fluid-liquid equilibria for carbon dioxide and fish oil related compound. Ind Eng Chem Res 34: 2473–2484, 1995. 48. Y Iwai, T Morotomi, K Sakamoto, Y Koga, Y Arai. High pressure vapor-liquid equilibria for carbon dioxide+limonene. J Chem Eng Data 41:951–952, 1996. 49. A Vetere. Predicting the vapor pressures of pure compounds by using Wagner equation. Fluid Phase Equil 62:1–10, 1991. 50. CH Tu. Group-contribution method for the estimation of vapor pressures. Fluid Phase Equil 99:105–120, 1994. 51. JH Hildebrand, RL Scott. The Solubility of Nonelectrolytes, 3rd ed. New York: Reinhold Publishing Corp., 1950. 52. JC Giddings, MN Myers, JW King. Dense gas chromatography at pressures to 2000 atmospheres. J Chromatogr Sci 7:276–283, 1969. 53. JC Giddings, MN Myers, L McLaren, RA Keller. High pressure gas chromatography of nonvolatile species. Science 162(10):67–73, 1968. 54. JW King, JP Friedrich. Quantitative correlations between solute molecular structure and solubility in supercritical fluids. J Chromatogr 517:449–458, 1990. 55. JM Wong, KP Johnston. Solubilization of biomolecules in carbon dioxide based supercritical fluids. Biotechnol Prog 2:29–39, 1986. 56. E Stahl, KW Quirin, D Gerard. Dense Gases for Extraction and Refining. Berlin: Springer-Verlag, 1987. 57. RL Eissler, JP Friedrich. Estimation of supercritical fluid-liquid solubility parameter differences for vegetable oils and other liquids from data taken with a stirred autoclave. J Am Oil Chem Soc 65:764–767, 1988. 58. R Fujishiro, JH Hildebrand. The liquid–liquid solubility of cyclohexane and perfluorotributylamine at 25°C. J Phys Chem 66:573–574, 1962.

Supercritical Fluid Extraction

91

59. E Stahl, W Schilz. Solubility behavior of polar natural substances in supercritical gases in the pressure range up to 2500 Bar. Chem-Ing-Tech 50:535–537, 1978. 60. M Cygnarowicz-Provost. Design and economic analysis of supercritical fluid extraction process. In: JW King, GR List (eds.). Supercritical Fluid Technology in Oil and Lipid Chemistry. Champaign, IL: AOCS Press, 1996, pp. 155–179. 61. PC Simoes, R Eggers, PT Jaeger. Interfacial tension of edible oils in supercritical carbon dioxide. Eur J Lipid Sci Technol 263–265, 2000. 62. MC Jones. Mass transfer in supercritical extraction from solid matrices. In: TJ Bruno, JF Ely (eds.). Supercritical Fluid Technology: Reviews in Modern Theory and Applications. Boca Raton, FL: CRC Press, 1991, pp. 365–381. 63. M Poletto, E Reverchon. Comparison of models for supercritical fluid extraction of seed and essential oils in relation to the mass-transfer rate. Ind Eng Chem Res 35:3680–3686, 1996. 64. G Brunner. Mass transfer from solid material in gas extraction. Ber Bunsenges Phys Chem 88:887–891, 1984. 65. H Peker, MP Srinivasan, JM Smith, BJ McCoy. Caffeine extraction rates from coffee beans with supercritical carbon dioxide. AIChE J 38(5):761–770, 1992. 66. M Goto, M Sato, T Hirose. Extraction of peppermint oil by supercritical carbon dioxide. J Chem Eng Jpn 26:401–407, 1993. 67. NR Bulley, M Fattori. Supercritical fluid extraction of vegetable oil seeds. J Am Oil Chem Soc 61:1362–1365, 1984. 68. AKK Lee, NR Bulley, M Fattori, A Meisen. Modeling of supercritical carbon dioxide extraction of canola oilseed in fixed beds. J Am Oil Chem Soc 63:921–925, 1986. 69. H Sovova, J Kucera, J Jez. Rate of the vegetable oil extraction with supercritical CO2. II. Extraction of grape oil. Chem Eng Sci 49:415–420, 1994. 70. E Reverchon, G Donsi, OL Sesti. Modeling of supercritical extraction from herbaceous matrices. Ind Eng Chem Res 32:2721–2726, 1993. 71. M Goto, BC Roy, T Hirose. Shrinking core leaching model for supercritical fluid extraction. J Supercrit Fluids 9:128–131, 1996. 72. Y Ikushima, N Saito, K Hatakeda, S Ito, T Asano, T Goto. A supercritical carbon dioxide extraction from mackerel (Scomber japonicus) powder: Experiment and modeling. Bull Chem Soc Jpn. 59:3709–3713, 1986. 73. NT Dunford, M Goto, F Temelli. Modeling of extraction with supercritical carbon dioxide from Atlantic mackerel (Scomber scombrus) at different moisture contents. J Supercrit Fluids 13:303–309, 1998. 74. JM Dobbs, JM Wong, KP Johnston. Nonpolar co-solvents for solubility enhancement in supercritical fluid carbon dioxide. J Chem Eng Data 31(3):303–308, 1986. 75. JP Calame, R Steiner. Extraction in flavor and perfumery industries. Chem Ind 10: 399–403, 1982. 76. NT Dunford, F Temelli. Extraction of phospholipids from canola with supercritical carbon dioxide and ethanol. J Am Oil Chem Soc 72(9):1009–1015, 1995. 77. L Montanari, P Fantozzi, JM Snyder, JW King. Selective extraction of phospholipids from soybeans with supercritical carbon dioxide and ethanol. J Supercrit Fluids 14:87–93, 1999. 78. E Stahl, E Shutz, HK Mangold. Extraction of seed oils with liquid and supercritical carbon dioxide. J Agric Food Chem 28:1153–1157, 1980.

92

Dunford et al.

79. JP Friedrich, GR List. Characterization of soybean oil extracted by supercritical carbon dioxide and hexane. J Am Oil Chem Soc 30:192–193, 1982. 80. JM Snyder, JP Friedrich, DD Christianson. Effect of moisture and particle size on the extractability of oils from seeds with supercritical CO2. J Am Oil Chem Soc 61:1851–1856, 1984. 81. M Fattori, NR Bulley, A Meisen. Carbon dioxide extraction of canola seed: Oil solubility and effect of seed treatment. J Am Oil Chem Soc 65:968–974,1988. 82. JW King, HJ Johnson, JP Friedrich. Extraction of fat tissue from meat products with supercritical carbon dioxide. J Agric Food Chem 37:951–954, 1989. 83. E Stahl, D Gerard. Solubility behavior and fractionation of essential oils in dense carbon dioxide. Perfum Flav 12:29–37, 1985. 84. N Gopalakrishnan, CS Narayanan. Supercritical carbon dioxide extraction of cardamom. J Agric Food Chem 39:1976–1978, 1991. 85. R von Eggers, W Stein. Hochdruck-extraktion von olsaates. Fette Seifen Anstrich 86:10–16, 1984. 86. NT Dunford, F Temelli. Extraction conditions and moisture content of canola flakes as related to lipid composition of supercritical CO2 extracts. J Food Sci 62:155–159, 1996. 87. NT Dunford, F Temelli, E LeBlanc. Supercritical CO2 extraction of oil and residual proteins from Atlantic mackerel (Scomber scombrus) as affected by moisture content. J Food Sci 62:289–294, 1997. 88. NT Dunford. Use of SC-CO2 for edible oil processing. PhD dissertation. University of Alberta, Edmonton, AB, Canada, 1995. 89. A Bertucco, F Sanmartin, G Storti. Simulated moving bed technology for continuous, countercurrent solid-fluid supercritical extraction. J Supercrit Fluids 8:138– 148, 1995. 90. M Bork, JP Korner. Description of an industrial-size supercritical gas extraction plant for semicontinious operation. Proceedings of 2nd International Symposium on Supercritical Fluids, Boston, MA, May 20–22, 1991, pp. 368–369. 91. JP Friedrich, EH Pryde. Supercritical CO2 extraction of lipid-bearing materials and characterization of the products. J Am Oil Chem Soc 61:223–228, 1984. 92. WB Nilsson, EJ Gauglitz, JK Hudson. Supercritical fluid fractionation of fish oil esters using incremental pressure programming and a temperature gradient. J Am Oil Chem Soc 66:1596–1600, 1989. 93. NT Dunford, JW King. Using a supercritical carbon dioxide fractionation technique for phytosterol enrichment in rice bran oil. J Food Sci, 65(8):1395–1399. 94. NT Dunford, JW King. Supercritical carbon dioxide fractionation of crude rice bran oil using a packed column with characterization of the resultant fractions. Proceedings of the 5th International Symposium on Supercritical Fluids (ISSF) (proceedings on CD). Atlanta, Georgia, April 8–12, 2000. 95. NT Dunford, JW King. Phytosterol-enriched trygliceride fractions from vegetable oil deodorizer distillate using supercritical fluid fractionation technology. Separation Science and Technology 37(2):1–2, 2002. 96. E Reverchon. Supercritical fluid extraction and fractionation of essential oils and related products. J Supercrit Fluids 10:1–38, 1997.

Supercritical Fluid Extraction

93

97. KP Johnston. New directions in supercritical fluid science and technology. In: KP Johnston, JML Penninger, eds. Supercritical Fluid Science and Technology. Washington, DC: American Chemical Society, 1989. 98. LG Randall. Present status of dense (supercritical) gas extraction and dense gas chromatography: Impetus for DGS/MS development. Sep Sci Technol 17(1):1–7. 99. G Brunner, S Peter. Zum Stand der extraction mit komprimierten gasen. ChemIng-Tech 53(7):529–542, 1981.

4 Extraction Systems Rudolf Eggers and Philip T. Jaeger Technische Universita¨t Hamburg-Harburg Hamburg, Germany

This chapter deals with different methods and equipment used for solvent extraction of food components from natural sources. A main distinction is made between systems using common liquid solvents that are industrially applied at greater scale since the first decades of the 20th century and an alternative extraction method using compressed gases, which is called supercritical fluid extraction (SFE). SFE started gaining importance in the mid-1970s, especially as a result of rising consciousness with respect to environmental aspects and the recent development of appropriate high-pressure equipment. In the first section, conventional extraction systems using liquid solvents are discussed, including equipment necessary for pretreatment and solvent recovery. The second section is concerned with selection and design of equipment required for SFE.

I. CONVENTIONAL EXTRACTION SYSTEMS Design of extraction systems and detailed selection of suitable equipment depends on the objective of the process and physical properties of the material to be extracted as well as of the obtained product. The solubilizing ability and selectivity of liquid solvents that are mainly based on water, hydrocarbons like hexane, or alcohol are used to leach or extract certain desired components from the applied source material, which is a naturally obtained solid in this case. This section first focuses on the extraction process itself describing different processing principles and giving an overview of the required equipment. Product quality and extraction efficiency depend very much on conditioning of the solid feed material ahead of solvent extraction, which will also be discussed. Posterior to 95

96

Eggers and Jaeger

extraction, the solvent has to be recovered from the product and the exhausted meal, taking care of the product quality and increasing environmental demands. Finally, an example of a complete plant used for hops extraction by alcohol is described. A. Classification In the past, extraction or leaching was often divided into percolation and immersion methods, referring to whether the solid is completely submerged or the solvent is just trickled through a solid bed (1). In general, classification has become clearer since technical development has concentrated on a few different types of extractors, most of them working continuously as percolators (2). On the other hand, immersion leaching of solid-solvent slurries using agitators or screws has largely lost industrial relevance. Even though many of the former extractor types (3) have nearly disappeared, they are still worth mentioning because older equipment may still be perfect for certain applications. Therefore, it is useful to start out by listing all possible operation modes and the relative working principles in order to document technical improvements in the past decades but finally focusing on the actual state of the art. 1. Operation Mode Batch Extraction. Within batch processing, extraction is carried out in vessels that are filled with the solid matter to be extracted. Afterward solvent is either percolated through the solid bed or added to the vessel until the solid is completely submerged. In the latter case, the solvent-solid mixture may be stirred so as to enhance mass transfer. After a designated holding time, the solvent-extract mixture, called miscella, is drawn from the vessel and the solid matter is discharged. This way of processing has almost completely disappeared because of the required interruptions of the operation for charging and discharging and high amounts of required solvent. Only for some special applications with small product rates, such as extraction of flavors, is batch extraction still used (e.g., extraction of lavender). Operating several batch extractors in parallel with cross-current solvent flow allows achievement of higher product rates but does not make use of the entire solvent capacity. Quasi-continuous Extraction. In order to increase extractor efficiency, several batch extractors can be operated in series using the solvent that is loaded in one extractor for passing it through another solid bed that still contains higher amounts of extractable substances. In this way, solute concentration is enhanced, gradually approaching the total solvent capacity. One or two extractors are always off-stream for discharging and charging. After a certain interval, input and

Extraction Systems

97

output ports are changed so that the solid bed extracted to the highest extent is shut off for discharging and a freshly charged extractor is switched in-line. Usually, extraction is performed countercurrently for maintaining continuously a sufficient concentration gradient of the solute in the solid matter and the solvent. Therefore, the freshly entering solvent is contacted with the solid extracted to the highest extent. Afterward, the solvent is passed through the extractors in the order of decreasing bed age until the solvent is almost completely loaded and is brought into contact with the latest charged solid. Continuous Extraction. For complete continuous operation, the solid matter has to be charged and discharged continuously to and from the extractor. Different types of conveying systems exist depending on whether percolation or immersion extraction is applied. These are presented in the following. In the case of extraction performed under conditions other than atmospheric pressure, either adequate sluice systems for the solid matter are required or quasi-continuous operation must be applied. If, for instance, proper extraction temperatures are above boiling temperature at atmospheric pressure, extraction is to be performed at elevated pressures. With the use of flammable organic solvents their escape through leakages can be prevented by operating under slight vacuum. Table 1 gives an overview of different types of extractor systems, including their field of application with some specific examples. 2. Working Principle Single-Stage Extraction. The simplest apparatus for liquid solvent extraction is a single-stage vessel used for immersion extraction. In order to confine the extraction period, the vessel may be stirred. Therefore, the solvent-solid mixture should form a flowable slurry. The extraction time has to be optimized because after a while extraction kinetics becomes quite slow and additional leaching becomes time consuming. The loaded solvent is retrieved and replaced by fresh solvent. This procedure is repeated several times until the solid matter is extracted to an acceptable extent. Multistage Extraction. In multistage static-bed extraction using a countercurrent operation mode, the fresh solvent enters the vessel containing the most exhausted solid. The extract being retrieved from this vessel is successively passed through a battery of extractors until arriving at the vessel most recently loaded. The extract retrieved from this one is discharged for further processing, i.e., desolventizing. The purpose of this operation is to increase the solute load of the solvent to a maximal value for posterior product separation and for solvent recovery to be as economical as possible. This method of quasicontinuous processing can be carried out with immersed solid as well as a solid bed exposed to percolating solvent. A typical application is the extraction of

Working principle Pharmacy Spices

Field of application

Vegetable oil Vegetable oil, spices, instant material Vegetable oil, spices

Sugar Sugar, vegetable oil Vegetable oil Sugar

Rotocel Carrousel

Stationary basket

Sieve tray belt; sliding cell Crown loop extractor Basket elevator Screw conveyer

Multistage percolator battery Instant material, sugar

Stirred vessel Single-stage percolator Multi-stage percolator

Extraction system

Extraction Systems: Characteristics and Applications

Immersion extraction Static bed percolation Static bed cross-current percolation Quasi-continuous Stationary bed, countercurrent percolation Continuous Rotating cells, countercurrent percolation Rotating bed, countercurrent percolation, stationary sieve tray bottom Stationary bed, countercurrent percolation, rotating feed/ discharging locations Horiz. moving bed, countercurrent percolation Horiz. moving bed, co-/ countercurrent percolation Vert. moving bed, co-/ countercurrent percolation Moving bed, countercurrent, immersion

Batch

Operation

Table 1

Sugar beets

Flaked oil seeds

Sugar cane, soybean oil

Sugar from beets/cane

Wheat germ, paprika

Soybean oil, paprika, pepper, hops

Instant coffee, sugar from beets

Alkaloids Pepper

Examples

98 Eggers and Jaeger

Extraction Systems

99

coffee for instant coffee production by a battery of percolators. At temperatures of up to 180°C and pressures of up to 20 atm, around 40–60% of water-soluble material is extracted from coffee grounds by water in an upstream or downstream fashion. At temperatures above 140°C hydrolysis of polysaccharides takes place enhancing their solubility, which increases the total extraction yield. These sugars are also capable of retaining flavors. A series of four to eight percolators allows the establishment of temperature gradients along the extraction line for taking influence on product yield and quality due to differing solubilities of the various substances present in roasted coffee (4). Figure 1 shows a battery of percolators for instant-coffee extraction. Vessel diameters range from 0.25 to 0.75 m. Typical capacities are around 1 t resulting in bed heights of 4.5–6 m. Applying vacuum to the vessel facilitates loading of the percolators. The ball valve at the top is opened and the grinded coffee enters the vessel. Grind sizes range from 3 to 5 mm, the result of a compromise between favoring mass transfer and limiting pressure drop. After being sufficiently leached, the spent coffee ground is discharged by “shooting” through the ball valve at the bottom of the vessel. Holdup is maintained at 30–35 min showing a tendency to shorter times. An extractor system developed by NIRO (Denmark) for coffee extraction, called FIC (fast instant coffee), comprises seven percolators arranged in a circle as a compact unit (Fig. 2). The percolators themselves are about half the size of conventional percolators reducing residence time to half. By their relatively compact design, high liquid velocities are reached that enhance mass transfer from the ground coffee particles. Operation is carried out in two steps based on water temperatures of 100°C for aroma extraction and 180°C for hydrolysis of polysaccharides. The process works with high efficiency although concentration of the produced liquid extract is somewhat lower than in the case of normal percolators. The complete extraction cycle takes about 1.5 h from the fresh water entering the unit to withdrawing the concentrated coffee solution. For extraction of sugar from sugar beets 10–16 columns are used, each having a diameter of around 1.6 m and a height of 2.6–3.2 m, resulting in volumes of 3–4 m3. The scalded beets (cossettes), resembling shoestring potatoes of 3–7 mm width and 5–8 cm length, are loaded to the vessel by means of hinged covers. Extraction is carried out using water at 40–75°C. Holdup is kept between 50 and 120 min. Different apparatus alternatives also exist such as versatile, chain belt, and ring extractors. Moving-Bed Extraction. Extractors with continuously moving beds are distinguished according to the mechanism of transporting the solid matter and contacting the solvent. Moving-bed extractors can be divided into percolators and immersion extractors. In percolation extraction, the solvent phase is passed through the solid bed, which is mainly stationary with respect to the solid-

Figure 1 Schematic of a percolator battery. Each percolator can be bypassed for discharging/charging as indicated only in the case of the second percolator.

100 Eggers and Jaeger

Extraction Systems

101

Figure 2 FIC extractor for instant-coffee production. (Courtesy of Niro, Denmark.)

containing cell. Most extractors that make use of the percolation principle have conveyer elements such as conveyer belts, frames, cells, or baskets that carry the solid matter through the extractor. Depending on the actual position, either fresh solvent or miscella is sprayed on the solid passing by. Commonly, fresh solvent is sprayed on the most exhausted solid. The miscella leaving here is recirculated and used for extraction of freshly entering solid. Recirculation is also used for enhancing contact time by submerging the bed to a greater extent. As much as possible countercurrent flow of solid with respect to solvent must

102

Eggers and Jaeger

be achieved. Accounting for the flow direction of the draining miscella, the general flow pattern is a cross-countercurrent flow. Nevertheless, when making use of gravity for miscella flow, as in basket elevators or loop extractors, some places of cocurrent flow are unavoidable. If the static solid bed is mixed up at least once during extraction, e.g., by dumping the content of one basket into another, a slightly higher yield may be achieved. To a certain extent, solid mixing already occurs in loop extractors. Cross-current operation of a moving bed means that fresh solvent is used throughout the entire extractor, resulting in rapid leaching of the solid but also causing a high consumption of solvent. An intensive solid–liquid contact, which includes continuous mixing of the solid, is carried out in immersion extraction using screws for conveying the solid through the extractor that is filled with solvent. The extractor may even consist of several different compartments dividing zones of different miscella concentrations. In this case, solid transport is achieved by screws from one trough to the other throughout the whole extractor. The solid is submerged partly within the solvent realizing countercurrent flow between the solid and the solvent. Immersion extraction is keener in facilitating entrainment of small particles than percolation where there is always some sort of self-filtration. Thus, solid content in the miscella after immersion extraction may easily rise to 1– 10%, whereas there are only several parts per million after percolation. B. Industrial Extractors Some general trends concerning efforts for improving extraction efficiency and performance can be observed. Development of an extractor implies a compromise between simplicity with respect to the number of moving parts and transporting systems, such as solvent pumps and solid-conveying systems, and efficiency of recirculation pattern for the miscella. Extractors containing compartments, as opposed to simple conveyer belts, facilitate submersion of the solid, which usually leads to higher extraction efficiencies. Shallow beds that are not divided into separated stages are in danger of forming lakes of solvent freely flowing on the bed surface and mixing miscella of different concentrations. Instead, in deep beds the absolute residence time is often enhanced so that the liquid (miscella or solvent) is sprayed on such a moving bed and dissolving solute on its way, then is drained from the solid in a subsequent section that does not correspond to its solute concentration. For example, full miscella drained from the descending leg of a basket elevator might be collected in the half-miscella tank below the rising leg. In the following, different types of extractors working with a moving bed are presented. Most of these extractor types have existed for several decades. Technical improvements are mostly confined to some details concerning the position of inlet/outlet streams and recirculation of miscella. The extractors are

Extraction Systems

103

subdivided into those containing rotating parts and those in which longitudinal movements are carried out. The latter machines make use of conveying elements such as conveyor belts or chains. In general, capital costs are lower than in case of the rotating principle. Moreover, they can be delivered preassembled to a great extent. Since they mostly work with a shallow bed, extraction results may differ from the previously mentioned rotating deep-bed extractors depending on the material being extracted. In general, the range of application of shallow-bed extractors is somewhat confined due to the extractability and permeability of the meal. The Rotocel extractor is a well-established extraction system developed by Blonox in the early 1960s (5). Then the successor Dravo closed down and fusion of licensees Krupp and Extraktionstechnik in 1990 resulted in production of the Carrousel extractor. The Rotocel consists of up to 18 cells located in a circle, each of them containing perforated bottoms that are periodically opened when the cells pass the discharging section during their rotation. One rotation lasts more than 1 h depending on the extractability of the solid. The rotational energy requirement is fairly low because in spite of large diameters there is little frictional force to be overcome. The Rotocel is a so-called “deep-bed” extractor, with a bed height of 1.8–3 m. The flakes, which may also be introduced as a slurry using miscella, are not moved relative to their neighbors. In this way fragile solid structures can be handled, but there is no additional mixing effect that otherwise could enhance extraction yields. Miscella drawn from the bottom of the cells is collected and used for leaching solid of less bed age. This recirculation system consists of up to 10 miscella pumps. The Carrousel extractor is similar to the Rotocel, except that only the frame of the extraction compartments rotates atop a static sieve tray, the structure of which is shown in Fig. 3. In this way, friction of particles scrapping along the sieve tray bottom needs to be overcome; but on the other hand, particles are kept in movement relative to each other, which enhances mass transfer.

Figure 3 Segment of the sieve tray bottom of a carrousel extractor. Original (concentric) (a) and actual (b) orientation of sieve gaps.

104

Eggers and Jaeger

One rotation takes about 1 h, which means that at a diameter of 8 m the highest relative velocities of particles with respect to the sieve tray bottom do not exceed 7 mm/s. After being exposed to different extraction stages during one cycle, each compartment arrives at the solid outlet position where the complete solid bed of the respective compartment is discharged through the open bottom. The separation walls of the compartments have a conical cross-section (Fig. 4) that helps in discharging the exhausted meal downward. Carrousel extractors have diameters of up to 15.3 m. At a bed height of 2 m an extractor of 8 m in diameter has a capacity of 75 m3. Double-deck carrousels containing diameters of up to 8 m are also in use (Fig. 4). After passing one cycle in the upper deck, the solid drops onto the lower level for extraction during another rotational period. Such an extractor is capable of extracting oil from 2000 t/d soybeans containing 18% of oil using hexane; the residual oil content amounts to 0.8%. Triple-deck carrousels have also been constructed, but poor accessibility to the middle deck for cleaning and repairing is a disadvantage. Figure 5 gives an overview of solid and liquid flow, also indicating the respective extract concentrations in countercurrent extractors like the Carrousel extractor. The weight of rotating parts can be reduced by using a stationary basket extractor originally fabricated by French Oil Mill Machinery Co. In such an extractor, the compartments filled with solid are stationary and only feed spouts and the positions of the solid discharging facility and draw-off of miscella rotate. Usually, 12–20 cells are applied, each of them 1.8–3 m deep. In the meantime, the French Company switched from the stationary basket principle to carrousel-type extractors. Next to rotating extractors also the conveyer belt principle is applied to oil extraction. DeSmet uses sieve tray belts for transporting the meal in their belt extractor. Either fresh solvent or miscella provided by a system of miscella recirculation is sprayed onto the shallow solid bed usually containing heights lower than 0.6 m. The solvent containing the freshly extracted solute drops from the belt into collecting trays. Before leaving the extractor, the solid is treated once more with pure solvent (benzene). A slight vacuum within the extractor is obtained by Venturi nozzles. Belt extractors have the advantage of low apparatus costs and fairly uncomplicated installation. Extraction results depend on a precise adjustment of transporting velocity, amount of liquid sprayed on the solid, and solid bed permeability. Following efforts to submerge the solid bed to the highest extent possible, lakes may be formed on top of the solid moving bed freely flowing to all sides and mixing half and full miscella with one another. Similar to rotational extraction systems, horizontal/vertical working extractors offer different ways of moving the solid for passing it through different stages of extraction. In general, recent development is leaning toward stationary screen plates or sieve trays on which the solid is pushed by cells or frames fixed

Extraction Systems

105

Figure 4 Interior of a double floor carrousel extractor. (Courtesy of Krupp, Hamburg.)

to chain conveyors, as in the case of the Crown loop extractor (Fig. 6). The loop extractor is quite compact with one cocurrent and two countercurrent extraction zones. In the sliding cell extractor from Lurgi (Fig. 7), U-shaped cells run on roller tracks pushing the solid material over stationary screen plates. The screen plates consist of rods aligned in a flow direction. The cross-section of the rods

106

Eggers and Jaeger

Figure 5 Concentration profiles of countercurrent extraction, e.g., Carrousel extractor.

Figure 6 Crown loop extractor.

Extraction Systems

107

Figure 7 Lurgi sliding cell extractor.

is V shaped to prevent clogging. The feed material is introduced into the cells of the upper belt by a filling device. After approximately half of the extraction time, the screen plate ends and the feed is dumped through the open cell bottom into the cell, just arriving at the lower screen plate in the feed transfer stage, as indicated in Fig. 7. Before being discharged, the feed is passed through a final drainage zone. The solvent passes through the feed material countercurrently by spraying the collected miscella onto feed at a previous position; it becomes enriched with extract until finally leaving the extractor. Recently, a sliding bed extractor was developed by Krupp containing a chain-frame conveying system on top of a static sieve tray passing two levels (Fig. 8). Miscella collection is also divided into two levels with a sophisticated recycling system. Full miscella is liberated from fines by integrated hydrocyclones. Bed height is adjusted at 0.5–1.3 m. Baskets fixed to a conveyer belt allow either for construction of tall extractors that take little floor space or for horizontal extractors in case one-floor operation is required. A variety of methods concerning collection and recirculation of the miscella and solvent-solid feed positions exist, always intending to maximize the number of regions of countercurrent flow while confining the number of pumps required. The basket elevator (Fig. 9), also known as the Bollman extractor (6), contains a rising and a descending leg, each of which contains around 15 baskets that continuously descend and rise undergoing different stages of charging/discharging of solid, fresh solvent, and circulated ex-

Figure 8

Sliding bed extractor. (Courtesy of Krupp, Hamburg.)

108 Eggers and Jaeger

Extraction Systems

109

Figure 9 Basket elevator.

tract. The solid bed formed in the baskets is rather shallow taking heights between 0.5 and 0.7 m. The solid is fed to the top basket of the descending leg. At this place, half miscella originating from drainage through the more exhausted solid beds of the rising leg is introduced draining downward through the descending baskets in a cocurrent fashion. The miscella draining from the bottom basket of the descending leg is collected in a sump and drawn off for further processing. Fresh solvent is sprayed on the top basket of the rising leg draining downward through the rising baskets in a countercurrent fashion, while extract concentration is enhanced resulting in the formerly mentioned half miscella, which is collected in a sump at the bottom. After arriving at the top and being contacted to fresh solvent, the exhausted solid is discharged by inverting the basket. A couple of extractor types make use of the screw transporting principle, such as the vertical screw tower from Buckau Wolf, Germany, the Hildebrandt extractor formerly applied to soybeans, and the horizontal helix from Raffinerie

110

Eggers and Jaeger

Tirlemontoise used for sugar beets, all of which have not been in operation to a great extent (5). Two helicoidal screws transport the solid through the doublescrew conveyor called Contex, offered at present by Niro, Denmark. The extraction liquid flows through the solids as a submerged stream by means of gravity due to a slight inclination of the vessel. Complications during operation are caused by disintegration of solid particles and flow conditions that depend on solid compacting. Multiplate tower extractors like the Bonotto extractor make use of rotating plates or paddles for transporting oilseed flakes until they fall through openings in the plates to the floor below. Solvent and miscella are transported upward in a countercurrent fashion. Such problems as bypassing, fines entrainment, and back mixing of miscella inhibited commercial application. C. Safety Aspects Working with volatile and flammable solvents implies risks. During normal operation, a number of measures, such as the use of explosion-protected equipment, working at slight vacuum and continuous control of escaping gases by ignition detectors can for the most part guarantee safe handling. Thus, most accidents involving ignition of solvents or even explosions occur as a result of equipment failure (7). When the plant is shut down and vessels are opened for repair, strict safety guidelines might be missing and residual solvent vapors might come into contact with air, producing flammable mixtures. The U.S. National Fire Protection Association has formed a committee dedicated to safety of solvent extraction plants, which issued the following statement given in part: NFPA 36 Committee on Solvent Extraction Plants: Par. 5–8.3: Extractors, Desolventizers, Toasters, Dryers, Spent Flake Conveyers shall be of a design that minimizes the possibility of ignition of product deposits. Such equipment shall be protected by extinguishing systems using inert gas, steam, or a combination of the two, controlled from a safe remote location. Par. 5–8.1.7: The extractor shall be provided with means to remove solvent vapors so that the concentration of vapors inside the unit in the area where work is required can be maintained at or below 25% of the lower flammable limit, e.g., by a purge fan sized so that it changes the empty air volume of the vessel once every 3 min. D. Conditioning 1. Mechanical Pretreatment Extraction efficiency is highly contingent on preparation of the solid matter that undergoes extraction. Small particles are advantageous to small diffusion

Extraction Systems

111

resistance within the particles. On the other hand, powders of very small particle size require great efforts of milling or grinding. While preparing the solid bed, reagglomeration may even occur and during operation as a trickle bed extractor channels might be formed, resulting in insufficient extraction yields. If the bed’s permeability decreases pressure drop might increase and lakes may be formed on top of the solid bed. Drainage is retarded, which may result in undesired back mixing in case the solid bed is moved through successive extraction zones within the extractor. Furthermore, the so-called fines of very small particle sizes are at risk of being entrained. In general, the content of particles of diameter below 0.5 mm should not exceed 5–10%. Oilseed processing preparation methods may be distinguished depending on whether they are combined with mechanical deoiling or are applied for obtaining solid material of defined size and structure. In the case of oil content above 25 wt%, mechanical deoiling by expression is suitable for economical reasons. Rapeseeds and sunflower seeds both containing more than 40% and corn germs with about 50% oil always undergo previous pressing. Cottonseeds containing about 25% are the limiting case for previous mechanical deoiling. Corn and soy, containing less oil, are usually directly extracted after conditioning. Mechanical deoiling implies changed characteristics of the feed material for extraction, e.g., moisture and cell structures differ from their natural state due to high pressing temperatures. Even agglomerates may be formed that have to be crushed prior to further processing. Furthermore, different equipment for mechanical pretreatment exist due to the type of force applied. The fluted rolling mill (Fig. 10) mainly cuts larger particles into pieces. A hammer mill (Fig. 11) inserts kinetic energy in particles, which is converted to surface energy by increasing surface area during impact with the agitator or the wall. In this case, the fines content is higher than that of the formerly described cutting principle. A roller mill works by pressing two cylinders against each other. In case there is no differential circumferential speed, the seeds are flaked only by com-

Figure 10 Working principle of a fluted breaker rolling mill.

112

Eggers and Jaeger

Figure 11 Working principle of a hammer mill.

pressing. A slight differential speed gives rise to shear forces, and seeds may also be ruptured. Extruders being high-shear devices are also capable of rupturing oil cells prior to solvent extraction or mechanical pressing. For processing of high oil content material the extruder may be provided with a drainage cage similar to those used for screw presses described below. In order to obtain particles of defined size, pelletizing or granulating may be applied by pressing, cutting, or adding moisture. The adequate pretreatment depends on the individual feed material and must be determined by experience. As already mentioned, if the content of extractable substances is fairly high, prepressing is carried out prior to extraction. Commonly, the screw presses used easily reduce the oil content in solids to 10% or less. Following solvent extraction requires less solvent because of the reduced amount of extractable substances. In addition, cell structures are destroyed, mechanically liberating enclosed substances. Using a pressure measurement technique described by Eggers et al. (8), the screw geometry may be adjusted to obtain an adequate pressure profile for achieving an optimal pressing result. Presses of the EP series manufactured by Krupp contain a shaft with a rising outer diameter in flow direction at a constant inner diameter of the strainer cage (Fig. 12) in order to achieve adequate compacting. Recent developments tend to obtain the complete final product only by mechanical treatment (full or final pressing; see section

Extraction Systems

113

Figure 12 Screw press. (Courtesy of Krupp, Hamburg.)

below). Therefore, different steps of pressing according to different operating temperatures may be applied for obtaining products of specified quality and quantity. Usually, elevated operating temperatures result from frictional forces. In order to avoid temperature rising, cooling of the press is needed. On the other hand, rising solid temperature before feeding may help performance and enhance product yield. A press recently developed by Krupp contains different temperature zones within one machine. Here prepressing is carried out at lower temperatures resulting in higher product quality but low yield. Final pressing at elevated temperatures has the objective of enhancing the product yield. Monforts has developed a compact double-acting screw press called Komet having a broad range of applicable source material (9) but without any special facility for cooling. Screws of various slopes are used for solids of different hardness, but the shafts always have the same constant outer diameter. Compacting is additionally regulated by use of different nozzles at the cake discharge spout. Very hard solids should be broken or crushed before being fed to the press. If the residual press cake still contains a considerable amount of extractable substances, solvent extraction follows pressing but the compacted solid cake should pass through a cake breaker first. Presses manufactured by Anderson International under the name Expeller are in use for prepressing and full pressing of a variety of oil containing material such as cottonseed, peanuts, corn germ, and sesame seed. After conditioning for careful adjustment of its temperature, the feed material enters the downspout where it receives a first pressing by a vertical screw (Fig. 13). Leaving this section, the solid arrives at the horizontal barrel containing a second screw for final pressing. The compacting pressure of the solid cake is regulated by a hydraulic cone choke at the position of solid discharge. A couple of different

Figure 13

Pressing by Anderson Expeller. (Courtesy of Anderson International Corp.)

114 Eggers and Jaeger

Extraction Systems

115

Expeller types are offered to meet specific demands. The Expeller 33 prepress leaves 15% residual oil content in the cake for further solvent extraction working at rates of up to 90 t/d feed material. The 33 duplex press was developed to handle especially hard fibrous and high oil content materials such as copra and palm kernels. The residual solid contains about 7% oil. The high-capacity Model 55 allows processing of more than 25 t/d with residual oil contents of 4–5%. Commonly, Anderson expellers are combined with thermal conditioning like cooking and drying of the feed prior to pressing. 2. Thermal Pretreatment Materials that are destined for oil production and contain a large amount of proteins, such as cottonseeds, soybeans, flaxseeds, sesame seeds, and peanuts, must be cooked prior to pressing in order to coagulate proteins and allow efficient recovery of the liberated oil. Therefore, the raw material is maintained for about 20 min at a moisture content of around 10% and temperatures of 90– 95°C. Afterward the material must be dried in a separate step to approximately 3% moisture before entering mechanical pressing so as to stiffen the particles. Materials containing small amounts of proteins only undergo the drying procedure. Drying time strongly depends on the physical properties of the solid particles, e.g., size, moisture, and porosity. Cooking of proteins and starches is combined to adjustment of moisture and porosity by the expander technology. Vapor is introduced into the solid structure during extrusion in a screw press–like device containing steam inlet nozzles at the circumference (Fig. 14). The solid-vapor mixture is expanded at the outlet gap resulting in increased porosity due to explosive-like evaporation from the solid cells. Amandus Kahl offers a ring gap expander disposing of an adjustable ring-formed gap that keeps a defined pressure of the solid moving bed. The resulting material is homogeneous having a high porosity at an increased bed density (compacting). Penetration of liquid in posterior solvent extraction is facilitated while the resulting homogeneous structure and high density facilitate enhanced solid throughput and safe performance of percolators. The expander-extruder-cooker fabricated by Anderson also allows blending of water into the feed material for cooking at a prescribed moisture level. An alternative method of preparing source material for extraction is so-called electropermeabilization. High-intensity electric field pulses cause cell damage and release of liquid contained in the cells (10). Because of its high viscosity, oil cannot be obtained by this technique but it may be applied to fruit juice extraction. E. Solvent Recovery The extracted cake and the product need to be liberated from any solvent residuals by stripping or distillation procedures. Therefore, the miscella leaving the

Figure 14 Schematic of an expander.

116 Eggers and Jaeger

Extraction Systems

117

extractor is passed through a distillation step for stripping the solvent and obtaining a solvent-free product. This product is ready for further treatment, such as liquid extraction of undesired components, enrichment of desired ones, or chemical modification. The exhausted meal is discharged from the extractor and transported to the desolventizing step by conveyer belts or screws. Here the solvent is evaporated in order to obtain a solvent-free solid residual that can be used, e.g., as animal food. Usually, the desolventized meal is toasted to increase its nutritional value as food material. Desolventizing and toasting is usually carried out in two separate steps. Lurgi offers a toaster including so-called pre-desolventizer stages at the top end of the same tower. Steam is passed from below through vapor duct trays holding the meal at various heights of the tower. Double-arm agitators move the meal atop these trays until it drops through outlet holes to the tray below. Within the trays there are also steam-heated sections for indirect heating. Using solvents that are partly miscible with water (e.g., alcohol, acetone), steam cannot be applied in view of posterior separation and solvent recovery. In this case, the so-called flash desolventizing process can be applied contacting the meal with superheated solvent. If volatile flammable organic solvents are used, operation of extraction, distillation, and desolventizing is carried out at slight vacuum to prevent the risk of explosion in case of leakage. If less volatile solvents are used, e.g., water, high temperatures must be applied, e.g., in spray drying of the extract-solvent mixture. Often, high temperatures are disadvantageous with respect to valuable compounds such as flavors and fragrances. Since the 1930s freeze drying is increasingly applied in order to evaporate water, e.g., from the extracted solubles of coffee. Here temperatures of 60–85°C are applied for sublimation of water at 50 Pa from coffee that is frozen at temperatures below −30°C. The highly concentrated frozen coffee is either filled into trays that are moved onto heated plates through a vacuum tunnel or directly scrapped over heated plates by agitators. In the former case, heat transfer is worse but the latter method leads to abrasion of the solid particles. In general, environmental protection and consumer demands afford development of solvent recovery systems that provide adequate cleaning of exhaust air and careful treatment of conserving valuable product components. F. Final Pressing Systems For production of small amounts of edible oil, mere mechanical pressing may be suitable. Physical limits of the minimal residual oil content exist due to strong adhesive forces. In case of rapeseed, a content of 7% of oil remaining in the solid matrix can be achieved only by pressing. Further treatment using solvents is not economic. Accounting for the loss in residual oil compared with a content

118

Eggers and Jaeger

of 1.5% after combined prepressing and solvent extraction and high costs of maintenance in the case of full pressing, production costs of oil coming from either process are usually equal at production rates above 500 t/a. Fruit juices are commonly produced only by mechanical treatment. Belt filter presses, e.g., manufactured by Flottweg or Zentrifuges from Westphalia, both in Germany, are used to mechanically extract juice, such as from apples and cherries. In processing of citrus juice, crushing of the fruit must be avoided since oil and bitter compounds originating from the peel have to be kept low in the juice. The FoodTech Extractor System from FMC, Florida, extracts juice and oil simultaneously by scratching off the peel using two approaching cups while the peeled fruit moves into a strainer tube. Here the juice is separated from the seeds and the rest of the fruit. In general, around half of the weight remains as solid residue subsequently processed to cattle feed supplement. Furthermore, about 0.4% is formed by citrus oil situated in the peel. Next to pressing of the peel, water is used for rinsing the oil, which on its turn requires separation of the obtained emulsion by centrifugation. Quality of peel oil depends on the amount of water used. While the oil obtained directly from pressing may be sold as cold-pressed citrus oil, the oil obtained from a subsequent aroma recovery step using multistage evaporation and distillation, and thus lower in quality is applied for production of soaps and detergents. Within the juice extractor offered by Brown International Corp., the peel oil is obtained before juice extraction. The peel is cut and rinsed with water. Centrifuges carry out separation of the resulting emulsion. G. Complete Extraction Plants Figure 15 shows a flow diagram of a hop extraction plant using ethanol as solvent. Cone hops is fed to a double-deck Rotocel extractor with 16 compartments after being dried. The miscella drawn from the extractor is concentrated by passing a four-stage vacuum evaporator at gentle temperatures. Complete elimination of the alcohol is carried out in a posterior separation step. The spent hops discharged from the extractor is desolventized in a dryer and subsequently pelletized for animal feed application. Recovered ethanol-water mixture is adjusted to the desired composition by rectification and recycled. Compared to alternative CO2 extraction (discussed in the next section), ethanol possesses little selectivity resulting in a product that contains almost the natural composition of extractables given by the feed material.

II. SUPERCRITICAL FLUID EXTRACTION (SFE) Extraction from solid material using supercritical fluid extraction (SFE), especially carbon dioxide (CO2) extraction, is established on an industrial scale for

Figure 15 Flow diagram of a hops extraction plant using ethanol as solvent. (Courtesy of Hallertaler Hopfenveredelungsgesellschaft.)

Extraction Systems 119

120

Eggers and Jaeger

a wide range of applications. Besides some large plants used for industrial production of decaffeinated coffee and hops extract, there are a number of smaller multipurpose plants that obtain extracts from a variety of natural materials, such as spices, herbs, and valuable vegetable/essential oils (11). A. Extraction System In Fig. 16 a general flow sheet of a supercritical fluid extraction is shown. Such a processing line mainly consists of a pressurizing device, a pressure vessel for extraction, one for separation (i.e., solvent recovery), and a couple of heat exchangers. If separation is performed in the most common way by pressure release, the fluid must be vented through a butterfly valve before entering the separator. Separation is carried out at lower pressure than extraction. The type of pressurizing device used to recirculate the fluid by rising pressure back to extraction conditions depends on the state of the solvent fluid coming from the separator. If the pressure in the separator is high enough for the fluid to be liquified by chilling in a reasonable temperature range, piston pumps may be applied. On the other hand, gas compressors working with much lower volumetric efficiencies are needed if the fluid at the pump inlet is in a gaseous state. Nevertheless, the corresponding low separation pressures might be of interest for highly volatile components that are well solubilized by the compressed fluid. Pressure must be reduced considerably for precipitating these solutes. Alternative methods of solvent recovery also exist for a complete isobaric solvent cycle. The solute can

Figure 16 General flow sheet of supercritical fluid extraction.

Extraction Systems

121

either be absorbed by an additional liquid, e.g., water, or adsorbed on a fixed bed, e.g., of activated carbon. Therefore, the pump is just needed for maintaining fluid flow and overcoming relatively low-pressure drops along the processing line. For arriving at the operating pressure and compensating solvent losses, a relatively small additional pump has to be installed. The solvent cycle is commonly represented by a T-S diagram from which energy balances may be drawn that are needed for heat exchanger design. For a usual SC-carbon dioxide (CO2) extraction with liquid CO2 at the pump inlet, Fig. 17 shows this clockwise turning cycle in such a T-S diagram. After being liquified in the chiller C1 the fluid is compressed by the pump P1. Pressure increase is supposed to take place nearly reversibly (isentropically). HE1 heats up the fluid to extraction temperature. Adiabatic expansion (no significant change in enthalpy) occurs in the butterfly valve RV1. Due to the JouleThompson effect, the carbon dioxide is cooled down arriving at saturation conditions. For better separation, the loaded fluid is completely evaporated just before entering the separator in HE2. Besides some large-scale plants for extraction of α-acids from hops (12), there are quite a number of smaller, multipurpose plants for obtaining extracts of a variety of natural materials such as spices, herbs, and solids containing flavors or fragrances, all of them using similar setups as depicted in Fig. 16 (13). For

Figure 17 CO2 cycle, depicted in a T-S diagram.

122

Eggers and Jaeger

decaffeination of green coffee beans, either the beans are moistened or moist carbon dioxide is used (14). Separation and regeneration of the supercritical solvent after decaffeination is usually performed by isobaric adsorption on activated carbon or absorption by water (15). In a similar way, caffeine-free tea can be produced (16). An alternative procedure has been proposed by Buse GmbH for decaffeination of green coffee beans using water saturated with carbon dioxide at pressures of up to 30 MPa (17). B. Batch and Continuous Processing Up to now, supercritical extraction of solids at industrial scale is mostly performed in a discontinuous manner due to the lack of reliable sluice systems for continuous inflow and outflow of solids to and from high-pressure vessels. In order to save expensive operation time, two to four extractors are often operated in turns. Figure 18 schematically shows a plant for solid extraction containing two extractors and an optional second separator in case stepwise pressure reduction is necessary. Figure 19 shows a time schedule of discontinuous supercritical extraction including charging and discharging of four vessels in turns. One vessel is always off-line for depressurizing, discharging, charging, purging, and pressurizing. Different methods have recently been proposed to achieve continuous charging and discharging of solids to and from high-pressure vessels. By using screw conveyers or extruders, the solid is compacted entering into the extractor (18, 19). The resulting pressure drop along the moving solid bed prevents pressure loss while charging the extractor. An alternative multistage sluice system containing various compartments on an axially moving frame has problems of scaling up due to lack of appropriate sealing systems. In case a slurry is formed, continuous processing can be carried out using common piston and membrane pumps (20). The solid–liquid dispersion is introduced into rather tall and thin extractors or columns where the supercritical fluid is directed either co- or countercurrently with respect to the liquid flow. Anyway, using a liquid “carrier” enhances mass transfer resistance from the solid particles into the supercritical fluid phase, which can partly be overcome by finely dispersing the liquid within the supercritical solvent. Finally, discharging of solid powders formed inside the extractor by dissolving the original carrier phase completely within the supercritical fluid may be realized by simply opening a discharge valve at the bottom and blowing out the solid particles together with the supercritical solvent. For pressure release of vessels containing, for example, pressurized carbon dioxide, one must also take into account temperature drops toward the vapor–liquid line or even surpassing the triple point that is situated at a slightly elevated pressure. As a consequence, outflow from the vessel partly takes place as a two-phase flow and blocking by

Figure 18

Flow diagram of an extraction plant with two extractors and fractionated separation.

Extraction Systems 123

124

Eggers and Jaeger

Figure 19 Time schedule of supercritical–solid batch extraction using four extraction vessels in turns, 120 kg per batch, 1200 kg CO2/h at 250 atm.

dry ice (solidified carbon dioxide) may occur (21). The interior of the vessel is easily cooled down to −30°C (Fig. 20). C. Recovery Systems As mentioned above, different principles of solvent (supercritical fluid) recovery have been proposed that are also applied at industrial scale. Next to the energyconsuming method of depressurizing the supercritical solvent, adsorption and absorption methods are used especially in case high amounts of supercritical

Extraction Systems

125

Figure 20 Pressure and temperature decrease during depressurizing of moist carbon dioxide from a 50-L vessel.

fluid must be circulated and the substances dissolved by this solvent are destined for elimination. If the objective is to obtain a valuable product as the extract phase, separation by pressure reduction is usually most suitable. Nevertheless, caffeine that is dissolved by supercritical carbon dioxide for decaffeination of green coffee beans may be absorbed by water in some type of high-pressure liquid–fluid extraction plant such as a cocurrent spray tower, countercurrent packed column, or mixer-settler (22), concentrating the caffeine in a posterior membrane module and obtaining a product of elevated purity (23). The highpressure countercurrent principle is described by Jaeger focusing on the wetting behavior of packing materials at elevated pressures (24). At industrial scale, adsorption of caffeine on activated carbon dominates, but up to now the caffeine is burned for thermal recovery of the adsorbent. Each recovery implies a 10% loss of activated carbon. Recent developments are aimed at obtaining the adsorbate without being destroyed during regeneration. The use of alternative adsorbents, such as ion exchangers (25), is also proposed to enhance processing rates or others with hydrophilic properties in order to facilitate their regeneration by water (26). For dimensioning an adsorber, one has to consider a sufficient length in order to establish plug flow within the adsorbent bed. Therefore, the minimal length-to-diameter ratio is about 10. The required length further depends on the amount of substance to be adsorbed.

126

Eggers and Jaeger

Adsorption capacities range between 25 and 80 g/kg adsorbent. Back mixing should be prevented by keeping fluid velocities at values below a few centimeters per second by choosing an adequate inner diameter. D. Pretreatment Similar to conventional liquid extraction, the solid feed material has to be conditioned properly. Mass transfer should be favored by maintaining small particles, eventually removing skins by pealing or dehulling. On the other hand, the structure of the solid bed should be porous and as homogeneous as possible. Large particles result in high void fraction, but at the same time the mass transfer from inside the particles into the bulk fluid is slowed down. Solid particles that tend to stick to each other (e.g., at high moisture content) often form channels by the fluid or even complete blocking. As a consequence, the solid material is extracted heterogeneously and to a low extent. Figure 21 shows the influence of pretreatment on the kinetics of supercritical extraction of sunflower oil. Extraction of dehulled and flaked sunflower seeds proceeds faster. In general, flaking

Figure 21 Kinetics of oil extraction from sunflower seeds at 50 MPa, 60°C. DHF = dehulled, flaked; UDHM = undehulled, milled; UDHP = undehulled, pelletized.

Extraction Systems

127

appears to be an appropriate approach to pretreatment since the thin layers minimize transport resistance while leaving a firm and porous structure to the bed. E. Vessel Design In supercritical extraction, pressure vessels are needed for supply and recovery of the solvent, the extraction (loading of the solvent) itself, and a number of heat exchangers. According to their dimensions and way of operation, different types of construction and factoring of these vessels are applied (27). Figure 22 summarizes the most important methods of pressure vessel construction. In general, vessels with solid walls and those containing compound (layered) walls are distinguished. Solid-walled vessels are normally produced as single forgings. Using this method, the diameter is limited to about 1 m and the wall thickness to about 150 mm. Greater heights may be obtained by joining several cylinders by circumferential welding. Wall thickness in excess of 200 mm is achieved by joining two half shells by longitudinal welding. The technological limitations of weight and size imposed on the mentioned methods for solid-walled vessels are overcome to a large extent by the use of vessels with laminated walls. The presence of such multiple layers while normally beneficial, can produce complications; the insertion of an adapter or a nozzle in a layered wall requires very careful design. Working with corrosive substances, the multilayer principle has the advantage of separating the corrosion problem from the required pressure resistance. The inner layer is made of a corrosion-resistant material with less tensile strength, whereas the outer layers resist the high pressure. A disadvantage of this type of vessel is reduced thermal conductivity. Usually, contact between the layers is not ideal leaving small gaps that give rise to enhanced thermal resistance. However, at high pressure, this effect is partially reduced due to additional pressing of the layers toward each other. Calculation of wall thickness is carried out according to national codes, such as the Boiler and Pressure Vessel Code of the American Society of Mechanical Engineers (ASME). Especially for design of extractor vessels, rules concerning some construction principles are based on experience gained in the past two decades of industrial application of SFE. The ratio of inner length to inner diameter influences the performance of extraction and should therefore be carefully chosen. At too low an inner diameter, wall effects become noticeable. Although axial dispersion may be relatively low, back mixing becomes relevant within a tall extractor. On the other hand, high inner diameters may result in heterogeneous extraction with respect to the radial position. In most cases, solid extraction is performed upflow. The fluid enters the bottom of the extractor at its center. The extractor contains a so-called product basket that has a sieve tray bottom or, even better,

Figure 22

Methods of pressure vessel construction.

128 Eggers and Jaeger

Extraction Systems

129

a sinter metal bottom. At its circumference, the basket must be sealed towards the inner wall of the extractor to prevent bypassing of the fluid. Since the basket enters the extractor by opening the extractor top, this top must be removable. Therefore, any fixed tubing at the top would have to be replaced for opening the extractor. Therefore, the outlet is positioned just below the top to one side of the extractor. This has to be considered for a homogeneous flow within the extractor because having the outlet on one side results in asymmetrical flow. In the case of discontinuous charging and discharging of solids, the vessel must contain quick-acting closures. Furthermore, cleaning must be facilitated if there is a risk of accumulating precipitates. F. Heat Exchangers The specific heat transport properties of the respective supercritical fluid and their variation depending on operating conditions must be taken into account for heat exchanger design. The rate of heat to be transferred within the fluid cycle, e.g., CO2, may be deduced from the T-S diagram taking the respective points of specific enthalpy. Coming from the pump (Fig. 16) the supercritical fluid, e.g., CO2, enters the heater HE1 at, say, 15°C and 30 MPa. The corresponding enthalpy of CO2 amounts to about −284 kJ/kg. Raising the temperature to 100°C, which gives an enthalpy of −116 kJ/kg, 168 kJ/kg of heat needs to be transferred. At a CO2 mass flow of 500 kg/h the transferred heat flux comes out to be 23 kW. For cleaning purposes, heat exchangers should be constructed as tube bundles or double tubes. In particular, the evaporator following the throttling valve is at risk of being blocked by precipitated extract. Vertical orientation of this heat exchanger helps downflow of precipitating liquid. For the condenser position, different possibilities are discussed leaving some doubts for the best solution. In the course of cooling, a condensate film is formed on the walls increasing heat transfer resistance to the gaseous phase. A diagonal orientation down toward the fluid outlet allows the freshly formed liquid to drop off the tube walls right away, leaving only a thin film of condensate. For laboratory-scale plants, heat exchangers may also be constructed as a coil of high-pressure tube placed in a thermostat bath to keep dimensions small despite relatively high heat transfer area. G. Pumps and Compressors In general, pumps are used to transport liquids through pipes. Every pump has certain operating characteristics due to which mass flow and pressure increase are related for the installed piping. Figure 23 qualitatively shows operating characteristics for centrifugal and piston pumps. Centrifugal pumps that are usually

130

Eggers and Jaeger

Figure 23 Characteristic curves of different types of pumps.

not applied to high-pressure technology show a reciprocal behavior of pressure with respect to mass flow: at higher pressure mass flow diminishes. The pump is actually operated at the point of intersection of the operating line and the characteristic line of the piping due to pressure losses caused by friction. On the other hand, piston pumps that are commonly used for high-pressure purposes ideally maintain a constant mass flow no matter the pressure at the outlet, assuming a noncompressible liquid. Cavitations, i.e., evaporation due to pressure loss at the pump inlet, must be avoided in the case of both centrifugal and piston pumps. So-called net pressure suction height (NPSH) is a measure of the minimal inlet pressure that ensures complete liquid pumping, usually based on properties of water. Since piston pumps have a certain constant volume per stroke, it is very dangerous to shut valves right behind the pump. Pressure will increase until either tubes burst or major damages occurs, e.g., at the pump transmission. Therefore, security devices such as security valves and rupture discs are obligatory. If the fluid to be circulated is highly compressible and the pressure is to be increased, compressors with multistage working principle need to be used. Pressure is increased stepwise with an intermediate cooling step. In each step pressure may be raised by a factor of 3–4. Compressors have a much lower volumetric efficiency than pumps but can work with almost any suction pressure. Supercritical fluid extraction works with fluids under conditions ranging from the need of compressors to the possibility of applying pumps. Usually, the

Extraction Systems

131

circulating fluid enters the pump at elevated pressure. Whether or not a liquid state can be assured at this point depends on conditions of the previous separation step and the chilling capacity. In the following we will assume that a pump is used. The two main different types of reciprocating pumps used for supercritical fluids are plunger (piston) and membrane pumps. Membrane pumps have certain advantages, especially concerning sealing. While piston pumps need special dynamic sealing packing to guarantee hermetic operation, the membrane itself separates the pressure chamber from the pressure-transmitting liquid and the transmission of the pump (Fig. 24). For food and pharmaceutical applications sealing toward, say, lubricants may be decisive for pump selection. On the other hand, the fact that the efficiency of piston pumps is higher than that of membrane pumps is important for realizing high mass flows. For circulating fluids on a high-pressure level but relatively small pressure drops in the cycle, canned centrifugal pumps (hermetic) may be applied. 1. Pump Efficiency (Volumetric Efficiency) Multiplying the stroke volume and the pumping frequency gives a theoretical value of the volume flow of the pump. This volume flow is never reached because of various factors that are quantified by using the so-called pump efficiency, ηp. ηp is composed of a systematic efficiency accounting for back flow through valves, fluid losses, and so forth, as well as an elastic efficiency taking into account elasticity of the pumping head, stagnant volumes, and compressibility of the fluid. The elastic efficiency that usually comes close to the total pump efficiency is defined by (28): ηE = 1 − (εTκ + λE)∆p

H h

with H/h denominating the stroke ratio. In case of a nonelastic piston (λE = 0) and a normal stagnant volume (εT = 1), the pump efficiency for a single acting piston pump comes out as: ηE = 1 − ∆V/V0 where ∆V/V0 is the relative compressibility, which can also be defined as ∆V/ V0 = κ ∆p, κ being the compressibility coefficient. In Fig. 25, the relative compressibility is depicted as a function of pressure for CO2. The compressibility is not only important to the pump efficiency but also to the pulsation of the pump. The volume flow characteristics of a double-acting plunger pump can be seen in Fig. 26. In the case of incompressible fluids one peak is directly followed by the other. For compressible fluids the first part of the peak is cut away and the flow becomes similar to that of a single-acting pump.

Figure 24

Schematic of a piston (a) and a membrane (b) pump.

132 Eggers and Jaeger

Extraction Systems

Figure 25 Relative compressibility of CO2.

Figure 26 Volume flow characteristics of a double-acting pump.

133

134

Eggers and Jaeger

2. Dimensioning of Pumps For solid extraction using supercritical CO2, velocity with respect to the empty cross-section of the extractor should amount to a few millimeters per second. In many applications the resulting mass flow (in kilograms per hour) comes out as about 20 times the content of the extractor (in kilograms), of course still depending on the solubilities in detail. Whether or not a sufficient mass flow can be achieved depends strongly on conditions at the pump inlet and on the desired extraction pressure. Taking into account conditions on the suction and the highpressure side of the pump, an estimation may be carried out using the volumetric efficiency described above. 3. Formation of Gas Hydrates In general, low temperatures are required for assuring liquid state of the fluid at the pump inlet. For many gases, including CO2, there are limits given in case water is present in the cycle. In spite of moist CO2 containing only around 0.02–0.2 wt% water, gas-hydrate crystals may be formed in or near the condenser, growing on the inner walls and possibly blocking tubes and pumps. These hydrates were found to be formed below 10°C and be quite stable, eventually lasting for days after stopping operation (31). H. Industrial Plants In the meantime, several applications for SFE have been established. Two of the most important industrial scale applications are extraction of α acids from hops and caffeine from green coffee beans. The purpose of using supercritical fluids is mainly to obtain a solvent-free product of high quality, carefully treated with respect to temperature and to save costs for environmental protection. A selection of industrial operated plants is shown in Table 2. The product value depends on whether the objective of the process is to obtain the fluid-soluble extract or the remaining nonsoluble substances. Because of rather low solubilities within supercritical CO2 in most cases, product costs are much higher when aiming for the extract. In detail, the costs depend on the solubility of the desired component in the solvent phase; on the possibilities of continuous, quasi-continuous, or semicontinuous operation; and on the type of regeneration of the solvent. Since the used supercritical solvent may have an increased selectivity in comparison with conventional liquid solvents, the final composition of the extract mostly differs from its natural composition within the solid matrix. This fact has to be taken into account considering product stability since many natural components disintegrate rapidly after being isolated or simply when nonsoluble natural stabilizing agents or antioxidants are absent in the obtained extract.

Extraction Systems

135

Table 2 Industrial Applications of SCF-Extraction Company/Country Yasuma (Japan) Fuji Flavor (Japan) Sago Koryo (Japan) Mori Seiya (Japan) Idemitsu Sekiyu (Japan) Takeda Seiyaku (Japan) Essences (Italy) CUB (Australia) Universal Flavors Ltd. (UK) HAG (Germany) Barth (Germany) SKW (Germany) Sumitomo Seika (Japan)

Capacity (L) 100 300 300 500 1000 1200 1200 1000 1000 45.000 12.000 18.000 100

Products Spices, pigments, food additives (19) Tobacco, flavors Flavors, pharmaceutical compounds (19) Flavors, pigments (19) Flavors (19) Pharmaceutical compounds (19) Essential oils (20) Flavors (21) Flavors (21) Decaffeinated coffee (21) Hops (22) Hops (22) Coffee extract (19)

REFERENCES 1. FA Norris. Extraction of fats and oils. In: Bailey’s Industrial Oil and Fat Products, Vol. 2, 4th ed. New York: John Wiley & Sons, 1982, pp. 230–237. 2. MA Williams. Extraction of lipids from natural sources. In: FD Gunstone, FB Padley eds. Lipid Technologies and Applications. New York: Marcel Dekker, 1997, pp. 127–135. ¨ le. Stuttgart: Ulmer, 1993, pp. 319–333. 3. M Bockisch. Nahrungsfette und -O 4. General Foods Corp.: Verfahren zur Herstellung von Kaffee-extrakt. German Patent 2125545, May 22, 1971. 5. HG Schwartzberg. Continuous counter-current extraction in the food industry. CEP: 67–85, 1980. 6. RE Treybal. Mass-Transfer Operations, 3rd ed. New York: McGraw-Hill, 1980, pp. 740–744. 7. CL Kingsbaker: Extractor failure: Safety procedures. Inform 10:1142–1147, 1999. 8. R Eggers, H Boeck, W Stein. Druck- und Temperaturprofile beim Abpressen von o¨lsaaten in seiherschneckenpressen. Fette Seifen Anstrichmittel 87:494–499, 1985. 9. L Falla, J Ospina, E Posada. Determinacio´n de las condiciones de extraccio´n de aceite de cafe´ por extrusio´n. Proceeding of the 13th ASIC Colloquium, Paipa, 1989, pp. 232–235. 10. A Angersbach, MN Eshtiaghi, D Knorr. Impact of high intensity electric field pulses on plant cells and tissues. In: G Barbosa-Canovas (ed.), COFE 97. New York: AICE, 1997, pp. 11, 18–21. 11. E Stahl, K-W Quirin, D Gerard. Dense Gases for Extraction and Refining. Berlin: Springer Verlag, 1987. 12. Brewing Research Foundation. Hop extraction with carbon dioxide. US Patent 4,218,491, Aug. 19, 1980.

136

Eggers and Jaeger

13. Norac Technologies Inc. Process for the supercritical extraction and fractionation of spices. European Patent 0511673 A1, Apr. 30, 1992. 14. HAG AG. Method for the decaffeination of raw coffee. US Patent 3,879,569, Apr. 22, 1975. 15. Studiengesellschaft Kohle mbH. Process for recovering caffeine. US Patent 3,806,619, Apr. 23, 1974. 16. O Vitzthum, P Hubert. Method for the manufacture of caffeine free black tea. US Patent 4,167,589, Sept. 11, 1979. 17. Buse GmbH. Verfahren zur Entcoffeinierung von Rohkaffee. European Patent 90122400.6, Nov. 23, 1990. ¨ berkritischem CO2. Fat Sci Technol 18. R Eggers. Extraktion von Fettrohstoffen mit U 96:513–518, 1994. 19. G Kaschel. Die Schneckenmaschine mit Gassperre als Ein- und Austragsystem fu¨r die Hochdruckextraktion von Pfanzlichen Naturstoffen. Dissertation, Technische Universita¨t Hamburg-Harburg, 1992. 20. Nestle´. Decaffeination process, European Patent 0010637, Oct. 3, 1979. 21. A Fredenhagen, R Eggers. Vessel release of pressurized carbon dioxide containing nitrogen or water. Two-Phase Flow Modeling Exp 1:623–627, 1999. ¨ berkritischem Kohlendioxid an den 22. A Pietsch. Die Gleichstromverspru¨hung mit U Beispielen Hochdruckentcoffeinierung und Carotinoidaufkonzentrierung. Dissertation, Technische Universita¨t Hamburg-Harburg, 2000. 23. A Pietsch, R Eggers. The mixer-settler principle as a separation unit in supercritical fluid processes. J Supercrit Fluids 14:163–171, 1999. 24. PT Jaeger: Grenzfla¨chen und Stofftransport in Verfahrenstechnischen Prozessen am Beispiel der Hochdruck—Gegenstromfraktionierung mit u¨berkritischem Kohlendioxid. Dissertation, Technische Universita¨t Hamburg-Harburg, 1998. 25. HAG AG. Verfahren zur Entcoffeinierung von Ro¨stkaffee. German Patent DE 3303679 A1, Aug. 16, 1984. 26. D.E.J. International Research. Process for extracting caffeine from solutions thereof in carbon dioxide under high pressure. US Patent 4,364,965, Dec. 21, 1982. 27. R Eggers. Design and operation of the pressure vessels used in near-critical extraction processes. In: MB King, TR Bott (eds.). Extraction of Natural Products Using Near-Critical Solvents. Glasgow: Blackie Academic, 1993, chapter 8. 28. R Eggers, G Fischer. Zum Einfluss der Kompressibilita¨t verdichteter Fluide auf den Fo¨rdervorgang in Verfahren zur Extraktion mit Gasen. Pumpentagung Karlsruhe Oct 6th –8th, 1992, B4-02. 29. V Hung, MA Unger: Applications of supercritical fluid extraction in the Japanese food industry. Food Aust 46:235–239, 1994. 30. E Reverchon. Supercritical fluid extraction and fractionation of essential oils and related materials. J Supercrit Fluids 10:1–38, 1997. 31. MV Palmer, ST Ting. Application for supercritical fluid technology in food processing. Food Chem 52:345–352, 1995.

5 Optimization Constantina Tzia National Technical University of Athens Athens, Greece

I. INTRODUCTION Optimization is the act of obtaining the best result possible or the effort for achieving the optimal solution under a given set of circumstances (1). In design, development, processing operation, and maintenance of engineering systems, common goals are either to minimize the cost or maximize the desired profits as product quality and operation yield. A systematic and efficient way to meet these goals is to place the emphasis on design and process optimization for manufacturability/performance (or yield), quality, and cost (2, 3). Such operations should be made efficient by applying the relevant optimization methods and taking the appropriate technological and managerial decisions among all possible alternatives. Optimization can be defined as the process of finding the conditions that give the optimum (maximum or minimum) value of a function of certain decision variables subject to restrictions or constraints that are imposed (2). Optimization may be the process of maximizing a desired quantity or minimizing an undesired one. The conditions (values of the processing variables) that produce the desired optimum value are called optimum conditions while the best of all feasible designs is called optimal design. In its most general meaning, optimization is the effort and process of making a decision, a design, or a system as perfect, effective, or functional as possible. Optimization for a system may mean the design of system parameters or the modification of its structure to minimize the total cost of the system’s products under boundary conditions associated with available materials, finan137

138

Tzia

cial resources, protection of the environment, and governmental regulation, taking into account the safety, operability, reliability, availability, and maintainability of the system. Optimizers or decision makers use optimization in the design of systems and processes, in the production and in systems operation. Some examples of the optimization use are: selection of processes or size of equipment, equipment items and their arrangement, operation conditions (temperature, pressure, flow rate, chemical composition of each stream in the system), equipment combination in specific processes to increase the overall system availability, etc. Formal optimization theory encompasses the special methodology, techniques, and procedures used to decide on the one specific solution in a defined set of possible alternatives that will best satisfy a selected criterion or function. The application of scientific methods and techniques to decision-making problems based on mathematical programming techniques may achieve the optimum of the operation result—the maximization or minimization of the criterion or function. A number of optimization methods have been developed for solving different types of optimization problems and hence various methods of efficient experimentation and simulation are available for performing the optimization. In many applications, the process to be optimized can be formulated as a mathematical model and the optimization experiments may be contacted or simulated in software. Today, even very large and complex systems can be modeled by means of computers, and optimization can yield substantially improved benefits. Optimization methods have been found effective in many areas of engineering design and operation or even in business systems. In chemical processes, optimization methods were developed in an effort to produce high-quality products under normal manufacturing and working conditions, reducing their functional variations under real manufacturing circumstances (low quality of raw materials, poor manufacturing equipment, etc.) and further lowering the cost of making the product (including development and manufacturing). A wide variety of problems in the design, construction, operation, and analysis of chemical plants or industrial processes can be solved by optimization. These methods have particular economical and technological interest for food engineers, as they are widely used in food processes to achieve high efficiency in design, development and manufacturing. Now optimization methods are used routinely in industrial processes of foods. In this chapter, an overview of the principles involved in design and process optimization is presented, the appropriate methods of optimization are described, and the applications of optimization in food engineering, especially in food extraction processing, are reviewed.

Optimization

139

II. OPTIMIZATION THEORY A. Quality Engineering—The Design Optimization Problem The quality of a product or a manufacturing process can be quantified in terms of the deviation from the target performance due to undesirable effects by factors external to the product, manufacturing imperfection or deterioration/changing of product performance characteristics (3). In a representative design optimization problem of a product or a process the common elements must be considered (Fig. 1). The response (y) is influenced by various factors; the dependence of the response y on the factors can be denoted by a function (f) or a mathematical model. Input or signal factors (or pre-assigned parameters) (s) are selected by the engineering knowledge and are set by the operator to attain the intended output (target performance). Control factors (or design or decision variables) (x) are the product or process parameters specification, and their best values (levels) are determined by the designer using a number of criteria. Noise factors (z) are the uncontrollable factors and their levels change from the environmental conditions of manufacture. One approach to reduce the variation of a product or process is to limit or eliminate the noise factors. The role of design optimization is first to center the design parameters specifying the levels of control factors in order to minimize sensitivity to all noise factors. Product or process design can make the product or process robust against all factors, whereas manufacturing process design and actual manufacturing can reduce only the variation due to manufacturing imperfection. A typical engineering problem can be posed as a process represented by some equations or by experimental data, while a performance criterion as minimum cost, maximum yield, etc. The process or model and the performance criterion comprise the optimization problem. The goal of optimization is to find

Figure 1 Block diagram of a product or a process in an optimization problem.

140

Tzia

the values of the variables in the process that yield the best (maximum or minimum) value of the performance criterion while satisfying the constraints—this is the optimization of a given problem. B. Classification of Design Problems Design problems can be broadly classified into static and dynamic problems depending upon the absence or presence of signal factors, respectively (3). Static refer to problems in which the values of the dependent variables remain constant with respect to time, while dynamic problems represent the ones where the process-dependent variables change with time (2). Static problems are also classified depending whether the response is continuous or discrete. The desired value may be the maximum or the minimum value or be categorized into ordered categories. Examples of static problems are the maximum yield of a desired constituent during an isolation process, the minimum product loss during a manufacturing process, or the best sensory quality and acceptability of a food. Dynamic problems are classified depending on the nature of the signal factor and the response variable (continuous or discrete). Examples of dynamic problems are fermentations in food processing in which the response (i.e., yield as product weight or concentration of a constituent, etc.), results by continuous monitoring of a process (viscosity increasing during curd formation in yogurt production, ethanol content increasing during wine fermentation). C. Elements of Optimization Problems—Definitions Optimization determines the values of independent variables that result in an optimal value of a dependent variable. This involves a process for finding the unique set of process conditions that produces the best results, usually after establishing a mathematical model of the product or process. The theory, methods, and techniques used to attain an optimum and to locate the optimum operating conditions are the subject of optimization. Following are definitions of the basic elements and terms that are common to all optimization problems (2, 4, 5). The representation of the relationship of the elements in an optimization problem and solution is in Fig. 2. Performance function or objective function or response or criterion or criterion function or performance measure: This is a function of the design variables that quantifies the desired result. It is the measured variable that determines and reflects the performance of the system or process; it is the quantity to be optimized (maximized or minimized). It is often the profit, cost, product yield, energy consumption, and particularly for food systems the shelf life, consumer preference, quality characteristics, physical properties, etc.

Optimization

141

Figure 2 Elements of an optimization problem.

Decision (independent) variables or parameters or (control) factors: These are the parameters in the process that affect the value of the objective function and can be adjusted to improve performance. They are free and independent variables that must be specified in the process model. In food systems, these commonly include temperature, pressure, flow rates, pH, concentration, moisture content, concentration of ingredients, etc. Constraints: These are the constraints on the allowed solutions that may have a problem to be optimized. The constraints, equality or inequality, limit the values of the dependent variables, the decision variables or even the performance functions of the process. Equality constraints are the fixed values that variables will have in the boundaries. Inequality constraints refer to upper or lower values of the variables that the system may approach at boundaries. Mathematical model: The model is the mathematical representation of the process that determines the performance functions in terms of the decision variables or other independent variables subject to constraint. Modeling of a process is the application of engineering sciences and knowledge to describe mathematically (using mathematics, statistics, numerical analysis, computer software) a process and its performance. Simulation is the use of the model to assess different scenarios. The simulation of the process with a mathematical model facilitates the process optimization against to costly experiments predicting process results for any one set of decision variables. Furthermore, simulation helps to gain understanding, to evaluate alternatives, and to answer specific questions. Optimization is described as the simulation performed aiming to maximize (or minimize) a certain process objective; the search for the desired optimum is usually done using mathematical algorithms.

142

Tzia

Optimization method or technique or procedure: This is the method of searching to find the optimum combination of decision variables possible within the boundary of the constraints. It can be as simple as selecting the combination among all possible combinations that produces the best results from the objective functions in the mathematical model. However, in complex problems where there are many decision variables with a wide range of values, a structured technique based on mathematical algorithms must be followed for solving of the problem. Computers and associated software make the computations involved in the selection (solution) feasible and cost-effective. Feasible solutions: These are values of the design variables that satisfy all the constraints. D. Problem Optimization Procedure In a general case of optimization, the problem must first be formulated and then the system to be optimized must be defined. Following, a function (mathematical model) that describes the system must be constructed and then the system’s optimum solution must be found. The simulation of the process by a mathematical model as well as the selection of the appropriate optimization method is fundamental to a successful process optimization. In all optimization problems, the above elements must be considered (6). A typical optimization problem procedure including the individual optimization steps and the relationships between the important optimization elements are presented in Fig. 3. 1. Optimization Problem Formulation Formulation of problem optimization requires identifying the essential elements of a given system and organizing them into a mathematical form, namely: (a) the objective function (criterion) and (b) the process model and constraints (2). It means that the variables that affect the performance of the system and the variables that determine and reflect the system’s behavior must be specified. The objective function represents profit in terms of the (key) variables of the process being analyzed and the process model and constraints describe the interrelationships of the (key) variables. A systematic approach for assembling the physical and empirical relations and data involved in an optimization problem and procedures are recommended. The need for formulation of the problem has been illustrated by examples of relative optimization problems. Problem formulation and establishment of a satisfactory function that describes the behavior of the criterion as a function of the independent parameters is the most critical aspect in a problem optimization and usually the most difficult step of a successful optimization study; artistry, judgment, and experience is required during the problem formulation step of optimization.

Optimization

143

Figure 3 Problem optimization procedure.

For the formulation of problem optimization the following steps must be carried out: 1. Description of the system to be optimized. The system boundaries must be clearly defined so system parameters become independent of external parameters. All the subsystems that significantly affect the performance of the system should be included in the optimization problem. When dealing with complex systems, they are usually divided to subsystems that are optimized individually (suboptimization); similarly, during suboptimization the subsystem boundaries must be carefully selected.

144

Tzia

2. Definition of a criterion (parameter). A single dependent parameter is defined, that serves as the overall evaluation criterion for the specific optimization problem. It will be maximized or minimized to satisfy the objectives and measures the degree the solution satisfies the desired objectives of the problem. Often, only one primary criterion is used as an optimization performance measure and all other criteria are treated as problem constraints or parameters. However, in complex problems, where the specification of the optimum solution is complicated by conflicting criteria, a single parameter must be established for optimization needs, taking into account the relative importance of all relevant criteria. In practice, if it is desirable to develop a design that is “best” with respect to many, usually competitive, criteria, advanced techniques for solving the optimization problem must be applied, i.e., simultaneously minimizing cost and environmental impact, while maximizing efficiency and reliability. The selection of criteria on which the system design will be evaluated and optimized is a key element in formulating an optimization problem. Optimization criteria may be economical (total capital investment, total annual costs, annual net profit, return on investment), or technological (profitability evaluation criteria) (thermodynamic efficiency, production time, production rate, reliability, total weight, etc.), and environmental (e.g. emitted pollutants). An optimized design is characterized by a minimum or maximum value, as appropriate, for each selected criterion. 3. Definition of controlled or independent parameters. These are parameters that affect the performance of the process or system and can be adjusted, changed, and controlled. Their values determine the criterion parameter value and must lie within the boundaries of the defined problem. The most critical element in optimization problems is the selection of all the appropriate variables that contribute to the decision-making process of attaining the optimum. The variables must include all the important variables that affect the performance and cost effectiveness of the system, while not including details or variables of minor importance. Functional and regional constraints: Functional constraints represent physical or functional interrelationships that exist among the independent parameters, while regional constraints limit the range over which the independent parameters can vary; they must be considered in addition to the parameters in optimization problems. Other parameters: Formal optimization depends on a clear definition of the system to be optimized. It only works for the specific system described where all criteria and parameters are defined within the system and are isolated and independent from other parameters outside the boundaries of the system. Therefore, formal optimization theory should be used only to find optimum parameters in well-defined systems with unique, quantitative-dependent and -independent parameters, criterion function, and functional and regional con-

Optimization

145

straints. So, the clearer the description of a given system, the more sure will be in obtaining the best solution. Furthermore, after the optimum set of parameters has been determined, other parameters, not included in the original problem, may be used and then compared with the others. Suboptimization: It is usually applied to complex systems, where the optimization of the entire system may not be feasible, either due to the problem formulation or the inadequate optimization techniques. It can be also applied for economic and practical reasons. Then, the optimization of one subsystem as part of the whole problem is done, ignoring some variables that affect the objective function or other subsystems; it does not however ensure the optimization of the overall system. 2. Development of Mathematical Model—Design and Evaluation The further procedures for formal optimization and solution techniques include the development of a model, model design, and application. That means the forming of the model, establishing and treating constraints, determining feasible solutions, and assigning of performance measures (6). In most cases of optimization, models are used instead of trial-and-error experiments and statistic techniques are applied. Design includes general description and specification of the programming technique and algorithms, formulation of the mathematical description, and simulation of the model. In order to obtain a mathematical description of the process or system to be optimized, a mathematical model of the process or system is formed (7, 8). A mathematical model is a description in terms of mathematical relations of the functions of a physical system; the way variables are interrelated and the independent variables affect the performance criterion are described by mathematical expressions. Models should closely represent the system and allow the determination of the performance functions in terms of the decision variables; consequently, development of a model requires a good understanding of the system. The optimizer has to select the specific mathematical representation to be used in the model, as well as the assumptions and limitations of the model. All the important aspects of the problem should be included, so that they will be taken into account in the solution (specific values of the variables, assigned variables that are functions of time, other independent variables, satisfaction of constraints or certain goals, etc.). Care must be taken not to omit any significant factor to save time. The degree of accuracy needed in the model must be determined. A successfully developed model helps to solve of the optimization problem. Process design simulations and flow-sheeting software are also very useful during modeling. Model building is completed by evaluation and application; it

146

Tzia

is an iterative process as improved models can be produced by feedback of information. The mathematical model for an optimization problem consists of: 1. Objective function to be maximized or minimized f(x) objective function 2. Equality and inequality constraints h(x) = 0 equality constraints g(x) ≥ 0 inequality constraints

(a) (b) (c)

where x is a vector of n variables (x1, x2, . . . , xn), h(x) is a vector of m1 equations, and g(x) is a vector of m2 inequalities. The total number of constraints is m = (m1 + m2). 1. The objective function expresses the optimization criterion (C) as a function of the dependent and independent variables (x1, x2, . . . , xn) and may be represented by: C = f(x1, . . . , xn)

optimization criterion function

(d)

The equality and inequality constraints are provided by appropriate models as well as by appropriate boundary conditions. These models usually may be a cost function, a benefit or profit function, or functions associated with material and energy balance with engineering design. The models also contain equations and inequalities that specify the allowable operating ranges, the maximum or minimum performance requirements, and the bounds on the availability of resources. System models may be linear or nonlinear (set of algebraic, differential, or partial differential equations), static (focused on steady-state operation), or dynamic (focused on transient state), deterministic (not allowing variations), or stochastic (or probabilistic with time-varying parameters). Mathematical models exist for some food processes; for the rest they must be developed based on relevant engineering and scientific knowledge. Their complexity varies depending on the application, so in some food systems model development may be difficult or impossible. Then, an approximating function is developed as closely as possible to the system by performing an appropriate experimental design with limited number of experiments and by model-fitting techniques. 2. The constraints, which in most optimization problems may limit the values of dependent or independent variables and consequently the region of allowable solutions, must be identified. Constraints may be imposed by the particular characteristic of the system or represent external restrictions (equipment, legal, etc.). The system model must account for all the imposed constraints. Constraints restrict the values of the system variables. Constraints often are classified as being either equality or inequality constraints. The types of constraints involved in any given problem are determined by the physical nature

Optimization

147

of the problem and by the level of complexity used in forming the mathematical model. Constraints may be rigid, referred to as physical variables (restricted to non-negative), government regulations, or customer requirements, etc., or may be soft-negotiable to some degree (soft constraints). The former are viewed as absolute goals, while the latter as goals associated with target values. Functional constraints: These are physical principles of operation, which govern the relationship among the various independent parameters (x1, x2, . . . , xn) of the problem and are represented by equations: fci = fi(x1, x2, . . . , xn)

(e)

Regional Constraints: These are practical limits on the range in which each parameter (xi) or function of the parameters can be varied and are expressed as inequalities: l1 ≤ ri(x1, x2, . . . , xn) ≤ l2

(f)

In an optimization problem, functional constraints can be used to eliminate the number of independent parameters. Normally, the number of functional constraints must be less than the number of independent parameters, while there can be any number of regional constraints. Feasible solutions: After the constraints are established, the existence of feasible solutions for the given problem (points or region) that simultaneously satisfy all of the constraints must be examined, whereas the soft constraints may be relaxed in order to minimize the deviations from goals. Evaluation of the model: This is carried out according to the evaluation criteria established in the problem definition and sensitivity testing of the model inputs and parameters. Use actual data, which may entail statistical analysis of the fitted parameters, in the model when possible. Fitting functions to empirical data: A model relates the output, i.e., the dependent variable(s) to the independent variable(s). Each equation in the model usually includes one or more coefficients that are presumed constant. The term parameter as used here will mean coefficient and possibly input or initial condition. With the help of experimental data, the form of the model can be determined, and subsequently (or simultaneously) the value of some or all of the parameters in the model estimated. Model validation: This consists of validation of model assumptions and model behavior by comparison with historical input–output, literature, and performance data and simulation. In general, data used in formulating a model should not be used to validate it. No single validation procedure is appropriate for all models. The model should predict the desired features of the process performance with suitable accuracy.

148

Tzia

3. Performing of Optimization—Attaining the Optimum During the final step of the problem optimization process—attaining the optimum—the optimization must be performed by the appropriate selected method (5). Optimization methods have as scope to adjust, re-adjust, and locate the values of the decision variables that optimize (maximize or minimize) the performance function, while insuring that all variables (dependent or independent) satisfy the constraints of the system. The already developed model of the system usually dictates the approach of optimal solution. Such approaches are: unconstrained optimization, constrained optimization with a single performance measure or multiple performance measures, and optimization of systems that evolve over time (dynamic optimization). Assigning of performance measures: The performance measures that are to be optimized must be assigned (6). The selection of meaningful performance measures is critical for the optimization results. Usually, one of the performance measures is assigned as a target and the remaining are converted into soft constraints. Performance measures are incorporated into the optimization process either by contacting actual experiments or using numerical searching techniques. Solution of optimization problems: The reliability of the solutions of optimization problems depends on well-defining the problems. In complex problem optimization, initial considerations may be primarily based on the optimizer’s judgments of the relevant parameters until a clearer configuration of the problem parts can be defined, together with the relevant criteria, parameters, and constraints. Procedures for defining competing systems or alternate strategies and formulating the problem in order to apply formal optimization techniques are also common to operations research, systems analysis, and systems design. The general formal optimization problem is formulated in terms of the three previously referred equations: the criterion function, and the functional and regional constraints. The techniques used for solving the optimization problem depend on the complexity of the optimization equations. Detailed treatment of the various techniques for solving of equations represent the literature about optimization. Consequently, the formulation and solution of an optimization problem involves the establishment of an evaluation criterion based on the problem objectives, followed by the determination of the optimum values of the controllable or independent parameters that will best satisfy the evaluation criterion. It is accomplished either by performance of measures or analytically. In most optimization problems where there are conflicting criteria a compromise must be done weighting their relative value.

Optimization

149

III. OPTIMIZATION METHODS A mathematical theory of optimization has been developed and has found application in a variety of engineering situations (1, 2, 5, 9, 10). The development of the digital computer allowing rapid numerical calculations has made the utilization of optimization procedures practical in many design situations. Mathematical programming techniques are useful in finding the minimum of a function of several variables under a prescribed set of constraints. Stochastic process techniques can be used to analyze problems described by a set of random variables having known probability distributions. Statistical methods enable one to analyze the experimental data and build empirical models to obtain the most accurate representation of the physical situation. An optimization or a mathematical programming problem can be stated as follows:

再冎 再冎

x1 x2 find X = ⯗ which minimizes or maximizes f(X) xn in unconstrained optimization problems or x1 x2 find X = ⯗ which minimizes or maximizes f(X) under the constraints: xn hj(X) ≤ 0, j = 1, 2, . . . . . , m gj(X) = 0, j = 1, 2, . . . . . , p in constrained optimization problems, where X is an n-dimensional vector called the design vector and f(X) is termed the objective function; in the case of constrained problems hj(X) and gj(X) are known as inequality and equality constraints, respectively. The minimizing/maximizing point or minimizer/maximizer is denoted by x*. A. Basic Theoretical Background (1, 2, 9, 10) In any engineering system the design vector, defined during the design process by a set of variables, represents the selected design variables as X:

再冎

x1 x2 X = ⯗ where xi are the design variables i = 1, 2, . . . , n. xn

150

Tzia

A vector is also represented by x and usually refers to a column vector. A matrix is referred as X and its elements as Xij or xij. A column vector of n variables is also represented in n-dimensional space Rn by: x = [x1, x2, . . . , xn]T, where the superscript T signifies the transpose of a row vector to form the column vector x. In the n-dimensional space (Rn), each coordinate axis represents a design space (or design variable space). A point x in n-dimensional space is the vector (x1, x2, . . . , xn)T, where xi is the component in the i-coordinate direction. For a given function f(x) of n variables, points at which f(x) assumes local maximum and local minimum values are of interest. Each point in the n-dimensional design space, called design point, represents either a possible or an impossible solution to the design problem. Optimization methods by iterative processes generate a sequence of points x(k) or {x(k)}(where k is the iteration number) intending to find x*, the solution of the problem. In general, it is assumed that the problem functions are smooth, continuous and continuously differentiable (C1). Therefore for a function f(x) at any point x, there is a vector of first partial derivatives, or gradient vector.

再 冎

∂f/∂x1 ∂f/∂x2 䉮f(x) = ⯗ ∂f/∂xn

where 䉮 denotes the gradient operator (∂f/∂x1, ∂f/∂x2, . . . . . . , ∂f/∂xn)T. If f(x) is twice continuously differentiable (C2), then there is a matrix of second partial derivatives 䉮2f(x) with elements ∂2f/(∂xi ∂xj) that is called Hessian matrix and denoted by H(x). This matrix is square and symmetric. Since any column is 䉮(∂f/∂xj), the matrix can be written as 䉮(䉮fT). Special cases of many variable functions include the general linear function, which can be written as: n

l(x) = ∑ aixi + bi = aTx + b i=1

where a and b are constants. The general quadratic function, can be written as: q(x) = 1⁄2xTGx + bTx + c where G, b, and c are constant and G is symmetric. The basis for all optimization methods is the classic theory of maxima and minima. Mathematically, the theory concerned with finding the minimum or maximum (extreme points) of an unconstrained function of n variables f(x1, x2, . . . , xn) that can be interpreted geometrically as finding the point in an ndimension space at which the function has an extremum. An optimal point x*

Optimization

151

is completely specified by satisfying the necessary and sufficient conditions for optimality. Following the desirable features of functions, as well as the necessary sufficient conditions to guarantee the defined extremum (minimum or maximum), are presented. The knowledge of the basic properties of objective functions and constraints is considered necessary. It is preferable and more convenient to work with continuous functions of one or more variables as well as with functions having continuous derivatives. Then the extreme points may lie at the stationary points and consequently a necessary condition for a minimum or maximum is: f′(x) = 0 or 䉮f(x) = 0 (except in case of saddle points). Hence, in order to locate the points where the partial derivatives are zero, the solving of n-algebraic equations should be done: ∂f/∂xj (x1, x2, . . . , xn) = 0 (j = 1, . . . , n). The equations can be solved directly or in some cases an approximate solution can be obtained by minimizing the sum of squares of the residuals (least squares method). It is also better to work with a unimodal function that has a single extremum (minimum or maximum); that is, a stationary point. Multimodal function has two or more extrema (maximum or minimum); these are multiple stationary points. The global extremum is the biggest or smallest among a set of extrema; local extrema may be significant in practical optimization problems involving nonlinear functions. Sufficient conditions for a unique (isolated) or extremum to exist are: f″(x) = 䉮2f (x) = H(x) > 0 for unique (global) minimum f″(x) = 䉮2f (x) = H(x) < 0 for unique (global) maximum f″(x) = 䉮2f (x) = H(x) = 0 neither maximum nor minimum (inflection point). The determination of convexity or concavity is helpful to establish if a local optimal solution is also a global optimal solution. A convex region is useful in optimization involving constraints. Equality constraints limit the feasible set of points on hyper-surfaces, curves, or even a single point, while inequality constraints specify a feasible region comprised of the set of points that are feasible. To simplify the above, the case of a function of one variable x is assumed. A point x* is called local minimum if f(x*) ≤ f(x + h) for all sufficiently small positive and negative values of h. Similarly, a point x* is called a local maximum if f(x*) ≥ f(x + h) for all values of h close to zero. A point x* will be a global minimum at x* if f(x*) ≤ f(x) or a global maximum of f(x) if f(x*) ≥ f(x) respectively for all x, and not just for all x close to x*, in the domain over which f(x) is defined. Examples of local and global optimum points are presented in Fig. 4. Local minimum or maximum can be located at the boundaries or at points at

152

Tzia

Figure 4 Types of stationary points of a function. A, inflection point (scalar equivalent to a saddle point); B, global maximum; C, local minimum; D, local maximum.

which the first derivative (f′) is zero or discontinuous (stationary points). One of the local minimum/maximum can be a global minimum/maximum. Points whose first derivative is zero, may be neither a maximum nor a minimum, but a mini-max, or better known as a saddle point and movement away form it will result in an increase or decrease on f(x) depending on the direction of the movement. Difficulties are caused when f(x) is a non-smooth function as its minima do not satisfy the same conditions as smooth minima. B. Basic Optimization Methods Many methods have been developed for the efficient solution of optimization problems; there is no general mathematical method for conducting the search for the optimal value. The optimization methods can be categorized according to: (a) the nature of the objective function; (b) the constraints; and (c) the decision variables involved. The objective function may: 1. Contain only a single decision variable (single or one-dimensional optimization) or many decision variables (multidimensional optimization) 2. Be continuous or contain discontinuities, and 3. Be linear or nonlinear (linear programming—LP or nonlinear programming—NLP) 4. Be one or more than one (single or multi-objective programming) Constraints may or may not exist in a problem and constrained or unconstrained optimization are the two main categories. The constraints may be expressed as linear or nonlinear equations or inequalities.

Optimization

153

The decision variables may be continuous, integers, or a combination. Depending on the nature of the design variables static or dynamic optimization methods may exist. Also, based on the deterministic nature of the variables, deterministic, or stochastic programming is classified. Graphical methods involve a procedure of finding a maximum or minimum point of the objective function by the graphical plotting of the objective function values. These methods are elementary, have reduced accuracy and are usually applied in the case of one or two-variable functions. Methods may be indirect or direct. Indirect methods determine extremum (a) by using derivatives and values of the objective function. A necessary condition for an extremum point of a differentiable objective function is that the point is a stationary point. Direct methods search for an extremum by directly comparing function values at a sequence of trial points without involving derivatives. These methods generally can more easily treat problems involving objective functions with discontinuities, points of inflection, and boundary points, while computers have enabled their application. Numerical methods are search techniques that have been developed to determine maxima and minima of functions. The objective function is computed at a starting set of the independent variables. A second set is then selected and the comparison of the new value of the objective function with the initial one indicates if the objective function is improving toward an optimum. Searches are simultaneous, when all sets of evaluation values are preselected, or are sequential when new sets of data are selected based on information from the previous sets of data. In applying numerical optimization methods, their efficiency should be taken into account before selecting the appropriate optimization method. When the objective function is continuous and continuously differentiable and not near the region limits, the optimization may be done analytically. This implies solving a set of differential equations of the first derivatives of the objective function with respect to each independent variable and following the usual mathematical procedures for maxima and minima. Additional mathematical procedures include the use of Lagrange multipliers and variational calculus. Given that a function and its derivatives are continuous, a method that exhibit quadratic convergence may be best; such methods can locate the exact maximum/minimum of a quadratic function (assuming it has a well-defined optimum) in a finite number of calculations. Gradient-based methods are search techniques that use derivatives; the gradient vector 䉮f(x) is central to these methods. At each point the gradient vector is perpendicular to the contour of the function and points in the direction of the greatest incremental increase in f(x). The gradient search works as follows: (a) evaluate a search direction r = 䉮f(x) at the current best x = x0; (b) let x = x0 + νr, where ν is a real scalar; (c) search for a maximum of f with respect to values of ν ≥ 0; and (d) reassign the current best x and return to step (a). This

154

Tzia

method is simple, however is inefficient when the contours are elongated or irregular in any way. Following, the usual cases of a single-variable function or a multivariable function with no constraints, and a multivariable function with equality and inequality constraints are presented. Also, some commonly used techniques applicable to these types of optimization problems are briefly discussed (1, 2, 5, 9–12). 1. Unconstrained Optimization Unconstrained optimization methods are applicable when searching for a minimum or maximum of a function that is not subject to any constraints. Unconstrained optimization may be single or one-dimensional or multi-dimensional optimization. Single or One-Dimensional Unconstrained Optimization. This is the most elementary type of optimization problem; the function f(x) has only one independent variable. The techniques applicable to this type of problem are important because some techniques applicable to multivariable functions involve repeated use of a single-variable search. To develop the necessary and sufficient conditions for a minimum or maximum of a function f(x), a Taylor series expansion (with n terms) about the presumed extremum x* can be performed. f(x* + h) = f(x*) + hf′(x*) +

h2 f″(x*) + . . . . + 2!

hn-1 (n-1) hn f (x*) + f (n)(x* + θh) (n-1)! n! If f′(x*) = f″(x*) = . . . . = f then f (x* + h) − f(x*) =

(n-1)

for 0 < θ < 1

(x*) = 0, but f

(n)

(1)

(x*) ≠ 0

hn (n) f (x* + θh) n!

(2)

f(x*) is a minimum if f (x*) > 0 and n is even, f(x*) is a maximum if f (x*) < 0 and n is even, and neither a minimum nor a maximum (inflection point) if n is odd. For example when n = 2, given that f′(x*) = 0 at the stationary point, the higher order terms are negligible compared to the second-order terms and hence equation becomes: h2 f(x* + h) = f(x*) + f″(x*) 2! (3) (n)

(n)

Then, the nature of f(x*) depends on the value of f″(x*) as already referred. That is at x* exists a minimum if f″(x*) > 0, a maximum if f″(x*) < 0 or an inflection point if f″(x*) is indefinite.

Optimization

155

Multidimensional Unconstrained Optimization. The theory for the minimum or maximum applied in one-dimensional optimization is generalized and extended in the case of a function of n independent variables. Therefore, for the necessary and sufficient conditions for minimum or maximum of an unconstrained function of several variables, the Taylor’s series expansion of a multivariable function about a point X* is: f(X) = f(X*) + d f (X*) +

1 2 d f (X*) + . . . . + 2!

1 N d f(X*) + RN (X*, h) N!

(4)

where the last term, called the remainder, is given by: RN (X*, h) =

1 dN+1f(X* + θ h) (N + 1)!

(5)

where 0 < θ < 1 and h = X − X*. It must he noted that the rth differential of f(x) at X* (partial derivative of r order) is the polynomial: n

n

n

d f(X*) = ∑ ∑ ⴢ ⴢ ⴢ ∑ hihj . . . hk r

i=1 j=1

k=1

∂rf(X*) ∂xi∂xj ⴢ ⴢ ⴢ ∂xk

(6)

r summations

The necessary condition for f(X) having an extreme point (maximum or minimum) at X = X* is the first partial derivatives of f(X) to exist at X* and to be 0. ∂f ∂f ∂f (X*) = (X*) = . . . . = (X*) = 0 ∂x1 ∂x2 ∂xn

(7)

A sufficient condition for a stationary point X* to be an extreme point depends on the nature of the matrix of second partial derivatives (Hessian ma∂f (X*) = 0 for trix) of f(X*). This results to the quantity Q (given that ∂xi ∂2f (X*) are continuous in the i = 1, . . . , n and second partial derivatives of ∂xixj vicinity of X*): n

n

Q = ∑ ∑ hihj i=1 j=1

where J



x = x*

=

冋

∂2f(X*) = hT J h* x=x* ∂xi∂xj

∂2f (∂xi ∂xj)



x = x*

册

(8)

is the Hessian matrix of f(X). Consequently, if

156

Tzia

J*x = x* > 0 then X* is a relative minimum point, and if J*x = x* < 0 then X* is a relative maximum point. In case that the Hessian matrix is semidefinite (but not definite), the stationary points should be investigated for sufficiency in actual practice. However, in the general case where the partial derivatives of f of all orders up to the order k ≥ 2 are continuous in the vicinity of a stationary point X*, and dk-2*x = x* = 0 dk*x = x* ≠ 0 (the first nonvanishing higher-order differential of f at X*) then, if k is even, when dk*x = x* > 0, X* is a relative minimum point, when dk*x =x* < 0, X* is a relative maximum point, and when dk*x = x* is semidefinite (but not definite), no general conclusion can be drawn (X* is not an extreme point, if k is odd). In the case of a function of two variables f(x,y), the Hessian matrix may ∂f ∂f = = 0. be neither positive nor negative definite at a point (x*,y*) at which ∂x ∂y That point is a saddle point. Unconstrained nonlinear multivariable optimization. The unconstrained nonlinear programming methods used for multivariable optimization are iterative procedures in which the following two steps are repeated: (a) starting from a given point choose a search direction and (b) minimize or maximize in that direction to find a new point. These methods mainly differ in how they generate the search directions. Direct/indirect methods. Direct methods for single variable functions include the region elimination methods, the two-point equal interval search, the bisecting method (or dichotomous search), the Fibonacci method, and the golden section method; the last two methods are considered the most efficient. The point estimation methods, or polynomial approximation methods usually involve a quadratic or cubic approximation of the objective function and for this Powell’s method is considered the most efficient. For multivariable functions, direct methods include the random search, the grid search, the univariate search, the sequential Simplex method, the HookeJeeves pattern search method, and Powell’s conjugate direction method. These methods are relatively simple to understand and execute. However, they are not as efficient and robust as many of the indirect methods. The indirect methods include the steepest descent/ascent gradient method (or Cauchy’s method), the conjugate gradient methods, Newton’s method, Marquardt’s method, the Secant methods, and the Broyden-Fletcher-GoldfarbShanno (BFGS) method. 2. Constrained Optimization Constrained optimization methods are applicable to locate stationary points of a function, but the solutions are subject to equality or inequality constraints.

Optimization

157

Optimization of the function must be carried out over a restricted domain of the independent variables. Optimization of Systems with Equality Constraints. Constrained optimization of a continuous function f(x1, x2, . . . , xn) of n independent variables subjected to m equality constraints gi(x1, x2, . . . . , xn) (i = 1, 2, . . . , m) will be considered. The problem is defined when m ≤ n. Three methods are mainly used for the solution of such problems: direct substitution, constrained variation, and Lagrange multipliers. Direct substitution methods involve the substitution of the m constraint equations directly into the objective function. The resulted objective function is not subject to any constraint and hence its optimum can be found by applying the unconstrained optimization techniques. This method is suitable for solving simpler problems, but cannot be applied to many practical problems due to nonlinearity of the constraints equations. Constrained variation methods in the general case of n variables with m constraints scope in finding an expression for the first-order differential of f (df) at all points where the constraints are satisfied. Each constraint equation is expressed as a linear equation of the variations dxi, i = 1, 2, . . . , n (m equations of n variations). Each one of m variations as well as df are expressed by the remaining n-m variations. The optimum points are obtained when df = 0. ∂f ∂f dx1 + . . . . . . . + dxn = 0 ∂x1 ∂xn ∂g ∂g dg1 = 1 dx1 + . . . . . . + 1 dxn = 0 ∂x1 ∂xn ............ ∂g ∂g dgm = m dx1 + . . . . . . + m dxn = 0 ∂x1 ∂xn df =

(9)

Hence, the necessary conditions for the extremum of f(X) are given by the n-m equations (Jacobian determinants):

J

冉

冊

f, g1, g2, . . . , gm = xk, x1, x2, . . . , xm

∂f ∂xk ∂g1 ∂xk ∂g2 ∂xk ⯗ ∂gm ∂xk

∂f ∂x1 ∂g1 ∂x1 ∂g2 ∂x1 ⯗ ∂gm ∂x1

where k = m + 1, m + 2, . . . . . , n.

∂f ∂x2 ∂g1 ∂x2 ∂g2 ∂x2 ⯗ ∂gm ∂x2

∂f ∂xm ∂g1 ... ∂xm ∂g2 ... ∂xm O ⯗ ∂gm ... ∂xm ...

=0

(10)

158

Tzia

Similarly, in the case of two independent variables x1, x2 where the function f(x1,x2) is subject to the constraint g(x1,x2), a necessary condition for f to have an extreme point at (x1*,x2*) is: df =

∂f ∂f dx1 + dx2 = 0 ∂x1 ∂x2

(11)

dg =

∂g ∂g dx1 + dx2 = 0 ∂x1 ∂x2

(12)

or by combining the equations:

冉

冊

∂f ∂g ∂f ∂g − ∂x1 ∂x2 ∂x2 ∂x1

=0

(13)

(x1*, x2*)

It is noted that the variations dx1, dx2 about the point (x1*,x2*) are called admissible variations. A sufficient condition for X* to be a constrained relative extremum is resulted by the Taylor series expansion of f, in terms of n-m variables (xm + 1, xm + 2, . . . , xn) about the extremum point X*: n

f(X* + dX) ⬇ f(X*) +

∑

i=m+1

where

冉冊

n

n

冉 冊

∂f ∂2f 1 dxi + dxidxj ∑ ∑ ∂xi 2! i=m+1 j=m+1 ∂xi∂xj g

(14)

g

冉冊 冉 冊

∂f ∂2f and are the first and second partial derivatives of f with ∂xi ∂xi∂xj g

g

respect to xi and xi xj (holding all the n-m variables constant) respectively, when x1, x2, . . . , xm are allowed to change so that the constraints gj (X* + dX) = 0, j = 1, 2, . . . , m, are satisfied. The quadratic form Q defined by: n

Q=

n

∑ ∑

i=m+1 j=m+1

冉 冊

∂2f dxidxj ∂xi∂xj

(15)

g

or the matrix:

冤

冉 冊冉 冉 冊冉

冊 冉

冊

∂2f ∂2f ∂2f ⴢ ⴢ ⴢ ∂xm+1∂xn ∂x2m+1 g ∂xm+1∂xm+2 g ⯗ ⯗ O ⯗ ∂2f ∂2f ∂2f ⴢⴢⴢ ∂xn∂xm+1 ∂xn∂xm+2 ∂x2n g g g

冊

冉 冊

g

冥

(16)

depending on their positive or negative value for all nonvanishing variations

Optimization

159

dxi are determinant for the optimum point X*. This method involves difficult computations as the constraints are more than two. Lagrange Multipliers method is the most commonly used method in constrained optimization problems with equality constraints. The Lagrange function L, for a problem of n variables and m equality constraints is defined for each constraint gj (X) as: L (x1, x2, . . . , xn, λ1, λ2, . . . , λm) = f (X) + λ1 g1 (X) + λ2 g2 (X) + . . . . . . + λm gm (X)

(17)

where the quantities λj are called Lagrange multipliers. The necessary conditions for the extremum of L are produced by the solution of a system of n+m equations in terms of the unknowns, xi and λj, and are given by: m

∂g ∂L ∂f = + ∑λj j = 0, i = 1, 2, . . . , n ∂xi ∂xi j=1 ∂xi

(18)

∂L = gj (X) = 0, j = 1, 2, . . . , m ∂λj

(19)

From the solution, the relative constrained extremum X* and the λ* result. It must be noted that the vector λ* of Lagrange multipliers is a sensitivity factor that indicates how tightly the constraint is binding at the optimum point. A sufficient condition for f(X) to have a relative extremum at X* is determined by the sign of the quadratic form Q: n

n

Q=∑∑ i=1 j=1

∂2L (X*,λ*) dxidxj ∂xi∂xj

(20)

or by the sign of the roots of the polynomial zi:



L11−z L21−z ⯗ Ln1−z zi = g11 g21 ⯗ gm1

where Lij =

L12 L22 ⯗ Ln2 g12 g22 ⯗ gm2

ⴢ ⴢ ⴢ L1n ⴢ ⴢ ⴢ L2n O ⯗ ⴢ ⴢ ⴢ Lnn−z ⴢ ⴢ ⴢ g1n ⴢ ⴢ ⴢ g2n O ⯗ ⴢ ⴢ ⴢ gmn

g11 g21 ⯗ g1n 0 0 ⯗ 0

g21 g22 ⯗ g2n 0 0 ⯗ 0

∂2L ∂g (X*, λ*), gij = i (X*) ∂xi xj ∂xj



ⴢ ⴢ ⴢ gm1 ⴢ ⴢ ⴢ gm2 O ⯗ ⴢ ⴢ ⴢ gmn ⴢⴢⴢ 0 =0 ⴢⴢⴢ 0 O ⯗ 0 0

(21)

(22)

So, if Q or each root of zi is positive or negative for all values of dX, X* will be a constrained minimum or maximum respectively. It must be noted that the

160

Tzia

point X* is not an extreme point if all of the roots of zi have not the same sign. The solution of a simple two-dimensional problem by Lagrange multipliers method is: L(x1, x2, λ) = f(x1, x2) + λ g(x1, x2)

(23)

Then, the necessary conditions for the extremum X* (x1, x2) are given by: ∂L ∂f ∂g (x1, x2) = (x1, x2) + λ (x1, x2) = 0 ∂x1 ∂x1 ∂x1

(24)

∂f ∂g ∂L (x1, x2) = (x1, x2) + λ (x1, x2) = 0 ∂x2 ∂x2 ∂x2

(25)

∂L (x1, x2, λ) = g (x1, x2) = 0 ∂λ

(26)

Optimization of Systems with Inequality Constraints. Constrained optimization subject to inequality constraints is treated as in the case of equality constrained problems after transformation of inequality constraints to equality ones by introducing the slack variables and solving the so formed constrained equations to define the feasible region and find the optimum solution. A usual problem of this category is the linear multivariable optimization with inequality constraints. For the general case of a function f (X) of n independent variables subject to m constraints gj(X) ≤ 0 (j = 1, 2, . . . , m), the constraints are transformed to equality ones as: gj(X) + yj2 = 0 (j = 1, 2, . . . , m)

(27)

where yi are non-negative slack variables. Hence, the problem becomes the finding of the extremum of f(X) subject to: Gj(X, Y) = gj(X) + yj2 = 0 (j = 1, 2, . . . , m)

(28)

where Y is the vector of slack variables and can be solved by using the previously referred Lagrange multipliers method as: m

L(X, Y, λ) = f(X) + ∑ λjGj(X,Y)

(29)

j=1

where λ is the Lagrange multipliers vector. Then, applying the necessary conditions for the stationary points, the following (n + 2m) equations of (n + 2m) unknowns result:

Optimization

161 m

∂f ∂L ∂g (X, Y, λ) = (X) + ∑ λj j (X) = 0, i = 1, 2, . . . , n ∂xi ∂xi ∂xi j=1

3.

(30)

∂L (X, Y, λ) = Gj(X, Y) = gj(X) + yj2 = 0, j = 1, 2, . . . , m ∂λj

(31)

∂L (X, Y, λ) = 2 λj yj2 = 0, j = 1, 2, . . . , m ∂yj

(32)

Optimization of Dynamic Systems

A dynamic system is characterized by time- or space-dependent behavior and variety by time or distance. The problems are called sequential decision problems or multistage decision problems as the decisions are to be made sequentially or at a number of stages respectively. Many dynamic systems are encountered in chemical engineering and food engineering (fermentations or thermal processes of foods). For the optimization of multistage decision problems, dynamic programming techniques are used. Dynamic programming methods decompose an optimization problem into a sequence of subproblems that can be solved serially. Each of the subproblems may contain one or few decision variables. A serial multistage decision process of n stages in decreasing order is represented schematically in Fig. 5. Each single-stage i decision process can be characterized by an input (input state parameter) (Si+1) which is the output of i + 1 stage, a decision variable (Xi), an output (output state parameter) (Si) and a return or objective function (Ri). The output is obtained as a result of making the decision, while the return depends on the effectiveness of the decision made and the output that results from the decision. The state transformation functions

Figure 5 Dynamic process of n stages.

162

Tzia

or design equations relate input to output and the return functions are represented as: Si = ti(Si+1, xi)

(33)

Ri = ri(Si+1, xi)

(34)

where xi is the vector of decision variables at stage i. The objective of dynamic optimization is, given the values of variables of the initial state or final state or both of the stages (boundary problem), to find the continuous decision control variable(s) that optimize some function of the individual stage returns. That is to find x1, x2, . . . , xn that optimize the objective function f(R1, R2, . . . , Rn), mathematically expressed by: fn(Sn) = opt [Rn(Sn, Xn) + fn-1(Sn-1)] (35) The functions of a system may differ in nature (i.e., differential). In order for a multistage problem to be solved by dynamic programming applying the decomposition technique, the objective function must be monotonic and separable, satisfying the requirements: n

n

i=1 n

i=1 n

f = ∑ Ri = ∑ Ri(xi,Si+1) f = Π Ri = Π Ri(xi,Si+1) i=1

(36)

i=1

where xi are real and non-negative. 4. Programming Optimization Linear programming (LP) is an optimization method applicable to problems in which the objective function and the constraints (equalities or inequalities) appear as linear functions of the design/decision variables. Simplex method of Dantzig is the most efficient and popular method for solving general LP problems, although other superior methods as Karmarkar’s have been developed. It is considered suitable for complex problems solution. In case there is a large number of variables and/or constraints, the decomposition principle can be used to solve the problem, while Karmarkar’s method has been shown more efficient than the simplex method in this case. Quadratic programming deals with problems that have a quadratic objective function, linear constraints and can be solved by suitably modifying the linear programming techniques. If the objective function and the constraints are fairly simple, expressed in terms of the design/decision variables, the classical methods of optimization can be used to solve the problem. But classical analytical methods can not be

Optimization

163

used if the expressions are not stated as explicit functions or they are too complicated to manipulate. Several methods are available for solving an unconstrained nonlinear optimization problem. The direct search (nongradient) methods require only the objective function values but not partial derivatives of the function in finding the optimum. The direct search (zero-order) methods use zero-order derivatives of the function. Direct methods are most suitable for simple problems involving small number of variables. The descent (gradient) techniques require, in addition to the function values, only first derivatives of the function (first-order methods) or both first and second derivatives of the function (second-order methods). Descent methods are generally more efficient than direct search techniques. There are many techniques available for the solution of a constrained nonlinear programming problem. In the direct methods, the constraints are handled in an explicit manner, whereas in most of the indirect methods, the constrained problem is solved as a sequence of unconstrained optimization problems. Geometric programming (GP) is a method of solving of nonlinear programming problems, where the functions and the constraints are in the form of polynomials. It places emphasis on the relative magnitudes of the terms of the objective function rather than the variables; instead of finding optimal values of the design/decision variables first, it first finds the optimal value of the objective function. Geometric programming is especially advantageous in cases that the optimal value of the objective function is of interest, as well as in optimization of a complicated problem reducing it to one involving a set of linear algebraic equations. Many problems in plant design and operation involve variables that are not continuous but instead have integer, discrete, or fractional values. If an integer solution is desired, it is possible to use any of the common techniques and round off the optimum values of the design variables to the nearest integer values. Integer programming (IP) refers to problems where all of the design/decision variables are restricted to be integers. A special case of IP is binary integer programming (BIP) (or zero-one programming), where all variables are either 0 or 1. Discrete programming refers to cases of problems where the variables are restricted to take only discrete values. In mixed integer programming (MIP), some of the variables are restricted to be integers while others may be continuous (fractional values). Many IP problems are linear in the objective function and constraints, hence are subject to solution by linear programming (MILP). Stochastic or probabilistic programming deals with situations, where some or all optimization problem parameters are described by stochastic (or random or probabilistic) variables. The stochastic problem is converted into an equivalent deterministic one that can be solved by familiar techniques (linear, geometric, dynamic, and nonlinear programming).

164

Tzia

5. Experimental Optimization—Response Surface Optimization (RSM) In some problems when the behavior of the system or process is not known and a model cannot be developed or the model developed is very complex, experimental optimization methods are used. Experiments are designed with appropriate methods and empirical models based on the experimental data result. The optimum is then approached by using statistical techniques, the most known is the response surface method (RSM), which is widely used in food applications. Assuming that the response of n independent variables is a function of the levels and combinations of these variables, response surfaces that result provide insight into the overall behavior and show the existence of optimal regions.

IV. OPTIMIZATION IN FOOD EXTRACTION PROCESSING A. Application of Optimization in Food Engineering Optimization has been applied in several areas in food engineering, and various optimization methods have been used both in processing and manufacturing. Areas of such applications are individual equipment or total system design, process design, as well as design, layout, control, management, and operation of the total plant. Following, more details for all these application areas of optimization approaches are presented. A selection of practical engineering perspectives referring to plant operation and optimization has been edited (13). Mathematical programming (linear, nonlinear, integer, combinatorial) and modeling techniques for various industrial applications relating to manufacturing, operation, decision-making, production scheduling, or management have been also presented (14). Especially, optimization in design and control of chemical engineering processes approached by global optimization methods (deterministic or probabilistic) and relative applications have advanced (15). Flow-sheeting optimization during the early stages of design, applying global optimization to modular process design approaches provides many advantages (16). Genetic algorithms (GA) or evolutionary ones (EA) have been investigated in chemical engineering problems and have been proved to be powerful and robust optimizing techniques either for total processes and/or particularly for extraction processes (17, 18). Optimization resources and specific information about optimization are available on the Internet in the NEOS—Network Enabled Optimization Technology Center (OTC)—which is a joint enterprise of Argonne National Laboratory and Northwestern University (19). The design and optimization of thermal systems is of primary interest for food engineering as is strongly related to cost as well as to food quality. Thermal

Optimization

165

processing is commonly applied in chemical processing industries and hence thermal systems are often encountered into manufacturing plants. Thermal systems are necessary in food processing plants either in thermal processes of food (pasteurization, sterilization, blanching, cooking, cooling, freezing, etc.) or in other complementary operations as storage under cooling or freezing. Consequently, thermoeconomic optimization involving thermodynamic optimization and modification of the structure and design parameters of a thermal system, allows minimization of the total cost (optimization criterion) of the system satisfying technological or environmental constraints (20). As far as individual equipment is concerned, the design and optimization of a falling-film evaporator and the construction of a mathematical model for its operation, aiming at both minimizing the overall operating cost (by minimizing water removal cost) and maintaining product quality of a large-scale production of concentrated apple juices has been cited (21). Optimization has found application in various processes widely used in foods, as in dehydration, heat processing, and formulation of foods. Examples of such areas where optimization techniques have been successfully used are presented. In thermal processing of foods, the optimization problem generally consists in the determination of processing conditions, the combination of process time and retort temperature, ensuring the constraint of required safety level but simultaneously minimizing the associated quality degradation (or maximizing product quality) (22). In classic problems of thermal processed canned foods, kinetic models both of bacteria spores’ inactivation and nutrients (i.e., thiamine) degradation have been proposed as basis of optimization. Optimization methods require studies of time–temperature dependent kinetics for each of these factors, or studies for finding the optimum temperature profile or the optimum container geometry of the thermally processed foods. Direct search by trial-and-error, mathematical algorithms and dynamic programming approach the optimization solution. A proper optimization technique is based on the Pontryagin’s maximum principle that searches out the optimum surface temperature as a smooth, continuous function of time, which maximizes the objective function (4). Furthermore for the design and optimization of thermal processing of foods, powerful dynamic models and techniques have been developed suitable and efficient for real-time industrial applications (optimization and control), i.e., for thermal sterilization (23, 24). As far as the food dehydration is concerned, it is related to dynamic optimization problem and the scope is to find an optimum temperature profile that would result in maximum nutrients retention (i.e., ascorbic acid) achieving the desired moisture content, given a specific drying time, while constraints for the final moisture or nutrient content of the dehydrated product may exist. Mathematical models describing the drying process of foods in accordance with the

166

Tzia

equipment used have been developed, while optimization techniques may be similar of those applied in thermal processing optimization that consider either continuous (Pontryagin maximum principle) or discrete-step optimum temperature profile (4). Optimization is usually applied in food products formulation, as a formulation process must meet a set of specifications for nutrient levels, be costeffective, and is subject to constraints, i.e., for combined weight or certain nutrient levels. Such problems may be solved by simple mathematical optimization technique as linear programming (25). Optimization of the nutritional quality of formulated foods in respect to different nutrients proportions has been investigated by evaluating the effect of the component proportion in the final product nutritional quality. Therefore, it is feasible to maximize the nutritional quality of mixtures by obtaining optimum formulation or by substituting a component with another in the product formula. A usual problem is the formulation of protein mixtures intended to satisfy human requirements with respect to essential amino acids. The optimization in nutritional experiments is very significant and particularly in protein mixtures problems, because the protein quality is evaluated in vivo and it is possible to express synergism effects due to complementarities of essential amino acids. Hence, the maximum nutritional quality is desired during product development, either by changing the concentrations of the protein ingredients (soybean protein products, milk proteins, whey proteins, etc.) or by the substitution of an animal origin protein by another (vegetable or less costly) in a formulated mixture of known nutritional quality (26). In nutritional evaluation experiments the mixture response surface methodology proved to be an extremely useful tool for the optimization of the mixture’s protein quality. Moreover, the estimation of the nutrient values in commercial food products is very important for the human diet. They must be taken daily in certain quantities, some of them are essential for human health (amino acids, linoleic acid), others are functional components (dietary fibers). Many missing nutrients values for nutrients of interest are encountered in widely used foods’ composition data due to lack of accepted analytical methods for some nutrients, high cost of chemical analysis, and proliferation of commercial foods. The missing nutrient value estimation is performed by various methods of different accuracy. Mathematical optimization proves more accurate to estimate nutrient values of food product than other techniques used (i.e., trial-and-error); linear programming as well as quadratic programming are comparably efficient in accuracy (27). Sensory optimization of a food product is another application area where optimization methods have been used. The product ingredients affect many attributes of the product that determine its acceptability by the consumers. Hence, the best product formulation is found by evaluating multiple sensory attributes

Optimization

167

or responses of food, carrying different weights in the perception of the product, using graphical optimization methods (28). In product optimization problems special (classical or novel) sensory methodologies and techniques are used (29). Mathematical models with linear or quadratic equations created by experimental designs, show the relationships between independent variables (food formulas, processing conditions) and the responses (rating attributes). These models are useful for the prediction of the likely attributes, the maximization of the acceptance, or for optimization problems under certain constraints of formulation (30, 31). An example of optimizing acceptability is the case of low-calorie products containing alternative sweeteners. Following a central composite design, response surface methodology (RSM) (32) can select the optimum product formulation (optimal concentration of sweetener) that optimizes the sensory quality of the product (maximum acceptability). Also during development of new food products, combining experimental design of both product and concept generates optimal products and concept within constraints, such as cost, desired sensory profile limits, or targeted population (33). The quality optimization of thermally processed foods (i.e., cooked) and the relative optimization methods employed for these processes are of interest in food engineering. Commercial processes try to meet the requirements for microbiological safety, nutritive value, and sensory acceptance of foods. Scope of the methods is to determine the suitable processing conditions, especially the temperature profile (temperature–time of processing), which maximize quality factors of foods undergoing thermal processing (i.e., retention or degradation of nutrients, microbial inactivation, etc.) (34). Dried potato cubes are an example of thermally processed food, the quality of which is optimized. The process includes potato blanching in water containing pectin and polydextrose, followed by a two-step drying procedure by a batch-type high-temperature fluidized bed drier (HTFB) at varying conditions, and a tunnel drier at standard conditions. The objective is to find biopolymers concentration, time of blanching, as well as temperature and time of drying in HTFB that obtain the maximum of rehydration ratio, puffing and water holding capacity, and minimum of nonenzymatic browning (7). In such problems, RSM is usually the method used for manipulation of experimental data. Using the same technique, the extrusion of a high-protein snack has also been optimized. Maximum value of expansion ratio and sensory rating of extrudates and minimum value of shear strength was obtained by controlling the feed moisture and temperature at the central zone of the extruder barrel (7). The optimization framework of heat-sensitive foods’ drying process, being also essential for a food engineer, has been developed by combining the equipment and material models to attain the cost-effective product drying (with efficient energy saving), and minimizing its quality degradation. The potato slice is such a material that suffers from loss of quality relating to color, nutrients, taste

168

Tzia

and/or texture and therefore the optimal operation of the dryer equipment may both maximize heat recovery and minimize quality degradation (i.e., loss of ascorbic acid, nonenzymatic browning), satisfying simultaneously the imposing constraints (drying time, food moisture content, ascorbic acid content, and enzymatic browning degradation) (35). Frying of foods is another thermal process in which the frying temperature significantly affects the quality characteristics (expansion volume, expansion ratio, color, texture parameters) and the sensory ones (appearance, texture score, acceptance score) of the fried product. As continuous process is commonly used involving certain consecutive deep frying steps, the corresponding optimum temperature values can be approached based on the quality and sensory product characteristics. A popular and effective optimization method for such multivariate problems is response surface methodology (RSM) (36). Enzymatic synthesis processes of certain valuable compounds have increased interest for investigation of parameters affecting the enzyme-catalyzed synthesis. Enzymatic produced compounds are preferred over the chemical-synthesized products and are widely used in foods and beverages with many industrial applications, i.e., low-molecular-weight esters by lipase-catalyzed esterification are flavor compounds of commercial importance. Consequently the optimization of such enzymatic processes for economical syntheses can be attained by central composite rotatable experimental design (CCRD) using response surface methodology (RSM) to predict best performance conditions (37). Modeling, simulation, and optimization are nowadays particularly valuable due to widespread use of computers and are important approaches to a number of problems in the food industry. Linear programming can be used for simple problems such as the optimization of product quality determining the concentrations of certain components that affect its quality. Examples of industrial processes in which linear programming has been applied are referred. LP has been applied to optimize the fixed process arrangement in an apple juice concentration plant which includes: apple crushing, juice extraction, further pressing of the resulting pomace after rehydration, mixing of juice streams, aroma stripping (for aroma recovery), juice clarification and concentration. Optimal plant operation can maximize the net profit by arranging the operating flow rates and taking into account the effects of costs, prices, apple varieties, and the operational capacity constraints for equipment. Also, LP was utilized for quality optimization in aseptic processing, where considering the microbial and enzyme inactivation, cooking quality and component quality retention (thiamin and chlorophyll), the optimal process (holding time and temperature) was derived. Another example of optimization using LP was in potato drying, where maximizing the rate of drying and minimizing undesirable side reactions (browning, ascorbic acid oxidation) with negative effect on finished product quality, the optimal drying profile was defined. (8). In addition, LP was used to quality optimization

Optimization

169

of potato chips, where given the quality as a function of moisture content, oil content and color characteristics of chips, the optimum frying conditions (temperature and time) were determined considering the constraints for temperature, oil, and moisture content of chip, and temperature and time of frying (7). Optimization and control by automatic systems of various food processes has been cited both for product quality and economic reasons, i.e., white wine fermentation, sugar crystallization, extrusion cooking, pasta goods production, or for other complementary industrial operations, such as collection of raw milk, reduction of product losses, and costs of water purification during cleaning in dairy industries. Optimal design and control of individual food processing equipment, i.e., climbing film evaporator for tomato juice, multistage hyperfiltration unit for apple juice is also cited (38). Optimization of industrial operations attempts to exploit cost, time, and quality profits. For example, a methodology is proposed for optimization of a vegetable oil hydrogenation unit. A mathematical model based on data from a hydrogenation plant is developed to select the optimum operating conditions of temperature and hydrogen pressure that provide the desired product in minimum time (39). On the other hand, optimization of the overall efficiency, total operating cost, yield, total return, quality of output, and throughput of the whole food manufacturing plant is necessary when changing the raw materials or the product properties. Of course, it can be applied to large food processing plants; it is evident that routine application of optimization methods during operation is practiced only in certain parts of industrial plants. However, with the increased use of computers and computer control in food manufacturing integrated optimization may be applied to every modern processing plant. The new approach for food process and plant optimization is optimization throughout the food chain. The optimization of a complete food chain consists of product, process, and raw materials optimization, and by analyzing the system efficiency (losses, wastes) leading to optimum plant, process, and equipment design and operation (40). Finally, optimization is also applied in management and control of food processing plants. Application of optimization in management of food processing and food service industries has been presented. In addition to the above applications of optimization in food processing, similar techniques are used in optimizing food analysis methods of high accuracy such as gas and liquid chromatographic methods (41). This has increased significance when the constituents of interest are related to food safety specifications. Also, optimization of instrumental measurements related to foods have been investigated, i.e., methodologies for extracting features from sensors output responses of instrument analyses (electronic nose, near infrared analyzer) (42, 43). In conclusion, it is clear that the increased use of optimization in food manufacturing enables the efficiency, productivity, and quality to be increased

170

Tzia

and the energy use, product loss, and environmental pollution to be reduced. Furthermore, the optimization philosophy, ideas, and techniques should be adopted throughout the food handling system—from the harvesting of raw materials to the transportation, processing, packaging, distribution, and consumption of the final products. B. Optimization of Extraction Processes Extraction is applied to recover or remove constituents and consequently the product of interest may be the extracted materials or the extracts. The design and optimization of an extraction process entails the knowledge and the role of the technological parameters of the process. Conventional solvent extraction involves the removal of certain constituents from a mixture of solids (leaching or solid extraction) or liquids (liquid extraction) by means of a solvent in which exhibit different solubility. Supercritical fluid extraction (SFE) involves the separating of a mixture by contacting it with a fluid under conditions of temperature and pressure above its critical point. The separation of solvent from the extracted solute in conventional extraction is carried out by distillation or evaporation, while in SFE separation is accomplished either by reducing the pressure at constant temperature or by raising the temperature at constant pressure (44). Following, the most important parameters that significantly affect the extraction efficiency and determine the process design and success are presented. In leaching, the important parameters are: 1. The preparation of solid material by size reduction (crushing, grinding, flaking or cutting into pieces or cosettes); solid size must be suitable (surface area per unit volume) to make the solute more accessible to the solvent and to favor the extraction but not very fine to cause packing of solids and impede free flow of solvent 2. The selection of solvent for extraction based on a number of characteristics (capacity, selectivity, chemical inertness, thermophysical properties, flammability, toxicity, cost, availability) 3. The selection of operating temperature—temperature must be high enough to give higher solubility of solute in solvents, but not very high to cause solvent losses, extraction of undesirable constituents or damage of sensitive components 4. The equipment depending on the mode of operations (batch or continuous), the solids handled (fixed bed, percolation, full immersion, intermittent drainage or dispersed/moving contact) or the performing arrangement (one stage or multistage) In liquid extraction the important parameters are:

Optimization

171

1. The selection of solvent as previously referred, mainly based on high solute capacity and selectivity; particularly, solvent interfacial tension must have low value to get a good dispersion with high interfacial area for extraction, but not very low to cause emulsion formation and create problems in separation 2. The equipment depending on the contacting of the phases (by gravity or by centrifugal force) In supercritical fluid extraction the important parameters are: 1. The capacity and selectivity of the extracting supercritical fluid and their dependence on temperature and pressure 2. The ratio of solvent mass flow rate to the mass of solids treated; knowledge of the solvent, raw material and extract physical properties is necessary 3. The operating pressure and temperature, which must not cause decomposition of the raw material 4. Bulk density of the solid feed depending on the density of the solids, the form and consistency of the material and its moisture content 5. The mechanical treatment of the raw material depending on the nature of raw material (raw material purification or extract recovery); mechanical treatment should be avoided in the case of solids recovery, whereas in the production of a soluble extract may be desirable 6. The time of extraction in respect to solvents, raw material feed and operating conditions The technical and economical feasibility of an extraction process is determined knowing the equipment size, operating conditions, solvent flow rates and extraction yields. Extraction optimization is involved both in the design and the operating conditions for the equipment (separation allocation, differences in physical and/or chemical properties for separation, equipment type and sequence separators, fixation of separated phases, entire process operating conditions). Optimization of liquid–liquid extraction processes has been investigated and examples for staged and continuous models of extraction appeared in the literature (45). In staged processes treated as an integer variable, the optimization is more difficult, while in continuous processes for either cocurrent or countercurrent flow, integer variables are avoided and optimization can be carried out by other techniques. Steady-state continuous countercurrent liquid extraction has been modeled; a plug flow model was proved sufficiently accurate for a continuous pilot-scale extraction column. It can be used to determine the maximum extraction rate to various applications. A common problem in optimization of extraction processes is that operation time for the lowest cost is usually different from the time that gives the

172

Tzia

best yield (effectiveness), so that it is impossible to have both optimal cost and yield simultaneously. In this case the operation time becomes the independent parameter whose value determines the value of the criteria: minimum cost and maximum yield. The relative importance of the two conflicting criteria must be judged in order to obtain an optimum extraction time. So, if lowering of the operation cost is of interest, the time of extraction will be optimized, while the yield will be the primary criterion in an isolation of desired components or removal of toxic substances from food sources. Suboptimum with respect to operation cost and yield or optimum with respect to the combined criteria of cost and yield may be found. An overall criterion function can be established applying a more formalized optimization. Regional constraints on time might be imposed; a very large time above a certain value is not cost effective or the yield is not accepted at time below a certain value. After the optimum operation time has been determined other parameters not included in the original problem (extraction medium, extraction system) may be used, thus decreasing the operation cost and increasing yield by formal optimization techniques. It must be noted that formal optimization theory should be used only to find optimum parameters in well-defined systems with quantitative-dependent and independent parameters, criterion function, and functional and regional constraints. C. Application of Optimization in Food Extraction Processing Extraction is a separation process widely used in food processing industry for various applications, while recently with supercritical fluid extraction is possible to improve the quality of food products. Extraction may be a basic step in processing of many food products (i.e. sugar, edible oils, etc.), used for the recovery of active constituents or for the removal of undesirable constituents from raw materials, while important components may be separated from natural products with many applications to food industries (flavors, antioxidants, etc.). Optimization of protein isolation process by optimizing the protein extraction step has been studied from oilseeds or beans as: tomato seed, pigeon pea, peanut, and flaxseed (46, 47, 48, 49). The protein isolation process from vegetable sources includes mainly extraction or leaching of proteins by aqueous solutions and isoeletric precipitation; therefore extraction of proteins, evaluated by protein extraction yield, is determinant for the total protein yield of the whole protein isolation process. The optimization of this process should attain the maximum both of protein extraction yield or total protein yield as well as of the protein quality of the isolated product. It must be mentioned that the conditions optimizing the above responses are not the same; usually maximizing the protein extraction yield or the total protein yield decreases protein isolate content. Thus, the basic factors affecting protein solubilization during extraction such as: parti-

Optimization

173

cle size of proteinaceous material, temperature, extracting medium (water or diluted salt solution), solid to liquid ratio, pH (neutral or alkaline region) have been studied by proper experimental design and using response surface methodology (RSM) or steepest-ascent gradient method. Mathematical models for the protein extraction yield or total protein yield as well as for the protein content of protein isolate were obtained, while the extraction conditions that lead to the maximization of the responses were determined. Surimi usually used as raw material in processed foods (fish sausage, fish cake, or kamaboko) is water-leached, minced flesh of deboned fish. Surimi processing starts with leaching and additional processes follow, such as grinding, setting, heating, and frozen storage. Because the processing conditions significantly affect the quality characteristics of surimi products (gel strength, whiteness) the optimization of surimi processing should be conducted. An optimization study has been presented that uses a surface response method with a central composite design of surimi processing experimentation. Among the processing conditions the leaching parameters (leaching water in each leaching cycle, number of leaching cycles) were examined as well as parameters of the following processing steps. The optimal processing conditions that maximize gel strength and minimize whiteness of surimi products by canonical analysis and ridge analysis as well as fitting of a suitable model was investigated (50). Optimization has been widely applied to oil processing industry, in oil solvent extraction as well as in oil expression or mechanical extraction. Mechanical extraction is an alternative method for oil expression by crushing the seeds in a mechanical screw press. In this case, both oil yield and energy pressing cost are affected by the preprocessing conditions of the seed (dehulling, preheating, flaking or grinding, seed moisture content, seed temperature prior of pressing) as well as by the design and operation characteristics of the press (pressure, temperature, pressing time) and their interactions. Mathematical models based on expression conditions have been developed to predict the maximum sunflower oil recovery (51). Olive oil is characterized as a “natural food product” because it is the only oil produced by mechanical method that can be consumed without further processing. In addition, due to its nutritive and health properties, olive oil is recognized as a functional food product. This oil, virgin olive oil, should meet certain specifications according to regulation EC No. 2568/91 related to quality characteristics (oil acidity or oxidative status, etc.). For the above reasons, special mechanical methods have been developed. The classical method of extraction of olive oil is based on the mechanical pressing of olives. An alternative method also has been developed, which involves centrifugal separation of olive oil using horizontal decanters. Special quality levels can be attained in virgin olive oil production when all operations (olive collection and storage, extraction processing, packaging and storage of end product) are carefully carried out.

174

Tzia

Efficiency of expression is usually evaluated by extraction yield expressed as percent of oil extracted to the percent of oil in the olives. The expression parameters affecting oil yield have been examined: pressure compaction rate, cake thickness, rigid solid (pits) content in the olive paste, and the optimization of olive paste expression process has been studied experimentally (52). Consequently, the simultaneous variation of the parameters must be investigated to obtain the maximum olive oil extraction yield and the optimal operation of industrial olive pressing systems to be determined. The pressure raise increases oil yield, however it is important for olive oil quality the pressure to be kept under constrained values. Therefore, the effect on olive oil quality of parameters, such as olive ripening and storage prior to processing and olive paste mixing time and temperature, should be studied so extraction can be optimized for oil quality requirements (53). In recent years, extraction of vegetable oils using supercritical fluid extraction (SFE) as an alternative to the current industrial processes (expeller pressing, solvent extraction) and its optimization has been investigated (54, 55). Carbon dioxide, which is the most commonly used solvent to SFE processes, is also used in oil extraction; supercritical pentane has also been cited (54). The efficiency of SFE of oils depends on the variables: temperature, pressure, particle size of seeds, contact time between the extracting fluid and the oil-bearing material, ability of the fluid to penetrate the oil-bearing material, and solubility of the oil in the extracting fluid. Optimization of SFE of various vegetable oils such as soybean oil, canola oil, and sunflower oil, has been studied and the conditions that maximize oil recovery have been determined. In particular, the effect of extraction variables on oil recovery has been estimated by factorial designs and the optimum extraction conditions have been found using modified simplex method for the case of soybean oil. Simulation and modeling of a sunflower oil SFE plant and the optimization of the continuous process operation (extraction and CO2 recovery) was carried out and the optimal operating conditions found (55). Mathematical model of a fixed-bed extractor of canola oil has been presented giving the concentration of oil on both the solid and fluid phases and determining the overall volumetric mass transfer coefficients; a one-dimensional unsteady state model was used and the predicted results were in good agreement with the respective experimental results (56). Extractive processes used in alcoholic food/beverage products have been studied and optimized. The design, optimization, and control of extractive alcoholic fermentation in continuous mode are preferred. Combined with selective ethanol extraction, fermentation is enhanced; the combination of fermentation and selective ethanol extraction enables efficient control of the process. The dynamic behavior of the process was studied by factorial design simulating industrial process, and a dynamic matrix control (DMC) algorithm was used to

Optimization

175

control the extractive process. Modeling and simulation with response surface analysis was used to determine the operational conditions that maximize alcohol yield and productivity (57). Also, a countercurrent supercritical fluid extraction process on a pilot plant of distilled alcoholic drinks has been studied to optimize the efficiency of ethanol extraction yield. Thus, by factorial experimental design the extraction and fractionation conditions determined the ethanol percentage and the total yield of extracted ethanol of the residual extracted drink and of each isolated fraction, allowing the production of concentrated extracts with a rich aroma and low ethanol content (58). Other analytical extractive processes that have been optimized are the separation of certain amino acids (i.e., DL-tryptophan), which are important food components from aqueous solution using an emulsion liquid membrane. The critical extraction parameters: carrier concentration in the membrane system, pH, initial amino acids concentration were studied and using response surface methodology (RSM) the optimum separation conditions were found where the maximum amino acid extraction yield was attained (59). Extraction (or acid leaching) is a special step or pretreatment in many cases of analytical procedures of some determinations in food matrixes: vitamins, trace elements, pesticides residues, etc., in foods, industrial products, water, or soil. The analytes should be isolated and/or preconcentrated without changing their original chemical forms and subsequently analyzed using analytical or instrumental methods (i.e., atomic absorption spectrometry in trace elements, chromatographic methods such as gas chromatography (GC), highperformance liquid chromatography (HPLC) in pesticides or vitamins, spectrophotometry in tannins, etc.). The optimization of an analytical procedure often involves the study of the effects of experimental conditions particularly of time consuming and expensive ones as the extraction pretreatment processes (60–66). Critical variables in such extraction (or leaching) procedures are: type of solvent (organic solvent, saponification reagent, acid/oxidant reagent type and concentration), leaching volume, time, temperature, particle size of ground food product (matrix), extraction method. The particular variables relative to the extraction method are: (a) soxhlet: reflux frequency; (b) solid-phase extraction (SPE): type of sorbent, eluting solvent flow rate, sample pH, sample volume, elution volume, addition of modifier, water sample flow rate; (c) supercritical fluid extraction (SFE): CO2 pressure, temperature, static, and dynamic time; and (d) ultrasound or microwave assisted: sonication, frequency of ultrasound energy or microwave power, exposure time, temperature (67, 68). Experimental (central composite, Plackett-Burman—PBD, orthogonal array—OAD) design and simplex optimization method or response surface methodology (RSM) are effectively used to study the effects of extraction variables and find the optimum values of extraction variables in which maximum percent recovery of the extracted components is attained.

176

Tzia

NOMENCLATURE xi(i = 1, 2, . . . n)

x, (x1, x2, . . . . . , xn) X Xij or xij x* x = [x1, x2, . . . . . . , xn]T x(k), {x(k)} k = 1, 2, . . . f(x), f(X) h(x), hj(X) g(x), gj(X) C, f(x1, . . . , xn) fci = fi(x1, x2, . . . . . . . . , xn) l1 ≤ ri(x1, x2, . . . . . . . . , xn) ≤ l2 f′(x), dx f″(x) dr f(x) 䉮f(x) 䉮, (∂f/∂x1, ∂f/∂x2, . . . . . . , ∂f/∂xn)T 䉮2f(x) 䉮2 H(x), 䉮(∂f/∂xj), 䉮(䉮fT) J*x=x* = [∂2f/(∂xi ∂xj)* x=x*] dk * x=x* J

冉

f, g1, g2, . . . , gm xk, x1, x2, . . . , xm

L λj Rn BIP CCD CCRD GMP

冊

independent parameters, variables, design variables in optimization problem vector of n variables, n-dimensional vector, design vector matrix elements of matrix X extremum (minimizer/ maximizer) column vector iterates in an iterative method objective function vector of equality constraints vector of inequality constraints optimization criterion function functional constraints regional constraints first derivative of function second derivative of function rth differential of f(x) (partial derivative of r order) first partial derivative or gradient vector first derivative operator (elements ∂xi/∂xj) second partial derivative second derivative operator (elements ∂2/ ∂xi ∂xj) Hessian matrix Hessian matrix of f(X) k-order differential of f at X* Jacobian determinants Lagrange function Lagrange multipliers n-dimensional space binary integer programming central composite design central composite rotatable design geometric programming

Optimization

IP LP MILP MIP NLP OAD PBD RSM SFE SPE

177

integer programming linear programming mixed integer linear programming mixed integer programming nonlinear programming orthogonal array design Plackett-Burman design response surface optimization supercritical fluid extraction solid-phase extraction

REFERENCES 1. SS Rao. Engineering Optimization: Theory and Practice, 3rd ed. New York: John Wiley & Sons, Inc. 1996. 2. TF Edgar, DM Himmelblau. Optimization of Chemical Processes. Singapore: McGraw-Hill Book Co., 1989, Chapters 1–9, p. 3–438. 3. G Taguchi, MS Phadke. Quality engineering through design optimization. In: D Khosrow, ed. Quality Control, Robust Design, and the Taguchi Method. Pacific Grove, CA: Wadsworth & Brooks/Cole, Advanced Books & Software, 1989, pp. 77–96. 4. AA Teixeira, CF Shoemaker. Process optimization. In: Computerized Food Processing Operations. New York: AVI, Van Nostrand Reinhold, 1989, pp. 169–195. 5. J Floros. Optimization methods in food processing and engineering. In: YH Hui, ed. Encyclopedia of Food Science and Technology, Vol. 3. Toronto: John Wiley & Sons, Inc., 1992, pp. 1952–1965. 6. DA Pierre. Optimization. In: McGraw-Hill Encyclopedia of Science & Technology, Vol. 12, 9th ed. New York: McGraw-Hill, Inc., 2002, pp. 479–485. 7. E Rotstein, J Chu, IS Saguy. Simulation and optimization. In: KJ Valentas, E Rotstein, RP Sing, eds. Handbook of Food Engineering Practice. CRC Press, 1997, pp. 559–580. 8. I Saguy, L Levine, S Symes, E Rotstein. Integration of computers in food processing. In: HG Schwartberg, MA Rao eds. Biotechnology and Food Process Engineering. New York: Marcel Dekker, 1990, pp. 415–441. 9. JS Arora. Introduction to Optimum Design. Singapore: McGraw-Hill, 1989. 10. R Fletcher. Practical Methods of Optimization, 2nd ed. Chichester, UK: John Wiley & Sons, 1987. 11. G Dieter. Optimization. In: Engineering Design, 2nd ed. New York: McGraw-Hill, 1991, pp. 199–230. 12. WD Baasel. Optimization techniques. In: Preliminary Chemical Engineering Plant Design, 2nd ed. New York: Van Nostrand Reinhold, 1982, pp. 450–469. 13. GF Nalven. Plant Operation and Optimization. Practical Engineering Perspectives. New York: American Institute of Chemical Engineers, 1996. 14. TA Ciriani, RC Leachman. Optimization in Industry. Mathematical Programming and Modeling Techniques in Practice. Chichester, UK: John Wiley & Sons, 1993.

178

Tzia

15. CA Floudas. Global optimization in design and control of chemical process systems. J Process Control 10:125–134, 2000. 16. RP Byrne, IDL Bogle. Global optimization of modular process flowsheets. Ind. Eng. Chem. Res. 39:4296–4301, 2000. 17. B Gross, P Roosen. Total process optimization in chemical engineering with evolutionary algorithms. Comp Chem Eng 22:S229-S236, 1998. 18. MF Yeo, EO Agyei. Optimizing engineering problems using genetic algorithms. Eng Comp 15(2):268–280, 1998. 19. http://www.mcs.anl.gov/home/otc 20. A Bejan, G Tsatsaronis, M Moran. Thermoeconomic optimization. In: Thermal Design & Optimization. Toronto, Canada: John Wiley & Sons, Inc., 1996, pp. 463–513. 21. M Moresi. Design and optimization of falling-film evaporators. In: S Thorne, ed. Developments in Food Preservation—3. Essex, UK: Elsevier Applied Science Publishers, 1985, pp. 183–244. 22. A Van Loey, M Hendrickx, C Smout, T Haentjens, P Tobback. Recent advances in process assessment and optimization. Meat Sci 43:S81-S98, 1996. 23. E Balsa-Canto, AA Alonso, JR Banga. A novel, efficient and reliable method for thermal process design and optimization. Part I: Theory. J Food Eng 52:227–234, 2002. 24. E Balsa-Canto, AA Alonso, JR Banga. A novel, efficient and reliable method for thermal process design and optimization. Part II: Applications. J Food Eng 52: 235–247, 2002. 25. SK Sharma, SJ Mulvaney, SS Rizvi. Product formulation and process optimization using linear programming. In: Food Process Engineering, Theory and Laboratory Experiments. New York: John Wiley & Sons, Inc., 2000, pp. 1–19. 26. IA Castro, J Tirapegui, RSSF Silva. Protein mixtures and their nutritional properties optimized by response surface methodology. Nutr Res 20(9):1341–1353, 2000. 27. BJ Westrich, IM Buzzard, LC Gatewood, PG McGovern. Accuracy and efficiency of estimating nutrient values in commercial food products using mathematical optimization. J Food Comp Anal 7:223–239, 1994. 28. MC Gacula, Jr. Product optimization. In: Design and Analysis of Sensory Optimization. Trumbull, CT: Food and Nutrition Press, 1993, pp. 105–229. 29. C Stampanoni-Koeferli, P Piccinali-Schwegler, D Hong-Chen. Application of classical and novel sensory techniques in product optimization. Lebensm.-Wiss. u.-Technol. 31:407–417, 1998. 30. H Moskowitz. Optimizing consumer product acceptance and perceived sensory quality. In: E Graf, IS Saguy, eds. Food Product Development: From Concept to the Marketplace. New York: AVI, Chapman & Hall, 1991, pp. 157–187. 31. JL Sidel, H Stone, HA Thomas. Hitting the target: sensory and product optimization. Cereal Food World 39(11/12):826–830, 1994. 32. MV Pastor, E Costell, L Izquierdo, L Dura´n. Optimizing acceptability of a high fruit-low sugar peach nectar using aspartame and guar gum. J Food Sci 61:852– 855, 1996. 33. H Moskowitz. Sensory segmentation and the simultaneous optimization of products and concepts for development and marketing of new foods. In: E Graf, IS Saguy, eds. Food Product Development: From Concept to the Marketplace. New York: AVI, Chapman & Hall, 1991, pp. 311–326.

Optimization

179

34. SD Holdsworth. Quality optimization. In: Thermal Processing of Packaged Foods. London: Blackie Academic & Professional, 1997, pp. 183–201. 35. JC Ho, SK Chou, AS Mujumdar, MNA Hawlader, KJ Chua. An optimization framework for drying of heat-sensitive products. Appl Thermal Eng 21:1779–1798, 2001. 36. C-S Chen, J-J Chen, T-P Wu, C-Y Chang. Optimizing the frying temperature of gluten balls using response surface methodology. J Sci Food Agric 77:64–70, 1998. 37. S Hari Krishna, AP Sattur, NG Karanth. Lipase-catalyzed synthesis of isoamyl isobutyrate—optimization using a central composite rotatable design. Proc Biochem 37:9–16, 2001. 38. M Renard, JJ Bimbenet. Automatic control and optimization of food processes. Proceedings of the International Symposium on the Automatic Control and Optimization of Food Processes (ACOFP), Paris, France, 1986. Essex, UK: Elsevier Science Publishers, 1988, pp. 417–557. 39. AS Moharir, S Bhatia, DN Saraf. Optimization of industrial vegetable oil hydrogenators. JAOCS 60(8):1543–1547, 1983. 40. P Filka. Optimization of food processing plants. In: JJ Bimbenet, ed. Automatic Control and Optimization of Food Processes. Proceedings of the International Symposium on the Automatic Control and Optimization of Food Processes (ACOFP), Paris, France, 1986. Essex, UK: Elsevier Science Publishers, 1988, pp. 405–415. 41. R Romero, D Ga´zquez, MG Bagur, M Sa´nchez-Vin˜as. Optimization of chromatographic parameters for the determination of biogenic amines in wines by reversedphase high-performance liquid chromatography. J Chrom A 871:75–83, 2000. 42. S Roussel, G Forsberg, V Steinmetz, P Grenier, V Bellon-Maurel. Optimization of electronic nose measurements. Part I: Methodology of output feature selection. J Food Eng 37:207–222, 1998. 43. T Naes, H Martens. Principal component regression in NIR analysis. J Chemom 2: 155–157, 1988. 44. KK Tiwari. Extraction technologies related to food processing. In: AG Gaonkar, ed. Food Processing: Recent Developments. New York: Elsevier, 1995, pp 269–302. 45. TF Edgar, DM Himmelblau. Optimization of Chemical Processes. Singapore: McGraw-Hill, 1989, pp. 474–480. 46. GN Liadakis, C Tzia, V Oreopoulou, CD Thomopoulos. Protein isolation from tomato seed meal, extraction optimization. J Food Sci 60:477–482, 1995. 47. IY Mizubuti, OB Ju´nior, LW de Oliveira Souza, RS dos Santos Ferreira da Silva, EI Ida. Response surface methodology for extraction optimization of pigeon pea protein. Food Chem 70:259–265, 2000. 48. IYS Rustom, MH Lo´pez—Leiva, BM Nair. Optimization of extraction of peanut proteins with water by response surface methodology. J Food Sci 56:1660–1663, 1991. 49. PKJPD Wanasundara, F Shahidi. Optimization of hexamrtaphosphate-assisted extraction of flaxseed proteins using response surface methodology. J Food Sci 61: 604–607, 1996. 50. SY Hsu. Optimization of the surimi processing system with a central composite design method. J Food Eng 24:101–111, 1995. 51. MS Singh, A Farsaie, LE Stewart, LW Douglass. Development of mathematical models to predict sunflower oil expression. American Society of Agricultural Engineers. St. Joseph, MI: USA Transactions of the ASAE, 1984, pp. 1190–1194.

180

Tzia

52. E Pagliarini, C Peri, B Zanoni, M Ghizzardi. Optimization of olive paste expression: Maximizing yield by central composite design approach. J Sci Food Agric 71:470–474, 1996. 53. E Monteleone, G Caporale, A Carlucci, E Pagliarini. Optimization of extra virgin olive oil quality. J Sci Food Agric 77:31–37, 1998. 54. FM Lanc¸as, MEC Queiroz, ICE Silva. Simplex optimization of extraction of soybean oil by supercritical pentane. Chrom 40(7/8):421–424, 1995 55. M Bravi, R. Rubbico, F Manna, N Verdone. Process optimization in sunflower oil extraction by supercritical CO2. Chem Eng Sci 57:2753–2764, 2002. 56. AKK Lee, NR Bulley, M Fattori, A Meisen. Modelling of supercritical carbon dioxide extraction of canola oilseed in fixed beds. JAOCS 63(7):921–925, 1986. 57. AC Costa, DIP Atala, F Maugeri, R Maciel. Factorial design and simulation for the optimization and determintation of control structures for an extractive alcoholic fermentation. Process Biochem 37:125–137, 2001. 58. FJ Sen˜ora´ns, A Ruiz-Rodrı´guez, E Iban˜ez, J Tabera, G Reglero. Optimization of countercurrent supercritical fluid extraction conditions for spirits fractionation. J Super Fluids 21:41–49, 2001. 59. E Bayraktar. Response surface optimization of the separation of DL-tryptophan using an emulsion liquid membrane. Process Biochem 37:169–175, 2001. 60. H Bagheri, M Saraji, M Chitsazan, SR Mousavi, M Naderi. Mixed-level orthogonal array design for the optimization of solid-phase extraction of some pesticides from surface water. J Chrom A 888:197–208, 2000. 61. P Bermejo-Barrera, O Mun˜iz-Naveiro, A Moreda-Pin˜eiro, A Barmejo-Barrera. The multivariate optimization of ultrasonic bath-induced acid leaching for the determination of trace elements in seafood products by atomic absorption spectrometry. Anal Clin Acta 439:211–227, 2001. 62. MC Garrigo´s, F Reche, ML Marı´n, K Pernı´as, A Jime´nez. Optimization of the extraction of azo colorants used in toy products. J Chrom A 963:427–433, 2002. 63. Z Jo´kai, J Hegoczki, P Fodor. Stability and optimization of extraction of four arsenic species. Microch J 59:117–124, 1998. 64. J Lee, L Ye, WO Landen, Jr., RR Eitenmiller. Optimization of an extraction procedure for the quantification of vitamin E in tomato and broccoli using response surface methodology. J Food Comp Anal 13,45–57, 2000. 65. B Martı´nez, F Rinco´n, MV Iba´n˜ez. Optimization of tannin extraction from infant foods. J Agric Food Chem 48:2097–2100, 2000. 66. CC Nascentes, M Korn, MAZ Arruda. A fast ultrasound-assisted extraction of Ca, Mg, Mn and Zn from vegetables. Microch J 69:37–43, 2001. 67. GA Csiktusna´di-Kiss, E Forga´cs, T Cserha´ti, T Mota, H Morais, A Ramos. Optimization of the microwave-assisted extraction of pigments from paprika (Capsium annuum L.) powders. J Chrom A 889:41–49, 2000. 68. P Larra´yoz, M Carbonell, F Iba´n˜ez, P Torre, Y Barcina. Optimization of indirect parameters which affect the extractability of volatile aroma compounds from Idiaza´bal cheese using analytical supercritical fluid extractions (SFE). Food Chem 64: 123–127, 1999.

6 Fats and Oils from Plant Materials Lei Xu and Levente L. Diosady University of Toronto Toronto, Ontario, Canada

I. INTRODUCTION Although “extraction” often describes the mass transfer process between two liquid phases, in the following the term “extraction” refers to the preferential dissolution of one or more constituents of a solid by contact with a liquid solvent. It is one of the oldest and most commonly used unit operations in the food industry. “Leaching” is another term often used to describe the operation, although originally referring only to percolation of a liquid solvent through a fixed bed of the solid. Sometimes the term “washing” is employed, as it is intimately associated with extraction as a complementary step. The fundamental purpose of extraction is the removal of a soluble fraction from an insoluble, permeable solid phase with which it is associated. The soluble fraction may be solid or liquid, and may be physically held within, chemically bound with, adsorbed on, or mechanically held in the pore structure of the insoluble matrix. Although the mining and metallurgical industries are the largest users of extraction technology, extraction is also a critical unit operation in the food industry. Many important foods and food ingredients are obtained by extraction from materials of life origin, including microbes, plants, and animals. For instance, sugar is extracted from sugar beets with hot water, tannin is dissolved out of various tree barks, and beverages such as tea and coffee are prepared both domestically and industrially by leaching operations. In terms of technological sophistication and production scale, vegetable oils are among the most important products recovered from seeds such as soybeans and rapeseed by extraction; therefore, these are discussed in greater detail in this chapter. Although in some cases mechanical pressing methods can be used, solvent extraction is a more 181

182

Xu and Diosady

efficient method for the recovery of oil from oil-bearing materials. It is the only efficient technique to recover oil from seeds or other materials low in oil (1). Since heat treatment can be minimized, oil produced by solvent extraction is of high quality, and thermal damage to meal, the residue after oil removal, is also minimal. The effectiveness and efficiency of extraction depend on many factors, including the solvent used, solid preparation, extraction temperature, modes of operation, and equipment. The theories of extraction have been well developed and are presented in detail in texts such as Treybal’s Mass Transfer Operations (2). Commercial processes of edible oil extraction are extensively reviewed in Bailey’s Industrial Oil and Fat Products (3), while essential oils are reviewed in Guenther’s The Essential Oils (4). The choice of solvent for extraction is largely determined by the solubility characteristics of the constituent of concern and the solid. Ideally the smaller component of interest should be highly soluble in the selected solvent, whereas the matrix or other major components should have little or no solubility in it. “Infinite” selectivity is never achieved in practice, and the process must be manipulated to obtain high yield and high purity of the desired components with minimal coextraction of undesired impurities. In addition to solubility, cost and safety are also taken into consideration when a solvent is chosen for extraction. To achieve complete extraction, a combination of several unit operations, such as successive extractions with two or more solvents, may be required. The pretreatment given to the oil-bearing solid frequently plays a very important role in the efficiency of the extraction process. In many instances of extraction, small particles of the soluble material are completely surrounded by a matrix of insoluble materials. The solvent must then diffuse into the mass, and the resulting solution must diffuse out before a separation can result. Particle size reduction by crushing and grinding of such solids greatly accelerates the extraction action, since the soluble portions are then made more accessible to the solvent. The extraction process must find a suitable compromise between the increased extraction rate obtained by reducing the particle size and increased difficulty in separating the small solid particles from the liquid solvent. Plants, microbes, animals, and their parts are cellular in structure, and the desired components are usually found in the cells. If the cell walls remain intact upon exposure to a solvent, the extraction depends solely on the diffusion of the solute through the cell walls, often controlled by osmotic pressure. Pretreatment techniques can be used to disrupt cell walls chemically or thermally. Alternatively, the aspect ratio is changed in a mechanical process. This allows the reduction of the penetration distance of the solvent while maintaining sufficiently large particle size to permit efficient separation from the solvent. As an example, cellular material may be cut into thin slices before extraction so as to reduce the penetration distance required for the solvent to reach the individual plant

Fats and Oils from Plant Materials

183

cells. In oil extraction, oilseeds are usually rolled to form flattened flakes with thickness ranging from 0.15 to 0.5 mm, while retaining major dimensions of up to 15 mm. The rolling and flaking evidently crushes some of the cell walls and opens up passageways for penetration of the solvent by capillary action (5, 6). It is impractical and sometimes even undesirable to grind the material small enough to release the contents of individual cells because of the difficulties excessively fine particles may cause in subsequent separation. Seed preparation includes heat and moisture adjustment to facilitate cell disruption and solvent penetration. It is usually desirable to extract at as high a temperature as possible because higher temperatures result in higher solubility of the solute in the solvent; thus, higher concentrations in the extraction liquor are possible. Moreover, the viscosity of most liquids is lower and the diffusivity greater at higher temperatures, leading to increased rates of extraction. Higher operating temperatures also reduce energy requirements in solvent recovery. However, the solvent selection and temperature must take into account that in some natural products high temperatures may cause deterioration and denaturation. Extraction operations can be conducted in batch (unsteady state) or continuous (steady state) mode. In each case, both stage-wise and continuous-contact types of equipment can be used. Extraction equipment uses two techniques for solid–liquid contact. The liquid can be sprayed and percolated over the solid, or, alternatively, the solid can be entirely immersed in the solvent. The choice of equipment depends on the physical and chemical characteristics of the matrix, the value of the products, and the desired throughput.

II. THEORIES OF EXTRACTION A. Rate of Extraction The performance of an extraction process is governed by both mass transfer and equilibrium phenomena. It can be affected by many physical and chemical factors, some of which are difficult to evaluate quantitatively. It is, therefore, not a simple matter to theoretically predict extraction rates, and quite frequently, such data have to be determined experimentally. If extraction only involves simple washing of a solute from the surface of a solid, it may be very fast, comprising merely the blending of solution and solvent, and its efficiency is thus determined almost entirely by the effectiveness of the mechanical separation of liquid from solid. Extracting a solute from the internal parts of a solid, on the other hand, will be much slower, in which case solids are made of a skeletal structure of insoluble substances with pores impregnated with the solute. Diffusion theory can be used to describe extraction processes that fall into this category. Beginning with Fick’s law, the following equation is derived (7):

184

Xu and Diosady

NA =

冉冊

NA DAB ρ NA + N B z M

ln

av

NA/(NA + NB) − xA2 NA/(NA + NB) − xA1

where NA and NB are molar fluxes of components A and B, respectively, DAB is the diffusivity of A in B in solution, ρ and M are the solution density and molecular weight, respectively, xA1 and xA2 are mole-fraction concentrations of component A at the beginning and end of the diffusion path, respectively, and z is the distance in the direction of diffusion. For most common cases where component A diffuses through nondiffusing B, NB = 0, where NA =

冉冊

DAB ρ z M

(xA1 − xA2)

av

The diffusivity varies significantly among liquids. In the absence of experimental data, it can be estimated by the following empirical correlation for nonelectrolytes: DAB =

(117.3 × 10−18(ϕMB)0.5T µνA0.6

where D AB is diffusivity of A in a very dilute solution in solvent B, MB the molecular weight of solvent, T the temperature, µ the solution viscosity, νA the solute molar volume at normal boiling point (0.0756 m3/kmol for water), and ϕ the association factor for solvent (2.26 for water, 1.9 for methanol, 1.5 for ethanol, 1.0 for unassociated solvents such as benzene and hexane). The complexity of the structures of natural products may make the application of these models difficult. In addition to the above attributive factors, the rate of diffusion can also be affected by the degree of cell rupture, the diversity of structures in the matrix, and the rate of dissolution of each constituent. All of these phenomena make it impossible to apply a single simple equation to model extraction. Although methods of dealing with solvent extraction of vegetable oils have been suggested (8, 9), very little has been published on modeling the extraction of other natural products. In oil extraction, the term “miscella” refers to the mixture of oil and solvent outside seed particles, and “raffinate” the mixture of the two within the particles. The design of large-scale solvent extraction apparatus for oilseeds extraction requires the knowledge of the rate at which equilibrium is attained between the miscella and raffinate. In most oilseeds, oil is stored in small, 1- to 10-µm oil bodies, which are enclosed by protein-embedded membranes. The membrane protein oleosin has high affinity for both oil and water on opposite ends of the molecule. The extraction proceeds first by washing out of free oil, which is held in large intercellular spaces and is not bound to the flake structure; it is followed by a diffusion process. Assuming that the cell walls are broken,

Fats and Oils from Plant Materials

185

the oil must still diffuse through the oleosin membrane of the oil droplets. The next step is the release of bound oil into the solvent, which may involve the breaking of hydrogen bonds or hydrophobic attractive forces. The solution then must diffuse back out through the solid matrix to the bulk miscella, which involves diffusion first through the solid itself, then through the laminar boundary layer on the surface of the solid. Therefore, oil extraction may be quite slow, particularly when the oil content of the seed is reduced to low levels (≤1.0%). Studies have shown that the oil extraction rate is influenced by a number of factors, including the thickness, size, shape, and internal structure of the seed particles; the intrinsic capacity for diffusion of solvent and oil, which is determined primarily by the viscosities of both; and, especially at low oil levels, the rate of extraction of other substances that are less readily soluble than glycerides, which are the primary components of interest in vegetable oils (1). Among these factors, the effect of the thickness of oilseed flakes is most evident. Othmer and Agarwar (5) correlated the rate of hexane extraction of soybean oil to flake thickness and remaining oil concentration as follows: −

dC = kF −3.97C 3.5 dt

where C is the concentration of oil in the flake, t the time, k the proportionality constant, and F the flake thickness. Since F has an exponent of almost 4, a small increase in flake thickness would result in a great decrease in the oil extraction rate. When using a homogeneous oil-impregnated material made of thin platelets of uniform thickness, approach of estimation of extraction rate becomes simpler as the application of diffusion theories is possible. The calculation has been thus developed by Boucher et al. (8) as follows: E=

8 π2

n=∞

1

冋

∑ (2n + 1) exp −(2n + 1) n=0

冉 冊 冉 冊册 2

2

π Dt 2 R2

where E is the fraction of total oil unextracted at the end of time t, R is one–half the platelet thickness, and D is the diffusivity of the oil within the platelets. Except for the initial stage where the free oil is washed out quickly, the above equation can be reduced to: E=

8 π

冉 冊

exp − 2

π2Dt 4R2

or logE = −0.091 − 1.07

Dt R2

186

Xu and Diosady

Thus, when the extraction proceeds past its initial stage, a plot of log E against t gives a straight line. The slope is a function of the diffusivity of the oil and the platelet thickness. The turbulence in the solvent, indicated by the Reynolds number, was found to have no significant effect on the extraction rate, showing that the limiting step in extraction is diffusion through the cell wall within the platelets, and the liquid-film resistance to the transfer of oil to the solvent was inconsequential (8, 10). The rapid release of oil during the initial stage of extraction was thought to be caused by the rupture of some oil bodies in slicing the seeds, plus the occurrence of void spaces in the seeds after drying (9). The diffusivity of the oil in the platelets was correlated to the viscosities of both solvent and oil by Boucher et al. (8) under their test conditions: D = 12.96 × 10−6(µ 0 µ s)−0.46 where µ o and µ s refer to the viscosities of oil and solvent, respectively. The numerical values in the formula are obviously specific to the structure of the platelets and therefore are applicable only to the lot used in their tests. B. Calculations for Steady-State Operation In steady-state operation, it is assumed that the solid is contacted with more than enough solvent to dissolve all the soluble solute and there is no preferential adsorption of either solvent or solute by the solid. Adequate contact time permitting, all the solute will be dissolved, and the oil concentration in the miscella will be equal to that in the solvent inside the seed matrix; thus, there is an equilibrium between the effluent solution and the solid. The insoluble solid is then separated by settling, filtration, drainage, or centrifugation. This is usually not achieved in practice as the solute may be incompletely dissolved due to inadequate contact time and some solute is usually adsorbed by the solid. Therefore, without corrections for these effects, the actual stage efficiencies are always lower than the calculated ones. Similarly to other unit operations, two typical calculations are performed in extraction: characterization and design. In the former case the initial solute content of the solid, and the number and amount of washings are known, and the final amount or concentration of solute in the extract is sought. This yields an estimate of the extent to which an extraction process proceeds. In case of design calculations, the final solute content of the solid is specified, with a known amount and concentration of solute in the solvent; whereas the number of washings, or the number of stages, is calculated in order to design an extraction process. Steady-state operation can be achieved in either a single-stage or a multistage manner. Single-stage extraction involves only one cycle of mixing the solid with the solvent and separating the resulting insoluble solid by physical means. Figure 1 shows all the streams involved in a complete process of single-

Fats and Oils from Plant Materials

187

Figure 1 Single-stage extraction (11).

stage extraction. Mass balance is performed for all components in the system to give (11): FyyF + R0 x0 = E1y1 + R1x1 F(1 − yF) + R0(1 − x0) = E1(1 − y1) + R1(1 − x1) F + R 0 = E 1 + R 1 = M1

for solute C for solvent A for solution A + C

M1 is the mass of the mixture, or the slurry, on an insoluble solid–free basis. The solid and solute concentrations in the slurry are, therefore: NM1 =

B B = F + R 0 M1

yM1 =

yFF + R0 x0 F + R0

respectively. By continuously contacting the solids with fresh batches of solvent, more solute can be extracted than a single batch extraction. This is called multistage cross-current extraction. All of the above equations are applicable to the calculations for addition stages, in which the already extracted solids from any stage are the feed solids to the next; thus, the procedure for a single stage is repeated with the changes in the subscripts to indicate different stages. Multistage crosscurrent extraction can be used to extract additional solute from the solids, but a much larger amount of solvent is required for this operation, and as a result, dilute solutions of the solute will inevitably be produced as it proceeds to the last stages of extraction. Although this is uneconomical use of solvent, the procedure allows experimental determination of the performance of countercurrent systems, using simple laboratory equipment. Countercurrent extraction minimizes solvent use and operating costs. In

188

Xu and Diosady

this approach, the final solution is withdrawn from contact with the fresh solid and the fresh solvent is mixed with the solid with most of the solute already extracted, keeping the driving force at the highest level throughout the process. A flowsheet of multistage countercurrent extraction is shown in Fig. 2. The process can be operated with any number of extraction and separation steps. An array of these steps is called an extraction battery. Like in single-stage extraction, calculations of multistage cross-current extraction are based on stage-wise mass balance. The solution (A + C) balance gives: F + RNp+1 = R1 + ENp = M The solute (C) balance gives: FyF + RNp+1xNp+1 = R1x1 + ENpyNp = MyM where M is total mass of the mixture (slurry) on an insoluble solid–free basis. The slurry is produced by mixing the solids and solvent such that: NM = yM =

B F + RNp+1 FyF + RNp+1xNp+1 F + RNp+1

where NM and yM are, respectively, the concentrations of solid and solute in the slurry on insoluble solid–free basis. The extraction process can be graphically modeled using a diagram as shown in Fig. 3, to indicate the approach to equilibrium obtained theoretically or experimentally, and the number of theoretical stages required for the extraction. The number of stages is dependent on the average stage efficiencies, which are affected by the mass transfer properties of each stage. In the special case where there is a constant value of N for slurry of all stages, the system approximates a constant R/E. When, in addition, the equilibrium between the solute concentration in the effluent solution, x, and that in the solid (insoluble solidfree basis), y, is of a linear relationship, i.e., m = y/x = constant, the final concentration in the solid, yNP can be calculated by the following equation: yF − yNp yF − mxNp+1

=

冉 冊 冉 冊 R mE

Np+1

R mE

−

Np+1

R mE

−1

Figure 2 Multistage countercurrent extraction (11).

Fats and Oils from Plant Materials 189

190

Xu and Diosady

Figure 3 Stage construction diagram (11).

Similarly, the number of stages, Np, can be estimated when the final concentration is specified. If no preferential adsorption of the solute occurs, the solute concentration is the same in both the solution and the solid, so that m = 1. To calculate the steady-state performance of a multistage countercurrent extraction, the concentrations in the intermediate stages can be experimentally estimated through batch simulation, using cross-current procedure as indicated earlier. The scheme for such a simulation was proposed by Scheibel (12) and Treybal (13). Fig. 4 shows an example of batch simulation of two-stage countercurrent extraction. Each circle in the figure represents a batch extraction. Starting from stage A, F amount of feed solid is contacted with S amount of fresh solvent. The mixture is then separated into a clear solution R′ and a solid EA. At stage B, the solid from stage A, EA, is again contacted with the same amount of solvent S before the separation to yield a solid E′. The whole simulation process is followed through as illustrated in Fig. 3. It consists of several runs.

Fats and Oils from Plant Materials

191

Figure 4 Batch simulation of a countercurrent cascade (13).

From each run R amount of solution and E amount of solid are produced. Although R and E at the beginning are clearly different in solute concentration from those produced by a countercurrent extraction, after several runs in this manner, they approach very closely the values obtained in a truly continuous multistage countercurrent extraction.

III. SOLVENTS FOR EXTRACTION For oil extraction, nonpolar solvents are usually used, as triglyceride-based oils are typically miscible with these solvents. The most common solvents in solvent

192

Xu and Diosady

extraction of oil are light paraffinic petroleum fractions. Both hexane (bp 66– 69°C) and heptane (bp 89–98°C) mixtures are widely used; sometimes cyclic hydrocarbons, such as cyclohexane (bp 71–85°C), are also used (16). While these solvents are efficient in oil extraction, the major disadvantage to their use is their flammability. Strict safety measures must be taken to avoid fire and reduce explosion hazard in the plants where they are used (17). Much effort has been taken to find alternative solvents, particularly since the Clean Air Act of 1990 designated hexane as a hazardous air pollutant. A methylpentane-type naphtha (bp 55–61°C) is now commercially available (18). Trichloroethylene (bp 86.7°C), due to its nonflammability, is a much safer solvent to handle in terms of prevention of fires and explosions (19, 20), but all chlorinated organic solvents are increasingly perceived as too toxic to be used in the production of food materials. There is pressure on the industry to switch to solvents that are perceived to be more benign: alcohols and even aqueous solutions. Both isopropanol (21) and ethanol (22, 23) have been used commercially to extract soybean and cottonseed oil. Recently, high-concentration isopropanol (⬃96%), recovered by pervaporation techniques, has been used instead of the commonly distilled azeotropic isopropanol (87.8%) for oil extraction in order to increase the oil solubility (24). Both cottonseed and soybean oils thus extracted, after refining, bleaching, and deodorization, well met commercial standards. However, although improving the flavor, the alcohols tend to lower functionality of soybean protein products, and due to low oil solubility and higher latent heat than hexane, they require significantly more energy for solvent recovery. Supercritical fluids have been tested for oil extraction (25, 26), but due to the extremely high pressures required, most equipment has low capacity, which currently limits the application of this technology to the production of high-value products such as coffee, hops, and flavor concentrates.

IV. SOLVENT EXTRACTORS Two types of commercial units are currently used for extraction: immersion and percolation. In the immersion type of extractor, solids to be extracted are conveyed through a pool of solvent. The particle size of the solid material must be reduced as much as possible to facilitate maximal contact between the solvent and the solid. However, in this method the separation of fine particles and the miscella is difficult, thus limiting its use to low-volume operations. Most largevolume units are percolation-type extractors that are considerably more efficient in terms of energy consumption and space requirements. In the percolation unit, the solid material (flakes or prepressed cake) forms a fixed bed, and the solvent is pumped and sprayed over the material, and drained through the bed, washing the oil down with it. Clearly the flakes or

Fats and Oils from Plant Materials

193

pieces of prepressed cake should be large enough to permit a reasonable flow rate of solvent through the solid material. Fine material can prevent the drainage, causing the bed to be flooded with solvent and resulting in improper washing and poor extraction. One of the first designs based on percolation was the basket-type extractor (Fig. 5), wherein the seed flakes are carried in baskets supported on endless chains. The baskets are loaded at the top of the descending leg, and the flakes are sprayed with half miscella that percolate through the beds of the flakes in these baskets as they go down. The miscella collected at bottom on the descending side is full miscella and thus is removed for oil and solvent recovery. On the ascent, the flakes are contacted with another stream of liquid that starts as pure solvent; thus, oil in the flakes is extracted in a countercurrent manner as the baskets move up. A short drainage time is provided at the top before the baskets are automatically flipped to unload the extracted and drained flakes into a discharging hopper, and they are then conveyed to the desolventizer. This early type of solvent extractor is bulky and requires intensive maintenance due to frequent chain breakdown. It has been largely superseded by rotary and horizontal extractors. A rotary extractor is, in fact, a vertical cylindrical shell with radially divided chambers called baskets or cells, either rotating or stationary, around a central shaft. Figure 6 shows a cut-away view of a rotary extractor manufactured by De Smet Process and Technology, Inc., the reflex extractor. Its liquid spraying nozzles and flakes hopper are fixed while the baskets in the shell rotate. The material is mixed with miscella as it enters the extractor and is thus slurry-fed to the rotating baskets. The miscella leaves the basket through the perforated bottom, and is collected and pumped to the previous basket. Thus, a countercurrent flow is set up between the moving seed beds and the stationary miscella or solvent headers. The most exhausted meal is contacted with fresh solvent, while the fresh seed is first contacted with nearly oil-saturated miscella. The totally sealed basket dividers ensure that each miscella stage flows vertically through the basket of material, and these dividers also allow the entire bed of material in the extraction zone to be thoroughly soaked in miscella. It is claimed that, since the miscella has more contact time to penetrate the flakes than in shallow-bed designs, even thicker flakes can be used to achieve desired residual oil content. After the extraction process is complete, a basket passes over an open portion of the screen, allowing the material to discharge into the dump hopper at the base of the extractor by gravity. A number of similar designs were developed based on the earlier Rotocel extractor by the Dravo Engineers and Contractors. Another variant of this design, developed by the French Oil Mill Machinery Company, called the stationary basket extractor, maintains the seed-holding baskets or cells immobile while rotating the solvent headers and bottom connector systems. Horizontal extractors feature the horizontal movement of the baskets while

194

Figure 5 Basket solvent extractor (1).

Xu and Diosady

Fats and Oils from Plant Materials

195

Figure 6 Reflex solvent extractor. (Courtesy of De Smet Process & Technology, Inc.)

the solvent is percolated through the moving seed bed. One typical example of this type is the Crown Model III continuous loop extractor made by the Crown Iron Works Company (Fig. 7), in which the solid material is deposited on veebar screen as a shallow bed (0.8 m) and travels a distance about 50 times the bed depth in a closed chamber shaped like a loop. The fresh material is fed to the extractor through an inlet hopper on the top, and moves through four stages on the upper level and another three on the lower level. In the first stage on the upper level it is washed with already concentrated miscella. The solids then move to the second stage concurrent to the miscella. The rich miscella drained from the second stage is pumped through a hydrocyclone before leaving the extractor as full miscella containing approximately 25% oil. The next five stages

Figure 7

Crown loop extractor Model III. (Courtesy of Crown Iron Works Company.)

196 Xu and Diosady

Fats and Oils from Plant Materials

197

are all operated in countercurrent manner. As the solids move from the upper level to the lower level between stages 4 and 5, the bed is completely turned over in the “tail” section so that both sides of the solids are washed with the miscella. Before being discharged, the extracted solids are drained of residual miscella in a drainage section. The mechanical features of this type of extractor promote solvent contact, rapid drainage, and complete extraction with a variety of products and allow utilization of fragile flakes with high content of fines. Extractors with capacities of 50–4000 t/d are in commercial service. Crown Iron Works Company also developed a Model IV extractor for countercurrent extraction of granular materials that are heavier than solvent (Fig. 8). The material is carried through a pool of solvent by a series of inclined drag conveyors. The solids pulled out of the solvent pool by the last conveyor is sprayed with fresh solvent and then are drained of residual miscella before being discharged. Full miscella is drawn off where solids are fed to the extractor. During the transfer of solids from one conveyor to another, the beds are completely turned over, thus ensuring good solvent–product contact.

V. EXTRACTION OF OILSEEDS The recovery of oil from oil-bearing plant materials has been a vital industry for thousands of years. Hence, its technology evolved from primitive manual operations in the early times to continuous processing by automated machinery in the modern days. This evolution was obviously driven by the need to maximize the oil yields, improve the oil quality, and produce a valuable residue after oil extraction. Oils from plants are usually contained in seeds, such as soybeans, sunflower seed, cottonseed, and rapeseed/canola, or fruits such as palm (Table 1). They contain 15–50% of oil and are the major source of vegetable oils for human consumption (Table 2). The oil extraction process is often followed by further processing to yield more refined oil products. Many processes have been developed to recover oil from oilseeds, but the most common ones are hydraulic pressing, expeller pressing, and solvent extraction. Developed in late 1700s, hydraulic pressing, also known as batch pressing, applies pressure using a hydraulic ram to batches of oilseeds confined in a barrel that allows oil to escape while retaining solids. Because it is a laborintensive, batch operation, its industrial use is uneconomical and thus declined drastically over the last two decades. Currently continuous expellers or screw presses are used for the mechanical extraction of oilseeds. These machines are able to create the pressures generated by a hydraulic press ram but require minimal labor because they operate continuously. The reduced labor cost and increased throughput and yield more than make up for their higher power requirement and maintenance cost (27).

198

Figure 8

Model IV Extractor. (Courtesy of Crown Iron Works Company.)

Fats and Oils from Plant Materials

199

Table 1 World Production of Major Oil-Bearing Seeds, Legumes, and Fruits in 1998–1999 Crop Year (in 106t) Soybeans Rapeseed/canola Cottonseed Sunflower seed Groundnuts, shelled Palm Linseed Sesame seed

154.70 35.78 33.63 27.26 20.88 16.71 2.62 2.59

Source: Ref. 14.

It is impossible to obtain complete oil removal by mechanical pressing, since the solids retain significant quantities of oil inside the matrix. In addition, as the oil content is reduced, the friction between particles escalates rapidly, and the heat generated by expeller causes very high temperatures that can degrade both the oil and the residual meal. To recover the last 20% of the oil, solvent extraction is the preferred operation. Solvent extraction as a batch process first appeared in 1870. Continuous solvent extraction was developed shortly after World War I. Since then, its use has expanded rapidly in oilseeds processing due to its high efficiency in oil removal and recovery. As solvent extraction is energy intensive, in oilseeds with high oil contents much of the easily removable oil is recovered by expelling prior to solvent extraction. Thus sunflower, rapeseed, and cottonseed are processed by a two-stage technique consisting of a prepress followed by solvent extraction, resulting in more complete removal of oil with a lower usage of solvent.

Table 2 Oil Content of a Number of Oil-Bearing Vegetable Materials Cottonseed Palm kernels Peanuts Rapeseed/canola Sesame seed Soybeans Sunflower seed Source: Ref. 15.

18–20 45–50 45–50 45–50 50–55 18–20 35–45

200

Xu and Diosady

A. Canola (Rapeseed) The technology to process canola or rapeseed is typical of high-oil-content seed processing. Although expeller pressing or direct solvent extraction may be used, these techniques either leave a fairly high residual oil content in the meal or are not energy efficient. The prevailing process in developed countries is prepresssolvent extraction, which is a combination of mechanical and solvent extraction, with the final portion of oil being removed with a solvent, usually hexane. In this process, much less residual oil is left in the meal. 1. Pretreatment and Prepressing of Oilseeds A typical oil extraction process begins with seed preparation (Fig. 9). The seeds usually carry foreign materials such as unwanted seeds, sticks, leaves, and even metal shreds. They are typically cleaned by passing through a magnetic separator and seed cleaners using a combination of air aspiration, agitation, and screens. In rapeseed, the enzyme myrosinase catalyzes hydrolysis of glucosinolates. Among the hydrolysis products are undesirable isothiocyanates, oxazolidinethiones, and nitriles (28), which are soluble in oil. In order to maintain the oil quality, it is necessary to keep the glucosinolates intact by rapidly inactivating myrosinase by “cooking” in a multistage cooker. The seed is first cracked to increase the area available for heat transfer. To achieve rapid myrosinase destruction and to minimize the time spent at the active temperatures of the enzyme (40–65°C), the cracked seed is contacted with live steam, which quickly raises the temperature by releasing the latent heat, and also adds water to aid in the denaturation of myrosinase. The excess water is removed by indirect heating in subsequent stages of the cooker employing stacked steam-jacketed trays with bottoms heated by steam. Since canola has much lower levels of glucosinolates than conventional rapeseed varieties, the cooking temperature for canola has been decreased from ⬃120°C to less than 100°C. The cooked seeds are then immediately pressed to separate oil by screw presses. A typical screw press consists of a hard-surfaced, horizontal worm shaft within a barrel composed of cages containing spaced lining bars. As the worm shaft rotates inside the cage assembly, it moves the seed through and out. Pressure on the seed increases as it moves through the barrel, expelling the oil between the lining bars. The pressure at the discharge end of the shaft forms the partially extracted seed into a cake. Important operating factors in prepressing are the speed of shaft rotation, the shaft assembly configuration, and setting of the choking mechanism at the discharge. Pressing cannot remove all of the oil without significant thermal damage to the meal. Typical residual oil content in fully pressed cake is 6–10%, which

Figure 9

Preparation and prepressing of canola.

Fats and Oils from Plant Materials 201

202

Xu and Diosady

makes this approach noncompetitive. Thus, in modern plants seeds are pressed only to the point where the meal protein is not seriously damaged, leaving 15– 22% oil in the prepressed material. This removes two-thirds of the oil, uses much lower pressures and considerably less energy than the equivalent fullpress operation, and results in better oil and meal quality. As the oil in the seed is contained in structures shaped like fibrous capillaries, application of too high pressures may seal these channels again and make further extraction difficult. Too much pressure may also cause the cake to become hard and impermeable to the solvent. It is, therefore, critical to control the pressure in the press and limit the amount of oil removed from the cake. To further free the oil, and to stabilize and strengthen the seed, another thermal step, usually referred to as “conditioning,” is required after prepressing. Conditioning involves heating the seed to reduce the viscosity of the oil and to make it easier for the oil to separate from the ruptured seed cell. As the seed is heated by the expeller, external heating may not be needed for this step. During conditioning, it is important to control the moisture content of the seed, as well as the temperature, because once the seed is crushed and flaked, microbial deterioration can take place rapidly, particularly if the material has a high moisture content or is of poor quality. Therefore, it is crucial to have a continuous and rapid flow of material from one piece of equipment to another. The flaked seeds are usually conditioned to a moisture content of 10–12% at a temperature of 80–90°C. As indicated earlier, oilseeds consist of oil bodies that contain liquid oil enclosed by a protein membrane. A typical oil body is 2–10 µm in diameter. The walls of oil bodies are essentially nonpermeable; therefore, for subsequent solvent extraction these walls must be broken to free the oil and facilitate the penetration of solvent into the cells. This is usually accomplished by flaking the prepressed seed in a roller mill after carefully adjusting the temperature and humidity. Flakes of 0.15–0.5 mm in thickness and up to 10 mm in diameter allow rapid solvent penetration with high bed permeability. 2. Solvent Extraction A simplified flow sheet of a typical solvent extraction process is shown in Fig. 10. The seed flakes after preparation or the cakes from prepressing are conveyed to the solvent extractor. As described in detail in the section II.D, every extractor performs two primary functions. First, it must provide retention time required to allow large volumes of solvent to penetrate and wash the solid material (flakes or cake). Second, it must separate the solids from the miscella to minimize solvent carry-over in the solids to the next phase of the operation, which is desolventizing of the extracted solid material.

Figure 10

Solvent extraction of prepressed canola.

Fats and Oils from Plant Materials 203

204

Xu and Diosady

3. Posttreatment After oil extraction, the solid material (meal) is transported in a closed conveyer system to the desolventizer-toaster (DT), where the residual solvent is removed by heating the meal enough to evaporate the solvent, but not so much as to deteriorate its nutritional value (Fig. 11). This unit is made of steam-heated trays mounted, one on top of the other, with a vertical shaft and sweep arms attached to move the meal around and down to the level through automatic openings in the trays. The top four trays make up the desolventizer and the bottom two trays

Figure 11 Desolventizer-toaster. (Courtesy of De Smet Process & Technology, Inc.)

Fats and Oils from Plant Materials

205

the toaster. The desolventizer may be divided into a pre-desolventizing and a desolventizing zone. By the time the meal reaches the toaster, most of the solvent has been evaporated and moved out the top of the desolventizer. Some moisture addition through steam injection is required to facilitate further evaporation by dissociation of bonding between the meal protein and hexane through hydrophobic interactions. The meal discharged from the DT may contain considerable moisture; thus, it must be further dried for final moisture adjustment and cooled. In some cases, all of these steps are accomplished in a single unit, dubbed DTDC for desolventizer-toaster-dryer-cooler. The hot solvent vapor from the desolventizer is used in most instances to heat other evaporation equipment before being condensed and sent back to the solvent tank for reuse. Finemeal dust is recovered prior to condensation and solvent recovery. To recover the crude oil from miscella, the solvent must be stripped from the oil in typical distillation columns. As mentioned above, the hot desolventized vapor is used to indirectly heat the vessel to evaporate some of the solvent. An additional stripping unit indirectly heated by steam is used to remove most of the remaining solvent and water. The final traces of solvent are removed by direct steam injection. The solvent vapors are then condensed in either watercooled or air-cooled condensers. During solvent extraction, all equipment is maintained under slightly negative pressure to minimize solvent losses. The air leaked into the system is passed through a vent condenser and a mineral oil absorption column before being vented to the atmosphere, both for explosion safety and solvent economy. After the solvent is stripped from miscella, the crude oil can be further processed before storage, to remove entrained compounds such as phospholipids or gums. B. Sunflower Seed Sunflower seed processing is similar to that of other high oil content oilseeds including canola and rapeseed. Therefore, this section, concentrates on differences in the operations. 1. Dehulling Sunflower seed contains 23–25% fibrous hull, which can interfere with the extraction process. Thus, the processor must decide whether to dehull prior to processing, which in turn determines the nature of the meal produced. Processing with the hulls produces a meal with a fiber level of about 18–20% and protein content of 28–30%. Removal of about 75% of the hulls produces a meal with a fiber level of 11–13% and a protein level of 40–42%. The market determines if dehulling is required.

206

Xu and Diosady

However, if the hulls are not removed, they tend to reduce the total yield of oil by absorbing and retaining oil in the press cake. To accommodate the additional volume of the hulls, larger equipment is required to process the extra tonnage. Moreover, wear on the prepress lining bars and worm shafts is increased, thus increasing the cost of the already expensive prepressing operation. Therefore, dehulling is often desirable. Two types of dehulling equipment are usually used: knife and impact, although a disk type is also available. Such machines break the hull fraction away from the seed, and the mixture is then separated using shaking screens and aspiration. The dehulling process must be finely tuned to leave a minimum of meats in the hull fraction to minimize oil losses. 2. Preparation The preparation of sunflower seed is similar to that of other high oil content seeds such as rapeseed. The seeds, or meats, are rolled to rupture and expose the oil cells. They are then conditioned, by heating, to reduce the viscosity of the oil and to make it easier to separate the oil from the meats or seeds. Proper temperature and moisture adjustments are critical to maximize the effectiveness of the prepressing stage that follows. This adjustment is usually termed as “tempering.” Cooking at high temperature is not required for sunflower seed. 3. Prepressing and Solvent Extraction These steps vary little from canola and rapeseed processing except for the adjustments made on the prepressing equipment including the shaft arrangement, spacing of the bars in the cage surrounding the shaft, the rotation speed, and the choking mechanism. After prepressing, the cake containing 16–18% oil is broken and granulated. To make sure that the residual oil content is less than 2% after solvent extraction, some manufacturers condition and flake the broken cake pieces again before conveying them to the solvent extractor. Others simply convey the material directly to the solvent extraction process. In both cases, production of fine particles must be minimized because large amounts of fine material may cause the solvent to flood the extractor bed instead of percolating through the material for proper extraction. C. Soybean Oil removal from soybeans is normally carried out by solvent extraction alone since soybeans have a relatively low oil content of 18–20%, as compared with that of rapeseed or sunflower seed. The clean, dry, and tempered beans are broken into four to eight pieces in cracking mills, usually composed of two pairs

Fats and Oils from Plant Materials

207

of fluted rolls turning at different speeds, cutting beans as they are fed through the two passes. The hulls of the dry beans are then easily separated by aspiration. The cracked and dehulled beans are conditioned by raising their temperature to 65°C and adjusting their moisture content using live steam. Conditioners are normally rotating drums with an internal steam coil. Conditioning enhances the performance of the subsequent flaking and extraction operations. The conditioned beans are fed to flaking mills to produce consistent flakes with an ideal thickness of 0.3 mm. Good flaking is essential to maximize oil extraction in the solvent extractor. Extruders are becoming the preferred means of pretreating soybeans. Water is added to the cracked seed, which is then heated and pressurized in the extruder. As the pressure is released at the exit, the absorbed water is rapidly vaporized, resulting in an expanded, porous structure where many of the cells are ruptured. This porous microstructure improves oil and solvent mobility. The solvent extraction of soybean oil is similar to that of other seeds (Fig. 12). During the process, the heat reduces the natural trypsin inhibitor levels in soybeans, thus increasing the nutritive value of the meal. D. Corn Since the corn kernel contains only about 5% oil, it is uneconomical to process corn simply to obtain the oil. Corn oil is obtained by processing the corn germ, which is a byproduct of the wet or dry milling of corn, a major industry. Corn germ contains about 50% oil. This oil is recovered by expelling or direct solvent extraction (dry-milled germ), or by their combination in prepress-solvent extraction (wet-milled germ). The actual processes involved are almost identical to those described above for other oilseeds.

VI. EXTRACTION OF ESSENTIAL OILS While edible oils are triglyceride-based oils mainly from oilseeds, essential oils are the volatile chemical components from various parts of plants, which are responsible for the characteristic aromas of these plants. To name a few, oils extracted from bitter almond, lemon and orange peels, rose and jasmine flowers, peppermint, and lovage roots are all essential oils. Although lipid-soluble, essential oils do not contain triglycerides, and they are complex mixtures of compounds that usually fall into the following four classes (29): 1. Terpenes 2. Straight-chain hydrocarbons and their oxygen derivatives

Figure 12 Solvent extraction of soybean oil.

208 Xu and Diosady

Fats and Oils from Plant Materials

209

3. Benzene derivatives 4. Volatile sulfur and nitrogen compounds Essential oils are widely used in the food and fragrance industries. Their active components contribute a significant portion of the flavor or aroma of a spice. They are often diluted with oil or emulsified to make them dispersible before addition to a flavor or fragrance system. To recover essential oils, distillation has always been most commonly used to take advantage of their volatility (30). The components in essential oils, however, have much higher boiling points than water; therefore, they are actually codistilled with steam or water to avoid thermal damage. Codistillation with water or steam distillation takes advantage of the fact that in the vapor phase the oil, water, and air do not interact. The composition of the vapor phase is directly proportional to the vapor pressures of the vapor components, i.e., water/ steam and the volatile oils. The steam acts as a carrier and removes the oil vapors, which have been evaporated well below their boiling point. This is especially important because many of the essential oil components have high boiling points and would break down thermally well below their normal boiling points. After condensation, the oils and water are immiscible and thus are easily separated. Like oilseeds processing, the production of essential oils also begins with the size reduction of the raw material, the comminution of the plant parts. As the essential oils are enclosed in “oil glands” or “oil cells” of the plant, the rate of oil vaporization will be entirely determined by the rate of hydrodiffusion if the plant parts are left intact, which is obviously a very slow process. Consequently, the plant material must be cut to some extent to rupture enough oil glands and meanwhile reduce the thickness of the material for the steam to diffuse, thus increasing the vaporization rate of the essential oils. The extent of comminution required varies with different plant parts. Seeds or fruits must be thoroughly crushed in order to disrupt as many of the cell wall as possible so as to make the oil directly accessible to the steam. This can be achieved by passing them through a roller as in oilseeds processing. Root, stalks, and all woody materials should be cut into small pieces to expose a large number of oil glands. They can be processed by a hay or ensilage cutter, which reduces the long natural parts of the plant to short lengths to be more easily handled for distillation. On the other hand, flowers, leaves, and other thin parts of the plant can be distilled without comminution since the structures in these parts are thin and permeable enough to allow rapid evaporation of the oil. The crushed or chopped material is ready for distillation. Three types of hydrodistillation are used in the essential oil industry: water distillation, water and steam distillation, and direct steam distillation. In water distillation, the material makes direct contact with boiling water by floating on the water or

210

Xu and Diosady

being immersed. The water can be brought to boil by fire, steam jacket, or coil, or even perforated steam coil. Some plant materials, such as almonds and rose flowers, can only be distilled in this manner since upon direct contact with steam they tend to agglomerate and form compact lumps, making it hard for the steam to penetrate. In water and steam distillation, the plant material is supported on a perforated screen placed above the boiling water, so that it is contacted by the steam instead of water. The steam remains saturated in this system (Fig. 13). In steam distillation, live steam (saturated or superheated) is applied through open or perforated steam coil. The choice among these distillation meth-

Figure 13 Retort for water and steam distillation of essential oils (30).

Fats and Oils from Plant Materials

211

ods is usually based on both their efficiency and their effect on the essential oils. Although higher steam pressures result in increased vaporization rate of the oils, the higher temperatures of the pressurized steam may cause thermal decomposition of the oils. The equipment for distillation of essential oils consists of three parts: the retort, the condenser, and the oil separator. The retort serves as a vessel where the steam or boiling water contacts the plant material to vaporize its essential oils. It can be as simple as a cylindrical container with a removable cover that can be clamped on the cylindrical section. A pipe is attached to the top of the retort to lead the vapor to the condenser. In the case of steam distillation, trays are placed close to the bottom of the container, and the steam is introduced through a open steam line. In multitray retorts, each tray is only filled with a relatively shallow layer of the material to ensure a uniform distribution of both the material and the steam. The height of a typical retort is 1.5–1.8 m. It is insulated to minimize heat loss. The oil vapor is converted to liquid and cooled down using a condenser. The most common condensers, used in the production of essential oils are coil condensers with the oil vapor and the steam passing inside the coil while cold water enters from the bottom of the condenser and flows outside the coil against the steam and oil vapor as shown in Fig. 14. The third important part of the distillation equipment is the oil separator, which, as indicated by its name, separates the essential oil from the condensed water. Many separators are built based on the principle of the ancient Florentine flask (Fig. 15). The condensate flows from the condenser into the separator where the water and the oil separate into two layers since they are immiscible and different in specific gravity. The oil is usually lighter and floats on top of the water. Both layers are then removed continuously from the separator through outlets at different levels. In addition to distillation, essential oils can also be processed by extraction. Extraction with cold fat is practiced with jasmine and tuberose flowers for their essential oils. Although this method gives a much greater yield than other methods, it is a lengthy and labor-intensive process. Extraction with volatile solvents is considered as the most technically advanced process, producing concentrates and alcohol soluble “absolutes,” the aromas of which truly represent the oils in their natural forms. Citrus oils, for instance, are often extracted with alcohol, from which two layers are obtained: one hydrophobic layer containing the terpene hydrocarbons and one aqueous alcohol layer with the polar compounds. They are separated and the polar compounds recovered. The essential oils may be used as collected or subjected to further purification, concentration, or separation. Vacuum distillation is the preferred method for refining most essential oils. With this technique the oil components can be

212

Xu and Diosady

Figure 14 Coil condenser (30).

fractionated according to their boiling points. Rectified, folded, and terpeneless oils are made through the selection of the various fractions obtained from redistillation. Extraction with supercritical carbon dioxide or propane is now practiced on small scales for highly valued components of plants in order to produce either flavor or aroma concentrates in the form of oils or resins.

Fats and Oils from Plant Materials

213

Figure 15 Florentine flask for oils lighter than water (30).

REFERENCES 1. FA Norris. Extraction of fats and oils. In: D Swern, ed. Bailey’s Industrial Oil and Fat Products, 4th ed., Vol. 2. New York: John Wiley & Sons, 1979, pp. 175–252. 2. RE Treybal. Mass Transfer Operations, 3rd ed. New York: McGraw-Hill, 1980. 3. YH Hui, ed. Bailey’s Industrial Oil and Fat Products, 5th ed., Vol. 4. New York: John Wiley & Sons, 1996. 4. E Guenther. The Essential Oils, Vol. 1. New York: D. Van Nostrand, 1948. 5. DF Othmer, JC Agarwal. Extraction of soybeans—theory and mechanism. Chem Eng Progr 51:372–378, 1955. 6. DF Othmer, WA Jaatinen. Extraction of soybeans. Mechanism with various solvents. Ind Eng Chem 51:543–546, 1959. 7. RE Treybal. Mass Transfer Operations, 2nd ed. New York: McGraw-Hill, 1968, pp. 15–37.

214

Xu and Diosady

8. DF Boucher, JC Brier, JO Osburn. Extraction of oil from a porous solid. Trans Am Inst Chem Eng 38:967–993, 1942. 9. HP Fan, JC Morris, H Wakeham. Diffusion phenomena in solvent extraction of peanut oil. Effect of cellular structure. Ind Eng Chem 40:195–199, 1948. 10. JO Osburn, DL Katz. Structure as a variable in the application of diffusion theory to extraction. Trans Am Inst Chem Eng 40:533–556, 1944. 11. RE Treybal. Mass Transfer Operations. 2nd ed. New York: McGraw-Hill, 1968, pp. 628–675. 12. EG Shreibel. Calculation of liquid-liquid extraction process. Ind Eng Chem 46: 16–24, 1954. 13. RE Treybal. Mass Transfer Operations. 2nd ed. New York: McGraw-Hill, 1968, pp. 408–488. 14. ISTA. Oil World Statistics Update. Hamburg, Germany: Mielke, 1998. 15. NOV Sonntag. Sources, utilization, and classification of oils and fats. In: D Swern, ed. Bailey’s Industrial Oil and Fat Products, 4th ed., Vol. 1. New York: John Wiley & Sons, 1979, pp. 271–288. 16. AL Ayers, JJ Dooley. Laboratory extraction of cottonseed with various petroleum hydrocarbons. J Am Oil Chem Soc 25:372–379, 1948. 17. NFPA 36. Solvent Extraction Plants. Quincy, MA: National Fire Protection Association, 1993. 18. MA Williams, RJ Hron Sr. Obtaining oils and fats from source materials. In: YH Hui, ed. Bailey’s Industrial Oil and Fat Products, 5th ed., Vol. 4. New York: John Wiley & Sons, 1996, pp. 61–157. 19. IJ Duncan. Commercial extraction of vegetable oils by means of trichloroethylene. J Am Oil Chem Soc 25:277–278, 1948. 20. OR Sweeney, LK Arnold. A new solvent extraction process for soybean oil. J Am Oil Chem Soc 26:697–700, 1949. 21. FC Magne, EL Skau. Phase equilibrium data pertaining to the extraction of cottonseed oil with ethanol and 2-propanol. J Am Oil Chem Soc 30:288–291, 1953. 22. RK Rao, LK Arnold. Alcoholic extraction of vegetable oils. IV. Solubilities of vegetable oils in aqueous 2-propanol. J Am Oil Chem Soc 34:401–404, 1957. 23. RK Rao, LK Arnold. Alcoholic extraction of vegetable oils. V. Pilot-plant extraction of cottonseed by aqueous ethanol. J Am Oil Chem Soc 35:277–281, 1958. 24. EW Lusas, LR Watkins, SS Koseoglu. Research in replacing hexane with isopropyl alcohol as an extraction solvent. Annual Meeting of American Oil Chemists’ Society, Chicago, 1991. 25. GR List, JP Friedrich, DD Christianson. Properties and processing of corn oils obtained by extraction with supercritical carbon dioxide. J Am Oil Chem Soc 61: 1849–1851, 1984. 26. GR List, JP Friedrich. Processing characteristics and oxidative stability of soybean oil extracted with supercritical carbon dioxide at 50°C and 8,000 psi. J Am Oil Chem Soc 62:82–84, 1985. 27. GC Mustakas. Recovery of oil from soybeans. In: DR Erickson, EH Pryde, OL Brekke, TL Mounts, RA Falb, eds. Handbook of Soy Oil Processing and Utilization. St. Louis: American Soybean Association, 1980, pp. 49–66.

Fats and Oils from Plant Materials

215

28. CH VanEtten, ME Daxenbichler, IA Wolff. Natural glucosinolates in foods and feeds. J Agric Food Chem 17:483–491, 1969. 29. AJ Haagen-Smit. The chemistry, origin, and function of essential oils in plant life. In: E Guenther, ed. The Essential Oils, Vol. 1. New York: D. Van Nostrand, 1948, pp. 15–83. 30. E Guenther. A. Distillation of essential oils. In: E Guenther, ed. The Essential Oils, Vol. 1. New York: D. Van Nostrand, 1948, pp. 85–187.

7 Proteins from Plant Materials Lei Xu and Levente L. Diosady University of Toronto Toronto, Ontario, Canada

I. INTRODUCTION As the world population continues to grow exponentially, the demand for food proteins will inevitably increase substantially. It has been forecast that within the next decade, in order to just maintain the present level of human nutrition, the supply of vegetable proteins must be doubled together with a fourfold increase in the production of animal-based proteins (1). Since such a dramatic growth in meat production is hard to achieve, human diet will likely rely more on vegetables and cereals as protein sources. In addition to continuing to sustain of human life as they have done for thousands of years, plant-based proteins (or vegetable proteins) offer great potential as functional food ingredients. In the past decades, many processes have been developed to produce proteins to be used as meat extenders in processed meat products or as protein-enriching agents in beverages. In the processing of vegetable proteins, much attention is paid to the preservation or improvement of their nutritional value as well as organoleptic and functional properties. Storage proteins in oilseeds, legumes, and cereals have been the focus of process and product development for food and industrial protein products. Protein products can be produced by the preferential extraction of nonprotein seed components, as is the case with soy protein concentrate, or by the dissolution, purification, and recovery of proteins in the form of protein isolates. For protein isolate production a number of aqueous and some nonaqueous organic solvents have been investigated (1). The storage proteins include a number of albumins, globulins, prolamines, and glutelins with a variety of molecular weights and a range of solubility characteristics. Albumins are typically soluble 217

218

Xu and Diosady

in water (pH 6–8), globulins in dilute salt solutions, prolamines in ethanol, and glutelins in dilute acid or alkali. Most of these proteins are highly soluble in strong alkali, although their functional properties may be altered by partial or complete denaturation at these extreme conditions. Soy proteins have been used as traditional foods in the orient. Accordingly, they have been studied extensively, and there are numerous commercial processes for the production of a wide range of commercial soy protein products. Many other protein sources were investigated in attempts to find alternatives to soy proteins. This was driven by the functional, nutritional, and allergenic problems of soy proteins, as well as the desire to develop value-added products from a number of underutilized plants or plant products, such as oilseed meals after oil extraction. Significant research advances were made in the production of wheat gluten, corn zein, and a number of oilseed proteins from peanuts, cottonseed, canola, and others. In cereal grains the main storage proteins are usually the alcohol soluble prolamines, while the salt-soluble globulins are abundant in oilseeds and legumes (2, 3). Regardless of their origins, these globulins all belong to two classes of different molecular sizes, usually denoted in the literature by their sedimentation coefficients as 7S and 11S (1). Most protein extraction processes are based on aqueous extraction using single- or multistage batch operation. The latest advances made in the methods for protein extraction feature the combinations of solvents with optimized extraction parameters and advanced separation technologies such as membrane processing to increase both the yield and quality of the protein products. In addition to their main use as functional ingredients in many food systems, proteins from vegetable sources are also used in some industrial processes as size, adhesive, or dispersive agents.

II. SOY PROTEINS The processing of soybean proteins has evolved since ancient times. For nearly 15 centuries people in Asia have combined the use of rice and the products of soybeans to produce an inexpensive diet that is reasonably well balanced in terms of essential amino acids. They learned that the greatest nutritional value from soybeans is obtained through a water extraction followed by coagulation of the resulting milky liquid into a curd. Crude as this ancient method is, it has provided the basis for the development of many modern processing methods. Nutritional studies show that soy proteins are an excellent source of food proteins. The amino acid composition of soy protein isolate nearly meets the essential amino acid pattern set by FAO/WHO/UNU as shown in Table 1 (3, 4). In combination with rice, the low methionine level of soy and the low lysine

Proteins from Plant Materials

219

Table 1 Comparison of Essential Amino Acid Content of Soy Protein Isolates to WHO/FAO/UNU Requirements WHO/FAO/UNU suggested pattern (mg/g protein) Essential amino acid Histidine Isoleucine Leucine Lysine Methionine and cysteine Phenylalanine and tyrosine Threonine Tryptophan Valine

2–5 years

10–12 years

Adult

Essential amino acid content of isolated soy protein (mg/g protein)

19 28 66 58 25 63 34 11 35

19 28 44 44 22 22 28 9 25

16 13 19 16 17 19 9 5 13

26 49 82 63 26 90 38 14 50

Source: From Ref. 3.

level of rice are both overcome. The high digestibility of soy proteins has been demonstrated by extensive nutritional studies on both animals and humans. Currently, four major classes of soy protein products are in commercial production: flour, protein concentrates, protein isolates, and textured products. Their total production exceeds one million tonnes. Soy flour can be made from either full-fat dehulled soybeans or dehulled defatted flakes. Full-fat soy flour contains fat in excess of 18%, while the oil content of defatted flour is usually less than 1%; thus, the protein content is much higher than that of full-fat flour. To process full-fat flour, high-quality, sound, clean, yellow soybeans are selected. Conventionally, these beans are first conditioned to a desirable moisture level and then cracked in roll mills. The cracked beans are dehulled by aspiration and screening. The meats are cooked at temperatures above 93°C by steam in another conditioner to inactivate enzymes such as lipoxygenase. After being dried, they are ground in two steps using a hammer mill to particle sizes small enough to pass through a 0.149-mm screen (No. 100 U.S. Standard Screen) (5). The production of defatted soy flour is directly linked to oil extraction from soybeans, which involves solvent extraction and desolventizing. The defatted and desolventized soy flakes with an appropriate moisture content are then ground in a hammer mill until 97% passes through a 0.149-mm screen (No. 100 U.S. Standard Screen). The term protein concentrate refers to products containing at least 70% protein produced by alcoholic extraction of carbohydrates, leaving behind the

220

Xu and Diosady

insoluble proteins, whereas protein isolate is usually used to describe products with more than 90% protein produced by the extraction, purification, and recovery of soy proteins. Although both protein concentrates and isolates can be made from full-fat soybeans, the products find limited use due to their appreciable fat content. Therefore, manufacturing of these protein products usually start with defatted soy flour after the grinding of the defatted flakes to facilitate subsequent extraction. The dehulled flour has a protein content of about 50%. Protein concentrates are produced by washing the meal to remove soluble carbohydrates with dilute acid, aqueous ethanol, or hot water, as none of these dissolves significant amounts of soy protein. With dilute acid, the pH of the extraction solution is adjusted to 4.5, the observed isoelectric point of soy proteins, where the solubility of soy proteins is minimal (6). The insoluble material containing most protein is thus separated from the soluble impurities in a batch operation that is repeated several times. The effect of ethanol is similar as most proteins are insoluble in 60–80% aqueous ethanol; thus, soluble oligosaccharides are extracted and separated (7). Aqueous alcoholic extraction also causes denaturation of the trypsin inhibitors present in the seed and removes some of the undesirable phenolic components, thus improving the quality of the product. The ethanol is removed from the protein concentrate and recovered by flash desolventizing. For hot water extraction, the starting material is first toasted to denature the proteins and decrease their nitrogen solubility index (NSI), and thus decrease protein solubility, while maximizing carbohydrate dissolution (8). In the treatment, the soy flakes are extracted with hot water at 66–93°C in either a batch or a continuous countercurrent manner, and pH is maintained in the range of 5.3–7.5. The isolation of soy proteins takes an approach opposite to protein concentration (5, 9–11), wherein the protein is dissolved while most of the impurities are left behind in the solids. A generic process is outlined in Fig. 1. The extraction is carried out with dilute alkali, pH 8–10, usually at elevated temperatures of 50–55°C. Although more protein could be extracted in more alkaline solutions, protein tends to deteriorate faster at high pH values. Extraction conditions, including pH, temperature, liquid-to-solids ratio, and additional reagents, vary among different manufacturers, and information about most of the detailed operating conditions remains proprietary. After extraction, separation of the aqueous extract from the solids is achieved by screening, centrifugation or decanting, and polish-filtration. Optionally, the extract may be further clarified by chemical purification by adsorbents, depending on the process adopted by the manufacturer. A food grade acid is then used to acidify the extract containing the soluble protein portion to pH 4–5. The most commonly used acids are acetic, hydrochloric, phosphoric, and sulfuric. Upon acidification a protein precipitate is formed, collected by centrifugation or filtration, and washed. The precipitate is usually neutralized with dilute food grade alkali to form sodium proteinates, which can

Proteins from Plant Materials

221

Figure 1 Commercial production of soy protein isolates (42).

be modified by jet cooking at 141–160°C, followed by treatment with a proteolytic enzyme for 1–30 min to improve the wettability and dispersibility of the product (10). Although spray drying is the preferred drying method, the product may be dried by other means such as a forced draft oven or a range drier. In the end, about one-third of the starting mass is recovered as a protein isolate containing more than 90% protein on a moisture-free basis (N × 6.25), one-third remaining as the insoluble residue, and one-third in the whey solution. After drum drying, the insoluble residue is used as a feed ingredient. The whey solution contains several undesired flavor components and trypsin inhibitors that must be heat inactivated before it can be used even for feed. Despite the generally desirable nutritional properties of soy protein concentrates and isolates, these products are typically powdery and do not exhibit the consistency required for palatable foods. In order to expand their use, novel processes have been developed to obtain soy protein products with the more desirable meat-like textures. Technologies involved in these processes include extrusion and spinning. In the 1960s, an extrusion cooking process was developed for the production of a fully toasted, full-fat, and texturized soy flour (12). The extrusion of soy protein is similar to other food extrusion processes. The extruders for texturizing are designed to handle high-moisture materials. In the process, the dehulled flakes are first cooked with steam at 93–100°C and preconditioned to a moisture content of 18%. The cooked material is then passed

222

Xu and Diosady

through a high-speed mixer where more steam is mixed with the flakes before entering a Wenger extruder, wherein the material is pushed along inside a barrel by a rotating screw while being heated by friction or indirect steam. High temperatures of 121–143°C are reached for 1–1.5 min within the barrel. At these high temperatures some hydrogen bonds break, unfolding the structured proteins, which are then elongated and aligned by shear forces. When the melt is forced through the die at the end of the barrel, it is rapidly expanded by the release of steam upon leaving the extruder, producing a porous matrix. As the proteins cool, new hydrogen bonds form to give the product a fibrous, meatlike texture. The cooled product is ground in a pin mill to desirable particle sizes and used in foods such as “bacon bits.” The spinning process was originally developed in the synthetic textile industry for the production of nylon fibers. It has been adapted to the commercial production of soy protein fibers (10, 11). A typical process for soy protein spinning is shown in Fig. 2. A soy protein isolate is first dissolved in strong alkali to make a “dope,” which is a solution with a protein concentration of about 20% at a pH of 12–13. After filtration, the dope is pumped through a spinneret made of a platinum plate with thousands of small holes less than 1 mm in diameter into a coagulating bath. As the dope contacts the acid and salt in the coagulating bath, the protein precipitates, forming protein fibers. Phosphoric acid and NaCl are usually used as the precipitating agents in the bath. To continue the texture-forming process, the fibers are combined to make tows about 0.5 cm in diameter to be stretched. The stretched tows are then washed to remove the acid and salt, and heated to be hardened. Binders such as egg albumin are added to improve the cohesiveness of the product, followed by the addition of fat, flavor, color, and supplementary nutrients in order to obtain a palatable food. Because the final product has a texture similar to that of processed meat, it is used as a meat analogue. The functional properties of these soy protein products play a critical role in determining their use in food systems. These include solubility, hydration and water absorption, viscosity, gelation, emulsification, foaming properties, and organoleptic properties such as color and flavor. Commercially available soy proteins exhibit a wide range of solubilities at pH 7 from 25% to 95% (13), and the solubility is dependent on pH and salt concentration. While higher pH results in high solubility, the effect of elevated salt level is to reduce solubility. Commercial soy proteins also have a wide range of water binding ability (14). Insoluble protein granules bind much more water than soluble proteins. Slurries of soy proteins have relatively low apparent viscosity and do not form strong gel networks; however, heat or alkali treatments can increase viscosity and improve gelation functionality (15). As with other functional properties, commercial soy proteins display a range of emulsion and foam properties. Kolar et al. (10) reported a range from 10 to 40 mL oil/100 mg protein for oil binding. Unlike

Proteins from Plant Materials

223

Figure 2 Spinning process for production of soy protein fibers (41).

other functional properties, color and flavor are primarily due to small nonprotein compounds and their interactions with proteins. A number of phenolic compounds and their oxidization products have been linked to the yellowish brown color of soy protein products (16). The products of lipid oxidation catalyzed by lipoxygenase are responsible for the undesirable beany flavor of most commercial soy proteins (17). Once formed, these compounds bind to proteins and thus are hard to remove. However, for commercial food applications these organoleptic problems can be avoided through careful control of food processing conditions. Presently a large variety of soy protein products are manufactured commercially worldwide. These are used in processed meat products, imitation cheese, whipped toppings, soy milk, nutritional beverages, and baked products. There is also a significant market for the industrial use of soy proteins in paper products, adhesives, and polymers.

224

Xu and Diosady

III. GLUTEN “Gluten” refers to the insoluble portion of proteins in cereal grains, first identified more than 200 years ago in dough from wheat flour, and isolated by simply washing the dough with water. Much research has been done on gluten and the flour proteins ever since. The wet impure gluten carries 65–70% water, has a creamy color, and feels like rubber. When dried, the crude gluten contains 75–80% protein and 5–15% carbohydrates, the remaining being lipids. Gluten can be divided into two fractions. One fraction is soluble in 60% aqueous alcohol, known as gliadin or prolamine. The other fraction, although insoluble in neutral water, is soluble in acidic or alkaline solutions, and is known as glutelin (18). The nutritional and functional properties of gluten have been well documented. Gluten has acceptable levels of most essential amino acids but is low in lysine. Its functional properties can be changed by reducing and oxidizing agents, and heat treatment. Oxidizing agents make gluten lose its extensibility and become brittle, whereas reducing agents increase its extensibility. Gluten may lose some or all of its “vitality” when subjected to heat treatment. In early industrial processes gluten was only a byproduct of wheat starch processing. The Alsatian method (19) is among the first methods to recover gluten, wherein whole wheat is steeped in water for 1–2 days. The softened wheat grain is then placed in bags of mesh to permit dissolution of starch while retaining the gluten. The bags are passed between a series of rolls with progressively smaller gaps, thus squeezing out starch but leaving gluten in the bags. The further separation of gluten from the hulls is long, and the yield of gluten is usually low. The Martin process uses wheat flour to make both starch and gluten (20). It starts with making a stiff dough containing about 40% water. After hydration for 1 h, the dough is rolled between fluted rolls under a spray of water to wash away the starch and leave the gluten in a single, coherent mass. This method produces good-quality gluten with high yield. Another process, called the batter process, involves making a batter, which is broken up while water is added to wash away the starch from the gluten (21). The gluten in this method is recovered as fine curds with more than 80% protein. Although developed decades ago, both the Martin and the batter processes are still used in separating starch and gluten from wheat flour. Figure 3 shows the major steps involved in either method. The gluten is separated from starch milk by a series of centrifuges. In the refining step, the lighter low-grade starch is separated from the heavier prime starch. The gluten is either flash or spray dried. The mass distribution among products is 59% prime starch, 8% tailing starch, and 15% gluten with a protein content of 80%. Based on the batter process, a continuous process has been developed (22). First a slack dough is made by mixing flour with water at a ratio of 0.7 : 1. Then the dough is heated to 48–57°C, followed by breaking up of the dough in the presence of additional water. The starch is

Proteins from Plant Materials

225

Figure 3 Manufacture of wheat starch (41).

thus largely washed out. The gluten is recovered as curds and separated from the starch by screening the slurry. Other processes include the alkali process (23), in which the wheat protein is dissolved in 0.03 N NaOH solution by mixing 1 part of flour with 18 parts of the alkaline solution, and after centrifugation to separate starch, the supernatant is acidified with H2SO4 to produce a precipitate containing 70–90% protein, which has no vitality. This method was modified with NH4OH to replace NaOH (24). As a result, the gluten is recovered with vitality. The vital gluten is used extensively as an ingredient in yeast-raised baked goods, particularly bread. Besides increasing the final protein content in the bread, it also results in a greater volume of better crumb texture and a longer shelf life of the bread. Canada has been a major producer and exporter of wheat gluten and contributed significantly to the commercial development of gluten extraction processes.

IV. RAPESEED AND CANOLA PROTEIN PREPARATION Although its oil has been consumed as food by humans for thousands of years, the idea of using rapeseed protein in food is relatively recent. One obvious

226

Xu and Diosady

reason for the delay is directly related to the organoleptic characteristics of rapeseed meal after oil removal. It has an unappealing dark green color and an unpleasant taste that may be characterized by both bitterness and astringency. According to modern studies, both problems are caused by phenolic compounds in rapeseed (16). Nevertheless, due to increasing demand for vegetable oils, the production of rapeseed has grown drastically in the last three decades. It is now ranked second in world production, exceeded only by soybeans (24a). The meal is used in animal feed but commands a significantly lower price than soy meal. Nutritional studies have shown that rapeseed proteins have well-balanced amino acid composition (25, 26), and are particularly high in lysine (27), which is a limiting amino acid in most cereal and oilseed proteins. Due to its abundance and high-quality proteins, rapeseed should play an important role in meeting the need of the world’s fast increasing population for food proteins. Consequently, there has been continued research interest in extracting rapeseed proteins for food. Rapeseed meal has a protein content of up to 40% after oil extraction; however, its direct use as a food protein source is prevented by its undesirable components. In addition to phenolic compounds, it contains high levels of glucosinolates (more than 100 µmol/g defatted meal), which upon hydrolysis release toxic compounds such as isothiocyanates and oxazolidinethiones (27a). Although the genetically improved “canola” varieties have much lower amounts of glucosinolates (20–30 µmol/g defatted meal), their meals are still unacceptable for incorporation into any food formulations. Canola meal also has a much higher phytate content of 3–5% than many other cereals and oilseeds (28, 29). Phytates are undesirable in foods as they tend to strongly bind minerals such as Fe and Zn, and make them unavailable for metabolism (30, 31). It is obvious that before rapeseed or canola protein can be used for food, much or all of these compounds must be removed. Although much process research has been directed to treating the meal, a variety of methods have been developed to isolate high-quality canola proteins. Most of the published methods were based on soy protein extraction technology: aqueous extraction followed by protein precipitation. Unlike soybeans, canola contains a wide range of proteins, with a broad band of isoelectric points and a wide range of molecular weights. For rapeseed protein extraction, alkaline aqueous solutions have been suggested as solvent. While over 80% extractabilities with dilute NaOH solutions have been reported by several researchers (32–34), these values cannot be attained using commercial meal as starting meal. To improve extraction, researchers used consecutive extractions at differing pH values. Blaicher et al. (35) extracted rapeseed protein at pH 9.5 and 12.0 in two consecutive stages, respectively, and achieved a 92% extractability. Although higher pH results in increased protein extractability, it could cause chemical modification of the proteins such as

Proteins from Plant Materials

227

the formation of harmful lysinoalanine (36) under extremely alkaline conditions (pH > 12). Since rapeseed proteins are predominantly globulins (37), dilute sodium chloride solution has been explored as a protein solvent. NaCl concentrations from 0.2 N to 2 N have been reported (25, 38–40). Extractabilities obtained with NaCl solutions were typically lower than those obtained with NaOH media. The recovery of salt-extracted protein is complicated by the need to remove the salt by dialysis. Following the example of soy technology, rapeseed proteins are usually recovered by isoelectric precipitation. The precipitate, which is usually washed and dried, constitutes the protein isolate. A wide range of isoelectric points from pH 2.6 to 10.0 are observed with rapeseed proteins (32, 34, 41, 42), due to their complex compositions as well as varietal difference among rapeseed strains. Consequently, protein recovery of single-step isoelectric precipitation is low, and the highest ever reported is 65.7% of the amount of protein extracted at pH 11, achieved at pH 3.6 by El Nockrashy et al. (43). Multistage precipitation at different pH values does not significantly increase the yield of protein precipitate (34, 44). Despite extensive studies in the past, no process has been commercialized due to low protein recovery, poor product quality, and high cost of the processing methods. To reduce the product losses and improve product quality, a membrane-based process was developed for rapeseed and canola protein isolation starting with defatted meals (45, 46). It consists of five main steps: alkaline extraction, isoelectric precipitation, ultrafiltration followed by diafiltration, and drying (Fig. 4). Two protein isolates are produced: soluble and precipitated, with a combined protein recovery of more than 70% of the protein in the meal. Both products are high in protein (>90%), low in phytates (5 cm long mass of cossettes 5 cm long to that