Fluid Phase Equilibria 194–197 (2002) 15–29
Thermophysical properties—Industrial directions R. Dohrn∗ , O. Pfohl Bayer AG Process Development, ZT-TE Fluid Process Technology—Thermophysical Properties & Thermodynamics, Building B310, 51368 Leverkusen, Germany Received 10 April 2001; accepted 1 October 2001
Abstract Changing conditions in the companies have necessarily led to changes in the work in thermodynamics groups in industry during the last 20 years. While many companies have reduced activities in the field of applied thermodynamics or even have closed their thermodynamics group, other companies, including Bayer, have put more emphasis in the field of applied thermodynamics. Here, ten industrial directions in the field of applied thermophysical properties are discussed. It is shown, where and when expertise in applied thermodynamics within the company is needed. At Bayer, this expertise can be delivered most efficiently by a group of thermodynamicists who use either internal sources, e.g. a laboratory, a database or estimation methods, or coordinate and supervise the use of external sources. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Thermodynamics; Industry; Costs; Service; Database; Equipment
1. Introduction The changes taking place all over the world due to globalization and the concept of share-holder value have influenced the structure of companies in the chemical and pharmaceutical industry. The way chemical engineers work has been changed by the availability of process simulators. Nowadays, process simulators are equipped with a variety of thermodynamic models and large parameter data banks which allow the calculation of thermophysical properties of pure substances and mixtures. Accelerated design, evaluation, and optimization of new processes require virtual tools based on reliable information. Essential to these tools are physical property models, which must be validated with accurate data. As Cox pointed out at the 6th International Conference on Properties and Phase Equilibria for Process and Product Design in Cortina d’Ampezzo, 1992, physical property data and methods are the raw material of chemical process design [1]. The process of making these data available costs money, no matter whether the data are retrieved from a data bank, are calculated with a model or are determined experimentally. Only if the use of thermophysical data leads to a sufficiently better process or product design, these costs are compensated ∗
Corresponding author. Tel.: +49-214-30-21787; fax: +49-214-30-81554. E-mail address:
[email protected] (R. Dohrn). 0378-3812/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 8 1 2 ( 0 1 ) 0 0 7 9 1 - 9
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Fig. 1. Flowsheet for the comparison of three process simulators for the separation of styrene and ethylbenzene using the SRK EOS [4]. Redrawn from Sadeq et al., AIChE Annual Meeting, 1995 [2].
or overcompensated by savings of investment or production costs. The question arises: Do we need more thermophysical data? There are three possible answers: (a) we have enough data, (b) we have not enough data, (c) we have too many data. People who answer with (a), we have enough data, have process simulators in mind, which are connected to some kind of property data bank, parameter data bank, or property estimation methods. With a few clicks the process simulator is ready for calculation. Apparently, without having to worry about thermophysical property data, the simulator will deliver results. That this belief in the numbers delivered by a computer might be very dangerous has been shown in the example of the classic styrene +ethylbenzene separation calculated with three popular process simulators [2,3]. Though, in all three cases the standard SRK equation of state [4] was used for the calculation, the results of three process simulators differ to a very high extent from each other (Fig. 1). The flow rate of ethylbenzene from the bottom of the second column differs between 2.90 and 8.55 kmol/h, or in other words, by a factor of about 3 at a given feed of 51.0 kmol/h ethylbenzene and 47.8 kmol/h styrene. The main reason for the discrepancies are different pure-component data that have been used by the simulators [2]. People who answer with (b), we have not enough data, might have in mind that for difficult separations small uncertainties in phase equilibrium data might have a huge impact on the design of a distillation column. In Fig. 2, the influence of an error of the separation factor α = K1 /K2 =(y1 /x1 )/(y2 /x2 ) on the minimum number of theoretical stages of a distillation column is given using the Fenske–Underwood equation (with purities of the products of 99.9 mol%). The closer the separation factor lies to 1, the larger is the possible relative error. This is due to the fact that the minimum number of stages is proportional to 1/(α − 1). When α approaches a value of 1, 1/(α − 1) goes to infinity. An underestimation of the correct value of α = 1.1 by 5% leads to a calculated column height that is more than 100% too high.
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Fig. 2. Influence of the errors of the separation factor, α = K1 /K2 =(y1 /x1 )/(y2 /x2 ), on the minimum number of theoretical stages of a distillation column.
This is very disadvantageous, because difficult separations (␣ close to 1) need many theoretical stages, e.g. between 100 and 200 stages, and high investment costs. For easy separations (with α > 1.5), the following approach is often used to account for the relative small influence of the separation factor on design: make the column 20% higher, e.g. six stages instead of five. For difficult separations, the uncertainty of number of stages needed is considerably higher. The approach of building the column higher to have a “safe” design is very expensive, e.g. for a distillation column of diameter 4.5 m and height 85 m, the investment cost is 4,500,000. At a separation factor of 1.1, an error of 5% for α more than doubles the number of stages. This would lead to the construction of two columns instead of one column, which leads to additional investment costs of 4,500,000. Similar examples have been given in the literature [5,6]. People who answer with (c), we have too many data, might have in mind that for standard pure components databanks offer many data sets for the same physical property, e.g. 26 data sets for the liquid viscosity of water. Which data set should be taken as “raw material” of a process simulation? This topic will be addressed further down.
2. Industrial directions Changing conditions in the companies have necessarily led to changes in the work in thermodynamics groups in the industry during the last 20 years. While many companies have reduced activities in the field of applied thermodynamics or even have closed their thermodynamics group, other companies, including
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Bayer, have put more emphasis in the field of applied thermodynamics. In which direction will the field of thermodynamics go? There is no single direction, but different trends can be identified. In the following section, 10 industrial directions will be discussed.
3. Financial efficiency One of the most influential driving forces of the daily work is the fact that in most industrial companies accounting systems are used to make sure that the group has to earn money to cover at least its own costs. Money can be earned by working in • large projects, like in strategic research projects, • typical projects, like the design of a new process or the revamp of an existing plant, or • very small projects, like the determination of the heat capacity of a substance. As can be imagined, many heat capacities would have to be measured in order to get the same income as being part of a strategic research project. In order to achieve the goal that by the end of the year the sum of income is higher than the sum of all costs different strategies can be used. Two extreme strategies are (1) reduction of costs, (2) increase of income. A reduction of cost leads to the advantage of lower prices for thermophysical data, at least in the short-run, so that more data will be measured for different projects in the company. This may lead to a higher income and to a good competitive position as compared to other sources of thermophysical properties, e.g. external laboratories. But there is the danger that ongoing cost reductions may lead to the end of the thermodynamics group. The most common way to reduce costs is to reduce the personnel. But in a thermodynamics group, the costs for the equipment and laboratory are relatively high, so that if cost reduction means reduction of personnel, the fixed costs remain and a higher income per capita for the remaining members of the group is needed. If this extra income cannot be earned, more cost reductions are likely to be demanded and the situation becomes worse. In industry the magic word is reorganization. For a group that is not financially efficient, it can mean the outsourcing of a group or the closing of the group, or other organizational measures, such as the organizational affiliation of the thermodynamics laboratory with the analytical service department with no thermodynamicists in the group. Drastic cost reduction programs also bear another danger. They can lead to a situation where investments in modern equipment are postponed or cancelled. The performance of the laboratory and the group is no longer state-of-art. Customers might be forced to look for alternative thermodynamic groups in order to get special problems solved—and then tend to give the standard jobs to the new group as well. The second strategy aims at the increase of the income. More academic personnel is hired, and thus more projects can be handled to the satisfaction of the customers, and thus more income is generated. Since the fixed costs for the laboratory remain, the costs per capita decrease. The extra money can be used to buy new equipment in order to stay modern. If the number of the personnel is not at the absolute minimum needed, the group is flexible to react to “fire brigade projects”, which are very urgent cases, where a very fast solution of a problem is needed. But there is also a danger in the strategy of income increase. A high annual income is needed, even in difficult times.
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4. Customer oriented service (worldwide) In order to get a sufficiently high number of projects, the customers have to be satisfied with the value they get for their money, so that they come back and new projects are started. For a customer-oriented service, it is important to identify who the customers of the group are. For small and medium size projects, these can be plant engineers and plant managers, process developers, process simulation experts, unit operation experts, e.g. for distillation, members of research departments of business groups or of affiliated companies. Larger projects are usually performed in a team. For strategic research projects, the board of directors is the customer, meaning the institution that gives the money for a project and gets the results of the project. The next question that has to be answered is: “What are the needs of our customers?” There is a long list of possible answers, e.g. safe plant design, process simulations with good property information, easy access to information, fast help and good advice. Often the customers are not directly interested in measuring data, they just want a problem to be solved. Only if measuring data is needed to solve the problem, they are willing to spend money for experiments. Sometimes the customers just want a piece of advice for a problem that might be solved using thermodynamics. Members of a thermodynamics group can be of help in different fields, e.g. as members of research project teams that can give advice in property related questions, or as a counselor for collaborations between an external institution, e.g. a university or research institution and an internal institution, such as a business group or an affiliated company. The role of thermodynamics experts in the industry has changed. Many of the traditional tasks still play an important role, but due to modern communication and computing facilities, some of them are performed much more efficiently than in the past. What abilities should the members of a thermodynamics group have? A sound theoretical and experimental background is still essential. A basic knowledge of economics is very helpful. In addition to scientific and technical skills, the thermodynamics expert must have good communication skills, e.g. be a good team player, or be able to supervise the lab personnel, or be a good counselor. It is most important that thermodynamicists in industry understand their customers and that they make themselves understood.
5. Initiation/counseling of collaborations, e.g. with universities Some projects or parts thereof can be performed outside the company, e.g. at a university. Whether this is advantageous as compared to an in-house solution depends very much on the circumstances. Advantages and disadvantages concerning time, money and knowledge are compared in Fig. 3 [7]. The biggest disadvantage of university projects is the slow response. In many cases, experimental data are needed at short notice. An industrial lab can deliver the data within days or even hours if needed. The delivery of chemicals to places outside the site of the plant is time consuming. Often safety reasons dictate against a measurement at a university, e.g. with dangerous or carcinogenic compounds. Standard measurements can be performed very efficiently and at low costs within the company. Labor-intensive work, such as basic research, software development and literature surveys, can be performed at lower costs at university. This is even more the case when the work has already been completed, e.g. for existing software. Concerning knowledge, there is always the problem of secrecy. Not everything can be covered by secrecy agreements. For some production processes, it is easier for somebody within the company to
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Fig. 3. Cooperation and/or competition with universities: advantages and disadvantages concerning time, money and knowledge.
gather the internal know-how than for somebody from outside the company. Universities often have the advantage of special equipment and specialized experts. If a project is given to an external laboratory, it is important that it is supervised by somebody in the company. This task can be performed by an expert, e.g. a member of the thermodynamics group. The supervising person is responsible for the knowledge transfer to and from the university, including the gathering of company’s internal know-how that is needed to expedite the external project. The supervision takes time and increases the cost of the external project, but without supervision even more external projects would fail than those that already fail with supervision. 6. Data management In many cases, the wish to get thermophysical property data can be fulfilled by searching the in-house databank. If the access to the database is complicated, time-consuming, or unknown, an engineer may estimate the properties even though experimental data may be available. A very easy access can be given through the intranet. In 1997, Bayer started to give free access to the in-house thermophysical property databank for intranet users all over the world within companies of the Bayer Group. Fig. 4 gives an overview of the data sets that are available in the databank. Most of the data originate from external sources, e.g. from the Dortmund Data Bank, which can be purchased from DDBST GmbH (Oldenburg) or Dechema (Frankfurt). 7. Evaluation/recommendation/parameterization of data For standard pure components many data sets for the same physical property exist. Which data set is the best one? A way out is the evaluation of the data, followed by a recommendation for the best data set or the parameterization of the data. Then, no longer the single data points are stored, but the parameters of an equation that describes the property in a certain range of temperature and pressure.
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Fig. 4. Overview of the Bayer thermophysical property databank. Used abbreviations: E: equilibria, V: vapor, L: liquid, S: solid, El: electrolyte, G: gas.
Often it is advantageous to have a collection of recommended data for a certain business, e.g. for the production of isocyanates: First, the components that are important for a production are defined, e.g. starting substances, intermediates, products, side products, and solvents. Then, physical properties are selected that are important for process design, e.g. vapor pressure, liquid density, or heat capacity. Components and properties form a matrix, for which the elements are filled during the process of data evaluation: • • • • •
Collect available data from internal and external sources; Evaluate the data quality; Evaluate the dependence on other properties; Select suitable equations; Adjust parameters for the equations.
If gaps remain in the matrix that are important for process design or product design, estimates are made or experimental data are taken, depending on the perceived importance of the missing property. 8. High precision data, for revamps, high product purities The following situation is common practice in production of chemicals. Based on more or less fair phase equilibrium data from external sources (e.g. from the data base of a commercial simulator), a distillation column was designed including an additional safety factor and it has been running successfully for many years. Now the production should be increased, or a different product should be distilled with the column. The question arises: Is the existing column suitable to fulfill the new requirements or not? It is a question of yes or no. In such a situation, the required accuracy of phase equilibrium data is very high. Often, some data sets from literature may lead to answer the question with “yes”, while other data sets lead to a “no”. A careful evaluation of the data sets is needed. Sometimes additional phase equilibrium measurements have to be made.
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Fig. 5. P–x–y diagram of the acetone + water system at different temperatures. Lines: calculations using the Wilson activity coefficient model with parameters Aij set according to the ratio of the molar volumes in order to obtain physically meaningful parameters, and one pair of Bij fit to all data [10,11] shown.
Fig. 6. Dependence of the separation factor on the fraction of water in the binary system acetone + water. Line: Wilson model with same parameters as in Fig. 5.
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Even the rather widely differing boiling point binary systems, such as acetone + water, might be problematic in a revamp situation, where, for example, distillation column reflux can only be varied within given limits. For high acetone purities, the system is approaching an azeotrope at certain temperatures and the separation factor is close to unity; one literature source [8] even reports an azeotrope in the region of interest for a recent project at Bayer, where the yes/no question could not be securely answered based on existing data, as depicted in Fig. 5. Within a very short period of time, measurements of the activity coefficient at infinite dilution and additional dynamic vapor–liquid equilibrium (VLE) measurements were performed in the range of high acetone purities (Fig. 6). As also shown in Fig. 6, the Wilson model [9] with parameters fit the data [10,11], shown in Fig. 5 before, predicts the new experimental data within experimental accuracy at high acetone purities, and could subsequently be securely used for a reliable process simulation up to high acetone purities.
9. Modern experimental equipment Most of the thermophysical property data, in industry are needed at short notice or even immediately. One reason is the fact that calculations and simulations are performed based on existing data. When the calculated results do not agree with the experiments, e.g. the purities of the distillate stream are different from the calculated compositions, additional information on phase equilibrium data are needed. During the time of data acquisition the simulations might be on hold. Therefore, a variety of experimental equipment has to be available. Usually, equipment is only installed and kept up-todate for those measurement methods that are most often required. Table 1 shows some of the methods that are available in the Table 1 Experimental methods and equipment (V: vapor, L: liquid, S: solid) Pure compounds, mixtures, intermediates, products
Thermal properties
Density Compressibility Thermal expansion
Caloric properties
Heat of vaporization Heat of sublimation Heat capacity
Phase equilibria
Transport properties
Vapor pressure Sublimation pressure Vapor-liquid equilibria Liquid-liquid equilibria High-pressure equilibria Critical points ␥∞ (␥ at infinite dilution) Thermal conductivity Viscosity Surface tension Diffusion coefficient
V
L
S
Example for method
•
• • •
•
Vibrating tube, aerometer Piston, vibrating tube Piston, vibrating tube
• • • • • • •
• • • • • • • • •
• • • •
•
Calorimeter Calorimeter Calorimeter Dynamic still, static still Vapor-pressure balance Dynamic or static method Cloud point determination Synthetic, analytical method Visual observation, synthetic Ebulliometry, GLC Transient hot wire method Capillary viscometer Droplet, ring method Pressure-decay method
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thermophysical property group of Bayer in Leverkusen. The spectrum of experimental methods include standard procedures for pure-component properties (also complying with the regulations for ‘Good Laboratory Practice’ by the Organization for Economic Co-operation and Development, OECD), as well as highly sophisticated apparatus for the investigation of high pressure multiphase equilibria or systems at extreme dilution. Because of their importance in chemical processes, vapor pressures are measured with high accuracy using static and dynamic methods. Transport properties, such as viscosity and thermal conductivity, are necessary for the design of equipment, e.g. boilers, and heat exchangers. New transport theories for mass transfer processes are gaining increasing attention for the simulation of distillation and absorption processes. These theories and the corresponding equations need precise and reliable information on transport properties, such as diffusion coefficients in gases, liquids or polymers. The improvement of experimental techniques in order to satisfy new needs is an ongoing task of a thermophysical property laboratory.
10. Modern calculation tools Tools to calculate thermophysical properties are needed to correlate experimental data and to generate data by an estimation method. Examples of correlative models are: • pure-component property correlations, e.g. vapor–pressure equations, • gE -models (Wilson [9], NRTL [12], UNIQUAC [13]), • equations of state (Peng–Robinson [14], SRK [4], SAFT [15,16]). If the required properties of the compounds and mixtures under consideration are not known and measurements are not feasible, the properties must be estimated from the known properties and the chemical structure of the compounds. For this purpose, predictive models can be used, for example, • group-contribution methods (Benson, UNIFAC [17], PSRK [18]), • models for solvent selection (MOSCED [19]), • models based on statistical thermodynamics (COSMO-term [20,21]). For many purposes, the accuracy of the estimated data is sufficient, e.g. for rough designs as the basis of first cost estimates. If greater accuracy is required, often experiments must be carried out. Predictive models can save a considerable amount of experimental effort [22]. If, for example, in high-pressure phase equilibrium measurements, the compositions of the coexisting phases are roughly known by making a prediction using the PSRK equation of state model, the experiments can be focussed on a certain temperature, pressure or composition range [23]. Nowadays, many predictive models are implemented in commercially available programs, e.g. in DDBSP [24] or as part of a process simulator. When those models are used in industry, they are often extended with additional coefficients or interaction parameters that have been fitted to unpublished in-house experimental data. As an example, the selection of a solvent for the extractive distillation of two narrow boiling isomers using the MOSCED model is discussed. The MOSCED model is an extension of the regular solution theory of Scatchard [25] and Hildebrand [26] to polar and associating systems. For the separation of narrow-boiling p-/m-isomers, some group parameters for the MOSCED model were not available.
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Therefore, the following stepwise procedure was applied. (1) Ebulliometric measurements to determine the activity coefficients at infinite dilution with several binary systems (m-isomer + n-nonane, n-hexanol and DMF, p-isomer + n-nonane, n-hexanol and DMF) were performed. (2) The missing MOSCED group parameters were fitted to the experimental data. (3) Many possible solvents were checked for the selectivity to separate the p-isomer from the m-isomer using the extended MOSCED model. (4) The selectivities of the most suitable “predicted” solvents were checked experimentally. The deviations between predictions and experimental data were in many cases smaller than 1%. By the use of the predictive method, the number of experiments needed to find a suitable solvent was decreased significantly. In a thermophysical property group, other models are also frequently used, e.g. in-house or commercial process simulators and many self-made tools for the daily work, such as spreadsheets.
11. New fields, e.g. polymers, reacting systems, electrolyte systems Many chemical companies have undergone a partial metamorphosis to become material companies. Therefore, the need for thermophysical data related to polymers has become an important task. For calculations in multicomponent systems, it has become a common practice to make simplifying assumptions about the phase behavior of some binary subsystems for which no experimental data are known. One of these simplifying assumptions is to assume ideal behavior when the boiling points of the substances differ considerably and when no liquid–liquid immiscibility is likely. For many applications, e.g. for the devolatilization of oligomers or polymers, this simplification can result in the wrong design of separation apparatus, like thin-film evaporators. With increasing difference in molar mass between the components in a binary system, increasingly strong negative deviations from Raoult’s law occur. The influence of the molar mass of the heavy component on phase equilibria in the system n-hexane + polydimethylsiloxane (PDMS) is depicted in Fig. 7. Experimental data have been taken from Ashworth and Price [27]. The boiling-point curve of the system decreases with increasing molar mass of the PDMS. The separation of the components is far more difficult than what would be calculated assuming ideal behavior. Table 2 shows the most important methods that are available in the thermophysical property groups of Bayer in Leverkusen for measuring phase equilibria in polymer systems (denoted by “χ ”, because they are often modeled with the Flory–Huggins equation [28–30]) and diffusion in polymer systems (denoted by “D”, the symbol for the diffusion coefficient). Modeling high-pressure equilibria (or equilibria where specific interactions are important) is often carried out using the Statistical Associating Fluid Theory (SAFT, “kij ”). The spectrum of experimental methods already covers a wide range of different apparatus, but there are ongoing efforts to extend the range where equilibria and diffusion processes can be measured to higher (polycarbonate) and lower (butyl) temperatures. One example of present work is the system n-pentane + PDMS [31] which serves as a model system when new apparatus for degassing polymers are tested. From literature, many data sets are known where equilibria at infinite dilution had been measured using inverse gas–chromatography (IGC/GLC). However,
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Fig. 7. Influence of the molar mass of the heavy component on phase equilibria in the system n-hexane + polydimethylsiloxane (PDMS). M n = number average molecular weight. Experimental data from Ashworth and Price [27].
Table 2 Experimental methods for the measurement of phase equilibria and diffusion coefficients in polymer systems at Bayer AG in Leverkusen Method
Characteristic
Sorption balance
Universal standard method; → χ , D
Pressure decay method
Universal method: liquid, foil; → χ , D Latex, slurry: corrected for 2nd L-phase → χ
View cell
VLE, LLE, SLE up to 35 MPa → χ or kij
Head space gas chromatography
Fast and inexpensive → χ, D Also suitable for multicomponent systems
Inverse gas chromatography
Fast and inexpensive; also screening method → χ, D
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Fig. 8. Activity of n-pentane in the system n-pentane + PDMS (molecular weight = 1, 03, 000 g/mol) at different temperatures [31] Calculation: Flory–Huggins equation for the activity coefficient, Peng–Robinson EOS for fugacity coefficients.
these data scatter and do not cover the desired temperature and concentration range. Thus, it became necessary to make our own high-precision measurements. According to the typical industrial approach, a thermodynamic model that reproduces densities and phase equilibria was set up in order to be able to extend the range where the data had been taken (35, 90, and 150 ◦ C, 0–80% pentane). The Flory–Huggins activity coefficient model and the SAFT equation of state were tested. Finally the Flory–Huggins equation was used for the liquid phase together with a Poynting correction, while the gas phase and reference state were modelled using the Peng the Robinson equation for fugacities and densities: Eq. (1) where, a is the activity, ϕ the fugacity coefficient, y the mole fraction in gas phase, P the pressure, the superscript Sat the saturation conditions of pure compound, Poy the Poynting correction, 1 the solvent. a1 =
ϕ1 y1 P Sat Sat ϕ1 P1 Poy1
(1)
The corrections for the gas phase became necessary because of the high degree of non–ideality of the standard state at 150 ◦ C (Psat (T ) = 16 bar; Tc = 196 ◦ C) and led to a much smaller influence of temperature on the binary χ parameter in the Flory-Huggins model—and thus better predictive capabilities outside the investigated temperature range. Using this γ –ϕ-approach, calculated results agreed with measured data within experimental accuracy (Fig. 8). The model is used as a thermodynamic basis when the efficiency of different apparatus for degassing polymers is compared.
12. Environmental aspects Environmental aspects have a very strong influence on the design of products and on the design of processes. One example should be given for each: polyurethane foams with environmentally benign
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blowing agents and the new design for the devolatilization process of Therban (hydrogenated acrylnitrilco-butadiene rubber), an elastomer that is stable at high temperatures and resistant to oil. Polyurethane rigid foams of the type commonly used for most refrigerator insulation are usually produced from two-component systems: Component A contains a polyol, including catalysts, stabilizers, and blowing agents; component B is a polyisocyanate. Low-boiling liquids and/or water are used as blowing agents. The water reacts with the polyisocyanate to form CO2 which serves as an additional blowing agent. The pressure generated by the gaseous blowing agent and CO2 in the closed cells has a strong influence on the stability of the foam. The insulation efficiency of the polyurethane foam is mainly (60–65%) determined by the thermal conductivity of the resulting gas in the closed cells. Until recently, CFC-11 (trichlorofluoromethane) was the most widely used blowing agent. Since CFCs are very stable, they can reach the ozone layer before being destroyed by a natural process. To find an adequate substitute for CFC-11, the vapor-phase thermal conductivity of several potential blowing agents has been measured. The experimental procedure is based on the transient hot-wire method [32]. The essential feature of this method is the precise determination of the transient temperature rise of a thin metallic wire. This is determined from measurements of the resistance of the wire over a period of a few seconds following the initiation of a heating cycle. Today, mixtures (e.g. cyclopentane/isopentane) trying to combine the best of both worlds, low thermal conductivity and high vapor pressure at low temperatures, have been introduced in the market. Our investigations [33–35], including experimental and theoretical work, aim at a better understanding of the relevant practical properties of market blowing agent (mixtures). An example of process integrated environmental protection is the new process of the production of Therban (see above). After the hydrogenation of the polymer, the solvent is removed from the polymer in a devolatilization process. To gain information for an improved process design, phase equilibrium data and diffusion coefficients have been determined experimentally. For example, the activity of the solvent as a function of the solvent fraction in the polymer has been measured. These thermophysical property data have been used as input information of a process simulation. The new design of the stripping process has led to savings of: • 20,000 tons of steam per year, • 5000 m3 of solvent containing gases per year, • 300,000 m3 of nitrogen per year. The new plant design received an award from the German Ministry of the Environment.
13. Conclusion The role of applied thermodynamics in industry is dependent on the importance of advanced process technology for the company. To find out this role, the following question can be asked: Which advantages does the company have as compared to the competitors? In case technology is one of the advantages, the next question arises: Is applied thermodynamics of importance for the core production processes or products? If this question is answered with “no”, applied thermodynamics is not essential for the company and tasks related to applied thermodynamics are outsourced to external sources, such as vendors and universities. If the question is answered with “yes”, there is a need for expertise in applied thermodynamics
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within the company. This expertise can be delivered efficiently by a group of skilled thermodynamicists who use either internal sources, e.g. a laboratory, a database, or estimation methods, or coordinate, and supervise the use of external sources. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35]
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