Mine Des

Is Tru Truck ck Queui Queuing ng Pro rodu duct ctiv ive e? Study of truck & shovel operations productivity using simulation platform MineDES

Dmitry Kostyuk Specialist Scientist, Group Resource and Business Optimisation 25 November 2014

Contents Simulation imu lation Modeling in Mine Pla Planni nning ng Truc ruck k and sho shove vell opera operatio tions ns sim simulation ulation platform MineD ineDE ES

• Overview  Ad d v an antt ag ages es •  A Bench nchma marki rking ng and valid valida atio tion n • Be Case Ca se Study Stu dy Conclusions Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 2

Mine Planning Challenges • Mines will continue to increase in depth, scale and complexity. • There is a strong demand for mine planners to:  –Produce achievable, optimized plans for these mines  –Appropriately size equipment fleets  –Design efficient, effective mine access systems  –Accurately estimate mining system productivity • Getting it wrong can negatively impact project NPV.

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 3

Conventional Mine Planning • Analytical methods – formula-based approach • High-level of abstraction – BIG PICTURE point of view • Normally doesn’t account for the impact on overall mining system productivity such factors as equipment interactions, parameters variability, randomness, uncertainty etc… • Fails to describe systems with dynamic behavior featuring:  – non-linear behavior   – non-intuitive influences between variables  – time and causal dependencies  – uncertainty, randomness and large number of parameters Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 4

Mine Planning using Simulation Modeling Simulation Modeling

• Method of solving problems that can’t be calculated analytically • Cheap and risk free experiments (“what if ?” studies) • Efficient for analyzing systems with dynamic behavior 

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 5

 Analytical Modeling vs. Simulation Modeling Queuing theory

Single lo ader 

Poisson stream (independent arrivals)  Ar ri val s: on aver age  trucks / hour 

Case #1:

Case #2:

Loading time exponentially distr ibuted:

Loading time arbitr ary distribut ed:

1/ – mean loading time Shovel utilization:   

 

 Average waiting time:  

1/ – mean loading time Shovel utilization:   

 

 Average queue length:   

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

 

 Average waiting time:   

     

,

where  is coefficient of variation of loading time.

Slide 6

 Analytical Modeling vs. Simulation Modeling Queuing theory Poisson stream (independent arrivals)

Multiple (K) loaders

 Ar ri val s: on aver age  trucks / hour 

Case #4:

Case #3: Loading time exponentially dist ributed:

1/ – mean loading time Shovel utilization:   

and  

1/ – mean loading time

 

 Average waiting time:    where P

 

,

   

 , !  

    ! 

Loading time arbitr ary distribut ed:

 

 ∑ 





!

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

 ANALYTICAL SOLUTION DOES NOT EXIST STARTING FROM HERE AND FOR ANY FURTHER COMPLICATION OF THE PROCESS!!!

Slide 7

 Advantages of Simulation Modeling • Enabling system analysis, and to find solutions where other methods fail • Once appropriate level of abstraction is selected, development of a simulation model is a more straightforward process than analytical modeling – less intellectual efforts, scalable, incremental and modular  • The structure of a simulation model naturally reflects the structure of the real system – it is visual, easy to verify and communicate to other people • Any state of the model is measurable and any entity, which is not below abstraction level is tractable – sensitivity analysis, statistical analysis • Ability to play and animate the system • Simulation models are a lot more convincing than Excel spreadsheets or Power Point slides

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 8

Contents Simulation Modeling in Mine Planning Truck and shovel operations simulation platform MineDES

• Overview •  Advantages • Benchmarking and validation Case Study Conclusions Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 9

MineDES - What is it? • Truck & shovel operations simulation tool that can be used to estimate mining movement and processing capability in the following dimensions:  – productivity statistics  – bottleneck processes and infrastructure  – truck queuing statistics  – the impact of different crew and maintenance schedules  – the impact of unscheduled random equipment and road sector downtime  – the influence of road maintenance vehicles and light vehicles on congestion  – the capacity of particular pit ramps, and the whole road network, to support planned material movements. • Primary design concept was to focus application on addressing strategic mine planning questions, but secondly to be applicable in the short term planning environment. Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 10

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 11

MineDES Features • Truck dynamics are calculated using full rim-pull curve data, taking into account truck payload as well as road gradient and quality • A fast, purpose-built simulation engine, and the modelling of trucks as agents to ensure realistic and intelligent behavior • Truck dispatch is predicated upon attempting to achieve a user-defined mining rate at each mining face • Realistic and flexible modelling of traffic rules at complex intersections • Modelling of payload and loading/dumping time variability • Flexibility in assigning legal digging and dumping destinations to different truck sets within each truck fleet • The optional application of a wide range of scheduled and unscheduled downtime for all mobile and static material processing infrastructure. • Integrated 3D visualization engine Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 12

 Advantages of truck & shovel simulator MineDES • In-house software development project with more than 4 years development history. • Developed from scratch and doesn’t use any commercial simulation engines. • Designed to address both long- and shortterm mine planning problems • Intuitive, flexible and fit for purpose. Development of a simulation scenario is straightforward process, which does not require programming skills and a lot of intellectual effort. • Benchmarked against other industry standard software products and tested in real operations environment

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 13

MineDES Benchmarking and Validation Truck dynamics model benchmarking against industry standard software 50 45 40 35

   )    h    / 30   m    k    ( 25    d 20   e   e   p 15   s    k 10   c   u   r 5    T

MineDES TALPAC

Distance (km)

0

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

600 m

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 14

Testing against real truck data. GPS real tr uck data plot:

Experiment results:

MineDES model - design view:

Truc k gr oun d sp eed pr ofi le. GPS vs. MineDES

40

115.4

   )35    h    / 30   m    k    (25    d   e20   e   p   s15    k   c10   u   r    T 5

115.2 115 114.8

0

   ) 114.6   m    k    (    Y114.4

0

5

10

15

20

Truc k t ravel dis tance pro fil e. GPS vs. MineDES

4.5 4

114.2

   )3.5   m    k 3    (   e2.5   c   n 2   a    t   s1.5    i    D 1

114 113.8 113.6

0.5 17

17.2

17.4

17.6

17.8

18

18.2

X (km)

0 0

5

10

15

20

Travel time (min)

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 15

Contents Simulation Modeling in Mine Planning Truck and shovel operations simulation platform MineDES

• Overview •  Advantages • Benchmarking and validation Case Study Conclusions Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 16

Case Study. Optimal fleet size. Truck Loading Time Distribution Graph [sec]:

Truck Payload Distribution Graph [tonnes]:

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 17

Case Study. Optimal fleet size. • A minimum 8 trucks are required to maximize productivity. • Adding more trucks to the fleet simply increases overall queuing time with no additional aggregate material movement - in fact, adding extra trucks above 8 can lead to an insignificant decrease in productivity (< 1%) due to increased traffic congestion.

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 18

Case Study. Optimal fleet size

The simulation experiment has shown that even in a simple case where non-deterministic behaviour is quite limited and the road network is simple (point-to-point), in an optimized configuration, we should expect to see trucks queuing at shovels to a not insubstantial extent. The simple financial model shows that this queuing is protective of productivity and operational value. Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 19

Conclusions • A new truck-shovel simulation tool called MineDES has been introduced • A simulation case study was undertaken using MineDES to address the question of whether having queuing trucks could be a feature of an optimally productive mining operation. • Our experiments have shown that in the case where non-deterministic factors are present in the system, typical of all real mining operations to a greater or lesser extent, then some degree of truck queuing will be observed in the most productive of configurations.

Dmitry Kostyuk, Specialist Scientist, Group Resource and Business Optimisation, 25 November 2014

Slide 20