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Simulation‐Based Multiobjective Optimization of Open‐Pit Mine Haulage System: A Modified‐NBI Method and Meta Modeling Approach

Author

Listed:
  • Milad Abolghasemian
  • Armin Ghane Kanafi
  • Maryam Daneshmand-Mehr

Abstract

A large number of engineering problems involve several conflicting objectives, which today are often solved through expensive simulation calculations. Methods based on meta‐models are one of the approaches to solving this group of problems. In this paper, multiobjective optimization in the extraction system of a copper open‐pit mine complex is presented by the modified‐NBI optimization method and regression meta‐model. For this purpose, two objective functions of maximizing the amount of total extraction, which is the sum of the extraction of sulfide, oxide, low‐grade ores, and waste in this mine, and minimizing the transport time of haulage according to the limitation of its storage capacity, transport equipment, and budget are considered. The Central Composite Design (CCD) method is used to build the Design of Experiments (DOE) for the design variables. The considered design variables are the number of trucks of 120 tons, 240 tons, 35 tons, and 100 tons. The number of targets considered in each design combination is considered the response surface. The suitable meta‐model to maximize the total extraction rate and minimize the transport time of the haulage, two modified functions of nonlinear regression have been determined. The accuracy of the models for selection has been done using PRESS and R2 statistics. The most common PRESS error has also been used to validate the meta‐models. Then the multiobjective optimization problem was solved using the modified‐NBI method. Finally, Pareto and optimal solutions using the proposed approach were presented and discussed.

Suggested Citation

  • Milad Abolghasemian & Armin Ghane Kanafi & Maryam Daneshmand-Mehr, 2022. "Simulation‐Based Multiobjective Optimization of Open‐Pit Mine Haulage System: A Modified‐NBI Method and Meta Modeling Approach," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:3540736
    DOI: 10.1155/2022/3540736
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    References listed on IDEAS

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    1. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
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