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Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore

Author

Listed:
  • Wen Zhang

    (Department of Industrial Engineering, College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Qinghe Yuan

    (Department of Industrial Engineering, College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    National Demonstration Center for Experimental Mining Engineering Education, Qingdao 266590, China)

  • Shun Jia

    (Department of Industrial Engineering, College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    National Demonstration Center for Experimental Mining Engineering Education, Qingdao 266590, China)

  • Zhaojun (Steven) Li

    (Department of Industrial Engineering & Engineering Management, College of Engineering, Western New England University, Springfield, MA 01119, USA)

  • Xianhui Yin

    (College of Quality and Standardization, Qingdao University, Qingdao 266071, China)

Abstract

In order to improve production control ability in the gold ore flotation process, the output index in this process was studied. Flotation is an effective gold recovery process. Gold concentrate grade and gold recovery rate are the key output indicators of the flotation process. However, in the existing studies exploring the impact of parameter changes on the output indicators, the control ability of the output indicators is insufficient, and the interaction between variables is inadequately considered. Therefore, a multi-objective optimization model based on response surface methodology and the non-dominated sorting genetic algorithm-II (NSGA-II) is proposed in this paper. Firstly, the experiment was designed based on the Box-Behnken principle. Based on the experimental results, the interaction between variables was analyzed and the response polynomial was fitted. Secondly, a multi-objective optimization model was constructed, and the NSGA-II was used to solve the model. Finally, an example of gold ore flotation was used to verify the effectiveness of the method. The optimal solution was a gold concentrate grade of 75.46 g/t and a gold recovery rate of 85.98%.

Suggested Citation

  • Wen Zhang & Qinghe Yuan & Shun Jia & Zhaojun (Steven) Li & Xianhui Yin, 2021. "Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8314-:d:601487
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    References listed on IDEAS

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