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Volume 10 Issue 11
Nov.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
X. L. Wang, L. M. Liu, L. Duan, and  Q. Liao,  “Multi-objective optimization for an industrial grinding and classification process based on PBM and RSM,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2124–2135, Nov. 2023. doi: 10.1109/JAS.2023.123333
Citation: X. L. Wang, L. M. Liu, L. Duan, and  Q. Liao,  “Multi-objective optimization for an industrial grinding and classification process based on PBM and RSM,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2124–2135, Nov. 2023. doi: 10.1109/JAS.2023.123333

Multi-Objective Optimization for an Industrial Grinding and Classification Process Based on PBM and RSM

doi: 10.1109/JAS.2023.123333
Funds:  This work was supported in part by the National Natural Science Foundation of China (62073342) and the National Key Research and Development Program of China (2018YFB1701100)
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  • The grinding and classification process is one of the key sub-processes in mineral processing, which influences the final process indexes significantly and determines energy and ball consumption of the whole plant. Therefore, optimal control of the process has been very important in practice. In order to stabilize the grinding index and improve grinding capacity in the process, a process model based on population balance model (PBM) is calibrated in this study. The correlation between the mill power and the operating variables in the grinding process is modelled by using the response surface method (RSM), which solves the problem where the traditional power modeling method relies on some unobservable mechanism-related parameters. On this basis, a multi-objective optimization model is established to maximize the useful power of the grinding circuit to improve the throughput of the grinding operation and improve the fraction of –0.074 mm particles in the hydrocyclone overflow to smooth the subsequent flotation operation. The elite non-dominated sorting genetic algorithm-II (NSGA-II) is then employed to solve the multi-objective optimization problem. Finally, subjective and objective weighting methods and integrated multi-attribute decision-making methods are used to select the optimal solution on the Pareto optimal solution set. The results demonstrate that the throughput of the mill and the fraction of –0.074 mm particles in the overflow of the cyclone are increased by 3.83 t/h and 2.53%, respectively.

     

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    Highlights

    • The mapping relationship between mill power and load is considered
    • The mill power model based on Response Surface Method is established
    • A multi-objective optimization model for maximizing power and product quality is established to improve throughput and grinding quality
    • The Multi-attribute decision criteria are used to determine the optimal set point

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