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

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.