Optimal-Setting for Ore and Water Feeding in Grinding Process Based on Improved Case-Based Reasoning

Abstract

The grinding product particle size is the most crucial operational index of mineral grinding processes. The size and consistency of the product directly affects the subsequent dressing and sintering. In this paper, a novel expert system is proposed for guiding the operating variables to keep the product stable with the wildly varying ore properties. First, case-based reasoning (CBR) is introduced to describe the whole grinding process with the historical data and expert experience. Second, the generative adversarial network (GAN) is employed to extend the raw data to enhance the flexibility of CBR. Moreover, the weights of different features in CBR is optimized by improved non-dominated sorting genetic algorithm II (NSGA-II). Finally, the proposed method is validated by a set of actual data collected from a Chinese dressing plant. The experimental result demonstrates the effectiveness of the proposed method.