Multi-Objective Optimization of Operating Parameters in Tumbling Mill with Neuro-Fuzzy Network

Due to the importance of tumbling mills in processing industries and factories and the lack of an acceptable model for identifying and predicting their performance, it is necessary to optimize these complexes, non-linear, and large systems. This paper aimed to study multi-objective optimization of operating parameters in a tumbling mill. To evaluate the effects of the mill working parameters such as mill speed, ball filling, slurry concentration, and slurry filling on grinding process, power draw, wear of lifters and size distribution of the mill product, it was tried to manufacture a pilot model with a smaller size than the actual mill. For this aim, a mill with 1×0.5m was implemented. The feed of the mill is copper ore with a size smaller than 1 inch. The experiments were done at 65 to 85% of the critical speed. In addition, the combination of the balls was used as grinding media with 10 to 30% of the total volume of the mill. Slurry concentration is 40 to 80% (the weight fraction of solid in slurry) and the slurry filling is between 0.5 and 2.5. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) based multi-objective optimization (NSGA-II) of tumbling mill is done. Level diagrams are used to select the best solution from the Pareto front. The results showed that the best grinding occurs at 70-80% of the critical speed and ball filling of 15-20%. Optimized grinding was observed when the slurry volume is 1-1.5 times of the ball bed voidage volume and the slurry concentration is between 60 and 70%.