سمیه میرزاده

Mill optimization has many economic benefits. Semi autogenous grinding mills are complex multi-input and multi-output systems that are difficult to optimize. The purpose of this study is to examine the functions of the wear of lifters, power draw and product size distribution. The design variables are mill speed, ball filling, slurry concentration and slurry filling. To achieve this aim, a pilot mill was carried out. The experimental results used to create training cases for the artificial neural network and then the optimization of the design variables is conducted by multi-objective genetic algorithm. Level diagrams are then used to select the best solution from the Pareto front. Finally, the response surface methodology has been used to study the interaction between the design parameters. 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 was 1-1.5 times of the ball bed voidage volume and the slurry concentration was 60-70%. Additionally, variables with the largest effect on the process are mill speed and ball filling.

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%.