Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network
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1.School of Mining Engineering, University of Science and Technology Liaoning, Anshan, Liaoning 114051, China
School of Resources and Environmental Engineering, Shandong University of Technology, Zibo, 255049, China
Central Metallurgical Research and Development Institute, Minerals Technology Department, Helwan, Cairo 11421, Egypt.
Publication date: 2023-09-11
Corresponding author
Ying Hou   

1.School of Mining Engineering, University of Science and Technology Liaoning, Anshan, Liaoning 114051, China
Physicochem. Probl. Miner. Process. 2023;59(6):172096
Grinding is commonly responsible for the liberation of valuable minerals from host rocks but can entail high costs in terms of energy and medium consumption, but a tower mill is a unique power-saving grinding machine over traditional mills. In a tower mill, many operating parameters affect the grinding performance, such as the amount of slurry with a known solid concentration, screw mixer speed, medium filling rate, material-ball ratio, and medium properties. Thus, 25 groups of grinding tests were conducted to establish the relationship between the grinding power consumption and operating parameters. The prediction model was established based on the backpropagation “BP” neural network, further optimized by the genetic algorithm GA to ensure the accuracy of the model, and verified. The test results show that the relative error of the predicted and actual values of the backpropagation “BP” neural network prediction model within 3% was reduced to within 2% by conducting the generic algorithm backpropagation “GA-BP” neural network. The optimum grinding power consumption of 41.069 kWh/t was obtained at the predicted operating parameters of 66.49% grinding concentration, 301.86 r/min screw speed, 20.47% medium filling rate, 96.61% medium ratio, and 0.1394 material-ball ratio. The verifying laboratory test at the optimum conditions, produced a grinding power consumption of 41.85 kWh/t with a relative error of 1.87%, showing the feasibility of using the genetic algorithm and BP neural network to optimize the grinding power consumption of the tower mill.
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