Research on energy-saving optimization of coal mine underground belt conveyor based on RBF neural network
Aiming at the problems of high energy consumption and large pollutant emissions in traditional coal mining processes,a new energy-saving optimization model for belt conveyors was constructed by using radial basis function optimization networks for energy-sav-ing optimization of conveyors.The results showed that the average energy-saving rate of the research model was 17.3％in 24 h.The av-erage energy-saving rate of the model based on multi-objective optimization algorithm was 10.5％in 24 h,and the average energy-saving rate of the model based on particle swarm optimization algorithm was 7.4％in 24 h.The energy consumption per hour of the study mod-el was 156.2 kWh,which was 423.53 kWh and 367.5 kWh less than that of the particle swarm model and the multi-objective model,respectively.To sum up,the conveyors based on the radial basis function optimized network can reduce energy consumption and achieve energy conservation and emission reduction during operation,and provide technical support for improving the intelligence of coal mine production equipment.
RBF neural networkbelt conveyorenergy-saving optimization model
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