Research Of Neural Network Algorithm In The Iron Ore Grinding For Energy-saving

In our country, storage of mineral resources is rich, while per capita energy is farbelow the world average, and the overall efficiency of energy use is only half ofequivalent to the developed countries. Industry property is the main national economicconstruction industries, and it is also the largest energy consumption areas, includingmetal mines leading to energy consumption itself.Underground mining of metal ore can not be used directly because of its complexcomponents, so it must first enter the beneficiation process to refine the pure monomer.Mineral main processes are ore crushing, grinding, classification, flotation andpostpartum treatment etc. Grinding process of energy consumption accounted for about50%of the concentrator. Therefore, the potential energy consumption of grinding stageconcentrator is significant. Because grinding and classification is involved many factorsand the large coupling between each factor. And there is no precise model, so control ofthe grinding and classification process is more difficult to achieve.With the development of computer control technology, intelligent control and othercutting-edge technology matures is quickly activity in many fields. However, currentlyintelligent control used in grinding process is only in the pilot study stage. It analyzedthree aspects of grinding and ball mill energy consumption process and active relation-ship with the loading. This design is using genetic algorithm to optimize the BPalgorithm to realize energy conservation control of the ore grinding and classificationprocess. It simulated test in MATLAB environment, and compared the results betweengenetic optimization BP control and BP control. It can draw a conclusion that geneticoptimization BP algorithm is feasible and superior used in the energy conservation ofiron ore grinding and classification.