Heuristic Optimization Algorithm for Automated Control Design of Bioleaching of Chalcopyrite
Microbially assisted recovery of copper from low-grade chalcopyrite has been reported to be a very difficult process, conventional hydrometallurgical methods were limited by many parameters. This study focus on the design and the training of a Multi-Layer Perceptron classifier for the optimized preparation conditions for bioleaching of chalcopyrite. The proposed approach uses the heuristic Backpropagation Neural Network generation and training (BPNN) algorithm to generate the neural network system. The optimization conditions for bioleaching of chalcopyrite in the system were discussed based on the artificial neural network model, in which the input conditions were selected as stirring speed, volume of inoculum, and process pH. The highest Cu dissolution of bioleaching was regarded as the optimization aim, along with constraints of each factors bounds. The highest copper recovery of 32.1% is obtained at pH 1.5, stirring speed of 140r/min, and 13% (v/v) inoculum concentration. The BP model based on heuristic optimization algorithm for bioleaching of chalcopyrite has practicability.
Wei Li Aili Yang Lei Liu
Energy and Environmental Research Center North China Electric Power University Beijing, China Department of Civil Engineering, Dalhousie University Halifax, Canada
国际会议
成都
英文
1-3
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）