Modeling and Optimization of Copper Flash Smelting Process Based on Neural Network |
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Authors: | WANG Jin-liang ZHANG Chuan-fu ZENG Qing-yun TONe Chang-ren ZHANG Wen-hai |
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Affiliation: | 1. School of Metallurgical Science and Engineering,Central South University,Changsha,Hunan 410083,China;Faculty of Material and Chemistry Engineering,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China 2. School of Metallurgical Science and Engineering,Central South University,Changsha,Hunan 410083,China 3. Faculty of Material and Chemistry Engineering,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China 4. China Nerin Engineering Co.,Ltd.,Nanchang,Jiangxi 330002,China |
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Abstract: | The copper flash smelting process neural network model (CFSPNNM) was developed, its input layer includes eightnodes: oxygen grade (OG), oxygen volume per ton of concentrate (OVPTC), flux rate (FR) and quantities of Cu, S, Fe, SiO2and MgO in copper concentrate; output layer includes three nodes: matte grade, matte temperature and Fe/SiO2 in slag, andnet structure was 8-13-10-3. Then, the internal relationship between the technological parameters and the objectiveparameters was built after the CFSPNNM was trained by using GA-BP algorithm. Moreover, the technological parameterswere optimized by using genetic algorithms (GA) to make energy consumption the lowest. Simulation results showed that theCFSPNNM had high prediction precision and good generalization performance. Compared with the practical average data, theenergy consumption can be reduced by 6.8% if the smelting process is controlled by adopting the optimized technologicalparameters. |
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Keywords: | neural network genetic algorithm copper flash smelting modeling optimization |
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