Modeling and Optimization of Copper Flash Smelting Process Based on Neural Network

【作者】 汪金良； 张传福； 曾青云； 童长仁； 张文海；

【Author】 WANG Jin-liang~(1,2) ZHANG Chuan-fu~1 ZENG Qing-yun TONG Chang-ren~2 ZHANG Wen-hai~3 1.School of Metallurgical Science and Engineering,Central South University,Changsha,Hunan 410083,China; 2.Faculty of Material and Chemistry Engineering,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China; 3.China Nerin Engineering Co.,Ltd.,Nanchang,Jiangxi 330002,China

【机构】 School of Metallurgical Science and Engineering,Central South University； Faculty of Material and Chemistry Engineering,Jiangxi University of Science and Technology； China Nerin Engineering Co.,Ltd.；

【摘要】 <正>The copper flash smelting process neural network model（CFSPNNM）was developed,its input layer includes eight nodes:oxygen grade（OG）,oxygen volume per ton of concentrate（OVPTC）,flux rate（FR）and quantities of Cu,S,Fe,SiO₂ and MgO in copper concentrate;output layer includes three nodes:matte grade,matte temperature and Fe/SiO₂ in slag,and net structure was 8-13-10-3.Then,the internal relationship between the technological parameters and the objective parameters was built after the CFSPNNM was trained by using GA-BP algorithm.Moreover,the technological parameters were optimized by using genetic algorithms（GA）to make energy consumption the lowest.Simulation results showed that the CFSPNNM had high prediction precision and good generalization performance.Compared with the practical average data,the energy consumption can be reduced by 6.8% if the smelting process is controlled by adopting the optimized technological parameters.更多还原

【Abstract】 The copper flash smelting process neural network model（CFSPNNM）was developed,its input layer includes eight nodes:oxygen grade（OG）,oxygen volume per ton of concentrate（OVPTC）,flux rate（FR）and quantities of Cu,S,Fe,SiO₂ and MgO in copper concentrate;output layer includes three nodes:matte grade,matte temperature and Fe/SiO₂ in slag,and net structure was 8-13-10-3.Then,the internal relationship between the technological parameters and the objective parameters was built after the CFSPNNM was trained by using GA-BP algorithm.Moreover,the technological parameters were optimized by using genetic algorithms（GA）to make energy consumption the lowest.Simulation results showed that the CFSPNNM had high prediction precision and good generalization performance.Compared with the practical average data,the energy consumption can be reduced by 6.8% if the smelting process is controlled by adopting the optimized technological parameters.更多还原