A technology for recovering indium from Jinchuan copper-smelting ash was developed. Indium in the ash was first enriched to the leaching-slag in leaching process,and then recovered by sulfating roasting. The method in...A technology for recovering indium from Jinchuan copper-smelting ash was developed. Indium in the ash was first enriched to the leaching-slag in leaching process,and then recovered by sulfating roasting. The method included mixing the leaching-slag with sulfuric acid,making them into particles,roasting the mixture,and then leaching the calcine with hot water. Above 90% of indium in calcine could be dissolved into the leaching solution. The optimized conditions were determined as follows： the mass ratio of sulfuric acid to leaching slag was 0.1,the roasting time was about 1 to 1.5 h in the temperature range of 200-250℃,and the calcine was leached for 1 h with 5：1 of liquid/solid ratio at 60℃. Over 99% of indium in leaching solution was finally enriched by Zn substitution or sulfide precipitation.展开更多
Due to the importance of detecting the matte grade in the copper flash smelting process, the mechanism model was established according to the multi-phase and multi-component mathematic model. Meanwhile this procedure ...Due to the importance of detecting the matte grade in the copper flash smelting process, the mechanism model was established according to the multi-phase and multi-component mathematic model. Meanwhile this procedure was a complicated production process with characteristics of large time delay, nonlinearity and so on. A fuzzy neural network model was set up through a great deal of production data. Besides a novel constrained gradient descent algorithm used to update the parameters was put forward to improve the parameters learning efficiency. Ultimately the self-adaptive combination technology was adopted to paralleled integrate two models in order to obtain the prediction model of the matte grade. Industrial data validation shows that the intelligently integrated model is more precise than a single model. It can not only predict the matte grade exactly but also provide optimal control of the copper flash smelting process with potent guidance.展开更多
文摘A technology for recovering indium from Jinchuan copper-smelting ash was developed. Indium in the ash was first enriched to the leaching-slag in leaching process,and then recovered by sulfating roasting. The method included mixing the leaching-slag with sulfuric acid,making them into particles,roasting the mixture,and then leaching the calcine with hot water. Above 90% of indium in calcine could be dissolved into the leaching solution. The optimized conditions were determined as follows： the mass ratio of sulfuric acid to leaching slag was 0.1,the roasting time was about 1 to 1.5 h in the temperature range of 200-250℃,and the calcine was leached for 1 h with 5：1 of liquid/solid ratio at 60℃. Over 99% of indium in leaching solution was finally enriched by Zn substitution or sulfide precipitation.
基金Project(60634020) supported by the National Natural Science Foundation of ChinaProject(2002CB312200) supported by the National Basic Research and Development Program of China
文摘Due to the importance of detecting the matte grade in the copper flash smelting process, the mechanism model was established according to the multi-phase and multi-component mathematic model. Meanwhile this procedure was a complicated production process with characteristics of large time delay, nonlinearity and so on. A fuzzy neural network model was set up through a great deal of production data. Besides a novel constrained gradient descent algorithm used to update the parameters was put forward to improve the parameters learning efficiency. Ultimately the self-adaptive combination technology was adopted to paralleled integrate two models in order to obtain the prediction model of the matte grade. Industrial data validation shows that the intelligently integrated model is more precise than a single model. It can not only predict the matte grade exactly but also provide optimal control of the copper flash smelting process with potent guidance.
