A mathematical model of the particle heating process in the reaction shaft of flash smelting furnace was established and the calculation was performed.The results indicate that radiation plays a significant role in th...A mathematical model of the particle heating process in the reaction shaft of flash smelting furnace was established and the calculation was performed.The results indicate that radiation plays a significant role in the heat transfer process within the first 0.6 m in the upper part of the reaction shaft,whilst the convection is dominant in the area below 0.6 m for the particle heating.In order to accelerate the particle ignition,it is necessary to enhance the convection,thus to speed up the particle heating.A high-speed preheated oxygen jet technology was then suggested to replace the nature gas combustion in the flash furnace,aiming to create a lateral disturbance in the gaseous phase around the particles,so as to achieve a slip velocity between the two phases and a high convective heat transfer coefficient.Numerical simulation was carried out for the cases with the high-speed oxygen jet and the normal nature gas burners.The results show that with the high-speed jet technology,particles are heated up more rapidly and ignited much earlier,especially within the area of the radial range of R=0.3−0.6 m.As a result,a more efficient smelting process can be achieved under the same operational condition.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
基金funded by Jinguan Copper of Tongling Non-ferrous Metals Group Co., Ltd.
文摘A mathematical model of the particle heating process in the reaction shaft of flash smelting furnace was established and the calculation was performed.The results indicate that radiation plays a significant role in the heat transfer process within the first 0.6 m in the upper part of the reaction shaft,whilst the convection is dominant in the area below 0.6 m for the particle heating.In order to accelerate the particle ignition,it is necessary to enhance the convection,thus to speed up the particle heating.A high-speed preheated oxygen jet technology was then suggested to replace the nature gas combustion in the flash furnace,aiming to create a lateral disturbance in the gaseous phase around the particles,so as to achieve a slip velocity between the two phases and a high convective heat transfer coefficient.Numerical simulation was carried out for the cases with the high-speed oxygen jet and the normal nature gas burners.The results show that with the high-speed jet technology,particles are heated up more rapidly and ignited much earlier,especially within the area of the radial range of R=0.3−0.6 m.As a result,a more efficient smelting process can be achieved under the same operational condition.
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.
