The optimized leaching techniques were studied by technical experiment and neural network optimization for improving indium leaching rate. Firstly, effect of single technical parameter on leaching rate was investigate...The optimized leaching techniques were studied by technical experiment and neural network optimization for improving indium leaching rate. Firstly, effect of single technical parameter on leaching rate was investigated experimentally with other parameters fixed as constants. The results show that increasing residual acidity can improve leaching rate of indium. Increasing the oxidant content can obviously increase leaching rate but the further addition of oxidant could not improve the leaching rate. The enhancement of temperature can improve the leaching rate while the further enhancement of temperature decreases it. Extension leaching time can improve the leaching rate. On this basis, a BPNN model was established to study the effects of multi-parameters on leaching rate. The results show that the relative error is extremely small, thus the BPNN model has a high prediction precision. At last, optimized technical parameters which can yield high leaching rate of 99.5%were obtained by experimental and BPNN studies：residual acidity 50-60 g/L, oxidant addition content 10%, leaching temperature 70 ℃ and leaching time 2 h.展开更多
The accuracy of spatial forecasting is close relation to the selection of spatial forecasting model. Each model from special aspects using special spatial data has its own advantage or disadvantage. A more accurate sp...The accuracy of spatial forecasting is close relation to the selection of spatial forecasting model. Each model from special aspects using special spatial data has its own advantage or disadvantage. A more accurate spatial forecasting model can be obtained by a linear combination of some models. In this study, first-order spatial autoregressive （SAR（1）） model, Kriging algorithm interpolation （KAI） model and back-propagation neural network （BPNN） model are established by using cross-section data or time series data. A spatial linear combination forecasting （SLCF） model is obtained by the combination models mentioned above. An empirical research by these models is carried out with forecasting some areas＇ GDP per capita in Fujian, 2003. It is found that the best one is the SLCF model.展开更多
基金Project(2012BAE06B01)supported by the National Key Technologies R&D Program of China
文摘The optimized leaching techniques were studied by technical experiment and neural network optimization for improving indium leaching rate. Firstly, effect of single technical parameter on leaching rate was investigated experimentally with other parameters fixed as constants. The results show that increasing residual acidity can improve leaching rate of indium. Increasing the oxidant content can obviously increase leaching rate but the further addition of oxidant could not improve the leaching rate. The enhancement of temperature can improve the leaching rate while the further enhancement of temperature decreases it. Extension leaching time can improve the leaching rate. On this basis, a BPNN model was established to study the effects of multi-parameters on leaching rate. The results show that the relative error is extremely small, thus the BPNN model has a high prediction precision. At last, optimized technical parameters which can yield high leaching rate of 99.5%were obtained by experimental and BPNN studies：residual acidity 50-60 g/L, oxidant addition content 10%, leaching temperature 70 ℃ and leaching time 2 h.
基金This project is supported by Fujian Social Science Foundation of China （2003E171）.
文摘The accuracy of spatial forecasting is close relation to the selection of spatial forecasting model. Each model from special aspects using special spatial data has its own advantage or disadvantage. A more accurate spatial forecasting model can be obtained by a linear combination of some models. In this study, first-order spatial autoregressive （SAR（1）） model, Kriging algorithm interpolation （KAI） model and back-propagation neural network （BPNN） model are established by using cross-section data or time series data. A spatial linear combination forecasting （SLCF） model is obtained by the combination models mentioned above. An empirical research by these models is carried out with forecasting some areas＇ GDP per capita in Fujian, 2003. It is found that the best one is the SLCF model.
