介绍了废硫酸热分解的过程和热量计算,详细介绍了在硫铁矿沸腾焙烧炉中掺烧烷基化废酸制取硫酸的方法。利用硫铁矿沸腾焙烧释放的反应热使烷基化废酸裂解为SO_2。裂解气汇入硫铁矿沸腾焙烧炉气,进入制酸系统。掺烧1 t w(H_2SO_4)90.5%...介绍了废硫酸热分解的过程和热量计算,详细介绍了在硫铁矿沸腾焙烧炉中掺烧烷基化废酸制取硫酸的方法。利用硫铁矿沸腾焙烧释放的反应热使烷基化废酸裂解为SO_2。裂解气汇入硫铁矿沸腾焙烧炉气,进入制酸系统。掺烧1 t w(H_2SO_4)90.5%烷基化废酸费用为463.5元。当烷基化废酸处理费为250元/t时,硫铁矿制酸装置可考虑掺烧烷基化废酸。展开更多
The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzi...The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzing the roasting process, coal gas flux, aluminium hydroxide feeding and oxygen content were ascertained as the main parameters for the forecast model. The order and delay time of each parameter in the model were deduced by F test method. With 400 groups of sample data (sampled with the period of 1.5 min) for its training, a wavelet neural network model was acquired that had a structure of {7 211}, i.e., seven nodes in the input layer, twenty-one nodes in the hidden layer and one node in the output layer. Testing on the prediction accuracy of the model shows that as the absolute error ±5.0 ℃ is adopted, the single-step prediction accuracy can achieve 90% and within 6 steps the multi-step forecast result of model for temperature is receivable.展开更多
文摘介绍了废硫酸热分解的过程和热量计算,详细介绍了在硫铁矿沸腾焙烧炉中掺烧烷基化废酸制取硫酸的方法。利用硫铁矿沸腾焙烧释放的反应热使烷基化废酸裂解为SO_2。裂解气汇入硫铁矿沸腾焙烧炉气,进入制酸系统。掺烧1 t w(H_2SO_4)90.5%烷基化废酸费用为463.5元。当烷基化废酸处理费为250元/t时,硫铁矿制酸装置可考虑掺烧烷基化废酸。
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzing the roasting process, coal gas flux, aluminium hydroxide feeding and oxygen content were ascertained as the main parameters for the forecast model. The order and delay time of each parameter in the model were deduced by F test method. With 400 groups of sample data (sampled with the period of 1.5 min) for its training, a wavelet neural network model was acquired that had a structure of {7 211}, i.e., seven nodes in the input layer, twenty-one nodes in the hidden layer and one node in the output layer. Testing on the prediction accuracy of the model shows that as the absolute error ±5.0 ℃ is adopted, the single-step prediction accuracy can achieve 90% and within 6 steps the multi-step forecast result of model for temperature is receivable.
