摘要
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
In the non-linear microwave drying process,the incremental improved back-propagation (BP) neural network and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (the microwave power,the acting time and the rotational frequency) for microwave drying of selenium-rich slag.The optimum operating conditions obtained from the quadratic form of the RSM are:the microwave power of 14.97 kW,the acting time of 89.58 min,the rotational frequency of 10.94 Hz,and the temperature of 136.407 °C.The relative dehydration rate of 97.1895% is obtained.Under the optimum operating conditions,the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables.The verification experiments demonstrate the prediction accuracy of the network,and the mean squared error is 0.16.The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
基金
Project(50734007) supported by the National Natural Science Foundation of China
作者简介
Corresponding author： PENG Jin-hui, Professor, PhD; Tel： ＋86-871-5191046; E-mail： jhpeng@kmnust.edu.cn
