Research on Moisture Content Predict Model of Tobacco Exported from Drying Cylinder
The optimal process parameters for drying are difficult to confirm,and the prediction error of tabacco moisture content is large.To assist in improving the quality of finished tobacco products in terms of information technology,the extreme learning machine(ELM)-based model for predicting the moisture content of tobacco exported from drying cylinder is studied.The environmental temperature,humidity,water addition ratio and other process parameters of tobacco drying process in the loose moisture back,pre-mixing cabinet,wet leaf feeding process are selected.Through the random forest method,the processed effective data of the drying process parameters,are reordered by gradually reduce the order of the average accuracy,so that process parameters of the drying cylinder tobacco moisture content prediction of the role of the larger drying can be screened out.The filtered drying process parameters are used as input data for ELM to obtain the prediction results of tobacco moisture content.The number of neurons in the hidden layer of the ELM is optimized by taking the minimum average absolute error of moisture content prediction as the fitness function of the differential evolutionary algorithm,to improve the prediction accuracy of the moisture content of the tobacco at the exit of the drying cylinder.The experimental results show that the model can realize the prediction of the moisture content of tobacco at the exit of the drying cylinder,and the prediction error is less than 0.3%,with high prediction accuracy.The research helps to improve the quality of tobacco.
NETL
NSTL
万方数据
维普
