An adaptive control system of tobacco loosening and conditioning based on model predictive control
To solve the problems of poor adaptive and unstable control in traditional control of moisture regain.Based on the working principle of the loose moisture regain machine and the mechanism of moisture absorption of tobacco leaves,three key variables:the moisture content of the material inlet,the amount of water added and the return air temperature,were selected.With the outlet moisture content of the material as the target value,a prediction model was established.To address control errors caused by model deviations,using model predictive control(MPC),the backpropagation algorithm in the neural network model was used to optimize the loss function tailored to the characteristics of the moisture regain process.By enabling the predictive model to self-iterate and adapt during the control process,the adaptability of the model is improved.After system optimization,the average standard deviation of loose regain outlet moisture decreased from 0.29 to 0.20,a year-on-year decrease of 32%,and the average CPK increased from 1.132 to 1.479,a year-on-year increase of 30%.This effectively improved the control ability of loose moisture regain process.
tobacco loosening and conditioningmodel predictive controladaptive back propagation algorithmloss function
万方数据
维普
