Research and Application of Moisture Control of Tobacco After Drying by HDT Based on Neural Network

In order to improve the control level of tobacco moisture after HDT drying and reduce the fluctuation of the process, in view of the poor adaptability of traditional PID control to the load changes or interference factors of the controlled object, and the inability to effectively control the Pure hysteresis system, in the process of introducing closed-loop control, a neural network-based HDT post-drying moisture feed-forward-feedback control method was adopted, and realized by Siemens PLC programming, and the final results showed that the standard deviation SD of tobacco moisture after HDT drying was reduced from 0.06 to 0.047 by using the neural network-based HDT post-drying moisture feed-forward feedback control method, and the control level was significantly improved.