Enhancing prior-knowledge-based control with deep attention neural network for outlet moisture content of cut-tobacco

Abstract

Monitoring and controlling the drying process is crucial, particularly for rotary dryers, to ensure optimal quality and efficiency. However, due to the process’s nonlinear behavior, delays, and input disturbances, achieving this can be quite challenging. Previous research has not adequately explored online forecasting for rotary dryer control in real production settings. To bridge this gap, our study focuses on tobacco drying in an actual plant and presents a novel online prediction model for moisture content. We conducted comparative experiments to validate the model’s practical application in production settings and demonstrate its effectiveness in enhancing drying control. Our approach, called MCTFormer, has proven to be scalable to high-dimensional problems. The results of our study indicate a 45% improvement in product quality through accurate prediction of outlet moisture content, better control over moisture levels, and reduced energy consumption.