Article Details

Abstract

Moisture control in the tobacco leaf re-drying process is a critical determinant of final product quality in cigarette manufacturing. Traditional control methods often struggle to effectively address inlet moisture fluctuations, system lag, and time-varying operating conditions. To address these challenges, this study proposes an intelligent control strategy integrating feedforward compensation with adaptive parameter tuning. By continuously monitoring the moisture content at the inlet and in the conditioning chamber, along with the temperature in the drying zone, a feedforward compensation model is established. A gradient descent algorithm is employed to dynamically optimize the feedforward coefficients, thereby enabling precise real-time temperature regulation in the drying zone. Using FameView configuration software, scripts are programmed to read moisture data and issue temperature commands. Experimental results demonstrate that this control strategy effectively suppresses moisture fluctuations in the conditioning chamber caused by inlet variations, reducing the standard deviation from 0.89% (traditional manual control) to 0.21%, with maximum deviations controlled within ±0.3%. This strategy provides a robust and highly adaptable intelligent control solution for the tobacco leaf re-drying process, which is characterized by significant lag and strong external disturbances.

Keywords

tobacco leaf re-drying; adaptive feedforward control; tobacco leaf moisture control

References

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About the authors

Zhang Xiao (1997-), male, Han nationality, from Nanyang, Henan Province, assistant engineer, master's student, research direction: intelligent control of industrial processes, fault diagnosis, and automation of leaf cutting and re baking.