Smart PID control cuts energy use in tobacco-leaf oven drying, study reports

Researchers report an intelligent measurement and control approach for tobacco-leaf oven drying that relies on multi-parameter collaborative regulation, with the aim of improving both moisture measurement precision and control accuracy during cigarette manufacturing.

The method incorporates an adaptive genetic algorithm featuring an elite-commoner population mechanism to optimize proportional-integral-derivative (PID) control parameters online. Rather than fixing controller settings in advance, the algorithm continuously adjusts them during operation, allowing the drying system to respond to changing conditions inside the oven.

According to the experimental observations described in the study, energy consumption per unit mass of evaporated water fell from 3100 to 2850 kilojoules per kilogram. The energy efficiency ratio improved to 0.94, and the stability coefficient increased to 0.96. These figures indicate reduced energy demand per kilogram of water removed alongside tighter control stability in the reported experiments.

The authors state that the findings provide practical technical support for precise and energy-efficient tobacco-leaf drying in cigarette manufacturing. Because oven drying depends on maintaining moisture within narrow limits, online optimization of control parameters is presented as a route to combining product quality with lower energy use.

The study also notes a clear limitation: extending the approach to other biological drying materials would require material-specific parameter calibration and further validation. The reported results therefore apply to the tobacco-leaf drying experiments described, and the authors caution that performance on other materials has not yet been demonstrated.

Subject of Research: Online optimization of PID control parameters for energy-efficient tobacco-leaf oven drying

Article Title: Optimization and reliability analysis of oven moisture measurement for multi-parameter collaborative control

Article References: Wang, J., Wang, X., Li, Z., Chen, J., Sun, S., Ma, X., Li, S., Li, J., & Wang, S. (2026). Optimization and reliability analysis of oven moisture measurement for multi-parameter collaborative control. Discover Applied Sciences. https://doi.org/10.1007/s42452-026-09787-4

Image Credits: AI Generated

DOI: 10.1007/s42452-026-09787-4

Keywords: tobacco-leaf drying, oven moisture measurement, PID control, adaptive genetic algorithm, multi-parameter control, energy efficiency, drying optimization, cigarette manufacturing, process control, elite-commoner population, Optimization, reliability