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

To improve the moisture measurement and control precision of tobacco-leaf oven drying, an intelligent measurement and control approach based on multi-parameter collaborative regulation is developed. An adaptive genetic algorithm incorporating an elite-commoner population mechanism is introduced to perform online optimization of proportional-integral-derivative control parameters. Experimental observations show that the energy consumption per unit mass of evaporated water drops from 3100 to 2850 kilojoules per kilogram, accompanied by an improvement in the energy efficiency ratio to 0.94 and an increase in the stability coefficient to 0.96. The experimental findings provide practical technical support for precise and energy-efficient tobacco-leaf drying in cigarette manufacturing, while extension to other biological drying materials requires material-specific parameter calibration and further validation.