Research on Genetic Algorithm-Based Optimization Algorithm for Tobacco Flue-Curing Machine Process Parameters

Abstract

The tobacco industry, as one of the pillar industries in China, has made significant contributions to the country’s economy and tax revenue. In the processing of tobacco products, tobacco leaf drying is an extremely important step, and the quality of tobacco leaf drying directly affects the yield of high-quality tobacco products. Therefore, improving flue-curing technology has become a major focus for tobacco companies. This paper constructs a prediction model for the moisture content of the tobacco leaf at the outlet of the flue-curing machine based on the BP neural network. It also proposes a genetic algorithm-based optimization method for solving flue-curing machine process parameters and establishes a database for flue-curing machine process parameters. Additionally, a flue-curing machine control software combined with the process parameter database is developed for real-time adjustment of various process parameters during the operation of the flue-curing machine. Experimental results demonstrate that the proposed genetic algorithm-based process parameter optimization method is effective and reasonable in practice, reducing the occurrence of “dry head and dry tail” issues and improving the stability of the flue-curing process.