Research on Optimization of Secondary Leaf Watering Parameters by Particle Swarm Optimized Random Forest Algorithm
The moisture content and temperature stability at the exit of the secondary moistening leaves are the key indexes to evaluate the re-curing process of tobacco leaves.However,it is difficult to accurately control the outlet index of secondary moistening in a regrilling plant in Yunnan province due to parameters such as ambient temperature and water steam flow.Through the construction of random forest algorithm model based on particle swarm optimization,the influence of various parameters on the export index of two rungs under different working condi-tions was explored.After cleaning the historical data of secondary leaf wetting parameters,Pearson coefficient analysis was carried out after re-moving dirty data to find the key production control parameters closely related to export quality.Combined with field manual experience and correlation analysis,the random forest algorithm of particle swarm optimization was used to optimize the return air temperature,hot air temper-ature,drain damper and compensation steam valve opening,and compared with random forest,gray wolf optimization random forest and BP neural network.The results show that the mean square error of return air temperature and hot air temperature obtained by the proposed algo-rithm is 0.003,and the mean square error of the opening of the tidal damper and the compensating steam valve is 0.001.The algorithm pro-vides a theoretical basis for operators to adjust the equipment and improve the quality of tobacco recuring.
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