Simulation Of Air Distribution And Optimization Of Air Supply Parameters In VAV Bulk Curing Barn

In order to improve the efficiency and effectiveness of flue-cured tobacco in China ’s densely-cured flue-cured rooms,a simulation guideline for gas flow in variable-volume-dense flue-cured flue-cured rooms was used,and knowledge of fluid mechanics and machine learning models were used to learn and optimize the supply air parameters.The main work of this paper includes: combing and summarizing the mathematical model and related control equations of the calculation and analysis of the dense grill room;modeling the calculation space of the dense grill room,and discussing the impact of different gridding methods on the calculation results.At the same time,the mathematical expressions of the porous media model are summarized,and the pressure and temperature and flow velocity of the gas in the dense roasting room are simulated and analyzed using the established model.On the basis of the above calculation and analysis,dense roasting under different air volumes and load conditions The gas state inside the room was calculated and analyzed;finally,the optimization and improvement were performed for the three steps of "sampling","feature selection" and "fusion weighting" in the random forest model.In order to improve the calculation and prediction accuracy of the improved random forest model Based on the error correction,a two-layer random forest model based on error correction is constructed.This model is combined with the artificial bee colony algorithm to learn and predict the gas flow in the densely roasted room.The result verification shows that the model The forecast accuracy meets the needs of the project.Under gas flow amount condition Curing Barn internal laws to predict analyzed and targeted for blowing parameters were optimized bulk curing barn improved.The research in this paper has some reference and guidance significance for the design,experiment,operation and improvement of dense roasting room.