Study on Optimum Model of Temperature and Humidity Controlin Grain Bulk Basedon Particle Swarm Optimization Algorithm

Abstract

Temperature and moisture are the most important factors affecting the safe storage of grain. Too high or too low temperature and humidity will cause the decomposition of organic matter in grain and food security problems such as pests, diseases and mildew. The regulation of temperature and humidity in grain storage is a multi-variable coupling and multi-objective optimization problem. In this paper, by analyzing the characteristics of grain humidity and temperature regulation process, the parameters of model are optimized by using GPSO particle swarm optimization algorithm, then the model predictive control is realized, and the proposed algorithm is simulated and experimentally studied. Experiments show that the algorithm has a high degree of fitting for predicting the humidity and heat control process of large grain stacks, and has a good prediction effect for temperature and humidity trend change.