Study on Optimum Model of Temperature and Humidity Control in Grain Bulk Based on Particle Swarm Optimization Algorithm
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.