A Study on Drying Control of Seed Cotton based on BP Neural Network Model

Abstract

Abstract In order to solve the storage, transportation and processing problems caused by too high or too low percentage of moisture regain in cotton processing, a general online flexible combination intelligent drying process scheme was designed based on two drying equipment, vertical drying tower and tower dryer. Firstly, based on BP neural network model, the relationship between the initial percentage of moisture regain, target percentage of moisture regain and hot air temperature is established. Secondly, DS18B20 single-bus temperature sensor is used to construct the multi-point temperature detection feedback system of drying system. Then, based on the feedback signal of temperature measurement, the temperature set value output by BP neural network model and the fuzzy PID model of seed cotton drying, the opening control of the heat source regulating valve is realized, so that the measured temperature at the entrance follows the set temperature quickly and accurately. Finally, the percentage of moisture regain of seed cotton was adjusted by controlling the hot air temperature at the mixing point, and the seed cotton was dried to the most favorable condition for processing. The test results show that the whole set of intelligent control process has high accuracy and can be used in actual production.