A neural network model used in continuous grain dryer control system

Abstract

Due to the nonlinearity, strong coupling and hysteresis of the parameters used in measuring the grain drying process, it is always a challenge to perform accurate control of the drying system. The purpose of this paper is to describe the design of a suitable neural network model to control the grain dryer more effectively. First, the operating mechanism of a continuous grain dryer and the principle of the heat and mass transfer that can be used in the process of grain drying was analyzed before an intelligent control system was designed accordingly. Second, an intelligent control system based on the Back Propagation Neural Network (BPNN) was developed. The BPNN was the optimal model selected based on a series of comparative test results. According to the BPNN prediction of the moisture content of dried rice, the system could adjust the rate of grain discharge of the dryer, and then control the drying process accurately. Finally, the neural network control model was simulated using computer simulation technology, and was optimized by comparing analysis results with experimental results. The results showed that the optimized intelligent control system using BPNN has the advantage of strong stability and good noise handling, and could have great potential for future implementation studies.

Keywords:

• Back propagation neural network
• paddy
• continuous grain dryer
• intelligent control system

Acknowledgements

We would also like to thank the reviewers for their valuable suggestions that helped improve the quality of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

We would like to acknowledge the funding support received for the project “Starch Metabolism and Yellowing Mechanism of Rice and Corn” [grant number 2016YFD0400104].