Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Author:

Zhang Ye1,Fang Zhuangdong2,Li Changyou1,Li Chengjie1

Affiliation:

1. College of Engineering, South China Agricultural University, Guangzhou 510642, China

2. Shanwei Academy of Agricultural Sciences, Shanwei 516600, China

Abstract

In practical industrial-scale paddy drying production, manual empirical operation is still widely used for process control. This often leads to poor uniformity in the moisture content distribution of discharged grains, affecting product quality. Model Predictive Control (MPC) is considered the most effective control method for paddy drying, but its implementation in industrial-scale drying is hindered by its high computational cost. This study aims to address this challenge by proposing a deep-learning-based model predictive control (DL-MPC) strategy for paddy drying. By establishing a mapping relation between the inlet and outlet paddy moisture content and paddy flow velocity, a DL-MPC strategy suitable for multistage counter-flow paddy drying systems is proposed. DL-MPC systems are developed using long short-term memory (LSTM) neural networks and trained using datasets from single-drying-stage and multistage drying systems. Simulation and analysis are conducted, followed by verification experiments on a 5HNH-15 multistage counter-flow paddy dryer. The results show that the DL-MPC system significantly improves computational speed while achieving satisfactory control performance. The predicted paddy flow velocity exhibits a smooth variation and matches field data obtained from multiple transition points, confirming the effectiveness of the designed DL-MPC system. The mean absolute error between the predicted and actual paddy moisture content under the DL-MPC system is 0.190% d.b., further supporting the effectiveness of the control system.

Funder

National Natural Science Foundation of China

Guangzhou Science and Technology Plan Project

Publisher

MDPI AG

Subject

Plant Science,Health Professions (miscellaneous),Health (social science),Microbiology,Food Science

Reference47 articles.

1. Optimal nitrogen rate strategy for sustainable rice production in China;Cai;Nature,2023

2. Liu, C., Chen, S., Xiao, S., Ma, L., Zhang, Y., and Chen, S. (2023). Process research and performance verification of variable temperature homogeneous drying device for paddy. Trans. Chin. Soc. Agric. Mach., Available online: https://link.cnki.net/urlid/11.1964.S.20230925.0912.012.

3. Mesterházy, Á., Oláh, J., and Popp, J. (2020). Losses in the grain supply chain: Causes and solutions. Sustainability, 12.

4. Drying kinetics of paddy drying with graphene far-infrared drying equipment at different IR temperatures, radiations-distances, grain-flow, and dehumidifying-velocities;Du;Case Stud. Therm. Eng.,2023

5. Mujumdar, A.S., and Menon, A.S. (2020). Handbook of Industrial Drying, CRC Press.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3