文献类型：期刊文献

英文题名：A distributed parameter model predictive control method for forced air ventilation through stored grain

作者：Zhou, Huiling[1]; Liu, Jingyun[2]; Jayas, Digvir S.[3]; Wu, Zidan[4]; Zhou, Xiaoguang[1]

第一作者：Zhou, Huiling

通讯作者：Zhou, Huiling

机构：[1] Automation School, Beijing University of Posts and Telecommunications, P.O. Box 137, Beijing, 100876, China; [2] College of Automation, Beijing Union University, Beijing, China; [3] Department of Biosystems Engineering, University of Manitoba, Winnipeg, Canada; [4] Academy of State Administration of Grain, Beijing, China

第一机构：Automation School, Beijing University of Posts and Telecommunications, P.O. Box 137, Beijing, 100876, China

年份：2014

卷号：30

期号：4

起止页码：593-600

外文期刊名：Applied Engineering in Agriculture

收录：EI(收录号：20144500178067);Scopus(收录号：2-s2.0-84908548512)

语种：英文

外文关键词：Air - Energy utilization - Mass transfer - Model predictive control - Moisture - Moisture control - Moisture determination - Optimization - Particle swarm optimization (PSO) - Predictive analytics - Predictive control systems - Temperature - Ventilation

摘要：The grain temperature and moisture are non-uniformly distributed in grain stores. This article proposed a distributed parameter model predictive control (DP-MPC) method for the forced air ventilation process through stored grain. The goal of DP-MPC was to control the grain temperature and moisture and to save energy. The grain bulk was divided into different control units to consider the distribution of grain bulk characteristics. The controller was designed with a heat and mass transfer predictive model and an objective function. The objective function was solved with particle swarm optimization (PSO) algorithm. In each control cycle, one control unit was taken as the controlled objective. A control unit shift strategy was proposed to shift controlled objectives. Both simulations and experiments were carried out to validate the control effectiveness. Results showed that the proposed DP-MPC method could control the whole grain bulk temperature and moisture content and optimize the system energy consumption. The maximum grain temperature and moisture content differences between the controlled value and the set point were less than 1°C and 1%, respectively. The energy consumed during ventilation with the DP-MPC was 15.5% less than that consumed during ventilation without control. ? 2014 American Society of Agricultural and Biological Engineers