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, H.[1];Liu, J.[2];Jayas, D. S.[3];Wu, Z.[4];Zhou, X.[1]

第一作者：Zhou, H.

通讯作者：Zhou, H[1]

机构：[1]Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China;[2]Beijing Union Univ, Coll Automat, Beijing, Peoples R China;[3]Univ Manitoba, Dept Biosyst Engn, Winnipeg, MB, Canada;[4]Acad State Adm Grain, Beijing, Peoples R China

第一机构：Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China

通讯机构：[1]corresponding author), Beijing Univ Posts & Telecommun, POB 137, Beijing 100876, Peoples R 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：;WOS:【SCI-EXPANDED(收录号:WOS:000343529200008)】；

基金：This work is partially supported by the Programme of Introducing Talents of Discipline to Universities of China (B08004) and the 2013 National Science and Technology support program (2013 BAD17B06).

语种：英文

外文关键词：Stored grain; Forced air ventilation; Model predictive control; Process optimization; Temperature; Moisture content; Distributed parameter

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 degrees C and 1%, respectively. The energy consumed during ventilation with the DP-MPC was 15.5% less than that consumed during ventilation without control.