Distributed Model Predictive Control of Iron Precipitation Process by Goethite Based on Dual Iterative Method

作     者：Chen, Ning Dai, Jiayang Zhou, Xiaojun Yang, Qingqing Gui, Weihua 

作者机构：Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS》 (国际控制与自动化系统杂志)

年 卷 期：2019年第17卷第5期

页      面：1233-1245页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China [61673399, 61873285] National Natural Science Foundation of Hunan Province [2017JJ2329] 

主　　题：Control coupling distributed model predictive control dual iterative method iron precipitation process 

摘      要：Iron precipitation is a key process in zinc hydrometallurgy. The process consists of a series of continuous reactors arranged in descending order, overflowing zinc leach solution from one reactor to the next. In this paper, according to the law of mass conservation and the reaction kinetics, a continuously stirred tank reactor model of a single reactor is first established. Then, a distributed model of cascade reactors is built with coupled control based on the single reactor model, considering the unreacted oxygen in leaching solution. Secondly, four reactors in the iron precipitation process are considered as four subsystems, the optimization control problem of the process is solved by a distributed model predictive control strategy. Moreover, the control information feedback between successive subsystems is used to solve the optimization problem of each subsystem, because of the existing control coupling in their optimization objective function of pre and post subsystems. Next, considering the intractability of the optimization problem for subsystems with various constraints, a distributed dual iterative algorithm is proposed to simplify the calculation. With the consideration of its cascade structure and control couplings, the proposed algorithm iteratively solves the primal problem and the dual problem of each subsystem. The application case shows that distributed model predictive control based on dual iteration algorithm can handle coupled control effectively and reduce the oxygen consumption.