SCI和EI收录∣中国化工学会会刊

Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (5): 1087-1101.doi: 10.1016/j.cjche.2017.12.005

• Process Systems Engineering and Process Safety • Previous Articles     Next Articles

Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies

Jianxin Wang1, Na Yu1, Mengqi Chen1, Lin Cong2, Lanyi Sun1   

  1. 1 State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum(East China), Qingdao 266580, China;
    2 College of Information and Control Engineering, China University of Petroleum(East China), Qingdao 266580, China
  • Received:2017-09-09 Revised:2017-11-27 Online:2018-05-28 Published:2018-06-29
  • Contact: Lanyi Sun,E-mail address:sunlanyi@upc.edu.cn E-mail:sunlanyi@upc.edu.cn
  • Supported by:

    Supported by the National Natural Science Foundation of China (21676299, 21476261 and 21606255).

Abstract: The dividing wall column (DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control (MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol, n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and temperature inferential control are considered. The multiobjective genetic algorithm function "gamultiobj" in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and temperature inferential control, resulting in a more stable and superior performance with lower values of integral of squared error (ISE).

Key words: Dividing wall column, Composition control, Temperature inferential control, PI strategy, Model predictive control, Genetic algorithm