Model predictive control of vinyl chloride monomer process by Aspen Plus Dynamics and MATLAB/Simulink co-simulation approach

被引:7
作者
Chinprasit, J. [1 ,2 ]
Panjapornpon, C. [1 ,2 ]
机构
[1] Kasetsart Univ, Dept Chem Engn, Ctr Excellence Petrochem & Mat Technol, Fac Engn, Bangkok 10900, Thailand
[2] Kasetsart Univ, Ctr Adv Studies Ind Technol, Bangkok 10900, Thailand
来源
26TH REGIONAL SYMPOSIUM ON CHEMICAL ENGINEERING (RSCE 2019) | 2020年 / 778卷
关键词
STEADY-STATE; OPTIMIZATION; REACTOR;
D O I
10.1088/1757-899X/778/1/012080
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Characteristics of a vinyl chloride monomer (VCM) process is complex and nonlinear due to interactions between units in a reaction-separation network, multiple process streams, and multiple control loops involved. A fluctuation of the thermal cracking unit could result in a difficulty in maintaining downstream units at the setpoints. In this work, an approach to develop a model predictive control (MPC) for the VCM process by deploying a co-simulation between MATLAB/Simulink and Aspen Plus Dynamics is presented. The co-simulation provides more capability and comfortability to design the MPC controller, and evaluate controllability. The VCM process consisting of thermal cracking, quench and distillation is modelled by Aspen Plus Dynamics. The MPC is developed by integrating the concept of plant-wide control and subsystem partitioning. To reduce the burden of mathematical modeling, the MATLAB system identification toolbox is used to develop a multivariable linear model for the MPC controller by reconciling dynamics data from the VCM plant model. Performances of the developed MPC is evaluated under the regulatory test of the EDC feed disturbance. By comparison with the multiple single-input-single-output proportional-integral controllers through the efficiency indexes-an integral squared error, an overshoot and a settling time. Simulation results supported that the MPC controller outperforms proportional-integral controllers.
引用
收藏
页数:10
相关论文
共 15 条
  • [1] Application of self-tuning PID control to a reactor of limestone slurry titrated with sulfuric acid
    Alpbaz, M
    Hapoglu, H
    Özkan, G
    Altuntas, S
    [J]. CHEMICAL ENGINEERING JOURNAL, 2006, 116 (01) : 19 - 24
  • [2] [Anonymous], EXPERT SYSTEMS APPL
  • [3] Aspen Technology I, 2013, ASP PHYS PROP SYST P
  • [4] Using process simulators for steady-state and dynamic plant analysis - An industrial case study
    Bezzo, F
    Bernardi, R
    Cremonese, G
    Finco, M
    Barolo, M
    [J]. CHEMICAL ENGINEERING RESEARCH & DESIGN, 2004, 82 (A4) : 499 - 512
  • [5] Real time optimization (RTO) with model predictive control (MPC)
    De Souza, Glauce
    Odloak, Darci
    Zanin, Antonio C.
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2010, 34 (12) : 1999 - 2006
  • [6] Multivariable PID controller design for chemical processes by frequency response approximation
    Escobar, Marcelo
    Trierweiler, Jorge O.
    [J]. CHEMICAL ENGINEERING SCIENCE, 2013, 88 : 1 - 15
  • [7] Dynamic Matrix Control of a Bubble-Column Reactor for Microbial Synthesis Gas Fermentation
    Fu, Yao
    Chang, Liang
    Henson, Michael A.
    Liu, Xing Gao
    [J]. CHEMICAL ENGINEERING & TECHNOLOGY, 2017, 40 (04) : 727 - 736
  • [8] Model Predictive Control Tuning Methods: A Review
    Garriga, Jorge L.
    Soroush, Masoud
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2010, 49 (08) : 3505 - 3515
  • [9] SNOWBALL EFFECTS IN-REACTOR SEPARATOR PROCESSES WITH RECYCLE
    LUYBEN, WL
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1994, 33 (02) : 299 - 305
  • [10] CONSTRAINED MULTIVARIABLE CONTROL OF FLUID CATALYTIC CRACKING CONVERTERS
    MORO, LFL
    ODLOAK, D
    [J]. JOURNAL OF PROCESS CONTROL, 1995, 5 (01) : 29 - 39