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Economic Model Predictive Control for Post-Combustion CO 2 Capture System Based on MEA

Author

Listed:
  • Chenbin Ma

    (College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Wenzhao Zhang

    (College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Yu Zheng

    (College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Aimin An

    (College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China
    National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China)

Abstract

For the post-combustion CO 2 capture (PCC) system, the time variability of the economic performance is key to the production process of such an actual industrial process. However, the performance index used by the conventional model predictive control (MPC) does not reflect the economy of the production process, so the economic cost function is used instead of the traditional performance index to measure the economy of the production process. In this paper, a complete dynamic model of the PCC system is constructed in Aspen Plus Dynamics. The effectiveness of the model is verified by dynamic testing; subspace identification is carried out using experimental data, a state-space equation between flue gas flow and lean solvent flow; the CO 2 capture rate is obtained; and dynamic models and control algorithm models of accused objects are established in Matlab/Simulink. Under the background of the environmental protection policy, an economic model predictive control (EMPC) strategy is proposed to manipulate the PCC system through seeking the optimal function of the economic performance, and the system is guaranteed to operate under the economic optimal and excellent quality of the MPC control strategy. The simulation results verify the effectiveness of the proposed method.

Suggested Citation

  • Chenbin Ma & Wenzhao Zhang & Yu Zheng & Aimin An, 2021. "Economic Model Predictive Control for Post-Combustion CO 2 Capture System Based on MEA," Energies, MDPI, vol. 14(23), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8160-:d:695606
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    References listed on IDEAS

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    1. Akinola, Toluleke E. & Oko, Eni & Wu, Xiao & Ma, Keming & Wang, Meihong, 2020. "Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process," Energy, Elsevier, vol. 213(C).
    2. Jochen Oexmann & Alfons Kather & Sebastian Linnenberg & Ulrich Liebenthal, 2012. "Post‐combustion CO 2 capture: chemical absorption processes in coal‐fired steam power plants," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 2(2), pages 80-98, April.
    3. Di Wang & Xiao Wu & Jiong Shen, 2020. "An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant," Energies, MDPI, vol. 13(15), pages 1-24, July.
    4. Wu, Xiao & Wang, Meihong & Liao, Peizhi & Shen, Jiong & Li, Yiguo, 2020. "Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation," Applied Energy, Elsevier, vol. 257(C).
    5. Wu, Xiao & Wang, Meihong & Lee, Kwang Y., 2020. "Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control," Energy, Elsevier, vol. 206(C).
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    Cited by:

    1. José Ramón Fernández, 2023. "An Overview of Advances in CO 2 Capture Technologies," Energies, MDPI, vol. 16(3), pages 1-4, February.
    2. Skjervold, Vidar T. & Mondino, Giorgia & Riboldi, Luca & Nord, Lars O., 2023. "Investigation of control strategies for adsorption-based CO2 capture from a thermal power plant under variable load operation," Energy, Elsevier, vol. 268(C).

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