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Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process

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  • Akinola, Toluleke E.
  • Oko, Eni
  • Wu, Xiao
  • Ma, Keming
  • Wang, Meihong

Abstract

The flexible operation capability of solvent-based post-combustion capture (PCC) process is vital to efficiently meet the load variation requirement in the integrated upstream power plant. This can be achieved through the deployment of an appropriate control strategy. In this paper, a nonlinear model predictive control (NMPC) system was developed and analysed for the solvent-based PCC process. The PCC process was represented as a nonlinear autoregressive with exogenous (NARX) inputs model, which was identified through the forward regression with orthogonal least squares (FROLS) algorithm. The FROLS algorithm allows the selection of an accurate model structure that best describes the dynamics of the process. The simulation results showed that the NMPC gave better performance compared with linear MPC (LMPC) with an improvement of 55.3% and 17.86% for CO2 capture level control under the scenarios considered. NMPC also gave a superior performance for reboiler temperature control with the lowest ISE values. The results from this work will support the development and implementation of NMPC strategy on the PCC process with reduced computational time and burden.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220319472
    DOI: 10.1016/j.energy.2020.118840
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    References listed on IDEAS

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    1. Wu, Xiao & Shen, Jiong & Wang, Meihong & Lee, Kwang Y., 2020. "Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization," Energy, Elsevier, vol. 196(C).
    2. Wu, Xiao & Wang, Meihong & Shen, Jiong & Li, Yiguo & Lawal, Adekola & Lee, Kwang Y., 2019. "Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls," Applied Energy, Elsevier, vol. 238(C), pages 495-515.
    3. 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).
    4. Ü. Kotta & N. Sadegh, 2002. "Two Approaches for State Space Realization of NARMA Models: Bridging the Gap," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 8(1), pages 21-32, March.
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    Cited by:

    1. Akinola, Toluleke E. & Bonilla Prado, Phebe L. & Wang, Meihong, 2022. "Experimental studies, molecular simulation and process modelling\simulation of adsorption-based post-combustion carbon capture for power plants: A state-of-the-art review," Applied Energy, Elsevier, vol. 317(C).
    2. Patrón, Gabriel D. & Ricardez-Sandoval, Luis, 2022. "An integrated real-time optimization, control, and estimation scheme for post-combustion CO2 capture," Applied Energy, Elsevier, vol. 308(C).
    3. Tang, Zihan & Wu, Xiao, 2023. "Distributed predictive control guided by intelligent reboiler steam feedforward for the coordinated operation of power plant-carbon capture system," Energy, Elsevier, vol. 267(C).
    4. Dong, Zhe & Li, Bowen & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2022. "Power-pressure coordinated control of modular high temperature gas-cooled reactors," Energy, Elsevier, vol. 252(C).
    5. 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.
    6. 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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