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Distributed predictive control guided by intelligent reboiler steam feedforward for the coordinated operation of power plant-carbon capture system

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  • Tang, Zihan
  • Wu, Xiao

Abstract

Solvent-based post-combustion CO2 capture is the most mature technology to reduce carbon emissions from coal-fired power plants. However, the strong interactions and dynamic discrepancies between power plant and carbon capture process increase the operating challenges. To overcome this issue, this paper proposes distributed model predictive controller for the power plant-carbon capture system. Local predictive controllers with different sampling times are developed for the power plant and carbon capture subsystems respectively according to their unique dynamic features. The interactions between the two subsystems are handled through information exchange and iterative optimization of the local controllers to achieve a global optimal control. An intelligent reboiler steam feedforward control is integrated into the distributed control framework to flexibly manipulate the reboiler steam allocation between power generation and solvent regeneration. The simulation results demonstrate the superiority of the proposed controller in both the basic and future scenarios, where power tracking performance is improved by 33.1% and 20.6% respectively, with marginal degradations on the carbon capture performance. This paper points to an effective control approach for the flexible and coordinative operation of the integrated power plant-carbon capture system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222034557
    DOI: 10.1016/j.energy.2022.126568
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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).
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    3. Hou, Guolian & Huang, Ting & Zheng, Fumeng & Huang, Congzhi, 2024. "A hierarchical reinforcement learning GPC for flexible operation of ultra-supercritical unit considering economy," Energy, Elsevier, vol. 289(C).

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