Robust control and flexible operation for commercial-scale coal-fired power plant with solvent-based post-combustion carbon capture. (February 2023)

• Record Type: Journal Article
• Authors: Liao, Peizhi; Wu, Xiao; Wang, Meihong; Li, Zhongmei; Qian, Feng
• Is Part Of: International journal of greenhouse gas control. Volume 123(2023)
• Publication Date: 2023-02
• Subjects: Post-combustion carbon capture -- Chemical absorption -- Coal-fired power plant -- Flexible operation -- Model predictive control -- Disturbance rejection; Greenhouse gases -- Environmental aspects -- Periodicals; Air -- Purification -- Technological innovations -- Periodicals; Gaz à effet de serre -- Périodiques; Gaz à effet de serre -- Réduction -- Périodiques; Air -- Purification -- Technological innovations; Greenhouse gases -- Environmental aspects; Periodicals; 363.73874605
• DOI: 10.1016/j.ijggc.2023.103831
• Languages: English
• ISSNs: 1750-5836

Abstract

Highlights: Dynamic modeling of commercial-scale CFPP-PCC process implemented in gCCS. In-depth investigation on dynamic characteristics for the integrated CFPP-PCC process. Extended state observer based stable model predictive control for the integrated CFPP-PCC process. Robust control in a multi-disturbance situation for the integrated CFPP-PCC process. Flexible operation in a wide-range load variation scenario for the integrated CFPP-PCC process. Abstract: Coal-fired power plant integrated with post-combustion carbon capture (CFPP-PCC) process exhibits strong nonlinearity and multi-variable connections. These dynamics along with unknown disturbances make it a challenging task to realize robust control and flexible operation in CFPP-PCC system. For this reason, this paper develops an extended state observer based stable model predictive control (ESOSMPC) in order to reject unknown disturbances and to achieve flexible operation with closed-loop stability. In the proposed control structure, an extended state observer is utilized to give accurate estimation for plant behavior change and unknown disturbances. This information will be used in active disturbance rejection, which aims to alleviate negative effects of disturbances and to enhance control performance. Stable model predictive control with infinite input-output based objective function is developed in order to fulfill Lyapunov stability condition, guaranteeing a stable operation for the integrated process. The proposed controller is compared with conventional MPC/PID controller under varying working conditions. Simulation results show that the presented ESOSMPC is able to achieve robust control in a multi-disturbance situation and to attain flexible operation in a broad-range load variation scenario. Graphical abstract: Image, graphical abstract