Development and implementation of advanced control strategies for power plant cycling with carbon capture. (2nd February 2019)

• Record Type: Journal Article
• Authors: He, Xin; Lima, Fernando V.
• Is Part Of: Computers & chemical engineering. Volume 121(2019)
• Page Start: 497
• Page End: 509
• Publication Date: 2019-02-02
• Subjects: Nonlinear model predictive control -- IGCC -- MEA post-combustion carbon capture; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.11.004
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: MPC strategies are implemented on coal-fired power plants with carbon capture. Supercritical and combined cycle power plant control structures are considered. Scenarios that simulate power plant cycling are addressed. Benefits of NLMPC for coal-fired power plants with carbon capture are shown. Abstract: In this paper, three model predictive control (MPC) strategies are developed and implemented for coal-fired power plant cycling applications. These strategies correspond to a dynamic matrix control (DMC)-based linear MPC, the proposed nonlinear MPC (NLMPC) and a nonlinear MPC from the literature (L-NMPC) as benchmark. For application purposes, dynamic models of two different coal-fired power plants with carbon capture are addressed: an integrated gasification combined cycle power plant with a water-gas shift membrane reactor (IGCC-MR) and a supercritical pulverized coal-fired power plant with monoethanolamine-based post-combustion carbon capture (SCPC-MEA). Successful MPC implementations on IGCC-MR system and SCPC-MEA carbon capture subsystem are addressed, including cycling trajectory tracking and disturbance rejection scenarios. The closed-loop results show that the proposed nonlinear MPC (NLMPC) improves the control performance by up to 96% when compared to the DMC controller in terms of the integral squared error (ISE) results.