Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties

Abstract

This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (MPC) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO2 capture (PCC) plant (integrated plant). The agility and robustness of the developed control algorithm (MPC) is demonstrated through the control response time and efficiency of energy requirement including the financial and operational benefits of the plant subjected to plant and market uncertainties. While, the MINLP is utilized to forecast plant operational modes by ensuring the operational fidelity of integrated plant. This involves utilization of historical (2011) and forecast (2020) market conditions (electricity tariff and carbon price) subject to maximum plant net operating revenue. The outcomes show that the future power plant will operate in mixed operation modes, for instance in unit turndown and load following modes, which contribute to a minimum capture energy penalty at 3.13 MJth/tonne CO2. Moreover, under the same year (2020), MPC exhibits superior control performance by satisfactorily obtain 94% actual CO2 capture from the ideal cumulative CO2 capture. Additionally, the integrated plant is capable to resume approximately 96% actual revenue from the ideal net operating revenue projected by the control-based optimization algorithm. The algorithm demonstrates that the installation of control system package (MPC) into the flexible PCC plant associated with coal-power generator could contribute to efficient energy management subjects to unprecedented uncertainties.