Controllability and optimal scheduling of a CO2 capture plant using model predictive control

New environmental regulations on CO2 emission from coal-fired power plants will require significant carbon capture and sequestration capabilities. This will increase the need to develop advanced model-based control systems that are able to maintain the dynamic operability of CO2 capture plants in the presence of operational constraints and disturbances. This paper presents an advanced centralized multivariable model predictive control (MPC) technique, which considers energy and environmental constraints, to address the controllability of a post-combustion CO2 capture process from a coal-based power plant. To implement the MPC-based control strategy, an instant client-server data communication link between the process simulator and the control framework was implemented using component objective model technology. The case studies presented in this study show that MPC performs significantly better in terms of close-loop settling time, integral squared error and compliance of operational and environmental constraints when compared to a decentralized multi-loop control scheme based on proportional-integral (PI) controllers. The proposed MPC scheme is further implemented in a dynamic multi-objective optimization framework to address the optimal scheduling of the CO2 capture process under disturbances. The presented scheduling scenarios consider the dynamic performance of the system and can be used to design feasible and efficient operating policies for the CO2 capture plant.