Flexible operation and simultaneous scheduling and control of a CO2 capture plant using model predictive control

Post-combustion CO2 capture plants that employ amine-based solvents are regarded as the most feasible strategy to reduce the CO2 emission from coal-fired power plants. This study presents a dynamic flexibility analysis of a post-combustion CO2 capture process using model predictive control (MPC). The critical operating conditions in the plant's main load (flue gas flowrate) were initially identified in open-loop and closed-loop. Insights from this analysis have shown that oscillatory changes with high frequency content in the load are particularly harmful for this process in closed-loop. Taking this insight into account, a simultaneous scheduling and control framework was developed to identify optimal operating policies under critical operating conditions in the flue gas flowrate. The results obtained from this framework were compared against a conventional sequential control and scheduling approach. The results show that the proposed integrated framework specifies more economically attractive policies than those obtained from the sequential approach.