Improving the energy cost of an absorber-stripper CO2 capture process through economic model predictive control

Carbon dioxide (CO2) is the major source of greenhouse gas and its capture and recovery is the key to effective reduction of CO2 emissions. Optimization of the CO2 capture process plays a critical role in the reduction of energy cost. The current strategy only deals with the steady state optimization of the CO2 capture process but the CO2 concentration in the plant varies with time and as a result a dynamic study of the economic assessment will reflect the true cost better. The economic model predictive control (EMPC) that combines real-time economic process optimization and feedback control is applied to the optimization of CO2 capture process. The large energy requirement for solvent regeneration is optimized in dynamic settings. Unlike the conventional steady state consideration of the economic performance assessment, the proposed method allows the cost to be adjusted to the volatile market conditions that varies rapidly. Case studies are then presented to show the benefits of the EMPC optimization for CO2 capture process.