Economic model predictive control of an absorber-stripper CO2 capture process for improving energy cost

Abstract

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 plays a critical role in the reduction of energy cost. CO2 concentration in the plant varies with time and a dynamic study of the economic optimization reflects the true cost better when compared to the current strategy of the steady state optimization. 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 optimization, the proposed method allows the cost to be adjusted to the changing condition such as feed composition and utility cost. Case studies are then presented to show the benefits of the EMPC optimization for CO2 capture process.