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.

二氧化碳（CO ₂）是温室气体的主要来源，其捕获和回收是有效减少CO ₂排放的关键。CO ₂捕集工艺的优化在降低能源成本中起着至关重要的作用。当前的策略仅处理CO ₂捕获过程的稳态优化，但是工厂中的CO ₂浓度随时间变化，因此，对经济评估的动态研究将更好地反映真实成本。将实时经济过程优化和反馈控制相结合的经济模型预测控制（EMPC）应用于CO ₂的优化捕获过程。在动态设置中优化了溶剂再生所需的大量能源。与常规的经济绩效评估的稳态考虑不同，所提出的方法允许将成本调整为迅速变化的动荡的市场条件。然后提供案例研究，以显示EMPC优化对CO ₂捕获过程的好处。