Using optimum conditions in CO2 capture processes to maximize CO2 capture capacity and rich amine temperature leads to saving energy and reducing costs. In the present article, an industrial CO2 capture process with aqueous MEA was studied using the sensitivity analysis and an optimization method. The process was simulated using a rate-based model. The results were validated against four different industrial operational data. In the four different industrial situations, the average relative error was 1.38%–3.85%. The liquid temperature profiles and CO2 absorption (%), calculated by the model agree with the real operational data. In the second part of the work, a sensitivity analysis of the absorption column's important variables was carried out to determine sensitive parameters for CO2 absorption capacity and rich MEA temperature. The variables are gas flow rate, solvent flow rate, flue gas temperature, inlet solvent temperature, CO2 concentration in the flue gas, loading of inlet solvent, and MEA concentration. Based on the results, all of the operational parameters except the inlet solvent and the inlet liquid temperature are considered influential. In the third and final part of the article, the operational conditions are optimized to maximize CO2 absorption (%) and rich solvent temperature. Response surface methodology (RSM) is used as a statistical optimization tool. The experimental design data were analyzed by analysis of variance (ANOVA) and fitted to the second-order polynomial equation using multiple regression analysis. By applying the optimum operational conditions in the model, CO2 absorption (%) and rich solvent temperature of 95.63% and 56.15 °C can be obtained, which indicate 13% and 17% increase, respectively compared to the base case. The results showed that the absorber's energy consumption is decreased by 16%, when applying optimum operational conditions. In contrary to previous studies, it is found that rich solution temperature is not a functio
