Aqueous amine is a solvent considered for carbon dioxide (CO2) recovery from the flue gas of a refinery gas turbine by chemical absorption/desorption process. The performance and the economics of this process depend on the choice of the amine absorbent, the concentration of the amine absorbent, the number of stages in the absorber and stripper columns, and the operating conditions. We used response surface methodology (RSM)-a simulation-optimization technique, which uses local searches to estimate an appropriate direction to reduce the objective function-to optimize the amine-based CO2 capture process in a previous work [Nuchitprasittichai and Cremaschi Comput. Chem. Eng. 2011, 35, 1521-1531] However, RSM does not provide any information about the quality of the obtained solution. In this paper, the RSM results are compared to those obtained by optimizing a global surrogate model of the system over the whole decision space with a global solver. We used an artificial neural network (ANN) as the global surrogate model. Depending on the accuracy of the global surrogate models, the solutions obtained using them can be shown to be global within the bounds of the data used to generate them. The comparison is used to assess the quality of the RSM results and their relative computational costs. Monoethanolamine (MEA), diglycolamine (DGA), diethanolamine (DEA), methyl diethanolamine (MDEA), triethanolamine (TEA), and blended aqueous solutions of these amines are considered in our analyses. The results reveal that the RSM algorithm yielded optimum solutions close to those obtained by the ANN approach for all solvents.