Efficient multi-objective optimization and operational analysis of amine scrubbing CO2 capture process with artificial neural network

• A surrogate-assisted framework for efficient stochastic optimization of amine scrubbing CO 2 capture process. • Multi-objective optimization runs were implemented to find the optimal operating points for different flue gas CO 2 concentration and desired capture levels. • Each optimization run with laborious model evaluations took only 10–40 min. Amine scrubbing processes for post-combustion CO 2 capture have been extensively studied and significantly improved via various novel designs. However, the amine scrubbers implemented nowadays were usually not optimized according to a number of different evaluation criteria. This is often due to the fact that, for high dimensional design spaces, the rigorous simulation runs needed to facilitate process optimization always calls for huge numbers of simulation software accesses and overwhelming iterative calculations. Therefore, in this study, the well-trained surrogate model was adopted to replace its rigorous counterpart for the purpose of ensuring efficient optimization runs in practical applications. In current study, two objectives, i.e., the specific equivalent work and the CO 2 capture level, were both rapidly and effectively optimized in various practical scenarios with different flue gas CO 2 concentrations. The corresponding operational parameters and utility consumptions were also easily obtained without additional effort. The computation results obtained so far showed that the proposed surrogate-assisted approach can be utilized to significantly reduce the computational load in practice.