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This study presents a methodology for surrogate optimization of cyclicadsorption processes, focusing on enhancing Pressure Swing Adsorption units forcarbon dioxide ($CO_{2}$) capture. We developed and implemented amultiple-input, single-output (MISO) framework comprising two deep neuralnetwork (DNN) models, predicting key process performance indicators. Thesemodels were then integrated into an optimization framework, leveraging particleswarm optimization (PSO) and statistical analysis to generate a comprehensivePareto front representation. This approach delineated feasible operationalregions (FORs) and highlighted the spectrum of optimal decision-makingscenarios. A key aspect of our methodology was the evaluation of optimizationeffectiveness. This was accomplished by testing decision variables derived fromthe Pareto front against a phenomenological model, affirming the surrogatemodels reliability. Subsequently, the study delved into analyzing the feasibleoperational domains of these decision variables. A detailed correlation map wasconstructed to elucidate the interplay between these variables, therebyuncovering the most impactful factors influencing process behavior. The studyoffers a practical, insightful operational map that aids operators inpinpointing the optimal process location and prioritizing specific operationalgoals.

Optimizing $CO_{2}$ Capture in Pressure Swing Adsorption Units: A Deep  Neural Network Approach with Optimality Evaluation and Operating Maps for  Decision-Making