Big Data-Based Optimization of a Pressure Swing Adsorption Unit for Syngas Purification: On Mapping Uncertainties from a Metaheuristic Technique

This article presents a systematic approach to optimize a pressure swing adsorption (PSA) unit where syngas is purified by porous aminofunctionalized titanium terephthalate MIL-125 that up to date has not been reported in the literature. The proposed method makes use of a particle swarm optimization (PSO)-based metaheuristic optimization technique that can generate a big data set of feasible solutions of the PSA process and, in turn, it allows to build probabilistic confidence regions around the optimal solution. Such a method of mapping uncertainties therefore not only finds the optimal operating conditions of the PSA unit for syngas purification but also provides more reliable decision-making that can be achieved with respect to its systematic monitoring and operational improvement.