Surrogate model optimization of vacuum pressure swing adsorption using a flexible metal organic framework with hysteretic sigmoidal isotherms

This study presents a process optimization study for a vacuum pressure swing adsorption (VPSA) process using a flexible metal-organic framework (MOF), which is gaining attention as a material to realize energy-efficient carbon dioxide capture processes. Many flexible MOFs exhibit sigmoidal adsorption isotherms with hysteresis, posing a challenge for simulation and optimization using a rigorous process model. In this study, we employ surrogate model optimization, where surrogate models using machine-learning algorithms were constructed from simulation of 903 operating conditions generated by Latin hypercube sampling. The surrogate models with the best performance were identified from 18 different surrogate options considering four design variables—adsorption pressure, desorption pressure, adsorption time, and desorption time. Using the best surrogate models, a multi-objective optimization problem was solved to identify the Pareto front among recovery, energy consumption, and bed size factor. Our analysis identified a distinct characteristic of VPSA using a flexible-MOF where purity and recovery are hardly affected by the feed volume.