Particle Swarm Optimization of Pressure Swing Adsorption for Hydrogen Purification from Depleted Gas Fields

Pressure swing adsorption (PSA) is a viable method for separating hydrogen from gas mixtures, an important aspect of long-term hydrogen storage in depleted gas fields. This study explores optimizing a 12-step PSA process for recovering high-purity hydrogen from varying compositions of hydrogen–methane mixtures, simulating the conditions likely encountered during hydrogen storage and recovery. Step-time optimization was performed on four different hydrogen–methane mixtures using the toPSAil simulation package—an open-source dynamic PSA simulator developed by researchers at the Georgia Institute of Technology—integrated with a particle swarm optimization (PSO) algorithm. The goal was to develop an optimization framework that can reliably identify PSA step times for different operating scenarios and satisfy specified purity and recovery constraints under fluctuating wellhead feed conditions. The optimization converged within 25–30 iterations, even in high-contaminant, low-pressure scenarios, where PSA performance is traditionally weak. The product purity in the optimized cycles was above 99.1% with more than 80% recovery for all cases, while fuel cell quality (99.7%) hydrogen was achieved in two out of the four scenarios. The purge-to-feed ratio of the best-performing cycles was between 0.07 and 0.32. These findings show the potential of the proposed approach in overcoming the difficulty of designing PSA cycles for non-constant gas compositions and achieving a hydrogen purification process suitable for variable feed conditions. The workflow generates a large synthetic dataset that can support surrogate or hybrid modeling. The results can help advance research in other gas separation areas with non-constant conditions, like flue gas or biogas purification.