Statistical analysis and comprehensive optimisation of zero-gap electrolyser: Transitioning catalysts from laboratory to industrial scale

Abstract

Advancements in lab-scale catalysts for alkaline water electrolysers (AWE) show promise for industrial application, yet their integration faces challenges due to the complex interplay of operating parameters. This study investigates the individual and interaction effects of critical factors—electrolyte flow rate, KOH concentration, temperature, and catalyst deposition time—on zero-gap AWE performance, utilising earth-abundant electrodes. Employing the Box-Behnken design, we developed models via Response Surface Methodology (RSM) and an Artificial Neural Network combined with Particle Swarm Optimization (ANN-PSO), uncovering significant interdependences among the parameters. Our analysis indicates that the increase in flow rates and deposition times markedly influence the optimum KOH concentration, increasing it by 30% and 40.8%, respectively. Additionally, higher deposition times and KOH concentrations influence the optimum flow rate by 19% and 57 %, respectively. Moreover, an increase in temperature inversely affects the optimal KOH concentration and flow rate. Through comprehensive optimisation using Genetic Algorithms (GA) and ANN-PSO, we achieved a 31.4% enhancement in AWE performance. Garson's method revealed KOH concentration as the most influential parameter, with flow rate being the least influential. The insights presented in this study could pave the way for a smoother transition of lab-developed catalysts to industrial applications in green energy systems, including fuel cells and CO2 conversion. Show more