A genetic algorithm-based framework for efficient optimization of alkaline electrolyzer flow channels to improve flow uniformity

Abstract

Alkaline electrolyzers exhibit considerable potential for large-scale hydrogen production. Optimizing flow channels to achieve uniform electrolyte distribution can efficiently improve the energy efficiency of electrolyzers, especially under high current density operation. This work proposes a genetic algorithm (GA) based framework for efficient optimization of flow channel design to improve the flow uniformity of electrolyte. The framework includes the selection of turbulence units in partitioned regions, identification of important parameters, GA optimization and result evaluation. Through feature importance analysis, six important parameters related to the lateral and longitudinal spacings of adjacent turbulence units in different regions of the flow channel are selected for global optimization. Enhancing the inlet uniformity is identified to significantly contribute to overall uniform flow performance in the channel. The flow channel design is optimized using GA through 38 generations before reaching convergence with a population size of 100. Results show that a combined improvement of 13.80 % in flow uniformity can be achieved based on a partitioned sub-region design and GA optimization. These findings offer crucial guideline for advancing and enhancing the design of large-sized electrolyzer flow channels.