Machine learning in proton exchange membrane water electrolysis — A knowledge-integrated framework

Abstract

In this study, we propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development. Given the significance of PEMWE in green hydrogen production and the inherent challenges in optimizing its performance, our framework aims to provide a systematic overview of incorporating data-driven models with domain-specific insights to address the domain challenges. We first identify the uncertainties originating from data acquisition conditions, data-driven model mechanisms, and domain expertise, highlighting their complementary characteristics in carrying information from different perspectives. Building upon this foundation, we showcase how to adeptly decompose knowledge and extract unique information to contribute to the data augmentation, modeling process, and knowledge discovery. We demonstrate a hierarchical three-level framework, termed the ”Ladder of Knowledge-integrated Machine Learning,” in the PEMWE context, applying it to three case studies within a context of cell degradation analysis to affirm its efficacy in interpolation, extrapolation, and information representation. Initial results demonstrate improvements in interpolation accuracy by up to 30%, robustness in extrapolation by enhancing predictive stability across varied operational conditions, and enriched information representation that supports autonomous knowledge discovery. This research lays the groundwork for more knowledge-informed enhancements in ML applications in engineering.