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 meld data-driven models with domain-specific insights systematically 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. This research lays the groundwork for more knowledge-informed enhancements in ML applications in engineering.

在这项研究中，我们提出采用一种新颖的框架，即知识集成机器学习，来推进质子交换膜水电解（PEMWE）的发展。通过融合数据驱动的模型和领域专业知识，系统地应对PEMWE在优化性能方面的挑战。我们展示了一种分层的三级框架，在PEMWE的背景下应用于细胞退化分析的三个案例研究中，证明了其在插值、外推和信息表示方面的有效性。这项研究为工程领域的知识驱动型增强的机器学习应用奠定了基础。

质子交换膜水电解中的机器学习 -- 第一部分：知识整合框架