Digital Twin and Machine Learning-Based Diagnostics for PEM Electrolyzer
PEM電解槽向けデジタルツインと機械学習に基づく診断 (AI 翻訳)
Diop M, Skorek AW, Moustapha Diop M
🤖 gxceed AI 要約
日本語
本論文は、PEM水電解槽の異常検知のためにデジタルツインと機械学習を組み合わせたハイブリッドシステムを提案。Azure Machine Learningを用いて電流、電圧、水素流量をリアルタイム予測し、デジタルツインで可視化する。予測モデルはExtreme Random TreesとElastic Netを採用し、決定係数0.99以上の高精度を達成。これにより故障予測と寿命延長が可能となり、グリーン水素生産の効率向上に寄与する。
English
This paper proposes a hybrid system combining digital twin and machine learning for real-time anomaly detection in PEM electrolyzers. Using Azure Machine Learning, it predicts current, voltage, and hydrogen flow rate, visualized in a digital twin. Models achieve R² > 0.99, enabling failure anticipation and lifespan extension for green hydrogen production.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では水素基本戦略に基づきグリーン水素のコスト低減と技術開発が急務であり、本手法は電解槽の高効率運用とメンテナンス最適化に寄与する。デジタルツインの活用は、SSBJやESG情報開示におけるサプライチェーン排出量の可視化にも応用可能。
In the global GX context
As global hydrogen production scales up, digital twin and ML diagnostics offer a pathway to reduce operational costs and improve electrolyzer lifespan. This work directly supports the efficiency targets of green hydrogen initiatives worldwide, aligning with the goals of the Hydrogen Council and national hydrogen strategies.
👥 読者別の含意
🔬研究者:Novel integration of digital twin and ML for PEM electrolyzer diagnostics with high prediction accuracy.
🏢実務担当者:Actionable methodology for real-time monitoring and predictive maintenance of electrolyzers in hydrogen plants.
🏛政策担当者:Supports policy goals for cost-competitive green hydrogen by showcasing technology that improves electrolyzer efficiency.
📄 Abstract（原文）
The degradation of the health state of Proton Exchange Membrane (PEM) water electrolyzer, caused by power supply variability, operating temperature changes, and other chemical factors, represents a major challenge for green hydrogen production efficiency. This paper presents an advanced hybrid system combining a digital twin and machine learning, enabling real-time anomaly detection of a PEM electrolyzer. This intelligent approach allows for the real-time prediction of operating parameters, namely current, voltage, and hydrogen flow rate, via Azure Machine Learning, and their visualization within the system's digital twin via Azure Digital Twins. Furthermore, the comparison between simulated data from the digital twin and those predicted by machine learning enables the anticipation of PEM electrolyzer anomalies. The selected prediction models rely on the Extreme Random Trees algorithm for current and voltage estimation, and on the Elastic Net algorithm for hydrogen flow rate prediction. The obtained results confirm the robustness of the proposed approach, with coefficients of determination of 0.99820, 0.99693, and 0.99665 for current, voltage, and hydrogen flow rate respectively, associated with Normalized Root Mean Square Errors (NRMSE) of 0.00870, 0.011278, and 0.11087. This high accuracy provides the digital twin with the capability to anticipate failures and extend the PEM electrolyzer's lifespan, with a view to optimizing the global efficiency of green hydrogen production.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.20944/preprints202605.1191.v1first seen 2026-05-21 04:22:29
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。