グリーン水素電解槽システムのリアルタイム監視と性能最適化のためのインテリジェントデジタルツインフレームワーク
Intelligent Digital Twin Framework for Real-Time Monitoring and Performance Optimization of Green Hydrogen Electrolyser Systems (原題)
Annu Dhull, D. Abdukhamidov, D. Yusupov, Hulkar Turobova, D. Rayimova, Z. Maxmudov
🤖 gxceed AI 要約
日本語
本論文は、グリーン水素電解槽のリアルタイム監視と性能最適化のためのデジタルツインフレームワークを提案する。物理モデルとAI予測を組み合わせ、変動する再生可能エネルギー入力下での効率向上と安定運用を実現する。温度と電流密度、水素生成量、消費電力、効率の関係を文献から確立し、高度な運用制御の必要性を示す。実用的なデジタル化運用手段を提供する。
English
This paper proposes a digital twin framework for real-time monitoring and performance optimization of green hydrogen electrolysers. It combines physics-based models with AI prediction to enhance efficiency and stability under fluctuating renewable energy inputs. The study establishes relationships between temperature and key performance metrics, demonstrating the need for sophisticated operational control. It offers a practical digitalized approach 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
日本の水素基本戦略やグリーンイノベーション基金の下で、グリーン水素のコスト低減と安定供給が課題となる中、本フレームワークは電解槽の運用最適化による効率向上に寄与する。SSBJ開示やカーボンニュートラル宣言に対応する企業にとって、水素製造の実績データと効率改善は重要な情報となる。
In the global GX context
Globally, green hydrogen is a key pillar of decarbonization strategies, and optimizing electrolyser performance is critical for cost competitiveness. This digital twin approach aligns with the growing emphasis on operational efficiency and digitalization in the energy transition. It provides a framework that can be integrated into broader sustainability reporting and transition finance considerations.
👥 読者別の含意
🔬研究者:Provides a novel integration of physics-based models and AI for electrolyser optimization, offering a basis for further research in digital twin applications for green hydrogen.
🏢実務担当者:Offers a practical framework for real-time monitoring and optimization of electrolyser systems, potentially reducing operational costs and improving hydrogen yield.
🏛政策担当者:Highlights the importance of digitalization and AI in enhancing green hydrogen production efficiency, informing policies that support technology adoption.
📄 Abstract（原文）
Real-time monitoring and adaptive optimization of green hydrogen electrolyser systems are critical for ensuring maximum efficiency, stability, and consistent hydrogen production when renewable energy inputs fluctuate. In this paper, we introduce a novel digital twin based intelligent framework for green hydrogen electrolysers that combines a physics-based electrolyser model, real-time sensor data, artificial intelligence-based prediction, and optimization driven decision support. The digital twin continuously synchronizes the physical electrolyser and the virtual electrolyser to predict current operating states and detect when performance deviates from those predicted, allowing the operator to modify operating conditions to optimize performance. Based on the scientific literature, we established a connection between temperature and the output of current density, how much hydrogen is produced, how much power is consumed, and how efficient the unit is operating, indicating the necessity of using a more sophisticated means of operating electrolysers. Our intelligent framework provides a practical means of enhancing efficiency, increasing reliability, and providing a digitalized means to operate electrolysers for producing green hydrogen.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.epj-conferences.org/articles/epjconf/pdf/2026/40/epjconf_icatfs2026_02003.pdffirst seen 2026-08-23 05:16:58 · last seen 2026-09-21 05:03:39
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