Multiphysics modeling of hybrid thermo-electrochemical energy storage integration for industrial energy systems: A path to sustainable manufacturing under dynamic policy scenarios
ハイブリッド熱電気化学エネルギー貯蔵のマルチフィジックスモデリングによる産業エネルギーシステムの統合：動的政策シナリオ下での持続可能な製造への道 (AI 翻訳)
Xu Cheng
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギー、炭素回収、ハイブリッド貯蔵を統合した産業エネルギーシステムに対し、過渡的なマルチフィジックスモデルと物理情報ニューラルネットワーク、Transformerを用いたハイブリッド深層学習サロゲート戦略を提案する。炭素価格や水素インセンティブなどの動的政策シナリオを組み込んだ確率的最適化により、貯蔵利用率38-52%向上、エクセルギー損失14.7%低減、ライフサイクルコスト9.3-16.8%削減を実現する。シミュレーションとデジタルツインによる検証であり、実プラント展開ではないが、スケーラブルで政策認識型の枠組みを示す。
English
This paper proposes a transient multiphysics hybrid energy storage framework integrating physics-based battery, thermal storage, and supercapacitors with a hybrid physics–deep learning surrogate strategy using PINN and Transformer models. It incorporates dynamic policy scenarios (carbon pricing, hydrogen incentives) via stochastic optimization, achieving 38–52% higher storage utilization, 14.7% lower exergy losses, and 9.3–16.8% lower lifecycle costs in simulated digital-twin validation. The framework is scalable and policy-aware, though not yet deployed in real plants.
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
日本の製造業はSSBJ気候開示義務化の下でエネルギー最適化と炭素価格対応が急務であり、本論文の動的政策シナリオエンジンはトランジション計画策定に示唆を与える。
In the global GX context
Aligned with ISSB/TCFD emphasis on climate resilience and energy efficiency, this paper offers a policy-aware digital twin framework that can inform corporate transition planning and regulatory stress testing globally.
👥 読者別の含意
🔬研究者:Novel hybrid AI-physics surrogate and MPC-RL control strategy for energy storage optimization in industrial settings.
🏢実務担当者:Potential to reduce energy costs and carbon footprint in manufacturing through optimized hybrid storage and policy-aware operation.
🏛政策担当者:Dynamic policy scenario framework can guide design of carbon pricing and subsidy schemes for industrial decarbonization.
📄 Abstract（原文）
Industrial energy systems integrating renewable generation, carbon capture, and hybrid storage are essential for sustainable manufacturing, yet many existing approaches rely on steady-state assumptions and neglect dynamic thermo-electrochemical behavior, safety constraints, and evolving policy incentives. This study proposes a transient multiphysics hybrid energy storage framework that combines a physics-based P2D battery model, thermal energy storage, and supercapacitors within a unified simulation environment. To accelerate prediction and optimization, a hybrid physics–deep learning surrogate strategy using physics-informed neural networks and Transformer-based models is introduced for Carbon Capture, Utilization, and Storage reactor kinetics, renewable forecasting, and multi-scenario operational planning. A dynamic policy scenario engine incorporating carbon pricing, hydrogen incentives, and renewable subsidies is integrated through stochastic optimization to evaluate policy-driven performance. Simulation-based digital-twin validation demonstrates improvements of 38%–52% in storage utilization, a 14.7% reduction in exergy losses, and 9.3%–16.8% lower lifecycle costs, while surrogate models reduce computation time by 27%. A Model Predictive Control - Reinforcement Learning (MPC-RL) hybrid control strategy further enhances operational resilience and decreases peak thermal loading by 22% under fluctuating industrial demand. Results are derived from simulation and surrogate-assisted digital-twin studies rather than real plant deployment, highlighting a scalable and policy-aware framework for efficient hybrid energy management.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1063/5.0322499first seen 2026-06-26 05:44:28
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