食品産業におけるグリーン水素ベースの蒸気生産のためのデジタル意思決定支援フレームワーク
A Digital Decision-Support Framework for Green Hydrogen-Based Steam Production in the Food Industry (原題)
Andreas Poyias, Panayiotis Mourtopallas, Diamanto Platanou, C. Politi, Despoina Georgopoulou, Antonis Peppas
🤖 gxceed AI 要約
日本語
食品産業の蒸気生産（エネルギー使用の57%を占める）の脱炭素化に向け、グリーン水素統合を最適化するデジタルフレームワークを開発。LightGBMを用いた予測モデルは、PV発電と気象データを学習し、15分・1時間・翌日予測で高い精度（R2 0.868, 0.832, 0.701）を達成。実測PV発電に基づくH2/CH4混焼で、高日射時にCO2排出を34%削減。
English
This study develops a digital framework for optimizing green hydrogen integration in food industry steam production, which accounts for up to 57% of energy use. LightGBM models trained on PV and weather data achieve high forecasting accuracy (R2 0.868 for 15-min, 0.832 for 1-hour, 0.701 for day-ahead). A real-time platform calculates optimal H2/CH4 blending, achieving 34% CO2 reduction during high-solar periods.
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
日本の食品産業でも蒸気生産の脱炭素化は重要課題であり、水素混焼や再生可能エネルギー活用の意思決定支援は、SSBJ開示やカーボンニュートラル目標達成に寄与する。本フレームワークは、中小企業を含む事業者が導入可能な実用的なデジタルツールとして参考になる。
In the global GX context
This framework aligns with global efforts to decarbonize industrial heat, a major source of emissions. It demonstrates a practical AI-driven approach for integrating green hydrogen, relevant to ISSB/CSRD reporting and transition finance. The methodology for forecasting and blending optimization can be adapted to various industrial contexts, supporting credible decarbonization strategies.
👥 読者別の含意
🔬研究者:Provides a novel application of LightGBM for solar forecasting with regime segmentation, offering insights into predictive modeling for renewable integration.
🏢実務担当者:Offers a TRL 6 decision-support tool for optimizing hydrogen blending in steam production, enabling measurable CO2 reductions and operational efficiency.
🏛政策担当者:Highlights the potential of digital solutions to facilitate industrial decarbonization, informing policies that promote hydrogen adoption and renewable integration.
📄 Abstract（原文）
The decarbonization of industrial steam production, representing up to 57% of energy use in the food industry, is critical for achieving EU climate neutrality goals. This study developed an integrated digital framework for the research project Hy4GreenSteam to optimize green-hydrogen integration through advanced predictive modeling. The employed LightGBM gradient-boosting algorithms were trained on 68,697 PV power measurements and 57,000 meteorological observations from 2020 to 2022. A “Production-Split” methodology was introduced for 24 h ahead forecasting, segmenting training into high (>2 kW) and low (≤2 kW) production regimes to manage solar heteroscedasticity. Results show the 15 min model achieved an R2 of 0.868 and the 1 h model an R2 of 0.832, while the day-ahead model—trained exclusively on information available at forecast issue time—achieved an R2 of 0.701, a 70% relative improvement over same-time-yesterday persistence. A complementary regime analysis shows that the production regime is predictable with 90.7% accuracy and quantifies the accuracy headroom of regime-specialized models (oracle R2 0.794). These methods were integrated into a real-time React-based platform that calculates optimal H2/CH4 blending; for the reference pilot configuration, driven by measured on-site PV generation, the computed CO2 emission reduction reaches 34% relative to natural-gas-only operation during high-solar operating intervals. Predictive modeling combined with a Digital Twin interface provides a TRL 6 decision-support solution, demonstrated in a relevant industrial environment, for managing renewable sources in industrial hydrogen applications.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/eng7080414first seen 2026-08-20 05:10:02 · last seen 2026-09-21 05:03:07
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