多目的バッチ・エネルギー計画による酸素燃焼ガラス炉の脱炭素化
Decarbonizing oxy-fuel glass furnaces through multi-objective batch and energy planning (原題)
Arshad, Hossein, Caccamo, Chiara, Lima Silva, Thiago, Cattaneo, Ernesto
🤖 gxceed AI 要約
日本語
本研究は、酸素燃焼ガラス炉におけるバッチ組成とエネルギー配分を同時最適化する多期間・多目的線形計画モデルを開発した。天然ガス、緑・青・灰水素、再生可能・系統電力を対象に、コストと排出のトレードオフを評価する。欧州の容器・繊維ガラス3事例では、カレット利用率向上で排出最大25.6%減、緑水素拡大で排出10.7%減（コスト約4%増）が示された。
English
This study develops a multi-period, multi-objective linear programming model that jointly optimizes batch composition and energy allocation for oxy-fuel glass furnaces across natural gas, green/blue/grey hydrogen, and renewable/grid electricity. Evaluated on three European container- and fiberglass cases, higher cullet use cuts emissions up to 25.6%, energy 19.3%, and cost 18.9%; emission-priority green hydrogen cuts emissions 10.7% at ~4% cost increase.
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
ガラス・窯業など高温産業は日本のGX推進でScope1削減が難しく、水素・電化・カレット活用の組合せ最適化はSSBJのScope1/2開示や移行計画策定に直接資する。国内窯業・ガラスメーカーの脱炭素ロードマップ検討に有用な意思決定枠組みを提供する。
In the global GX context
Hard-to-abate high-temperature industries like glass are central to ISSB/CSRD transition-planning disclosure, where Scope 1 abatement pathways must be credible. This model offers a structured, quantitative approach to evaluating hydrogen, electrification, and cullet trade-offs that can inform transition finance and decarbonization target setting.
👥 読者別の含意
🔬研究者:多目的最適化と産業脱炭素の統合手法として、水素・電化・リサイクル材の同時評価モデルの設計知見を得られる。
🏢実務担当者:自社ガラス炉のバッチ組成・エネルギー調達・カレット利用の意思決定に、コストと排出のトレードオフ定量化フレームを応用できる。
🏛政策担当者:水素価格・電力炭素強度・カレット規制が高温産業の脱炭素コストに与える影響を評価する政策設計の参考になる。
📄 Abstract（原文）
Abstract Decarbonizing high-temperature glass production requires coordinated decisions on batch composition, energy sourcing, and furnace operation. However, existing research has not sufficiently integrated virgin raw materials, internal and external cullet, multiple hydrogen pathways, electric boosting, inventories, lead times, and operational constraints within a unified short-term production planning and control framework for oxy-fuel glass furnaces. This study develops a multi-period, multi-objective linear programming model to address this gap. The model jointly optimizes batch composition and energy allocation across natural gas, green, blue, and grey hydrogen, and renewable and grid electricity. It links material choices to melting-energy requirements, process emissions, and direct and indirect energy-related emissions, while incorporating storage, electricity availability, ordering decisions, ramping limits, hydrogen-blending limits, and energy-mix constraints. Two conflicting objectives are considered: minimizing operating cost and emissions. Lexicographic optimization generates cost- and emission-priority solutions, while a normalized weighted-sum approach identifies intermediate trade-offs. The model is evaluated using three European industrial cases in container-glass and fiberglass production over a 24-h planning horizon. Results show that higher cullet utilization can reduce emissions by up to 25.6%, energy use by up to 19.3%, and cost by up to 18.9%. Under emission-priority operation, green-hydrogen scale-up reduces emissions by up to 10.7% at a cost increase of approximately 4%. Cost-priority solutions remain dominated by natural gas, whereas lower-emission operation requires greater use of green hydrogen and renewable electricity. Although the model is deterministic and does not capture detailed furnace physics, it provides structured decision support for plant-specific decarbonization planning.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/23335671first seen 2026-10-14 04:34:00
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