AI-Driven Optimization of Green Hydrogen Production Networks for Saudi Arabia's Industrial Cities Under Vision 2030
AI駆動によるサウジアラビア産業都市のグリーン水素生産ネットワーク最適化：ビジョン2030の下で (AI 翻訳)
Mohamed Vajeebu Mohamed Hussain Pareeth
🤖 gxceed AI 要約
日本語
本レビューは、サウジアラビアの産業都市（NEOM、ヤンブー、ジュベイル等）におけるグリーン水素生産ネットワークのAI駆動最適化を提案する。機械学習、デジタルツイン、確率的最適化、説明可能な意思決定システムを統合し、コスト削減、水ストレス緩和、再生可能エネルギー出力抑制の低減、電解槽劣化の抑制、オフテイク不確実性の低減を目指す。再生可能エネルギー予測、淡水配分、電解槽スケジューリング、貯蔵サイズ、アンモニア転換、港湾輸出計画、ESG報告を統合する枠組みを提示し、AIをネットワーク全体に組み込むことの重要性を強調する。
English
This review proposes an AI-driven optimization framework for green hydrogen production networks in Saudi industrial cities (NEOM, Yanbu, Jubail, etc.). It integrates machine learning, digital twins, stochastic optimization, and explainable decision systems to reduce costs, water stress, renewable curtailment, electrolyzer degradation, and offtake uncertainty. The framework links renewable forecasting, water allocation, electrolyzer scheduling, storage sizing, ammonia conversion, port export planning, and ESG reporting, emphasizing that AI is most valuable when embedded across the entire network.
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
日本は水素基本戦略を掲げ、グリーン水素の国際調達・国内生産を進めており、本論文のAI最適化フレームワークは日本の水素サプライチェーン構築や産業クラスターの効率化に示唆を与える。特に、SSBJ開示やカーボンニュートラル目標に向けた投資判断において、AIを活用したネットワーク最適化とESG報告の統合は、日本企業の競争力強化に寄与する可能性がある。
In the global GX context
This paper contributes to global GX scholarship by demonstrating how AI can optimize green hydrogen networks at a national scale, aligning with international efforts to scale hydrogen as a decarbonization vector. It offers a framework that integrates technical optimization with ESG reporting, relevant for countries and companies developing hydrogen hubs and seeking to attract transition finance. The emphasis on interoperable data standards and bankable models resonates with global discussions on hydrogen certification and infrastructure investment.
👥 読者別の含意
🔬研究者:Provides a structured framework for AI-driven optimization of hydrogen networks, highlighting research gaps and integration opportunities.
🏢実務担当者:Offers insights into how AI can reduce costs and improve efficiency in hydrogen production, useful for project developers and industrial planners.
🏛政策担当者:Suggests policy levers such as data standards and phased roadmaps to accelerate hydrogen deployment, relevant for national hydrogen strategies.
📄 Abstract（原文）
Green hydrogen is becoming a strategic industrial fuel for Saudi Arabia because it can convert world-class solar and wind resources into exportable molecules, decarbonized feedstocks, and resilient energy services for industrial cities. This review paper develops an AI-driven optimization perspective for green hydrogen production networks in NEOM, Yanbu, Jubail, Ras Al-Khair, Dammam and related logistics corridors under Vision 2030. The aim is to explain how machine learning, digital twins, stochastic optimization, and explainable decision systems can reduce cost, water stress, renewable curtailment, electrolyzer degradation, and offtake uncertainty while strengthening industrial competitiveness. A structured review methodology was used to synthesize recent literature, policy reports, and applied hydrogen system studies published between 2020 and 2025. The paper proposes an integrated framework that links renewable forecasting, desalinated water allocation, electrolyzer scheduling, storage sizing, ammonia conversion, port export planning, and ESG reporting. Findings show that AI is most valuable when it is embedded across the full network rather than limited to plant-level control. The study concludes that Saudi industrial cities can accelerate green hydrogen deployment by adopting interoperable data standards, bankable optimization models, cybersecure control rooms, and phased implementation roadmaps aligned with Vision 2030.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.36348/gajeb.2026.v08i04.030first seen 2026-08-08 05:24:17
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