PSOおよびGA最適化逆ニューラルコントローラによる合成燃料向けオンデマンドグリーン水素製造
Intelligent On-Demand Green Hydrogen Production for Synthetic Fuels via PSO- and GA-Optimized Inverse Neural Controllers (原題)
Marisol Coba-Martínez, Jarniel García-Morales, G. Guerrero-Ramírez, M. Cervantes-Bobadilla, E. Guerrero-Ramírez, Ivetteh-Viginia Medina-Medina, M. Adam-Medina
🤖 gxceed AI 要約
日本語
再生可能エネルギーの変動性に対応するため、水素製造を需要に応じて制御するインテリジェント制御戦略を提案。アルカリ水電解のANNモデルを構築し、PSOとGAで最適化した逆ニューラルコントローラが、メタノール合成の化学量論比を維持しながら水素供給を追従。応答時間は約1秒で、脱炭素化に寄与する。
English
Proposes an intelligent control strategy for alkaline water electrolysis to produce green hydrogen on-demand, matching the stoichiometric requirements of methanol synthesis. ANN models with classical and conformable activation functions are optimized via PSO and GA, achieving rapid tracking of hydrogen demand with H2/CO2 ratio within ±2% in about 1 second. Contributes to decarbonization of industrial and transport sectors.
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、グリーン水素の活用を推進。本技術は再生可能エネルギー由来の水素を効率的に利用し、合成燃料（e-fuel）の生産を最適化するもので、日本のカーボンニュートラル目標や水素サプライチェーン構築に貢献する。
In the global GX context
Global push for green hydrogen and synthetic fuels (e-fuels) as decarbonization pathways. This control strategy enhances efficiency of Power-to-Liquid processes, aligning with international efforts to scale up green hydrogen production and utilization, supporting climate goals under the Paris Agreement.
👥 読者別の含意
🔬研究者:Provides a novel application of AI-based control for green hydrogen production, relevant for researchers in process control and renewable energy systems.
🏢実務担当者:Offers a practical approach to optimize hydrogen production in PtL plants, potentially reducing operational costs and improving efficiency.
🏛政策担当者:Highlights the potential of AI in enabling flexible and efficient green hydrogen production, informing policies that support e-fuel adoption.
📄 Abstract（原文）
Green hydrogen is a key energy carrier in Power-to-Liquid (PtL) pathways for the production of sustainable synthetic fuels, contributing to the decarbonization of the industrial and transport sectors. However, the intermittent nature of renewable energy sources and the variable hydrogen requirements needed to maintain the appropriate stoichiometric ratio for synthesis processes necessitate regulating hydrogen production according to process demand, rather than maximizing its generation. This article proposes an intelligent control strategy for alkaline water electrolysis, in which the hydrogen production target is determined from the stoichiometric requirements of synthetic methanol production, based on available carbon dioxide. ANN models were developed using the experimental data, incorporating both classical and conformable activation functions in the hidden layer. Based on the selected models, the ANNi was formulated, and PSO and GA were used to determine the required feed current according to hydrogen demand. The proposed methodology was evaluated under a dynamic hydrogen-demand profile derived from the stoichiometric requirements of methanol synthesis. The results show that the proposed controllers closely track changes in hydrogen demand. After each change in the setpoint, the H2/CO2 ratio returned to a ±2% band around the stoichiometric setpoint in approximately 0.98 s for ICANNi-PSO and 0.96 s for ICANNi-GA. Furthermore, some conformable activation functions achieved performance comparable to that of classical activation functions while using fewer neurons in the hidden layer. Both optimization algorithms provided comparable tracking performance under the evaluated conditions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/eng7080426first seen 2026-08-27 05:29:24 · last seen 2026-09-21 05:02:34
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