AI-Driven Catalyst Optimization in Methane Steam Reforming: A Hybrid HGBO–VIKOR and ConvLSTM Framework for Sustainable Hydrogen Production
メタンスチーム改質におけるAI駆動の触媒最適化：持続可能な水素生産のためのハイブリッドHGBO–VIKORおよびConvLSTMフレームワーク (AI 翻訳)
Haitham Al Qahtani
🤖 gxceed AI 要約
日本語
本研究は、メタンスチーム改質（MSR）プロセスにおける触媒と運転条件の最適化を目的とし、HGBO、VIKOR、ConvLSTMを統合したAIフレームワークを提案。620件の実験データを用いて評価し、水素収率98.5%、エネルギー効率99%、CO2排出量0.85 kg/hを達成した。産業用水素製造の効率化と脱炭素化に寄与する。
English
This study proposes an AI framework integrating HGBO, VIKOR, and ConvLSTM to optimize catalyst-condition combinations in methane steam reforming for hydrogen production. Evaluated on 620 experimental cases, it achieves hydrogen yield up to 98.5%, energy efficiency near 99%, and reduced CO2 emissions of 0.85 kg/h, offering practical guidance for industrial hydrogen systems.
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
日本は水素社会実現を目指し、ブルー水素を含む多様な水素供給源を検討中。本フレームワークは既存のMSRプロセスの効率改善と排出削減に貢献し、日本の水素戦略（特に既存インフラ活用）に示唆を与える。
In the global GX context
Globally, hydrogen is a key decarbonization pathway, but current MSR-based production is carbon-intensive. This AI-driven optimization reduces emissions and improves efficiency, bridging the gap towards cleaner hydrogen production. The framework is applicable to industrial retrofits and aligns with transition finance goals for blue hydrogen.
👥 読者別の含意
🔬研究者:Provides a novel hybrid AI framework (HGBO-VIKOR-ConvLSTM) for multi-objective optimization in chemical engineering, applicable to catalyst and process optimization.
🏢実務担当者:Offers a data-driven decision tool for selecting catalysts and operating conditions to improve yield, efficiency, and reduce emissions in existing hydrogen plants.
🏛政策担当者:Highlights potential efficiency gains in conventional hydrogen production, supporting blue hydrogen as a transition step while green hydrogen scales up.
📄 Abstract（原文）
Methane steam reforming (MSR) is the most widely used industrial process for hydrogen production. However, catalyst deactivation, carbon emissions, and energy inefficiencies limit its sustainable performance. Therefore, improving catalyst selection and optimizing operating conditions are essential for efficient hydrogen generation. This study proposes an artificial intelligence-driven framework to optimize catalyst–condition combinations in MSR systems. The framework integrates Hybrid Golden Beetle Optimization (HGBO), VIKOR-based multi-criteria decision making, and Convolutional Long Short-Term Memory (ConvLSTM) modeling. HGBO explores the solution space and generates Pareto-optimal combinations of catalysts and operating conditions. These solutions are then ranked using the VIKOR method. The ranking considers hydrogen yield, methane conversion, energy efficiency, CO2 emissions, and catalyst lifetime. Economic feasibility is also included in the decision process. ConvLSTM modeling captures spatiotemporal relationships in catalyst and process data and predicts catalyst degradation under different operating conditions. The framework is evaluated using 620 experimentally reported MSR cases collected from the published literature within industrial ranges of 600–1200 °C, 1–40 bar, and H2O/CH4 ratios of 1–6. The optimized configurations achieve hydrogen yields up to 98.5%, energy efficiency approaching 99%, and reduced CO2 emissions of about 0.85 kg h−1. The results provide practical guidance for catalyst selection and process optimization in industrial hydrogen production systems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18083717first seen 2026-05-06 00:06:46
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