Optimization of Biogas Steam Reforming Toward Low Carbon Hydrogen Production Using Integrated Artificial Neural Network and Genetic Algorithm
人工ニューラルネットワークと遺伝的アルゴリズムを統合した低炭素水素製造のためのバイオガス水蒸気改質の最適化 (AI 翻訳)
I. P. Okwuosa, O. J. Odejobi
🤖 gxceed AI 要約
日本語
本研究は、バイオガスを原料とする水蒸気改質プロセスをANNとGAを用いて最適化し、低炭素水素製造の効率化を図った。Aspen HYSYSによるシミュレーションデータでANNを訓練し、GAで運転条件を最適化した結果、水素モル分率0.5536を達成し、検証誤差は2.67%と高い精度を示した。提案手法は、水素製造の運転条件決定に系統的アプローチを提供する。
English
This study optimizes biogas steam reforming for low-carbon hydrogen production using ANN integrated with GA. The ANN was trained on Aspen HYSYS simulation data, and GA optimized operating conditions to achieve a hydrogen mole fraction of 0.5536 with a validation error of 2.67%. The hybrid framework offers a systematic approach to enhance hydrogen yield and operational reliability.
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
日本では水素社会実現に向けた取り組みが進み、バイオガス由来の低炭素水素は重要な選択肢である。本研究成果は、国内の水素製造プロセスの効率化やコスト削減に寄与し、水素基本戦略やグリーン成長戦略に沿った技術開発に示唆を与える。
In the global GX context
Globally, low-carbon hydrogen is key to decarbonization, and biogas steam reforming offers a renewable pathway. This study's ANN-GA optimization framework can improve process efficiency and reduce costs, supporting the scale-up of green hydrogen production and aligning with international climate goals.
👥 読者別の含意
🔬研究者:Provides a validated ANN-GA optimization framework for biogas reforming, useful for process optimization research.
🏢実務担当者:Offers a data-driven method to optimize hydrogen production conditions, potentially reducing operational costs and improving yield.
🏛政策担当者:Highlights the potential of biogas-derived hydrogen as a low-carbon energy source, informing policy support for renewable hydrogen technologies.
📄 Abstract（原文）
Abstract Hydrogen has been identified as a versatile energy carrier, offering a viable route to decarbonize and meet escalating global energy demands. Biogas produced from the anaerobic digestion of organic matter can potentially serve as a feedstock for hydrogen production using the steam reforming process. This research investigates the optimization of a steam reforming process utilizing biogas feedstock for low-carbon hydrogen production using Artificial Neural Network (ANN) integrated with Genetic Algorithm (GA). An equilibrium based steady-state simulation of the process was developed using Aspen HYSYS to generate data for neural network training, validation and testing. Key process parameters considered for optimization include: biogas flow rate, steam flow rate, reformer temperature and reformer pressure with hydrogen mole fraction at reformer outlet as the response variable. A two-layer feedforward neural network with 4-12-1 architecture was trained on simulation data, achieving a correlation coefficient (R-value) of 0.99. This ANN model was integrated within the fitness function of GA to iteratively optimize process parameters subject to a steam-to-carbon ratio constraint ≥ 2.5 to maximize hydrogen mole fraction while reducing the risk of catalyst deactivation via coking. The optimal parameters identified were 63 kg/h biogas flow rate, 62.04 kg/h steam flow rate, 1000°C reformer temperature, and 12.34 bar reformer pressure corresponding to a maximum hydrogen mole fraction of 0.5536 at the reformer outlet as predicted by the ANN model. Validation of these optimal parameters against the Aspen HYSYS model showed a relative error of 2.67% and 98.53% hydrogen yield at the reformer outlet. The proposed hybrid ANN-GA framework provides a robust, systematic approach for determining optimal operating conditions that enhance yield while maintaining operational reliability and efficiency.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.2118/234848-msfirst seen 2026-08-11 04:37:35
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。