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<p>The rising global energy demand and the urgent need to transition toward cleaner fuels have driven interest in sustainable hydrogen production technologies. Aqueous phase reforming (APR) has emerged as a promising route for generating hydrogen-rich syngas from oxygenated feedstocks under moderate conditions. However, the process efficiency and gas selectivity remain highly dependent on catalyst type and process parameters. This study aims to investigate the impact of various catalysts, including Pt/Al<sub>2</sub>O<sub>3</sub>, Ni/Al<sub>2</sub>O<sub>3</sub>, and Ru/Al<sub>2</sub>O<sub>3</sub>, on the syngas composition derived from APR at different temperatures and pressures. Experimental trials were conducted using a high-pressure batch reactor, and the resulting gas yields were analyzed using gas chromatography. Additionally, a predictive model using an artificial neural network (ANN) was developed and validated against support vector regression (SVR) and multiple linear regression (MLR) models. The results showed that Ru/Al<sub>2</sub>O<sub>3</sub> produced the highest hydrogen yield of 68% at 450&#xa0;°C and 40&#xa0;bar, while Pt/<sub>2</sub>O<sub>3</sub> demonstrated good performance in the lower temperature range (57–60% H<sub>2</sub> at 200–250&#xa0;°C). The ANN model achieved a prediction accuracy of 98%, significantly outperforming conventional techniques. These findings highlight the role of catalyst choice in optimizing syngas quality and validate AI-based modeling for process optimization. Future studies should focus on catalyst longevity, feedstock flexibility, and real-time integration with AI for dynamic process control.</p>

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Catalytic performance and AI-predicted optimization of hydrogen-rich syngas from biomass-derived feedstocks

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • M. Ezhumalai,
  • M. Govindasamy,
  • R. Dhairiyasamy,
  • D. Varshney,
  • S. Singh

摘要

The rising global energy demand and the urgent need to transition toward cleaner fuels have driven interest in sustainable hydrogen production technologies. Aqueous phase reforming (APR) has emerged as a promising route for generating hydrogen-rich syngas from oxygenated feedstocks under moderate conditions. However, the process efficiency and gas selectivity remain highly dependent on catalyst type and process parameters. This study aims to investigate the impact of various catalysts, including Pt/Al2O3, Ni/Al2O3, and Ru/Al2O3, on the syngas composition derived from APR at different temperatures and pressures. Experimental trials were conducted using a high-pressure batch reactor, and the resulting gas yields were analyzed using gas chromatography. Additionally, a predictive model using an artificial neural network (ANN) was developed and validated against support vector regression (SVR) and multiple linear regression (MLR) models. The results showed that Ru/Al2O3 produced the highest hydrogen yield of 68% at 450 °C and 40 bar, while Pt/2O3 demonstrated good performance in the lower temperature range (57–60% H2 at 200–250 °C). The ANN model achieved a prediction accuracy of 98%, significantly outperforming conventional techniques. These findings highlight the role of catalyst choice in optimizing syngas quality and validate AI-based modeling for process optimization. Future studies should focus on catalyst longevity, feedstock flexibility, and real-time integration with AI for dynamic process control.