Journal of Applied Science and Engineering

Published by Tamkang University Press

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Quantitative Assessment of Operational Components Regarding Supercritical Water Gasification Systems’ Hydrogen Yield

 2027

 Volume 36

Yu Qian

Fushun Vocational Technology Institute, Fushun 113122, Liaoning, China

Received: December 31, 2025
Accepted: May 04, 2026
Publication Date: October 09, 2026

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Representation of the XGB’s optimization process by the POA metaheuristic model.

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Supercritical Water Gasification is a promising technology for the production of hydrogen-rich gas from biomass, but it suffers from high operating costs because of the extreme temperature and pressure requirements. A reasonable estimation for hydrogen produced by this process is required for the optimization of process and scaling it for industrial purposes. The present study aims to provide a predictive framework for hydrogen mole fraction estimation in the absence of expensive experimental trials. For accurate modelling of water gasification high-dimensional space, the current research provides comparative machine learning based farmwork, which are optimized using a novel Pufferfish Optimization Algorithm by hyperparameter optimization strategy. The performance comparison indicates that optimized Extremely Gradient Boosting model achieves the highest performance in hydrogen mole fraction prediction by yielding Coefficient of Determination of 0.9992, Mean Squared Error of 0.2325 during testing process. The interpretability analysis of this model demonstrates that the Temperature and Catalyst Type parameters influential parameters, that higher catalyst type negatively impact the model output, while elevated temperatures positively influence the model output.

Keywords: Supercritical water gasification, Hydrogen, Quantitative assessment, Wet biomass conversion

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