A surrogate model for the economic evaluation of renewable hydrogen production from biomass feedstocks via supercritical water gasification

• Published in: International journal of hydrogen energy Vol. 49; pp. 277 - 294
• Main Authors: Rodgers, Sarah, Bowler, Alexander, Wells, Laura, Lee, Chai Siah, Hayes, Martin, Poulston, Stephen, Lester, Edward, Meng, Fanran, McKechnie, Jon, Conradie, Alex
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 02.01.2024
• Subjects: Machine learning; Renewable hydrogen; Supercritical water gasification; Surrogate model; Techno-economic analysis; Techno-economic analysis; Renewable hydrogen; Surrogate model; Supercritical water gasification; Machine learning
• ISSN: 0360-3199; 1879-3487
• DOI: 10.1016/j.ijhydene.2023.08.016

Supercritical water gasification is a promising technology for renewable hydrogen production from high moisture content biomass. This work produces a machine learning surrogate model to predict the Levelised Cost of Hydrogen over a range of biomass compositions, processing capacities, and geographic locations. The model is published to facilitate early-stage economic analysis (doi.org/10.6084/m9.figshare.22811066). A process simulation using the Gibbs reactor provided the training data using 40 biomass compositions, five processing capacities (10–200 m3/h), and three geographic locations (China, Brazil, UK). The levelised costs ranged between 3.81 and 18.72 $/kgH2 across the considered parameter combinations. Heat and electricity integration resulted in low process emissions averaging 0.46 kgCO2eq/GJH2 (China and Brazil), and 0.37 kgCO2eq/GJH2 (UK). Artificial neural networks were most accurate when compared to random forests and support vector regression for the surrogate model during cross-validation, achieving an accuracy of MAPE: <4.6%, RMSE: <0.39, and R2: >0.99 on the test set. [Display omitted] •A techno-economic surrogate model for supercritical water gasification is created.•Model predicts the LCOH for different biomass compositions, scales, and locations.•RF, SVR, and ANN algorithms were compared for the surrogate model.•ANNs achieved the highest prediction accuracy during cross-validation.•Final model is published to facilitate early-stage feedstock evaluation.