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

Rodgers, Sarah; Bowler, Alexander; Wells, Laura; Siah Lee, Chai; Hayes, Martin; Poulston, Stephen; Lester, Edward; Meng, Fanran; McKechnie, Jon; Conradie, Alex

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Aug 1, 2023
• Online Publication Date: Aug 18, 2023
• Publication Date: Jan 2, 2024
• Deposit Date: Oct 4, 2023
• Publicly Available Date: Oct 5, 2023
• Journal: International Journal of Hydrogen Energy
• Print ISSN: 0360-3199
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: A
• Pages: 277-294
• DOI: https://doi.org/10.1016/j.ijhydene.2023.08.016
• Public URL: https://nottingham-repository.worktribe.com/output/24425856
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S036031992303923X?via%3Dihub