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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

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


Authors

Sarah Rodgers

Alexander Bowler

Laura Wells

Martin Hayes

Stephen Poulston

Fanran Meng

JON MCKECHNIE Jon.Mckechnie@nottingham.ac.uk
Professor of Engineering Sustainability

Alex Conradie



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.

Citation

Rodgers, S., Bowler, A., Wells, L., Siah Lee, C., Hayes, M., Poulston, S., …Conradie, A. (2024). A surrogate model for the economic evaluation of renewable hydrogen production from biomass feedstocks via supercritical water gasification. International Journal of Hydrogen Energy, 49(A), 277-294. https://doi.org/10.1016/j.ijhydene.2023.08.016

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

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