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

Rodgers, S; Bowler, A; Wells, L; Lee, CS; Hayes, M; Poulston, S; Lester, E; ... Conradie, A; + view all (2023) A surrogate model for the economic evaluation of renewable hydrogen production from biomass feedstocks via supercritical water gasification. International Journal of Hydrogen Energy 10.1016/j.ijhydene.2023.08.016. (In press). Green open access

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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.

Type: Article
Title: A surrogate model for the economic evaluation of renewable hydrogen production from biomass feedstocks via supercritical water gasification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijhydene.2023.08.016
Publisher version: https://doi.org/10.1016/j.ijhydene.2023.08.016
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Supercritical water gasification, Surrogate model, Renewable hydrogen, Techno-economic analysis, Machine learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10176305
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