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).
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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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