D. Poggio
Modelling the anaerobic digestion of solid organic waste – Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach
Poggio, D.; Walker, M.; Nimmo, W.; Ma, L.; Pourkashanian, M.
Authors
Dr Mark Walker Mark.Walker@hull.ac.uk
Lecturer in Sustainable Engineering Systems
W. Nimmo
L. Ma
M. Pourkashanian
Abstract
This work proposes a novel and rigorous substrate characterisation methodology to be used with ADM1 to simulate the anaerobic digestion of solid organic waste. The proposed method uses data from both direct substrate analysis and the methane production from laboratory scale anaerobic digestion experiments and involves assessment of four substrate fractionation models. The models partition the organic matter into a mixture of particulate and soluble fractions with the decision on the most suitable model being made on quality of fit between experimental and simulated data and the uncertainty of the calibrated parameters. The method was tested using samples of domestic green and food waste and using experimental data from both short batch tests and longer semi-continuous trials. The results showed that in general an increased fractionation model complexity led to better fit but with increased uncertainty. When using batch test data the most suitable model for green waste included one particulate and one soluble fraction, whereas for food waste two particulate fractions were needed. With richer semi-continuous datasets, the parameter estimation resulted in less uncertainty therefore allowing the description of the substrate with a more complex model. The resulting substrate characterisations and fractionation models obtained from batch test data, for both waste samples, were used to validate the method using semi-continuous experimental data and showed good prediction of methane production, biogas composition, total and volatile solids, ammonia and alkalinity.
Citation
Poggio, D., Walker, M., Nimmo, W., Ma, L., & Pourkashanian, M. (2016). Modelling the anaerobic digestion of solid organic waste – Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach. Waste Management, 53, 40-54. https://doi.org/10.1016/j.wasman.2016.04.024
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 22, 2016 |
Online Publication Date | May 2, 2016 |
Publication Date | 2016-07 |
Deposit Date | Mar 22, 2021 |
Publicly Available Date | Mar 29, 2021 |
Journal | Waste Management |
Print ISSN | 0956-053X |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 53 |
Pages | 40-54 |
DOI | https://doi.org/10.1016/j.wasman.2016.04.024 |
Keywords | Anaerobic digestion; ADM1; Model inputs; Substrate description; Food waste; Green waste |
Public URL | https://hull-repository.worktribe.com/output/3742488 |
Additional Information | This article is maintained by: Elsevier; Article Title: Modelling the anaerobic digestion of solid organic waste – Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach; Journal Title: Waste Management; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.wasman.2016.04.024; Content Type: article; Copyright: © 2016 The Author(s). Published by Elsevier Ltd. |
Files
Published article
(1.8 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
© 2016 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
You might also like
Downloadable Citations
About Repository@Hull
Administrator e-mail: repository@hull.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search