Biomethane production modelling from third-generation biomass

Verónica Córdoba, Marcela Bavio and Gerardo Acosta

Renewable Energy, 2024, vol. 234, issue C

Abstract: Third-generation biomass constitutes a renewable energy source that could substitute fossil fuels. This study evaluated the biomethane potential (BMP) of Ulva sp., Codium sp. and Undaria pinnatifida through anaerobic digestion. The daily production of biomethane was evaluated using different models, including Modified Gompertz, Chen and Hashimoto, First-order, Transfer and Cone models, as well as an Artificial Neural Network (ANN) model. The experimental BMP was 0.17, 0.26, and 0.32 Nm³/kg VS for Ulva sp., Codium sp., and U pinnatifida, respectively. Among the non-linear regression models, the Transfer model (R2 > 0.9915) and the First-order model (R2 > 0.9889) are the ones that best fit the experimental data. However, the ANN shows a better fit (R2 > 0.999 and RMSE<4.537) to the data compared to the non-linear regression models. Furthermore, ANN can capture the complexity of biological systems, allowing for more accurate and detailed modelling of the processes involved. The identification and optimization of the biomethane potential of macroalgae contribute to developing sustainable energy alternatives, offering a renewable energy source that could mitigate the environmental stress associated with traditional fossil fuels.

Keywords: Anaerobic digestion; Bioprocesses modelling; Biomethane; Macroalgae (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148124012795
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:234:y:2024:i:c:s0960148124012795

DOI: 10.1016/j.renene.2024.121211

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().