Citation
Abstract
To evaluate the feasibility of co-digestion of landfill leachate and liquidised food waste, batch experiments were conducted to explore the optimal mixing ratio and support carrier integration in up-flow anaerobic sludge blanket reactors. Continuous experiments were conducted to evaluate the long-term effects of support carriers on methane generation and digestate characteristics. To compare biogas accuracy, Biomethane Potential Tests 1 and 2 data were used to build artificial neural networks using feed-forward backpropagation and support vector machines using linear function. Red clay bio ring, ceramic bio ring and lava rock support carriers were used in the modified reactors. Significant improvement in the anaerobic process was observed during the co-digestion, along with the addition of a ceramic bio ring with biogas production (2150 mL/d), specific methane production (293.30 mL CH4/gCODadded, >66.10 compared to the conventional reactor), and chemical oxygen demand removal (>80) at an organic loading rate of 6 g/L/day. The Brunauer-Emmett-Teller method demonstrates that ceramic bio ring has a higher specific surface area (7.87 m2/g). The support vector machine model had superior predicted accuracy in biogas production with an R2 of 0.986, indicating it is more robust for large-scale applications. Thus, utilising a suitable proportion of substrates and selecting an appropriate support carrier can stimulate methanogenic activity, which can boost methane production, a promising renewable energy source, and produce higher-quality effluent.
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Engineering |
DOI Number: | https://doi.org/10.1016/j.bej.2024.109221 |
Publisher: | Elsevier |
Keywords: | Anaerobic digestion; Landfill leachate; Liquidised food waste; Machine learning applications; Support carrier |
Depositing User: | Ms. Zaimah Saiful Yazan |
Date Deposited: | 13 Jun 2024 02:36 |
Last Modified: | 13 Jun 2024 02:36 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.bej.2024.109221 |
URI: | http://psasir.upm.edu.my/id/eprint/106169 |
Statistic Details: | View Download Statistic |
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