Enhancing Hydrothermal Carbonization of Food Waste with Landfill Leachate: Optimization, Methane Recovery, and Sustainable Energy Generation
Publication: Journal of Hazardous, Toxic, and Radioactive Waste
Volume 28, Issue 4
Abstract
Hydrothermal carbonization (HTC) emerges as a promising avenue for converting food waste into valuable resources while addressing environmental concerns. This study investigated the optimization of HTC parameters, utilizing landfill leachate as a sustainable moisture source and exploring methane recovery from process water. Employing the response surface methodology (RSM) and artificial neural networks (ANN), the influence of key process variables on HTC efficiency was meticulously analyzed. Notably, the superior predictive capability of ANN over RSM was demonstrated, with a lower mean-squared error and a higher correlation coefficient. The identified optimal HTC conditions were 193°C for 175 min at a solid/liquid ratio of 0.2; under these conditions, RSM and ANN predicted mass yields of 66.37 ± 1.15% and 65.68 ± 0.11%, respectively. Furthermore, under these conditions, process water exhibited a remarkable biomethane production rate of approximately 283.11 ± 13.5 mL/g COD, fitting well with the modified Gompertz model. This study advances HTC optimization strategies and underscores the potential of integrating landfill leachate and methane recovery into food waste valorization processes, thereby paving the way for sustainable resource management practices.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
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Information
Published In
Journal of Hazardous, Toxic, and Radioactive Waste
Volume 28 • Issue 4 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Mar 7, 2024
Accepted: Apr 26, 2024
Published online: Jul 17, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 17, 2024
ASCE Technical Topics:
- Artificial intelligence (AI)
- Artificial intelligence and machine learning
- Business management
- Chemicals
- Chemistry
- Computer programming
- Computing in civil engineering
- Engineering materials (by type)
- Environmental engineering
- Landfills
- Leachates
- Materials engineering
- Methane
- Neural networks
- Organic compounds
- Organic compounds
- Pollutants
- Practice and Profession
- Recycling
- Sustainable development
- Waste management
- Waste sites
- Waste treatment
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