Fungal Pretreatment-Assisted Methane Recovery from Food Waste: A Literature-Informed Simulation Framework
4 affiliations
- 1School of Engineering, Department of Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
- 2Graduate Researcher, School of Engineering, Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC 3125, Australia
- 3Faculty of Pharmacy and Pharmaceutical Sciences, Fukuyama University, Fukuyama, Hiroshima 729-0292, Japan
- 4School of Science, Computing and Emerging Technologies, Department of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne, VIC 3122, Australia
Abstract
Food waste is a heterogeneous, moisture-rich substrate with substantial potential for renewable methane recovery, but variability in composition, hydrolysis and reactor stability constrains reliable anaerobic-digestion performance. This study presents a literature-informed computational scenario-screening framework that translates fungal pretreatment descriptors into model-ready variables for anaerobic digestion. Published experimental and modelling studies were used to parameterise food-waste composition, spent coffee grounds (SCG), enzyme-targeted pretreatment, anaerobic-digestion operating conditions, methane potential and heuristic risk indicators; no new experimental measurements were generated. The Python-based framework links fungal pretreatment severity with substrate solubilisation, hydrolysis, methane yield, raw-biogas CH4 fraction, gross methane energy recovery and operational-risk indicators. Under the specified model assumptions, heuristic stability-adjusted methane output methane screening output increased from 272.5 L CH4 kg−1 VS in the untreated control to 291.5 L CH4 kg−1 VS under high pretreatment and 295.5 L CH4 kg−1 VS under extended pretreatment, while increasing SCG inclusion and high organic loading reduced the corresponding screening outputs. These directional responses are partly imposed by the model structure and are therefore interpreted as comparative scenario outputs and experimentally testable hypotheses rather than independent quantitative predictions or process optima.
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BibTeX
@article{zaferanloo2026fungal,
title = {{Fungal Pretreatment-Assisted Methane Recovery from Food Waste: A Literature-Informed Simulation Framework}},
author = {Pouya Zaferanloo and Mohammad Imamul Hossain and Shoji Maehara and Mehran Ektesabi and François Malherbe and Bita Zaferanloo},
journal = {Molecules},
year = {2026},
month = oct,
doi = {10.3390/molecules31203594},
url = {https://www.mdpi.com/1420-3049/31/20/3594},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Fungal Pretreatment-Assisted Methane Recovery from Food Waste: A Literature-Informed Simulation Framework
AU - Pouya Zaferanloo
AU - Mohammad Imamul Hossain
AU - Shoji Maehara
AU - Mehran Ektesabi
AU - François Malherbe
AU - Bita Zaferanloo
PY - 2026
DA - 2026/10/01
JO - Molecules
DO - 10.3390/molecules31203594
UR - https://www.mdpi.com/1420-3049/31/20/3594
AB - Food waste is a heterogeneous, moisture-rich substrate with substantial potential for renewable methane recovery, but variability in composition, hydrolysis and reactor stability constrains reliable anaerobic-digestion performance. This study presents a literature-informed computational scenario-screening framework that translates fungal pretreatment descriptors into model-ready variables for anaerobic digestion. Published experimental and modelling studies were used to parameterise food-waste composition, spent coffee grounds (SCG), enzyme-targeted pretreatment, anaerobic-digestion operating conditions, methane potential and heuristic risk indicators; no new experimental measurements were generated. The Python-based framework links fungal pretreatment severity with substrate solubilisation, hydrolysis, methane yield, raw-biogas CH4 fraction, gross methane energy recovery and operational-risk indicators. Under the specified model assumptions, heuristic stability-adjusted methane output methane screening output increased from 272.5 L CH4 kg−1 VS in the untreated control to 291.5 L CH4 kg−1 VS under high pretreatment and 295.5 L CH4 kg−1 VS under extended pretreatment, while increasing SCG inclusion and high organic loading reduced the corresponding screening outputs. These directional responses are partly imposed by the model structure and are therefore interpreted as comparative scenario outputs and experimentally testable hypotheses rather than independent quantitative predictions or process optima.
SN - 1420-3049
ER -