Fungal Pretreatment-Assisted Methane Recovery from Food Waste: A Literature-Informed Simulation Framework

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.