Renewable biomethane production from sustainable agricultural waste co-digestion

Anaerobic digestion offers a practical route for converting agricultural waste into biomethane while supporting sustainable water and resource management. This study investigates mono-and co-digestion of six organic substrates (hereafter distinguished as cattle manure (CTM) and cow manure (COM), two compositionally distinct manure feedstocks sourced separately, softwood, fruit peels, vegetable leaves, and waste pulp) using wastewater as a dilution and co-digestion medium. Feedstock characterization and batch digestion experiments were conducted under mesophilic temperatures (37-45 degrees C) to assess how the carbon-to-nitrogen ratio, pH, total solids, and organic loading rate influence methane generation. Among the individual substrates, cow manure delivered the strongest mono-digestion performance. At an organic loading rate of 6.0 kg VS m-3 d-1, a hydraulic retention time of six days, and 45 degrees C, it produced 15 L/kg VS of biogas with a methane concentration of 96.6% in biogas measured by GC-TCD and a COD removal efficiency of 91.6%. This performance is attributed to its favorable C/N ratio of 27.6, volatile solids content of 65%, and moisture content of 75.3%, which collectively created a nutrient-rich, well-buffered environment for methanogens. This methane content is at the upper end of values reported for conventional anaerobic digestion (typically 55-75%, exceeding 90% only after biogas upgrading); the value was confirmed by triplicate gas-chromatographic analysis, and independent cross-validation is recommended as future confirmatory work. Co-digestion consistently outperforms mono-digestion through synergistic substrate interactions. Blend S-2 produced the highest volumetric biogas yield of 73.29 L/kg VS at pH 7.0 and, under continuous operation at 3 kg VS m-3 d-1, achieved the greatest specific methane production of 124.6 L/kg VS and a volumetric methane production rate of 2.1 L d-1. Blend S-8 recorded the highest methane concentration among all blends at 66.15% under batch conditions, improving further to 78.6-89.9% at pH 8.2. Kinetic modeling using the modified Gompertz equation and an artificial neural network both closely matched experimental data, with R2 values ranging from 0.92 to 0.999, confirming their reliability for process prediction and optimization. These findings underscore the importance of substrate compatibility, wastewater-assisted dilution, and pH regulation in maximizing biomethane yield, offering a scalable waste-to-energy framework that advances circular bioresource recovery and sustainable water-energy-environment systems.