Predictive modelling of methane yield in biochar-amended cheese whey and septage co-digestion: Exploring synergistic effects using Gompertz and neural networks

Abstract

This study performed bench scale studies on anaerobic co-digestion of cheese whey and septage mixed with biochar (BC) as additive at various dosages (0.5 g, 1 g, 2 g and 4 g) and total solids (TS) concentrations (5%, 7.5%, 10%,12.5% and 15%). The experimental results revealed 29.58% increase in methane yield (486 ± 11.32 mL/gVS) with 27% reduction in lag phase time at 10% TS concentration and 50 g/L of BC loading. The mechanistic investigations revealed that BC improved process stability by virtue of its robust buffering capacity and mitigated ammonia inhibition. Statistical analysis indicates BC dosage had a more pronounced effect (P < 0.0001) compared to the impact of TS concentrations. Additionally, the results were modelled using Gompertz model (GM) and artificial neural network (ANN) algorithm, which revealed the outperformance of ANN over GM with MSE 17.96, R² value 0.9942 and error 0.27%. These findings validated the practicality of utilizing a high dosage of BC in semi-solid anaerobic digestion conditions.

Keywords: Artificial neural network; Biogas production rate; Optimisation; Septage-derived biochar; Total solids.