Modeling and optimization of bioenergy production from co-digestion of poultry litter with wheat straw in anaerobic sequencing batch reactor: Response surface methodology and artificial neural network

This study investigated the sequencing batch co-digestion (Co-AD) of poultry litter (PL) and wheat straw (WS) for continuous methane production as a renewable bioenergy resource. Response surface methodology (RSM) and artificial neural network (ANN) were both used to model the methane content (MC, %) in the produced biogas and daily methane yield (DMY, mL CH4/g VS added) from the Co-AD process. The RSM models (R2 = 0.9554, RMSE = 0.813; R2 = 0.9618, RMSE = 4.940) were more accurate than the ANN models (R2 = 0.9163, RMSE = 1.114; R2 = 0.9037, RMSE = 7.847) in predicting MC and DMY, respectively. The optimal conditions for maximum DMY obtained by RSM were a C/N ratio of 22.73 (a mixing ratio of 0.46 g PL to 1.00 g WS on dry matter basis), total solids of 2.27%, and hydraulic retention time of 11.45 days, under which the validation trials showed MC of (58.37 ± 0.25)% and DMY of (184.36 ± 0.51) mL CH4/g VS added with the prediction errors by RSM (1.07% and 1.87%) being relatively lower than that by ANN (4.21% and 4.35%). The models and optimized parameters results could provide support for the operation and prediction of the continuous Co-AD of PL with WS for real bioenergy production applications.