Modeling and optimization of the hydrolysis and acidification via liquid fraction of digestate from corn straw by response surface methodology and artificial neural network

This study investigated liquid fraction of digestate (LFD) assisted hydrolysis and acidification (HA) of corn straw (CS) for enhanced biogas production. Response surface methodology-central composite design (RSM-CCD) and artificial neural network-genetic algorithm (ANN-GA) tools were used to optimize the acidogenic performance. Optimized performance was obtained at the hydraulic retention time of 8 d, temperature of 41 °C, the ratio of substrate and inoculum of 4, and pH 10. As a result of combining LFD and HA modification, performance in anaerobic digestion was improved. The highest observed methane yield under the optimal condition was 305.6 mL g−1·TS−1, which was 92.3% higher than the control group. In comparison to the RSM model (R2 = 0.921, RMSE = 20.3), the ANN-GA model indicated a higher R2 and a lower RSME value (R2 = 0.965, RMSE = 13.4), which demonstrated its advantages in determining the non-linear behavior of corn straw anaerobic digestion. The supplementation of LFD along with HA can be useful for enhancing the VFAs yield and bioenergy recovery from CS.