Modeling and optimization of the hydrolysis and acidification via liquid fraction of digestate from corn straw by response surface methodology and artificial neural network
2022
Liu, Yue | Gong, Hui | Shi, Chuan | Yuan, Hairong | Zuo, Xiaoyu | Chang, Yanqing | Li, Xiujin
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.
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