Optimizing the process parameters to maximize biogas yield from anaerobic co-digestion of alkali-treated corn stover and poultry manure using artificial neural network and response surface methodology

Large amounts of poultry manure (PM), corn stover (CS), and cattle manure are generated annually posing a serious environmental problem that must be addressed. Anaerobic co-digestion of poultry manure with alkali-treated corn stover is becoming more popular as a way to enhance biogas generation and organic waste management. The present study investigated the effect of four independent variables (temperature, hydraulic retention time, pH, and PM to alkali-treated corn stover ratio) on the biogas yield. Response surface methodology (RSM) and artificial neural network (ANN) have been used to optimize and predict biogas production via an anaerobic co-digestion process. The results revealed that the properly trained artificial neural network model was found to be more powerful modeling capability and accurate in prediction as compared to response surface methodology. The optimum conditions were found to be a temperature of 37 degrees C, a hydraulic retention time of 13 days, pH of 7, and 80% blending ratio (PM to alkali-treated corn stover). Under these conditions, the model predicted a biogas yield of 745 mL/g TS with a desirability value of 0.995. Generally, the findings of the study suggest that co-digestion of PM and alkali-treated corn stover is a promising way to increase the production of biogas by ensuring nutrient balance.

Automated summary

The study found that co-digestion of PM and alkali-treated corn stover can increase biogas production, with key conditions including temperature, hydraulic retention time, pH, and blending ratio. The artificial neural network model showed better accuracy and modeling capability in predicting biogas yield.