Anaerobic digestion of abattoir wastes for biogas production: optimization via performance evaluation comparison

Abstract

Anaerobic digestion of abattoir wastes under mesophilic conditions was carried out to investigate how different modeling tools affect biogas yield in order to be subsequently used for process optimization. Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) were employed to optimize the process, and to assess the individual and interactive effect of incubation time, temperature, and pH on biogas yield. The digester used in this study produced an average biogas yield of 0.00103 m³/kg VS daily from cow-dung. Gas chromatographic analysis of the produced biogas showed the methane content to be 66.8%. The conditions for optimum biogas yield as predicted by RSM were incubation time of 28.98 days, temperature and pH of 30.16°C, and 7.43, respectively. For ANNs, the incubation time, temperature, and pH for optimum biogas yield were 26.76 days, 30.94°C, and 7.27, respectively. With these conditions, biogas yield by RSM was Olayomi Abiodun Falowo m³/kg VS while that of ANNs was Olayomi Abiodun Falowo m³/kg VS. Model validation by experimental tests showed that ANN is better in terms of prediction and accuracy than the RSM, though, the two techniques complemented each other in interpreting the interactive effects of the input variables on the biogas production.