Optimisation of biogas yield from anaerobic co-digestion of dual waste for environmental sustainability: ANN, RSM and GA approach

The main objective of this study was to find the best way to turn food waste and animal manure into biogas. In this research work, an L25 orthogonal array was developed for three factors and five levels of parameters and optimised through the response surface method (RSM) and genetic algorithm (GA). Experiments were conducted to collect data on the variation of the cattle dung and food waste mixing ratios (25:75, 50:50, 75:25, 100:0 and 0:100 w/w %), retention times (7, 9, 11, 13 and 15 days), and digester temperatures (20, 25, 30, 35 and 40°C). Concerning the obtained data, an artificial neural network (ANN) model has been developed to estimate biogas production yield. The RSM and GA analyses showed that the optimal parameters were a 0:100 (w/w %) mixing ratio, 15-day retention time, and at 40°C digester temperature and the corresponding biogas yield was 551.774 ml/day and 551.776 ml/day, respectively.