Development of ternary models for prediction of biogas yield in a novel modular biodigester: a case of fuzzy Mamdani model (FMM), artificial neural network (ANN), and response surface methodology (RSM)

Fuzzy Mamdani Model (FMM), Artificial Neural Network (ANN), and Response Surface Methodology (RSM) models were adopted to model and optimise biogas production on assorted substrates of poultry wastes (PW) and cow dung in a modular biodigester system. The gas produced at retention time of 42 days was within the range of 50 to 52 ml/day. The temperature range was between the range of 20 and 40 °C, while the pH values were stable at optimal range of 8.0. The statistical analysis of the experimental result obtained from methane yield, using different computational technique, reveals that fuzzy Mamdani model reflect zero (0) values for average absolute deviation (AAD), mean average error (MAE), standard error of prediction (SEP), and root-mean-square error (RMSE) and average coefficient of determination (R²) of one (1.0). ANN result obtain showed AAD value of 0.00011991, MAE value of 0.000061538, SEP value of 0.0000044369, and RMSE value of 0.00022188 and R² value of 1.0, and RSM result reveals absolute AAD of 0.0030, MAE of 0.0015, SEP of 0.00011092, RMSE of 0.0055, and R² of 0.9998. It can be concluded from the result of the analysis that RSM gave a good prediction of the model while ANN and FMM models produced slightly better results. The result obtained from the FMM model seems marginally better and superior in terms of forecasting of biogas and methane production rate with satisfactory outcome.