Development of ternary models for prediction of biogas yield in a novel modular biodigester: a case of fuzzy Mamdani mod
- PDF / 921,269 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 105 Downloads / 175 Views
ORIGINAL ARTICLE
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) Modestus O. Okwu 1,2 & Olusegun D. Samuel 1 & Omonigho B. Otanocha 1 & Lagouge K. Tartibu 2 & Henry O. Omoregbee 3 & Victor M. Mbachu 4 Received: 5 September 2020 / Revised: 21 October 2020 / Accepted: 23 October 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords FuzzyMamdani model . Artificialneuralnetwork . Responsesurface methodology . Anaerobicdigestion . Feedstock . Biogas yield
1 Introduction In recent past, biogas production has proven to be an ecofriendly energy source and a successful way of reducing pollution and the greenhouse gas emission [1]. Biogas production * Modestus O. Okwu [email protected] 1
Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, Warri, Delta State, Nigeria
2
Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg, South Africa
3
Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria
4
Department of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
and its usage will greatly reduce global warming and offer an opportunity for extenuation of greenhouse gas emission [2]. Hence, research in this field of study has attracted the interest of academia and researchers. Innovations on possible biological means of improving biogas production have been documented [3]. The focus has been on efficien
Data Loading...
