%0 Conference Paper
%A Okwu, M.O.
%A Oyejide, O.J.
%A Oyekale, J.
%A Ezekiel, K.
%A Maware, C.
%A Orikpete, O.F.
%A Okonkwo, C.P.
%D 2024
%F scholars:20078
%I Elsevier B.V.
%K Anaerobic digestion; Energy conversion; Forecasting; Mammals; Membership functions; Stochastic systems, Accurate prediction; Anaerobic co-digestion; Biodigesters; Codigestion; Decomposable waste; Energy conversion systems; Fuzzy mamdani model; Mamdani model; Waste to energy conversion; Yield prediction, Biogas
%P 2259-2268
%R 10.1016/j.procs.2024.02.045
%T Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes
%U https://khub.utp.edu.my/scholars/20078/
%V 232
%X Accurate prediction of biogas yield is crucial for optimizing waste-to-energy conversion systems in anaerobic co-digestion processes. In this study, a double input and single output (DISO) fuzzy mamdani model (FMM) was developed for the prediction of biogas yield in a pilot scale of 105-L mesophilic anaerobic sludge bio-digester. The input variables considered are the combination of cow dung and pig waste and the retention time (RT), while the output variable is the experimental biogas yield. Triangular Fuzzy Membership Functions (TFMF) were utilized to define the input and output datasets, and rules were derived from de-fuzzification. Comparative analysis between the FMM's predicted results and experimental values showcased its effectiveness in forecasting biogas yield during the anaerobic co-digestion of the hybrid wastes. Significantly, the FMM consistently produced results with low error values for the sample dataset, underscoring its accuracy even under stochastic conditions. This study emphasizes the FMM's ability to generate predictions with minimal deviations, offering superior results. As a prospect for future research, the implementation of hybrid algorithms may further enhance biogas yield prediction accuracy within waste-to-energy systems. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
%Z cited By 0; Conference of 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 ; Conference Date: 22 November 2023 Through 24 November 2023; Conference Code:198427