eprintid: 20078
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/02/00/78
datestamp: 2024-06-04 14:19:49
lastmod: 2024-06-04 14:19:49
status_changed: 2024-06-04 14:16:33
type: conference_item
metadata_visibility: show
creators_name: Okwu, M.O.
creators_name: Oyejide, O.J.
creators_name: Oyekale, J.
creators_name: Ezekiel, K.
creators_name: Maware, C.
creators_name: Orikpete, O.F.
creators_name: Okonkwo, C.P.
title: Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes
ispublished: pub
keywords: 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
note: 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
abstract: 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)
date: 2024
publisher: Elsevier B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189778991&doi=10.1016%2fj.procs.2024.02.045&partnerID=40&md5=2303c78fced92972df1b352518a890a1
id_number: 10.1016/j.procs.2024.02.045
full_text_status: none
publication: Procedia Computer Science
volume: 232
pagerange: 2259-2268
refereed: TRUE
issn: 18770509
citation:   Okwu, M.O. and Oyejide, O.J. and Oyekale, J. and Ezekiel, K. and Maware, C. and Orikpete, O.F. and Okonkwo, C.P.  (2024) Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes.  In: UNSPECIFIED.