Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes

作     者：M.O. Okwu O.J. Oyejide J. Oyekale K. Ezekiel C. Maware O.F. Orikpete C.P. Okonkwo 

作者机构：Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Department of Mechanical Engineering Universiti Teknologi Petronas Seri Iskandar Malaysia Maintenance Engineering and Asset Management University of Manchester United Kingdom Fugio Cho Engineering Technology 210E Center for Robotics and Manufacturing Building University of Kentucky USA Centre for Occupational Health Safety and Environment (COHSE) University of Port Harcourt Choba Nigeria Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Nigeria 

出 版 物：《Procedia Computer Science》 

年 卷 期：2024年第232卷

页      面：2259-2268页

主　　题：Waste-to-Energy Conversion Fuzzy Mamdani Model biodigester co-digestion decomposable waste 

摘      要：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.