Comparative Analysis of Five Kinetic Models for Prediction of Methane Yield

Autor: S. Roberts, N. Mathaka, M. A. Zeleke, K. N. Nwaigwe
Rok vydání: 2023
Předmět:
Zdroj: Journal of The Institution of Engineers (India): Series A. 104:335-342
ISSN: 2250-2157
2250-2149
DOI: 10.1007/s40030-023-00715-y
Popis: A work on the analysis of five kinetic models for predicting methane yield is presented. The selected five common models for predicting methane yield include Gompertz, logistic, first-order, Richards and transfert models. Anaerobic digestion of orange and banana peels was simulated using the selected models. A comparative analysis of the models was carried out to determine the best-fit model. When predicting the methane yield of banana peels, all models reached a maximum of 99.23% and the model which was most accurate was the Gompertz model with 76% cumulative deviation and the least accurate model was the transfert model with 274.7% cumulative deviation. The prediction of methane yield on orange peels was 99.49% accurate for all models having the most accurate model being the Gompertz and first-order kinetic model with a cumulative deviation of 20.67% and the least accurate model being the transfert model with a cumulative deviation of 112%. This work presents the simulation considerations and application for each model.
Databáze: OpenAIRE
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