Development of a mathematical model and validation for methane production using cow dung as substrate in the underground biogas digester

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Abstract

South Africa is highly dependent on conventional fuels from non-renewable energy sources such as coal and fossil fuels. The methane from biogas is a renewable energy source for generating electricity using a combined heat and power plant. Therefore, the aim of the study is to develop and validate a mathematical model for predicting methane production in an underground biogas digester. The developed model was able to predict the production of methane gas as separate entity differing from other models. A total of 286 datasets were used as a trained dataset for the model development, and 144 datasets served as test data for the validation of the model, making a total of 430 measured datasets of all the predictors. The determination coefficient (R2) and the p-value of the predicted and calculated methane yield were 0.962 and 0.920, respectively. The high R2 in the present study confirms a good correlation between the model and experimental value. Hence, the model is of significance because it is applicable in predicting the performance of methane production of systems of the same design used in different locations, thereby arriving at the same constant values. From the study, the ambient weather factors (ambient temperature, relative humidity, and global horizontal irradiance) affected the methane production. Additionally, the indoor parameters (pH, gas temperature, slurry bottom and slurry top temperature) impacted on the yield of the methane production because the scaling factors associated with these quantities are non-zero real numbers. Hence, the scope of the study did not consider the volume of the biogas digester as an input parameter to the response.

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Obileke, K. C., Mamphweli, S., Meyer, E. L., Makaka, G., & Nwokolo, N. (2021). Development of a mathematical model and validation for methane production using cow dung as substrate in the underground biogas digester. Processes, 9(4). https://doi.org/10.3390/pr9040643

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