Estimation of biogas reactor constants using multiple regression analysis

Thesis Abstract

Biogas generation is accomplished by anaerobic digestion of biodegradable material in a biogas digester. Anaerobic digestion as a process requires dynamic model for predicting process performance. Mathematical model for biogas production from anaerobic digesters described in this study was used to predict biogas generation using experimental data. Multiple regression analysis was used in analyzing two sets of data, the first sets of data contains four different experimental results of biogas yield in which the biogas production rate was determined weekly. The second sets of data containing four different experimental results had biogas production rate determined daily, while the first data sets were used as a curve fitting model, the second sets were used for the verification of the model derived. The predicted values of biogas yield were close to the measured values with a maximum correlation coefficient, R = 0.91. It was further showed that the regression constants were dependent on temperature only. Hence, the predictive capability of the model can be improved by making those regression constants dependent on temperature.