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The need to provide a scientific basis for biogas technology planning, control and management cannot be overemphasized in the search for sustainable renewable energy options. Hence, a proactive study to develop a predictive tool that would assist in the design of bioreactor was undertaken. This was achieved by investigating the kinetics of anaerobic digestion of substrates for biogas production under laboratory conditions using an experimental setup of a 10 litre bioreactor. Chicken droppings and cow dung were used differently as test samples, while biogas production was monitored and collected by water displacement method. 58.5 and 12 litres of biogas were obtained from poultry droppings and cow dung substrates, respectively within optimum pH of 6.5-7.3. Kinetic parameters for biodegradation of substrates were determined by conducting biochemical tests of dissolved oxygen (DO), biochemical oxygen demand (BOD), and total suspended solids (TSS). Biodegradation growth kinetics were derived, and kinetic constants were obtained from test results. This aided the formulation of mathematical expressions for biogas production estimate. The mathematical models obtained were checked for homogeneity using a system of dimensional analysis. The formulae were used to predict daily biogas yield, which was compared with the experimental biogas production. The volume of biogas produced was affected by the yield coefficient (y), substrate mass (M), retention time (θ), rate of biogas production (R), and the biogas potential of substrate (Bg). Overall, the mathematical model developed could be a veritable tool for the design, assessment and prediction of biogas production of any substrate.
Chicken droppings; cow dung; bioreactor; anaerobic digestion; biogas
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Mathematical Modelling of Biogas Production from Animal Waste Via Anaerobic Biodegradation
How to cite this paper: Ozokolie Chukwunonso Sampson, Okonkwo Wilfred Ifeanyi, Cosmas Anyanwu, Tagne Takote Brice Clausel. (2025). Mathematical Modelling of Biogas Production from Animal Waste Via Anaerobic Biodegradation. International Journal of Statistics and Data Science, 1(1), 49-52.
DOI: http://dx.doi.org/10.26855/ijsds.2025.12.005