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.

Dairy cattle manure serves as a potential source of contamination and infection of animals, humans and the environment. Manure samples withdrawn from a balloon-type digester during anaerobic digestion were evaluated for the presence of antibiotic-resistant bacterial pathogens. The bacterial load of the samples was determined via a viable plate count method and the recovered isolates were subjected to characterisation and identification. These isolates were employed in antibiotic susceptibility testing using a disc diffusion method against a suite of 10 conventional antibiotics. The multiple antibiotic resistance (MAR) index was calculated and MAR phenotypes were generated. Although all the bacterial pathogens showed a certain degree of resistance to the studied antibiotics, a marked resistance was demonstrated by Campylobacter sp. to co-trimoxazole (87.5%) and nalidixic acid (81.5%). Remarkably, a high resistance (82.42%) was demonstrated against the antibiotic class, macrolide, followed by beta-lactams (40.44%), suggesting that bacterial resistance depended on the chemical structure of the antibiotics. However, individual bacterial isolates varied in resistance to particular antibiotics. Of the 83 bacterial isolates, 40(48.19%) observed MAR > 0.2 and, thus, were described as multidrug-resistant isolates. A total of 28 MAR phenotypes were revealed with the highest frequency of MAR phenotypes (37.5%) expressed against 3 antibiotics. Results indicated a high risk of exposure to various antibiotics and wide diversity of antibiotic resistance.