Development and Validation of a Multiple Linear Regression Model to Predict the Volume of Biogas Produced in a Temperature-controlled Biodigester

Biogas from anaerobic degradation of biomass has gained a significant share in the renewable energy mix in recent years. It can be produced from different organic substrates such as animal dung, plant residue and kitchen waste. The challenge is in predicting the amount of biogas that can be produced from a given amount of substrate. One possibility of achieving this is to use mathematical modelling. The present work seeks to develop and validate a regression model to forecast the volume of biogas produced using, pH and slurry temperature as predictors. The data used was collected from a 6-cubic meter temperature-controlled underground digester fed with cow dung. The full-scale experiments were conducted in batch mode for two 30-day retention periods. One dataset was used to train the model and the other to validate it. ReliefF test was used to order the importance of the input variables. An Arduino-based temperature-control system maintained the slurry temperature at 35 ± 0.5oC. The multiple linear regression model developed predicted the volume of gas produced accurately (R2 = 0.9597) during the retention period. The cumulative biogas produced was 24.68m3. The Chemical Oxygen Demand (COD) and volatile solids (VS) reductions were 71.78% and 60.60% respectively. The findings from the study can help researchers and stakeholders involved in designing and planning temperature-controlled digesters to correctly forecast the exploitable biogas in a sample of biomass to ensure a reliable source of energy for rural populace, thereby alleviating energy poverty, deforestation and the hustle to search for firewood in rural communities.