Kinetic modelling of the biomethane production potential of acidic pretreated groundnut shells

Process parameter optimization is of significant interest in the search for renewable energy from biomass. This study investigates the potential of three kinetic models to analyze and optimize biomethane production from acidic pretreated groundnut shells. First-order, logistic, and Gompertz models were investigated. Groundnut shells were pretreated with H₂SO₄ at different concentrations, temperatures, and exposure times before anaerobic digestion. The cumulative biomethane yield was used to validate the kinetic models developed. The model accuracy was determined using performance metrics of root mean square error (RMSE), Akaike’s information criterion (AIC), correlation coefficient (R²), and percentage difference (%diff). A comparative analysis was carried out to ascertain the best-fit model. The results show that H₂SO₄ pretreatment improves the biomethane yield by 62–178%. The value of the pretreated feedstock’s lag phase (λ) is lower (0.4) than that of the untreated feedstock (2.74), indicating pretreatment’s efficiency in reducing the retention time. The models’ performance shows an RMSE of 10.57–77.42, AIC of 253.94–281.74, R² of 0.9708–0.9967, and %diff of 0.18–1.55%. It was noticed from the model that pretreatment conditions are significant to the performance of the models. The performance metrics show that all the models can analyze and optimize the biomethane yield of pretreated lignocellulose feedstock, and the Gompertz model produces the highest accuracy. Findings from this study can be applied to optimize and predict biomethane production from acidic pretreated lignocellulose feedstock at the commercial scale.