<![CDATA[Biogas production through anaerobic fermentation is a promising renewable energy alternative that continues to gain attention. To improve the accuracy and efficiency of production predictions, kinetic modeling approaches that describe the underlying biological processes are essential. This study compares three kinetic models Gompertz, Logistic, and Transference in predicting biogas production under varying pH conditions, with the aim of identifying the model that best represents the experimental data. The models were evaluated based on parameters including maximum production capacity (Ym), maximum production rate (U), lag time (λ), and prediction errors quantified by the sum of squared errors (SSE), root mean square error (RMSE), and coefficient of determination (R²). The results demonstrate that the Transference model consistently outperforms the other models. At neutral pH (pH 7), the Transference model predicted a maximum biogas production of 2127.11 cm³, a maximum daily production rate of 158.23 cm³/day, a short lag phase of 0.947 days, a low SSE value of 3223.45, and an R² value of 1.000, indicating an excellent fit to the experimental data. Compared to the Gompertz and Logistic models, the Transference model exhibited greater stability, accuracy, and realism in representing the biogas production process. These findings indicate that the Transference model is a reliable predictive tool for the design and optimization of biogas production systems, particularly under optimal pH conditions.]]>
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