Biogas Prediction Enhancement for a Swine Farm Bio-Digester Using a Lag-Based Surrogate Machine Learning Model

Biogas production estimation has been one of the most important and challenging objectives for anaerobic digestion processes due to the complexity of its dynamics and the lack of high-quality open-access datasets. This study presents a hybrid modeling framework that combines a mechanistic model, based on ordinary differential equations (ODEs), with a machine learning model. Rather than relying exclusively on experimental data, the proposed approach leverages physics-informed synthetic data generation, complemented by a lag-based feature engineering to capture inherent temporal dependencies in the process dynamics available in operational data of a bio-digester. Two configurations were evaluated: a baseline model and an enhanced version incorporating lag features and a simplified temperature profile. This specific computational enhancement provides a robust predictive core that successfully avoids the severe predictive degradation observed in purely mechanistic approaches at high spatial discretizations. While the improved surrogate model achieved high predictive performance (R2=0.9788, RMSE=131.80 [L/d]), additional analyses reveal that this resilience is driven by temporal memory and remains sensitive to noise and feature composition. Instead of presenting the model as a final independent physical validation, this work is rigorously framed as a proof-of-concept digital twin core, acknowledging the gap that still exists between simulation-based ODE emulation and unstructured real-world reliability.