Benchmarking Tabular Foundation Models for Total Volatile Fatty Acid Prediction in Anaerobic Digestion

Monitoring the concentration of Total Volatile Fatty Acids (TVFA (M)) is critical for ensuring the stability and efficiency of the Anaerobic Digestion (AD) process although conventional laboratory methods are often time-consuming and hinder real-time control. This study develops soft sensors based on machine learning techniques to predict TVFA (M) levels using readily available parameters such as pH, pCO2, and Total Ammoniacal Nitrogen (TAN). A primary contribution of this work is the comprehensive benchmarking of the proposed approach against current State-of-the-Art (SOTA) deep learning and machine learning models including XGBoost, Random Forest, TorchMLP, and the advanced RealTabPFN-v2.5. Experimental results demonstrate that the RealTabPFN-v2.5 model outperforms other modern algorithms by achieving the highest accuracy with an R2 of 0.889 and the lowest error rate with an RMSE of 0.0079. SHAP (SHapley Additive exPlanations) analysis was employed to interpret the model’s predictions, identifying pH as the most influential factor in TVFA (M) prediction and confirming that the model’s decision-making process aligns with established biological principles. These findings highlight the significant potential of integrating SOTA machine learning models into intelligent monitoring systems for the automation and optimization of biogas production processes.