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

Amangeldy B. Baigarayeva Z. Tasmurzayev N. Boltaboyeva A. Imanbek B. Maulenbekov M. Zhussupbekov S. Wojcik W. Kozhamberdieva M. Konysbekova A.
February 2026 Multidisciplinary Digital Publishing Institute (MDPI)

Algorithms
2026 #19 Issue 2

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.

anaerobic digestion , biogas process monitoring , deep learning , neural networks , RealTabPFN , SHAP analysis , soft sensors , state-of-the-art (SOTA) models , total volatile fatty acids (TVFA (M))

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Joldasbekov Institute of Mechanics and Engineering, Almaty, 050010, Kazakhstan
Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty, 050040, Kazakhstan
LLP “Kazakhstan RD Solutions”, Almaty, 050056, Kazakhstan
Institute of Automation and Information Technology, Satbayev University, Almaty, 050013, Kazakhstan
Department of Automation and Control, Energo University, Almaty, 050013, Kazakhstan
Institute of Electronics and Information Technology, Politechnika Lubelska, Lublin, 20-618, Poland
JSC “Research Institute of Cardiology and Internal Diseases”, Almaty, 050000, Kazakhstan

Joldasbekov Institute of Mechanics and Engineering
Faculty of Information Technologies and Artificial Intelligence
LLP “Kazakhstan RD Solutions”
Institute of Automation and Information Technology
Department of Automation and Control
Institute of Electronics and Information Technology
JSC “Research Institute of Cardiology and Internal Diseases”

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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026