Thermodynamics and explainable machine learning assist in interpreting biodegradability of dissolved organic matter in sludge anaerobic digestion with thermal hydrolysis

Abstract

Dissolved organic matter (DOM) is essential in biological treatment, yet its specific roles remain incompletely understood. This study introduces a machine learning (ML) framework to interpret DOM biodegradability in the anaerobic digestion (AD) of sludge, incorporating a thermodynamic indicator (λ). Ensemble models such as Xgboost and LightGBM achieved high accuracy (training: 0.90–0.98; testing: 0.75–0.85). The explainability of the ML models revealed that the features λ, measured m/z, nitrogen to carbon ratio (N/C), hydrogen to carbon ratio (H/C), and nominal oxidation state of carbon (NOSC) were significant formula features determining biodegradability. Shapley values further indicated that the biodegradable DOM were mostly formulas with λ lower than 0.03, measured m/z value higher than 600 Da, and N/C ratios higher than 0.2. This study suggests that a strategy based on ML and its explainability, considering formula features, particularly thermodynamic indicators, provides a novel approach for understanding and estimating the biodegradation of DOM.