A sludge volume index model based on components of extracellular polymeric substances during temperature decline

ABSTRACTSludge bulking remains a persistent challenge in wastewater treatment plants due to the hardness of its prediction. In this research, sludge bulking was simulated in a sequencing batch reactor (SBR) under stepwise cooling conditions to simulate the temperature fluctuations typically happening in the early winter period. The variations in the formulation and concentrations of extracellular polymeric substances (EPS) and the Sludge Volume Index (SVI) during progressive temperature decline were systematically recorded and analysed. Correlation analysis indicated a positive correlation between SVI and the concentrations of soluble microbial products (SMP), loosely bound EPS (LB-EPS), and tightly bound EPS (TB-EPS). A multiple linear regression (MLR) model was developed to predict SVI based on EPS components, explaining 54.6% of the variation in SVI (adjusted R² = 0.546) with statistical significance (p < 0.01). Model validation confirmed its reliability, demonstrating that the proposed model effectively captures the impact of EPS variations on SVI. This study underscores the critical role of specific EPS components on SVI, providing a responsible approach for real-time monitoring and prediction of sludge bulking during temperature declining in early winter.