Identifying critical components causing seasonal variation of activated sludge settleability and developing early warning tool

Settleability of activated sludge is one of the most common problems that restricts the efficiency of activated sludge system. Obvious seasonal variation of settleability was found in the activated sludge system of a full scale wastewater treatment plant (WWTP) during 2 years of observation. Principal component analysis (PCA) was applied to study the correlation between diluted sludge volume index (DSVI), operational and environmental factors. As a result, temperature and mixed liquid suspended solids (MLSS) were found as the most significant variables relating with DSVI variation. Multivariate regression, partial least squares regression and support vector machine regression were applied to develop early warning models for DSVI prediction. The multivariate regression model was proved as a simple and easy-to-interpret early warning tool to be applied in practice. Based on the ratio of volatile substances in biomass, the original cause of seasonal variation of settleability was further discussed by referring the storage-biodegradation mechanism. Moreover, the results of this work also suggested that modern statistical techniques were important to investigate complicated engineering problems. This study provided insights of seasonal variation of activated sludge settleability by systematic investigation of long-term data of a full scale WWTP.