An evaluation model for the deep dewatering potential of municipal sludge based on PLS regression

Abstract

For constructing a new convenient and reliable system to evaluate the dewatering performance of sludge, the relationship between the water content of dewatered municipal sludge and its physicochemical properties was comprehensively investigated. Then, an evaluation and prediction model was established based on partial least squares regression (PLS), which could accurately predict the solid content of municipal sludge treated by high-pressure deep dewatering. Furthermore, the main factors affecting the sludge dewaterability were explored by the means of principal component analysis (PCA), and it was concluded that first principal component (PC1), containing most composition of extracellular polymeric substances (EPS), accounted for 53.1% of the X variation and 62.9% of information Y variation. Both protein in soluble EPS (PNS) and polysaccharide in a tightly bound EPS (PST), with the variable importance in the projection (VIP) values more than 1, were regarded as the most decisive factors to the deep dewatering performance of municipal sludge. Additionally, the validation results demonstrate the accuracy and practicability of the obtained model, specifically fit for a conditioning system for cationic polyacrylamide (CPAM) and lysozyme either separately or jointly.