Classification of Activated Sludge Settleability Using Linear and Nonlinear Classification Functions

Abstract

In this paper, two classifiers are proposed to distinguish between bulking and non-bulking situations in an activated sludge wastewater treatment plant, based on available image analysis information. The first classifier consists of a simple linear classification function, while the second classifier uses a highly nonlinear least squares support vector machine (LS-SVM) to distinguish between both situations. It is shown that the nonlinear LS-SVM classification function outperforms the linear classifier. Both exhibit identical misclassification rates, but fewer samples are located in the uncertainty area when using the nonlinear classifier. However, this better classification performance requires the identification of a substantial amount of model parameters, while the linear classifier is, except for the threshold values, parameterless.