Support Vector Machines Based Approach for Chemical Phosphorus Removal Process in Wastewater Treatment Plant

In this research, support vector machine (SVM) is investigated to model the uncertainty in chemical phosphorus removal processes in wastewater treatment plants. SVM is a machine-learning method based on the principle of structural risk minimization, which performs well when applied to data outside the training set. The prediction whether or not the concentration of total phosphorus as P in the effluent will exceed the maximum allowable limit (1.0 mg/L) for a certain input is considered a supervised-learning problem. Least squares support vector machines (LS-SVMs) algorithm, which is a reformulation of standard SVMs, is used to design the classifier. Performance of radial basis function (REF), polynomial and multi-layer perceptron (MLP) kernels has been evaluated and a high classification rate of 88.52% was achieved using radial basis function (RBF) kernel