Date of Award

2005

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Electrical and Computer Engineering

First Advisor

A Guergachi

Second Advisor

Gul Khan

Abstract

In order to meet the more stringent environmental regulations, the adaptive and optimal control strategies should be investigated for the biological nitrogen removal (BNR) processes in wastewater treatment plants. Because of the complex nature of the microbial metabolism involved, the conventional mechanistic models for nitrogen removal are difficult to formulate and the existing ones are still uncertain to some extent. Alternatively, the machine learning methods have been investigated as black-box modelling techniques. A new approach, Support Vector Machine (SVM) was proposed to be used to model the biological nitrogen removal processes in this thesis. Specifically, LS-SVM, a simplified formulation of SVM, was applied to predict the concentration of nitrate & nitrite (NO). The simulation results indicate that the proposed method has better generalization performance in comparison with generalized regression neural network, especially under weather conditions that are quite different from the training weather conditions.