Abnormal Event Detection Platform Design for a Wastewater Quality Monitoring System

Abstract

The introduction of water quality monitoring systems (WQMSs) into the city wastewater discharging and recycling system has been considered as an effective practice to address the potential risks to wastewater plants as well as public concerns associated with pollution source localization. Under such background, a WQMS was developed and deployed in a large Australia Research Council (ARC) Linkage project entitled “A New Management System for Effective Wastewater Source Control” to meet the requirements of several major Australian water utilities. A WQMS was developed for this ARC Linkage project, which aims to monitor the wastewater quality in city wastewater pipelines to safeguard purified recycled water (PRW) system in water treatment companies. The developed WQMS contains both welldesigned hardware and reliable software and implements primary functions including data collection, data processing, event detection, alarming, a central server and web-based user interface (UI). To explore the potentials of existing event detection methods using in wastewater application environment, six widely used event detection methods in drinking and natural water area are evaluated, including Cumulative Sum (CUSUM), Linear Prediction, Moving Median, Exponentially Weighted Moving Average (EWMA), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs). The evaluation is performed on real datasets collected from 10 different pump stations with WQMS deployed. Referring to the results of the performance evaluation on some existing event detection methods, we designed and developed two methods according to the requirements of the practical application. The first modified Bayesian analysis method achieves good performance in testing, and it has been applied in deployed WQMSs and trialed for over four years without any significant issues. The second one is an ANN-based method with multiple classifiers integrated. It achieves excellent overall performance among ten testing sites although the high computational cost limits its practical value.