title: Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model.
abstract: Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking.
authors: School of Automation Science &Engineering, South China University of Technology, Wushang Road, Guangzhou 510640, China.|Advanced Water Management Centre, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia.
authors: Yiqi
authors: Y
authors: Liu
authors: Advanced Water Management Centre, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia.
authors: Jianhua
authors: J
authors: Guo
authors: Advanced Water Management Centre, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia.
authors: Qilin
authors: Q
authors: Wang
authors: School of Automation Science &Engineering, South China University of Technology, Wushang Road, Guangzhou 510640, China.
authors: Daoping
authors: D
authors: Huang