Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater

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• 作者：Guido Carvajal^a ; ^{guido.carvajal@unsw.edu.au} ; David J. Roser^a ; ^{djroser@unsw.edu.au} ; Scott A. Sisson^b ; ^{scott.sisson@unsw.edu.au} ; Alexandra Keegan^c ; ^{Alex.Keegan@sawater.com.au} ; Stuart J. Khan^a ; ^{s.khan@unsw.edu.au}
• 关键词：Bayesian belief networks ; Chlorination ; Modelling ; Multilayer perceptron ; Removal efficiency ; Virus
• 刊名：Water Research
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：109
• 期：Complete
• 页码：144-154
• 全文大小：1939 K
• 卷排序：109

文摘

Disinfection of Coxsackievirus B5 and Adenovirus 2 was modelled using Bayes nets. Interpolation of pH and turbidity values was possible through a Bayesian multilayer perceptron model. The combined effects of pH and turbidity on disinfection performance were assessed. Prediction of target CT and log reduction values for various scenarios could be obtained. Individually, pH had a higher impact than turbidity on target CT values.