title: Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater.
abstract: Chlorine disinfection of biologically treated wastewater is practiced in many locations prior to environmental discharge or beneficial reuse. The effectiveness of chlorine disinfection processes may be influenced by several factors, such as pH, temperature, ionic strength, organic carbon concentration, and suspended solids. We investigated the use of Bayesian multilayer perceptron (BMLP) models as efficient and practical tools for compiling and analysing free chlorine and monochloramine virus disinfection performance as a multivariate problem. Corresponding to their relative susceptibility, Adenovirus 2 was used to assess disinfection by monochloramine and Coxsackievirus B5 was used for free chlorine. A BMLP model was constructed to relate key disinfection conditions (CT, pH, turbidity) to observed Log Reduction Values (LRVs) for these viruses at constant temperature. The models proved to be valuable for incorporating uncertainty in the chlor(am)ination performance estimation and interpolating between operating conditions. Various types of queries could be performed with this model including the identification of target CT for a particular combination of LRV, pH and turbidity. Similarly, it was possible to derive achievable LRVs for combinations of CT, pH and turbidity. These queries yielded probability density functions for the target variable reflecting the uncertainty in the model parameters and variability of the input variables. The disinfection efficacy was greatly impacted by pH and to a lesser extent by turbidity for both types of disinfections. Non-linear relationships were observed between pH and target CT, and turbidity and target CT, with compound effects on target CT also evidenced. This work demonstrated that the use of BMLP models had considerable ability to improve the resolution and understanding of the multivariate relationships between operational parameters and disinfection outcomes for wastewater treatment.
authors.affiliation: UNSW Water Research Centre, School of Civil & Environmental Engineering, University of New South Wales, NSW 2052, Australia. Electronic address: guido.carvajal@unsw.edu.au.
authors.forename: Guido
authors.initials: G
authors.lastname: Carvajal
authors.affiliation: UNSW Water Research Centre, School of Civil & Environmental Engineering, University of New South Wales, NSW 2052, Australia. Electronic address: djroser@unsw.edu.au.
authors.forename: David J
authors.initials: DJ
authors.lastname: Roser
authors.affiliation: School of Mathematics & Statistics, University of New South Wales, NSW 2052, Australia. Electronic address: scott.sisson@unsw.edu.au.
authors.forename: Scott A
authors.initials: SA
authors.lastname: Sisson
authors.affiliation: Research and Innovation Services, SA Water Corporation, Adelaide, SA 5000, Australia. Electronic address: Alex.Keegan@sawater.com.au.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Keegan
authors.affiliation: UNSW Water Research Centre, School of Civil & Environmental Engineering, University of New South Wales, NSW 2052, Australia. Electronic address: s.khan@unsw.edu.au.
authors.forename: Stuart J
authors.initials: SJ
authors.lastname: Khan
mesh_terms: D001499:Bayes Theorem; D002713:Chlorine; D004202:Disinfectants; D004203:Disinfection; D006801:Humans; D014780:Viruses; D062065:Wastewater
chemical_list: D002713:Chlorine; D004202:Disinfectants; D062065:Wastewater
keywords: Bayesian belief networks; Chlorination; Modelling; Multilayer perceptron; Removal efficiency; Virus