Modelling pathogen log10 reduction values achieved by activated sludge treatment using naïve and semi naïve Bayes network models.
Risk management for wastewater treatment and reuse have led to growing interest in understanding and optimising pathogen reduction during biological treatment processes. However, modelling pathogen reduction is often limited by poor characterization of the relationships between variables and incomplete knowledge of removal mechanisms. The aim of this paper was to assess the applicability of Bayesian belief network models to represent associations between pathogen reduction, and operating conditions and monitoring parameters and predict AS performance. Naïve Bayes and semi-naïve Bayes networks were constructed from an activated sludge dataset including operating and monitoring parameters, and removal efficiencies for two pathogens (native Giardia lamblia and seeded Cryptosporidium parvum) and five native microbial indicators (F-RNA bacteriophage, Clostridium perfringens, Escherichia coli, coliforms and enterococci). First we defined the Bayesian network structures for the two pathogen log10 reduction values (LRVs) class nodes discretized into two states (< and ≥ 1 LRV) using two different learning algorithms. Eight metrics, such as Prediction Accuracy (PA) and Area Under the receiver operating Curve (AUC), provided a comparison of model prediction performance, certainty and goodness of fit. This comparison was used to select the optimum models. The optimum Tree Augmented naïve models predicted removal efficiency with high AUC when all system parameters were used simultaneously (AUCs for C. parvum and G. lamblia LRVs of 0.95 and 0.87 respectively). However, metrics for individual system parameters showed only the C. parvum model was reliable. By contrast individual parameters for G. lamblia LRV prediction typically obtained low AUC scores (AUC < 0.81). Useful predictors for C. parvum LRV included solids retention time, turbidity and total coliform LRV. The methodology developed appears applicable for predicting pathogen removal efficiency in water treatment systems generally.
UNSW Water Research Centre, School of Civil & Environmental Engineering, University of New South Wales, NSW, 2052, Australia. Electronic address: guido.carvajal@unsw.edu.au.
Guido
G
Carvajal
UNSW Water Research Centre, School of Civil & Environmental Engineering, University of New South Wales, NSW, 2052, Australia. Electronic address: djroser@unsw.edu.au.
David J
DJ
Roser
School of Mathematics & Statistics, University of New South Wales, NSW, 2052, Australia. Electronic address: scott.sisson@unsw.edu.au.
Scott A
SA
Sisson
Australian Water Quality Centre, SA Water Corporation, Adelaide, SA, 5000, Australia. Electronic address: Alex.Keegan@sawater.com.au.
Alexandra
A
Keegan
UNSW Water Research Centre, School of Civil & Environmental Engineering, University of New South Wales, NSW, 2052, Australia. Electronic address: s.khan@unsw.edu.au.
Stuart J
SJ
Khan
D001499:Bayes Theorem; D016785:Cryptosporidium parvum / Q000502:physiology*; D016829:Giardia lamblia / Q000502:physiology*; D008962:Models, Theoretical; D012722:Sewage / Q000469:parasitology*; D014865:Waste Disposal, Fluid / Q000379:methods*; D018508:Water Purification
D012722:Sewage
Activated sludge; Bayesian belief networks; Modelling; Naïve Bayes; Pathogens; Removal efficiency