Disinfection By-Product Modeling Using Bayesian Networks

Issues of Disinfection by-products (DBPs) formation in response to chlorination in drinking water treatment systems is a common issue encountered by water treatment plant (WTPs) operators. This is complicated by the presence of zebra mussels, which may inhabit the raw water intake of WTPs. While chlorination at the intake to control zebra mussel populations is effective, the formation of DBPs is exacerbated. To evaluate alternative approaches of chlorination, a Bayesian network is developed using the Webweavr-IV Toolkit, utilizing causal relationships between raw water quality parameters in the form of conditional probabilities.

Four alternative chlorination scenarios are analyzed, one of which demonstrates the probability of high TTHM concentrations (>80µg/L) can be reduced from 25.2% to 24.5%; and the probability of high cancer risk from TTHM (>10^-5) can be reduced from 96.6% to 96.2%.