• Journal of Korean Society of Water and Wastewater
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• v.31 no.5
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• pp.421-430
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• 2017

In this study, a model was developed to predict for Disinfection By-Products (DBPs) generated in water supply networks and consumer premises, before and after the introduction of advanced water purification facilities. Based on two-way ANOVA, which was carried out to statistically verify the water quality difference in the water supply network according to introduce the advanced water treatment process. The water quality before and after advanced water purification was shown to have a statistically significant difference. A multiple regression model was developed to predict the concentration of DBPs in consumer premises before and after the introduction of advanced water purification facilities. The prediction model developed for the concentration of DBPs accurately simulated the actual measurements, as its coefficients of correlation with the actual measurements were all 0.88 or higher. In addition, the prediction for the period not used in the model development to verify the developed model also showed coefficients of correlation with the actual measurements of 0.96 or higher. As the prediction model developed in this study has an advantage in that the variables that compose the model are relatively simple when compared with those of models developed in previous studies, it is considered highly usable for further study and field application. The methodology proposed in this study and the study findings can be used to meet the level of consumer requirement related to DBPs and to analyze and set the service level when establishing a master plan for development of water supply, and a water supply facility asset management plan.

• Journal of Korean Society on Water Environment
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• v.26 no.3
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• pp.460-467
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• 2010

For D-WTP's sedimentation basin and distribution reservoir, and water tap the predictive models proposed tentatively herein included the models for estimating TTHM concentration in precipitated water, for treated water and for tap water, and the estimated correlation formula between treated water's TTHM concentration and tap water. As for TTHM-concentration predictive model in sedimentation water, the coefficient of determination is 0.866 for best-fitted short-term $DOC{\times}UV_{254}$ based Model (TTHM). As for $HAA_5$-concentration predictive model in sedimentation water, the coefficient of determination is 0.947 for the suitable $UV_{254}$-based model ($HAA_5$). In case of the predictive model in treated water, the coefficient of determination is 0.980 for best-fitted $DOC{\times}UV_{254}$ based model (TTHM) using coagulated waters, while the coefficient of determination is 0.983 for best-fitted $DOC{\times}UV_{254}$ based model ($HAA_5$) using coagulated waters, which described the $HAA_5$ concentration well. However, the predictive model for tap water could not be compatible with the one for treated water, only except for possibility inducing correlation formula for prediction, [i.e., the correlation formula between TTHM concentration and tap water was verified as TTHM (tap water) = $1.162{\times}TTHM$ (treated water), while $HAA_5$ (tap water) = $0.965{\times}HAA_5$ (treated water).] The correlation analysis between DOC and $KMnO_4$ consumption by process resulted in higher relationship with filtrated water, showing that its regression is $DOC=0.669{\times}KMnO_4$ consumption - 0.166 with 0.689 of determination coefficient. By substituting it to the existing DOC-based model ($HAA_5$) for treated water, the consequential model formula was made as follows; $HAA_5=8.35(KMnO_4\;consumption{\times}0.669-0.166)^{0.701}(Cl_2)^{0.577}t^{0.150}0.9216^{(pH-7.5)}1.022^{(Temp-20^{\circ}C)}$