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.

Ten empirical disinfection models for the plasma process were used to find an optimum model. The variation of model parameters in each model according to the operating conditions (first voltage, second voltage, air flow rate, pH, incubation water concentration) were investigated in order to explain the disinfection model. In this experiment, the DBD (dielectric barrier discharge) plasma reactor was used to inactivate Phytophthora capsici which cause wilt in tomato plantation. Optimum disinfection models were chosen among ten models by the application of statistical SSE (sum of squared error), RMSE (root mean sum of squared error), r2 values on the experimental data using the GInaFiT software in Microsoft Excel. The optimum models were shown as Log-linear+Tail model, Double Weibull model and Biphasic model. Three models were applied to the experimental data according to the variation of the operating conditions. In Log-linear+Tail model, Log10(No), Log10(Nres) and kmax values were examined. In Double Weibull model, Log10(No), Log10(Nres), α, δ1, δ2, p values were calculated and examined. In Biphasic model, Log10(No), f, kmax1 and kmax2 values were used. The appropriate model parameters for the calculation of optimum operating conditions were kmax, α, kmax1 at each model, respectively.

While a range of natural organic matter (NOM) types can generate high levels of disinfection by-products (DBPs) after chlorination, there is little understanding of which specific compounds act as precursors. Use of eight model compounds allows linking of explicit properties to treatability and DBP formation potential (DBPFP). The removal of model compounds by various treatment processes and their haloacetic acid formation potential (HAAFP) before and after treatment were recorded. The model compounds comprised a range of hydrophobic (HPO) and hydrophilic (HPI) neutral and anionic compounds. On the treatment processes, an ozone oxidation process was moderate for control of model compounds, while the HPO-neutral compound was most treatable with activated carbon process. Biodegradation was successful in removing amino acids, while coagulation and ion exchange process had little effect on neutral molecules. Although compared with the HPO compounds the HPI compounds had low HAAFP the ozone oxidation and biodegradation were capable of increasing their HAAFP. In situations where neutral or HPI molecules have high DBPFP additional treatments may be required to remove recalcitrant NOM and control DBPs.

The application of disinfection models on the plasma process was investigated. Nine empirical models were used to find an optimum model. The variation of parameters in model according to the operating conditions (first voltage, second voltage, air flow rate, pH) were investigated in order to explain the disinfection model. In this experiment, the DBD (dielectric barrier discharge) plasma reactor was used to inactivate Ralstonia Solanacearum which cause wilt in tomato plantation. Optimum disinfection models were chosen among the nine models by the application of statistical SSE (sum of squared error), RMSE (root mean sum of squared error), r2 values on the experimental data using the GInaFiT software in Microsoft Excel. The optimum model was shown as Weibull+talil model followed by Log-linear+ Shoulder+Tail model. Two models were applied to the experimental data according to the variation of the operating conditions. In Weibull+talil model, Log10(No), Log10(Nres), δ and p values were examined. And in Log-linear+Shoulder+Tail model, the Log10(No), Log10(Nres), kmax, Sl values were calculated and examined.