In this study, effects of five raw water quality parameters (turbidity, odor compounds caused by algae, filter clogging caused by algae, pH increase caused by algae, and organic matter) on improvements and operations costs of typical water treatment plant (WTP) were estimated. The raw water quality parameters were assumed the worst possible conditions based on the past data and costs were subsequently estimated. Results showed that new water treatment facilities were needed, such as a selective intake system, an advanced water treatment processes, a dual media filter, a carbonation facility, and a re-chlorination facility depending on water quality. Furthermore, changes needed to be made in WTP operations, such as adding powered activated carbon, increasing the injection of chlorine, adding coagulation aid, increasing the discharge of backwashed water, and increasing the operation time of dewatering facilities. Such findings showed that to reliably produce high-quality tap water and reduce water treatment costs, continuous improvements to the quality of water sources are needed.

Sewer deterioration models are needed to forecast the remaining life expectancy of sewer networks by assessing their conditions. In this study, the serious defect (or condition state 3) occurrence probability, at which sewer rehabilitation program should be implemented, was evaluated using four probability distribution functions such as normal, lognormal, exponential, and Weibull distribution. A sample of 252 km of CCTV-inspected sewer pipe data in city Z was collected in the first place. Then the effective data (284 sewer sections of 8.15 km) with reliable information were extracted and classified into 3 groups considering the sub-catchment area, sewer material, and sewer pipe size. Anderson-Darling test was conducted to select the most fitted probability distribution of sewer defect occurrence as Weibull distribution. The shape parameters (β) and scale parameters (η) of Weibull distribution were estimated from the data set of 3 classified groups, including standard errors, 95% confidence intervals, and log-likelihood values. The plot of probability density function and cumulative distribution function were obtained using the estimated parameter values, which could be used to indicate the quantitative level of risk on occurrence of CS3. It was estimated that sewer data group 1, group 2, and group 3 has CS3 occurrence probability exceeding 50% at 13th-year, 11th-year, and 16th-year after the installation, respectively. For every data groups, the time exceeding the CS3 occurrence probability of 90% was also predicted to be 27th- to 30th-year after the installation.

In this study, a pilot-scale (3 ㎥/day) membrane distillation (MD) process was operated to treat digestate produced from anaerobic digestion of livestock wastewater. In order to evaluate the performance and energy cost of MD process, it was compared with the pilot scale (10 ㎥/day) reverse osmosis (RO) process, expected competitive process, under same feed condition. As results, MD process shows stable permeate flux (average 10.1 L/㎡/hr) until 150 hours, whereas permeate flux of RO process was decreased from 5.3 to 1.5 L/㎡/hr within 24 hours. In the case of removal of COD, TN, and TP, MD process shows a high removal rate (98.7, 93.7, and 99% respectively) stably until 150 hours. However, in the case of RO process, removal rate was decreased from 91.6 to 69.5% in COD and from 93.7 to 76.0% in TP during 100 hours of operation. Removal rate of TN in RO process was fluctuated in the range of 34.5-62.9% (average 44.6%) during the operation. As a result of energy cost analysis, MD process using waste heat for heating the feed shows 18% lower cost compare with RO process. Thus, overall efficiency of the MD process is higher then that of the RO process in terms of permeate flux, removal rate of salts, and operating cost (in the case of using waste heat) in treating the anaerobic digestate of livestock wastewater.

In this study, air scouring cleaning was selected and applied among 5 small blocks (S1~S5) in domestic S cities to analyze the cleaning effect of particles causing discoloration. In order to identify the cleaning effect, 10 locations were selected as water quality investigation point, such as the stagnant or water mains ends. Removal of solids, variation of particle components, weight and concentration were analyzed. And the level of the cleanness of the surface inside water mains using endoscope was investigated. As a result of analysis, the solids discharged after cleaning were mainly sand and gravel, pieces related to pipe materials, and corrosion products. As a result of analyzing the concentrated particles of the filter before and after cleaning, it was found that the change in discoloration on the filter was large. In addition, as a result of comparing the weight and the concentration of the particles, it was found that the particles causing discoloration were significantly removed after cleaning. From the results of the endoscopy, it was confirmed that most of the precipitated and accumulated dark yellow discoloration matters inside water mains were removed through cleaning. Therefore, it seems that the particles causing discoloration in water decreased after cleaning. Therefore, it is expected that, if properly cleaning was applied, matters that cause discoloration can be removed from the water mains, and customer's complaints can also be reduced through water quality improvement.

In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm's ability to interpret seemed to be excellent.

Since sewer rehabilitation program requires long construction period and enormous capital investment, determination of rehabilitation priorities is important with systematic planning considering appropriate evaluation parameters. In this research, we applied PROMETHEE(Preference Ranking Organization METHod for Evaluations) known as very objective and scientific multi-criteria decision-making analysis, using the weights determined by AHP(Analytic Hierarchy Process) for the selected sewer evaluation items to calculate the rehabilitation priorities for each sewer sub-catchment in basin Gusan 1 of Seoul. Preference functions and preference thresholds were estimated for each criterion of ratio of lack of hydraulic capacity of sewers, defect ratio, ratio of sewers with velocity less than its minimum criteria, and density of sewers in the sub-catchment. As a result, it was found that region d had the first priority among four sub-catchments. For each and every sewer located in region d, we could also rank sewers to be rehabilitated urgently.