In this study, we have developed a movable defect detection system based on a vision module with machine-learning algorithm for distinguishing product quality. Machine-learning model determined the results in good or no good through images acquired from the vision module consisting of a camera, processor unit, and lighting. To ensure versatility for use in a variety of settings, we have integrated a robot arm and cart for the movable defect detection system, and the robot arm that adjusts the focus length is made to be able to rotate in all directions. The type of defect was divided into eccentricity defect and printing defect. As a result, it was confirmed that classification accuracy showed 0.9901 in our developed device.

In this study, the physical properties and fracture characteristics according to the tensile load are evaluated on the materials of the polymeric filler and carbon fiber-based composite sleeve technique. The polymeric filler and the composite sleeve technique are applied to areas where the pipe body thickness is reduced due to corrosion in large-diameter water pipes. First, the tensile strength of the polymeric filler was 161.48~240.43 kgf/cm2, and the tensile strength of the polyurea polymeric filler was relatively higher than that of the epoxy. However, the tensile strength of the polymeric filler is relatively very low compared to ductile cast iron pipes(4,300 kgf/cm2<) or steel pipes(4,100 kgf/cm2). Second, the tensile strength of glass fiber, which is mainly used in composite sleeves, is 3,887.0 kgf/cm2, and that of carbon fiber is up to 5,922.5 kgf/cm2. The tensile strengths of glass and carbon fiber are higher than ductile cast iron pipe or steel pipe. Third, when reinforcing the hemispherical simulated corrosion shape of the ductile cast iron pipe and the steel pipe with a polymeric filler, there was an effect of increasing the ultimate tensile load by 1.04 to 1.06 times, but the ultimate load was 37.7 to 53.7% compared to the ductile cast iron or steel specimen without corrosion damage. It was found that the effect on the reinforcement of the corrosion damaged part was insignificant. Fourth, the composite sleeve using carbon fiber showed an ultimate load of 1.10(0.61T, 1,821.0 kgf) and 1.02(0.60T, 2,290.7 kgf) times higher than the ductile cast iron pipe(1,657.83 kgf) and steel pipe(2,236.8 kgf), respectively. When using a composite sleeve such as fiber, the corrosion damage part of large-diameter water pipes can be reinforced with same level as the original pipe, and the supply stability can be secured through accident prevention.

In this study, as the proportion of aged pipelines increases rapidly, in the event of an accident caused by corrosion and structural deterioration of metal pipes, appropriate overlay welding is applied in the field to partially repair it. The size of the base steel plate and the selection of a stable welding method were evaluated, and possible problems caused by the overlay welding were identified, and improvement measures were proposed. For the test, a new steel pipe coated with epoxy lining on the inner surface and polyethylene on the outer surface was subjected to a tensile test by processing the repaired specimen through overlay welding with a steel plate after artificial cracking, and structural safety was evaluated after repair. Furthermore, the influences of the size of the throat and the size of the steel plate were analyzed. As a result of the tensile test by dividing the repaired steel plate overlay into a constraint and non-constraint conditions, the tensile load was concentrated in the welded part and damage occurred in the welded part. It was found that the maximum load leading to breakage increases as the size of the welding throat increases. In addition, it was found that the resistance to load increased slightly as the size of the overlaid steel plate increased, but the effect was not significant, confirming the need for repair in consideration of the site conditions. As a result of evaluating the damage to the coating material on the back side of the welding, it was confirmed that the coating material on the opposite side of the welding burned black(epoxy) or was greatly deformed by heat(polyethylene). Therefore, it is necessary to secure structural stability through repainting, etc. in order to prevent damage to the coating material on the opposite side during overlay welding.

The management of algal bloom is essential for the proper management of water supply systems and to maintain the safety of drinking water. Chlorophyll-a(Chl-a) is a commonly used indicator to represent the algal concentration. In recent years, advanced machine learning models have been increasingly used to predict Chl-a in freshwater systems. Machine learning models show good performance in various fields, while the process of model development requires considerable labor and time by experts. Automated machine learning(auto ML) is an emerging field of machine learning study. Auto ML is used to develop machine learning models while minimizing the time and labor required in the model development process. This study developed an auto ML to predict Chl-a using auto sklearn, one of most widely used open source auto ML algorithms. The model performance was compared with other two popular ensemble machine learning models, random forest(RF) and XGBoost(XGB). The model performance was evaluated using three indices, root mean squared error, root mean squared error-observation standard deviation ratio(RSR) and Nash-Sutcliffe coefficient of efficiency. The RSR of auto ML, RF, and XGB were 0.659, 0.684 and 0.638, respectively. The results shows that auto ML outperforms RF, and XGB shows better prediction performance than auto ML, while the differences between model performances were not significant. Shapley value analysis, an explainable machine learning algorithm, was used to provide quantitative interpretation about the model prediction of auto ML developed in this study. The results of this study present the possible applicability of auto ML for the prediction of water quality.

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

The quantified analysis of damages to wastewater treatment plants by natural disasters is essential to maintain the stability of wastewater treatment systems. However, studies on the quantified analysis of natural disaster effects on wastewater treatment systems are very rare. In this study, a total disaster index (DI) was developed to quantify the various damages to wastewater treatment systems from natural disasters using two statistical methods (i.e., AHP: analytic hierarchy process and PCA: principal component analysis). Typhoons, heavy rain, and earthquakes are considered as three major natural disasters for the development of the DI. A total of 15 input variables from public open-source data (e.g., statistical yearbook of wastewater treatment system, meteorological data and financial status in local governments) were used for the development of a DI for 199 wastewater treatment plants in Korea. The total DI was calculated from the weighted sum of the disaster indices of the three natural disasters (i.e., TI for typhoon, RI for heavy rain, and EI for earthquake). The three disaster indices of each natural disaster were determined from four components, such as possibility of occurrence and expected damages. The relative weights of the four components to calculate the disaster indices (TI, RI and EI) for each of the three natural disasters were also determined from AHP. PCA was used to determine the relative weights of the input variables to calculate the four components. The relative weights of TI, RI and EI to calculate total DI were determined as 0.547, 0.306, and 0.147 respectively.

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

The proper operation and safety management of water and wastewater treatment systems are essential for providing stable water service to the public. However, various natural disasters including floods, large storms, volcano eruptions and earthquakes threaten public water services by causing serious damage to water and wastewater treatment plants and pipeline systems. Korea is known as a country that is relatively safe from earthquakes, but the recent increase in the frequency of earthquakes has increased the need for a proper earthquake management system. Interest in research and the establishment of legal regulations has increased, especially since the large earthquake in Gyeongju in 2016. Currently, earthquakes in Korea are managed by legal regulations and guidelines integrated with other disasters such as floods and large storms. The legal system has long been controlled and relatively well managed, but technical research has made limited progress since it was considered in the past that Korea is safe from earthquake damage. Various technologies, including seismic design and earthquake forecasting, are required to minimize possible damages from earthquakes, so proper research is essential. This paper reviews the current state of technology development and legal management systems to prevent damages and restore water and wastewater treatment systems after earthquakes in Korea and other countries. High technologies such as unmanned aerial vehicles, wireless networks and real-time monitoring systems are already being applied to water and wastewater treatment processes, and to further establish the optimal system for earthquake response in water and wastewater treatment facilities, continuous research in connection with the Fourth Industrial Revolution, including information and communications technologies, is essential.

본 연구는 흑산도 산림유전자원보호구역을 대상으로 산림식생구조를 파악하고자 수행하였으며, 2017년 6월부터 2017년 8월까지 총 59개 조사구에서 식생조사를 실시하였다. Z-M 식물사회학적 방법으로 산림식생 유형을 분류하고 식생단위별 구성종의 중요치와 종다양도를 분석하였다. 산림식생유형분류 결과 최상위 단위에서 동백나무군락군으로 분류되었으며, 군락단위에서는 황칠나무군락(식생단위 1), 소사나무군락, 동백나무전형군락(식생단위 6)의 3개 군락으로 분류되었다. 소사나무군락은 회양목군(식생단위 2), 진달래군(식생단위 3), 왕머루군(식생단위 4), 소사나무전형군 (식생단위 5)의 4개 소군으로 분류되었다. 식생단위별 평균상대우점치 분석 결과 식생단위 1은 붉가시나무, 식생단위 2는 소사나무, 식생단위 3은 곰솔, 식생단위 4는 소나무, 식생단위 5와 6은 구실잣밤나무가 각각 우점치가 가장 높게 나타났다. 종다양도 분석결과 식생단위 2의 종풍부도, 종다양도, 종균재도가 가장 높았으며, 종우점도는 식생단위 6이 가장 높았다. 흑산도 산림유전자원보호구역의 6개 식생단위와 12개 종군에 대한 군집생태학적 접근의 관리방안이 필요할 것으로 판단되었다.

낙동강 유역에 조성된 신규습지에서 서식환경에 대한 어류의 분포 특성을 평가하고, 이를 기반으로 한 관리방안 도출을 위해 42개 습지에서 환경요인의 측정과 어류 조사를 시행하였다. 조사 기간 동안 총 30종의 어류가 출현하였으며, 이 중에서 배스(Micropterus salmoides)와 블루길(Lepomis macrochirus)과 같은 외래어종의 비율이 상대적으로 높았다. 특히, 밀어(Rhinogobius brunneus)나 끄리(Opsariichthys uncirostris amurensis), 피라미(Zacco platypus) 등의 어류는 흐름이 있는 환경을 선호하기 때문에 배스가 우점하는 습지(정체수역)에서 상대적으로 낮은 밀도를 가지는 것으로 사료된다. SOM (Self-Organizing Map)을 활용한 패턴분석 결과, 각 습지의 서식환경 특성에 따라 어류 종의 출현빈도가 상이한 것으로 분석되었다. 어류 종의 분포는 각 습지의 수심 변화와 수생식물 피도에 민감하게 영향 받는 것으로 나타났으며, 수온이나 pH, 용존산소 등의 이화학적 요인 변화에 대한 영향은 적었다. 특히 수생식물의 피도는 어류의 종다양성이나 밀도에 강한 영향을 주는 것으로 분석되었으며, 수변식생이 부족한 습지에서는 어류가 적은 풍부도와 다양성을 가지는 것으로 나타났다. 본 연구 결과를 기반으로 평가할 때 어류 등의 생물다양성 증진을 위해 호안사면의 높은 인공성이나 수변식생의 부족한 습지 등은 개선이 필요할 것으로 사료되며, 건강성 확보를 위한 지속가능한 관리방안이 마련되어야 할 것으로 판단된다.

일반적으로 SWRO의 경우 에너지 소비량은 3.5 kWh/m³ 이상의 에너지를 소비한다. 그중 RO트레인은 2.5～3.0 kWh/m³를 소모해 전체 에너지 소비량의 70% 이상을 소비하고 있으며, 전체 시스템 에너지 절감을 위해서는 RO트레인의 최적 설계가 중요하다. 따라서 당사는 다양한 RO트레인의 설계를 최적화 하여 소모되는 에너지의 양을 10% 절감하고자 한다. 1) ISDInternally Staged Desing)을 적용한 1st Pass의 설계최적화, 2) SPSP(Split Partial Sencond Pass) 적용을 통한 2nd 용량 최적화 설계, 3) 초고성능 막을 적용한 Single Pass 설계방법의 최적 조합을 통해 저에너지 역삼투막 시스템 설계 기술을 개발하고자 한다.

Relatively low efficiency in anaerobic digestion process is mainly caused by unproper mixing method. In this study, tray motion type agitator was applied in actual anaerobic digestion tank in order to improve the digestion efficiency, equalize the flow velocity distribution and energy saving. The impeller of tray motion type agitator was reciprocated vertically. Gas lift type agitator and tray motion type agitator appears almost same mixing efficiency include digestion rates. However, tray motion type agitator have shown that lower energy consumption compared to the conventional gas lift type agitator. Implementation of tray motion type agitator in the anaerobic digestion tanks contributed to the stabilization of mixing environment, efficiency and energy efficiency of the tank.

본 연구는 파인애플로 제조한 와인의 과실주의 제조 가능성을 조사하고, 첨가당의 종류가 발효과정에 어떠한 영향을 주는지를 알아보고자 실시하였다. 발효과정이 진행되는 동안 당도와 알코올 농도의 변화는 첨가한 당의 종류에 따라 다르게 변화 하였다. 당을 첨가하지 않은 과즙의 경우 가장 먼저 알코올의 증가가 종료되었으며, 특히, 포도당을 첨가한 와인에서 가장 많은 알코올(12.8%)이 생성되어 효모의 당 이용성이 높은 것으로 나타났다. 유기산은 모든 와인에서 citric acid와, malic acid, acetic acid, succinic acid 및 lactic acid가 검출되었으며, 그 밖에 oxalic acid도 소량 존재하였다. 그 중에서 설탕을 첨가한 와인에서 citric acid (0.335 mg/mL)와 malic acid (0.127 mg/mL) 함량이 높게 나타났으며, 또한, 가장 많은 유기산이 측정되었다. 총 페놀 함량 및 항산화 활성도(DPPH 라디칼 제거능)는 파인애플 제조 와인에서 약 950 mg/L 및 약 4,900 mg/L으로 나타났다. 관능검사 결과는 비전문가 집단과 비교하여 전문가 집단에서 기호도는 당을 첨가하지 않는 와인에서 가장 높게 나타났다. 특히, 와인전문가들은 알코올 함량이 적은 당을 첨가하지 않은 와인을 더 선호하는 것으로 확인되었다. 본 연구 결과와 같이 파인애플로 제조한 와인이 가당을 하지 않고서도 과실주로서의 가능성에 대해 긍정적인 평가를 내릴 수 있었다. 이처럼, 건강에도 도움을 주고, 풍미도 좋으며, 알코올 농도가 높지 않은 와인을 제조할 수 있으며, 다양한 소비층의 소비를 유도할 수 있을 것으로 판단된다.