• International journal of advanced smart convergence
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• v.8 no.2
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• pp.218-226
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• 2019

The use of Information and Communication Technologies (ICT) is the key to operate a change from the traditional manual reading of water meters and sensors to an automated system where high frequency data is remotely collected and analyzed in real time, one of the main components of a Smart Water Grid. The recent boom of ICT offers a wide range of both wired and wireless technologies to achieve this objective. We review and present in this article the most widely recognized technologies and protocols along with their respective advantages, drawbacks and applicability range which can be Home Area Network (HAN), Building Area Network (BAN) or Local/Neighborhood Area Network (LAN/NAN). We also present our findings and we give recommendations on the application of ICT in Smart Water Grids and future work needed.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.178-184
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• 2022

Renewable energy sources based on solar radiation, wind energy, geothermal energy, and biomass energy have now reached the level of industrial application. A new way to generate electricity using low-potential heat is to install a hydro-steam turbine. In hydro-steam turbines, hot water is supplied directly to turbine rotor nozzles without prior separation into steam and water in separators, which significantly increases the efficiency of hot water energy use. Such turbines are suggested to be used as autonomous energy sources in geothermal heating systems, heating water boilers and cooling systems of chemical reactors, metallurgical furnaces, etc. The authors conclude that the installation of hydro-steam turbines in heating plants and process boiler plants can also be effective if the used exhaust steam-water mixture at the turbine outlet is used to heat the network water or as return water.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.4
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• pp.413-428
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• 2013

In this study, Ant Colony Algorithm(ACO) was used for optimal model. ACO which are metaheuristic algorithm for combinatorial optimization problem are inspired by the fact that ants are able to find the shortest route between their nest and food source. For applying the model to water distribution systems, pipes, tanks(reservoirs), pump construction and pump operation cost were considered as object function and pressure at each node and reservoir level were considered as constraints. Modified model from Ostfeld and Tubaltzev(2008) was verified by applying 2-Looped, Hanoi and Ostfeld's networks. And sensitivity analysis about ant number, number of ants in a best group and pheromone decrease rate was accomplished. After the verification, it was applied to real water network from S water treatment plant. As a result of the analysis, in the Two-looped network, the best design cost was found to $419,000 and in the Hanoi network, the best design cost was calculated to $6,164,384, and in the Ostfeld's network, the best design cost was found to $3,525,096. These are almost equal or better result compared with previous researches. Last, the cost of optimal design for real network, was found for 66 billion dollar that is 8.8 % lower than before. In addition, optimal diameter for aged pipes was found in this study and the 5 of 8 aged pipes were changed the diameter. Through this result, pipe construction cost reduction was found to 11 percent lower than before. And to conclusion, The least cost design model on water distribution system was developed and verified successfully in this study and it will be very useful not only optimal pipe change plan but optimization plan for whole water distribution system.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.59-70
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• 2022

Water distribution systems (WDSs) are a representative infrastructure injecting chlorine to disinfect the pathogenic microorganisms and supplying water from sources to consumers. Also, WDSs prescribe to maintain the usual standard (0.1-4.0 mg/L) of residual chlorine. However, the user's usage pattern, water age, network shape, and type affect the hydraulic features (i.e. nodal pressure, pipe velocity) and water quality features (i.e., the residual chlorine concentration). Therefore, this study developed an optimization approach for optimizing WDSs considering water quality-hydraulic factors using Multi-objective Harmony Search (MOHS). The design cost and the system resilience were applied as the design objective functions, and the nodal pressure and the concentration of residual chlorine are used as constraints. The derived optimal designs through this approach were analyzed according to network characteristics such as the network shapes and type. These optimal designs can meet the safety of economic and water quality aspects to increase user acceptance.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.317-320
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A neural network model is developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, the turbidity in flocculator is regarded as a new input variable. And the genetic algorithm is utilized to identify the neural network structure. The ability of the proposed scheme validated through the field test is proved to be of considerable practical value.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.1-6
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• 2008

Biological and chemical sensors are the two most frequently used sensors to monitor the water resource. Chemical sensor is very accurate to pick up the types and to measure the concentration of the chemical substance. Drawback is that it works for just one type of chemical substance. Therefore a lot of expensive monitoring system needs to be installed to determine the safeness of the water, which costs too much expense. Biological sensor, on the contrary, can judge the degree of pollution of the water with just one monitoring system. However, it is not easy to figure out the type of contaminant with a biological sensor. In this study, an endeavor is made to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve (FIC) from a biological monitoring system. Wem-tox values are calculated from the amount of flourescence of contaminated and reference water. Curve fitting is executed to find the representative curve of the raw data of Wem-tox values. Then the curves are digitalized at the same interval to train the neural network model. Taguchi method is used to optimize the neural network model parameters. The optimized model shows a good capacity to figure out the toxicant from FIC.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1910-1914
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• 2007

This paper employs a relatively new technique of Artificial Neural Network (ANN) to forecast water demand of Daegu city. The ANN model used in this study is a single hidden layer hierarchy model. About seventeen sets of historical water demand records and the values of their socioeconomic impact factors are used to train the model. Also other regression and time serious models are investigated for comparison purpose. The results present the ANN model can better perform the issue of urban water demand forecasting, and obtain the correlation coefficient of $R^2$ with a value of 0.987 and the relative difference less than 4.4% for this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.276-276
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• 2018

In this paper the potential of the Principle Component Analysis(PCA) technique that can be used to detect leaks in water pipe network blocks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study. The algorithms were designed to extract a partial set of flow data from the original 24 hour flow data so that the variability of the flows in the determined partial data set are minimal. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The results showed that the effectiveness of detecting leaks may improve by applying the developed algorithm. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks in real-world water pipe networks.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.4 s.112
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• pp.44-51
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• 2005

Paper mills use and discharge lots of water. And so now the papermaking industry could be classified into major water consuming industry In order to analyze the process water network and to establish the mass, water balance of duplex board mill, computer aided simulation was made using water pinch method. Based on the pinch analysis results, reuse of process water, after regenerating by microfilter as much as $140\;m^3/hr$, could be suggested without significant accumulation of contaminants in process water. According to this suggestion about $3000\;m^3/day$ of recycled process water could be sub stituted by regenerated water and consequently $30\%$ of energy cost is expected to be reduced.