• Journal of Korean Society of Water and Wastewater
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• v.37 no.4
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• pp.215-229
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• 2023

In this study, non-destructive technologies that can be applied to evaluate the integrity of valve materials, safety against internal pressure caused by corrosion, and the blocking function of large-diameter water valves during operation without requiring specimen collection or manpower entering the inside of the valve were tested to assess the reliability of the technologies and their suitability for field application. The results showed that the condition of the graphite structure inside the valve body can be evaluated directly through the optical microscope in the field without specimen collection for large-diameter water butterfly valves, and the depth of corrosion inside the valve body can be determined by array ultrasound and the tensile strength can be measured by instrumented indentation test. The reliability of each of these non-destructive techniques is high, and they can be widely used to evaluate the condition of steel or cast iron pipes that are significantly smaller in thickness than valves. Evaluation of blocking function of the valves with mixed gas showed that it can be detected even when a very low flow rate of mixed gas passes through the disk along with the water flow. Finally, as a result of evaluating the field applicability of non-destructive technologies for three old butterfly valves installed in the US industrial water pipeline, it was found that it is possible to check the material and determine the suitability of large-diameter water valves without taking samples, and to determine the corrosion state and mechanical strength. In addition, it was possible to evaluate safety through the measurement results, and it is judged that the evaluation of the blocking function using mixed gas will help strengthen preventive response in the event of an accident.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.2
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• pp.67-75
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• 2012

Carbon-dioxide capture and storage (CCS) process is consisted by capturing carbon-dioxide from large point source such as power plant and steel works, transporting and sequestrating captured $CO_2$ in a stable geological structure. During CCS process, it is inevitable of introducing impurities from combustion, capture and purification process into $CO_2$ stream. Impurities such as $SO_2$, $H_2O$, CO, $N_2$, Ar, $O_2$, $H_2$, can influence on process efficiency, capital expenditure, operation expense of CCS process. In this study, experimental apparatus is built to simulate the behavior of $CO_2$ transport under various impurity composition and process pressure condition. With this apparatus, $N_2$ impurity effect on $CO_2$ mixture transportation was experimentally evaluated. The result showed that as $N_2$ ratio increased pressure drop per mass flow and specific volume of $CO_2-N_2$ mixture also increased. In 120 and 100 bar condition the mixture was in single phase supercritical condition, and as $N_2$ ratio increased gradient of specific volume change and pressure drop per mass flow did not change largely compared to low pressure condition. In 70 bar condition the mixture phase changed from single phase liquid to single phase vapor through liquid-vapor two phase region, and it showed that the gradient of specific volume change and pressure drop per mass flow varied in each phase.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.