• Journal of the Korean Institute of Gas
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• v.23 no.1
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• pp.27-35
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• 2019

In order to solve the global warming and reduce greenhouse gas emissions, $CO_2$ capture technology was developed by applying oxy-fuel combustion. But there has been such a problem that its economic efficiency is low due to the high price of oxygen gases. ASU is known to be most suitable method to produce large quantity of oxygen, to reduce the oxygen production cost, the efficiency of ASU need to be improved. To improve the efficiency of ASU, exergy analysis can be used. The exergy analysis provides the information of used energy in the process, the location and size of exergy destruction. In this study, the exergy analysis was used for process developing and optimization of large scale ASU. The process simulation of ASU was conducted, the results were used to calculate the exergy. As a result, to reduce the exergy loss in the cold box of ASU, a lower operating pressure process was suggested. It was confirmed the importance of heat leak and heat loss reduction of cold box. Also, the unit process of ASU which requires thermal integration was confirmed.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.191-199
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• 2021

A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.148-159
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• 2021

This study focused on searching for optimal locations for general high schools by considering the minimum move distance and the maximum student capacity upon starting a common curriculum based on a high school credit system by taking Seoul as an illustration. The main results were as follows. First, the results from P-median showed that the students' average move distance was below 625m when more than 30% of general high schools offer the common curriculum courses. In addition, the results from MCLP indicated that it was possible to hold all the students. Second, although all the universities located in Seoul open the common curriculum courses, it would not be available to hold all students. On the other hand, when more than 20% of the universities open the courses, MCLP indicated that it was possible to hold the same capacity. In addition, the Office of Education should support moving to the universities offering courses for students affiliated with high schools located in the southeastern area of Seoul and in poor transportation areas. It is expected that by suggesting a problem solving framework regarding space with a spatial optimization method, the study results can be used as a basic data for selecting schools offering common curriculum courses.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.99-106
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• 2021

To circumvent the skin problem from phenylethanol (PhE), we have studied on the enzymatic synthesis of phenylethanol galactoside (PhE-gal) as an alternative to PhE. Base on the previous study, we optimized the reaction conditions for PhE-gal synthesis from PhE using E. coli β-galactosidase (β-gal). The optimal amount of β-gal, PhE concentration, pH, and temperature for PhE-gal synthesis were 0.45 U/ml, 1%, 8.0, 40℃, respectively. Under these conditions, about 81.9 mM PhE was converted into about 47.4 mM PhE-gal, in which the conversion yield was about 57.9%. Meanwhile, when the reaction mixture containing PhE and PhE-gal was mixed and fractionated with water-immiscible solvent (EA or MC), it was observed that PhE-gal was distributed in water phase, and PhE was distributed in solvent phase. Additionally, PhE-gal was clearly distributed into water phase when MC was used, but PE-gal was not when EA was used. In the future, we are planning to carried out the continuing study on developing an alternative cosmetic preservative using PhE-gal.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.13-20
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• 2022

Aircraft weight is an important factor affecting performance and fuel efficiency. In the conceptual design stage of the aircraft, the process of balancing cost and weight is performed using empirical formulas such as fuel consumption cost per weight in estimating element weight. In addition, when an airline operates an aircraft, it promotes fuel efficiency improvement, fuel saving and carbon reduction through weight management activities. The relationship between changes in aircraft weight and changes in fuel consumption is called the cost of weight, and the cost of weight is used to evaluate the effect of adding or reducing weight to an aircraft on fuel consumption. In this study, the problems of the existing cost of weight calculation method are identified, and a new cost of weight calculation method is introduced to solve the problem. Using Breguet's Range Formula and actual flight data of the A350-900 aircraft, two weight costs are calculated based on take-off weight and landing weight. In conclusion, it was suggested that it is reasonable to use the cost of weight based on the take-off weight and the landing weight for other purposes. In particular, the cost of weight based on the landing weight can be used as an empirical formula for estimating element weight and optimizing cost and weight in the conceptual design stage of similar aircraft.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Wind and Structures
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• v.34 no.1
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• pp.127-136
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• 2022

In this work a multi-fidelity non-intrusive polynomial chaos (MF-NIPC) has been applied to a structural wind engineering problem in architectural design for the first time. In architectural design it is important to design structures that are safe in a range of wind directions and speeds. For this reason, the computational models used to design buildings and bridges must account for the uncertainties associated with the interaction between the structure and wind. In order to use the numerical simulations for the design, the numerical models must be validated by experi-mental data, and uncertainties contained in the experiments should also be taken into account. Uncertainty Quantifi-cation has been increasingly used for CFD simulations to consider such uncertainties. Typically, CFD simulations are computationally expensive, motivating the increased interest in multi-fidelity methods due to their ability to lev-erage limited data sets of high-fidelity data with evaluations of more computationally inexpensive models. Previous-ly, the multi-fidelity framework has been applied to CFD simulations for the purposes of optimization, rather than for the statistical assessment of candidate design. In this paper MF-NIPC method is applied to flow around a rectan-gular 5:1 cylinder, which has been thoroughly investigated for architectural design. The purpose of UQ is validation of numerical simulation results with experimental data, therefore the radius of curvature of the rectangular cylinder corners and the angle of attack are considered to be random variables, which are known to contain uncertainties when wind tunnel tests are carried out. Computational Fluid Dynamics (CFD) simulations are solved by a solver that employs the Finite Element Method (FEM) for two turbulence modeling approaches of the incompressible Navier-Stokes equations: Unsteady Reynolds Averaged Navier Stokes (URANS) and the Large Eddy simulation (LES). The results of the uncertainty analysis with CFD are compared to experimental data in terms of time-averaged pressure coefficients and bulk parameters. In addition, the accuracy and efficiency of the multi-fidelity framework is demonstrated through a comparison with the results of the high-fidelity model.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.6
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• pp.827-835
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• 2022

Commercial boiled frozen ark shell Scapharca subcrenata (BFAS) is generally used as a seasoned seafood products. One problem facing the industry is that quality decreases during thawing. This study investigated ways to improve quality and shelf-stability of BFAS for use as a non-thermally prepared seasoned seafood products. The Viable bacteria were detected in BFAS after thawing under running water, but were not detected after blanching for over 2 min at 95±5℃. Blanching and dewatering times were optimized by response surface methodology (RSM) to reduce the initial number of bacteria and improve BFAS texture. Experimental design was deemed appropriate because no significant difference (P>0.05) was observed between predicted and actual moisture content, hardness, and overall acceptance values. Optimal blanching and dewatering times were 210 s and 80 s, respectively. Optimized blanching and dewatering processes can significantly improve safety and BAFS qualities including texture. These results indicate that BFAS demand as a staple for home meal replacements can be increased by application of optimized blanching and dewatering processes, especially in Korean seafood processing companies where running water thawing is common.