• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1209-1215
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• 2022

The selective catalytic reduction (SCR) is known as a very efficient method to reduce nitrogen oxides (NOx) and the catalyst performs reduction from nitrogen oxides (NOx) to nitrogen (N₂) and water vapor (H2O). The catalyst, which is one of the factors determining the performance of the nitrogen oxide (NOx) ruduction method, is known to increase catalyst efficiency as cell density increases. In this study, the reduction characteristics of nitrogen oxides (NOx) under various engine loads investigated. A 100CPSI(60Cell) catalysts was studied through a laboratory-sized simulating device that can simulate the exhaust gas conditions from the power generation engine installed in the training ship SEGERO. The effect of 100CPSI(60Cell) cell density was compared with that of 25.8CPSI(30Cell) cell density that already had NOx reduction data from the SCR manufacturing. The experimental catalysts were honeycomb type and its compositions and materials of V₂O₅-WO₃-TiO₂ were retained, with only change on cell density. As a result, the NOx concentration reduction rate from 100CPSI(60Cell) catalyst was 88.5%, and IMO specific NOx emission was 0.99g/kwh satisfying the IMO Tier III NOx emission requirement. The NOx concentration reduction rate from 25.8CPSI(30Cell) was 78%, and IMO specific NOx emission was 2.00g/kwh. Comparing the NOx concentration reduction rate and emission of 100CPSI(60Cell) and 25.8CPSI(30Cell) catalysts, notably, the NOx concentration reduction rate of 100CPSI(60Cell) catalyst was 10.5% higher and its IMO specific NOx emission was about twice less than that of the 25.8CPSI(30Cell) catalysts. Therefore, an efficient NOx reduction effect can be expected by increasing the cell density of catalysts. In other words, effects to production cost reduction, efficient arrangement of engine room and cargo space can be estimated from the reduced catalyst volume.