• Journal of Radiation Protection and Research
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• v.39 no.1
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• pp.7-13
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• 2014

Phosphate rock, phosphogypsum, and products in phosphate processing facility contain naturally occurring radioactive materials (NORM). Therefore, they may give rise to enhanced radiation dose to workers due to inhalation of airborne particulates. Internal dose due to particle inhalation varies depending on particle properties. The objective of the present study was to characterize particle properties at the largest phosphate processing facility in Korea. A cascade impactor was employed to sample airborne particulates at various processing areas in the plant. The collected samples were used for characterization of particle size distribution, particle concentration in the air, and shape analysis. Aerodynamic diameters of airborne particulates ranged 0.03-100 ${\mu}m$ with the highest concentration at the particle size range of 4.7-5.8 ${\mu}m$ (geometric mean = 5.22 ${\mu}m$) or 5.8-9.0 ${\mu}m$ (geometric mean = 7.22 ${\mu}m$). Particle concentrations in the air varied widely by sampling area up to more than two orders of magnitude. The large variation resulted from the variability of mechanical operations and building ventilations. The airborne particulates appeared as spheroids or rough spherical fragments across all sampling areas and sampled size intervals. Average mass densities of phosphate rocks, phosphogypsums, and fertilizers were 3.1-3.4, 2.1-2.6, and 1.7 $gcm^{-3}$, respectively. Radioactivity concentration of uranium series in phosphate rocks varied with country of origin, ranging 94-866 $Bqkg^{-1}$. Among the uranium series, uranium was mostly concentrated on products, including phosphoric acid or fertilizers whereas radium was concentrated on byproducts or phosphogypsum. No significant radioactivity of $^{226}Ra$ and $^{228}Ra$ were found in fertilizer. However, $^{40}K$ concentration in fertilizer was up to 5,000 Bq $g^{-1}$. The database established in this study can be used for the accurate risk assessment of workers due to inhalation of airborne particles containing NORM. In addition, the findings can be used as a basic data for development of safety standard and guide and for practical radiation safety management at the facility.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.