Abstract

The impact of cold discharge (CD) released from Tongyeong LNG terminal was evaluated at different flow rates. At the actual flow rate of 10 m3 s−1, the impact of CD on surrounding seawaters was insignificant with a slight decrease in seawater temperature. The surface and bottom seawater temperature within a 1 km radius from the discharge outlet decreased by an average of 0.4 °C and 0.6 °C, respectively. At a simulation flow rate of 500 m3 s−1, CD settled to the bottom and widely diffused. The seawater temperature within a 1 km radius from the discharge outlet decreased by an average of 3.1 °C and 5.2 °C in the surface and bottom layers, respectively. A machine learning-based model was trained to estimate the diffusion area of CD using physical factors at the outlet as predictors. The trained model could identify not only the optimal sites of the LNG terminal to minimize the impact of CD on the surrounding seawaters but also worst-case site. This CD modeling process could enable low-cost impact assessments on seawaters, and the same process could be used for thermal discharge.

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