Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

This paper represents the cold energy for liquefied nitrogen gas and cascade refrigerator. In this study, the vaporizer of liquefied nitrogen gas has the fin coil tube type with the dimension of inside diameter of 10mm and outside diameter of 12mm. Also, the total length of vaporizer is 20,000mm. The main experimental parameters are the mean velocity in duct and the supplied flow-rates of liquefied nitrogen gas. For the cascade refrigeration system, the refrigerants are ethane(R-170) in the high pressure stage and R-22 in the low pressure stage.