Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Development of Artificial Intelligence Model for Outlet Temperature of Vaporizer

기화 설비의 토출 온도 예측을 위한 인공지능 모델 개발

  • Lee, Sang-Hyun (Dept. Industrial Engineering & IT-Bio Convergence System, Chosun University) ;
  • Cho, Gi-Jung (Dept. Industrial Engineering, Chosun University) ;
  • Shin, Jong-Ho (Dept. Industrial Engineering, Chosun University)
  • 이상현 (조선대학교 산업공학과 & IT-Bio 융합시스템 전공) ;
  • 조기정 (조선대학교 산업공학과) ;
  • 신종호 (조선대학교 산업공학과)
  • Received : 2021.05.26
  • Accepted : 2021.06.16
  • Published : 2021.06.30
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Abstract

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.

Keywords

Acknowledgement

This paper was researched with the support of Chosun University's academic research fund in 2017.

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