Deep learning neural networks to decide whether to operate the 174K Liquefied Natural Gas Carrier's Gas Combustion Unit

• 발행기관: 한국항해항만학회
• 저자명: Sungrok Kim Qianfeng Lin Jooyoung Son
• 간행물 정보: 『한국항해항만학회 학술대회논문집』2022 추계학술대회논문집, 383~384쪽, 전체 2쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 2022.11.10

영문 초록

Gas Combustion Unit (GCU) onboard liquefied natural gas carriers handles boil-off to stabilize tank pressure. There are many factors for LNG cargo operators to take into consideration to determine whether to use GCU or not. Gas consumption of main engine and re-liquefied gas through the Partial Re-Liquefaction System (PRS) are good examples of these factors. Human gas operators have decided the operation so far. In this paper, some deep learning neural network models were developed to provide human gas operators with a decision support system. The models consider various factors specially into GCU operation. A deep learning model with Sigmoid activation functions in input layer and hidden layers made the best performance among eight different deep learning models.

키워드

neural networks deep learning model activation function gas combustion unit boil-off gas