Boil‐Off Gas Two‐Stage Compression and Recondensation Process at a Liquefied Natural Gas Receiving Terminal

Abstract

Due to ship unloading and the change of downstream gas consumption, the boiloff gas (BOG) and liquefied natural gas outputs fluctuate constantly, causing difficulties in both the design and operation of the BOG handling process. The high energy consumption, instability, and inflexibility of the original process affect the operation cost and running safety of the terminal to a large extent. To address these problems, a two-stage compression and recondensation process is designed and optimized, based on the original one. Compared with the original process through dynamic simulation, the design process has better stability, benefiting from lower operating pressure and smaller fluctuations of both the pressure and the liquid level in the recondenser. In addition, the power and BOG emission are remarkably reduced, which will soon bring great benefit and recovery of investment cost.

Liquefied Natural Gas (LNG) Supply Chains: Recent Advances and Future Opportunities

“…The findings suggested that increasing the number of compression and reliquefaction stages, excluding a high-pressure (HP) pump, reduced energy consumption. Li and Wen 95 developed and optimized a twostage compression and recondensation process for BOG management, focusing on safety, operational stability, and energy efficiency. This approach led to improved dynamic performance and a more stable operation.…”

A new optimization method of energy consumption for dynamic boil-off gas liquefaction

“…The dynamic model for the liquefaction process of small mixed refrigerant is designed and the mixed refrigerant component is optimized for disturbances in temperature, pressure, flow rate and composition of raw gas 25,26 . Moreover, the total energy consumption of the system will be reduced through the combination of steady and dynamic optimization using dynamic simulation 27 . But there exists a shortage of comprehensive and systematic research for multi-factor coupling energy-saving optimization, such as a variety of dynamic control structure optimization analysis [28][29][30] and multiple variables optimization 31 .…”