Automated process design and integration of precooling for energy-efficient BOG (boil-off gas) liquefaction processes

• Published in: Applied thermal engineering 2020-11, Vol.181, p.116014, Article 116014
• Main Authors: Son, Hyunsoo, Kim, Jin-Kuk
• Format: Article
• Language: English
• Subjects: Ammonia; BOG liquefaction; CO2 precooling; Emissions; Energy consumption; Energy efficiency; Liquefaction; Liquefied natural gas; LNG-fueled ship; Marine environment; NH3 precooling; Optimization; Physical properties; Pollution; Precooling; Process optimization; Refrigerants; Robustness (mathematics); Sensitivity analysis; Solution space; System effectiveness
• ISSN: 1359-4311; 1873-5606
• DOI: 10.1016/j.applthermaleng.2020.116014

•Development of optimization methodology for precooling cycles for liquefaction.•System analysis of energy-efficient structures for CO2 and NH3 precooling cycles.•Enhancement of computational performance of optimization with sensitivity analysis.•Systematic evaluation of energy saving potentials in boil-off gas liquefaction. As awareness on air pollution in marine environment increases, transition of ship fuel to natural gas has been recognized as a practical solution for emission minimization. It is realized with LNG-fueled ship, whose implementation in practice demands cost-effective and energy-efficient re-liquefaction of BOG. The purpose of this study is to introduce precooling cycle to BOG re-liquefaction process to enhance energy efficiency and explore the most appropriate usage of pre-cooling refrigerants between CO2 and NH3. Three different configurations for precooling are compared, and operating conditions are optimized with an objective function to minimize energy consumption. Pre-screening based on sensitivity analysis, before optimization, is carried out to exclude any potential infeasible solution space. It allows robust optimization, leading to high quality optimal solution. Up to 7.9% reduction in overall energy consumption is achieved with the integration of precooling cycles, illustrated by high dependence on physical properties of saturated refrigerants. The modeling and optimization framework proposed in this study can serve as an effective application to the wide range of refrigeration systems subject to various refrigerants.