Design and optimization of an onboard boil-off gas re-liquefaction process under different weather-related scenarios with machine learning predictions

Abstract

In this study, a novel boil-off-gas handling process was proposed to optimize refrigerant mix and flow rate considering different boil-off gas rates under different weather conditions. The proposed design utilizes a nitrogen expander and mixed refrigerant cycle to generate subcooled liquified natural gas to cool the tank, preventing boil-off gas generation. The optimized design in terms of exergy destruction minimization is assessed in a multiple steady -state simulation based on seasonal boil-off gas and heat ingress rates which are predicted by a machine learning algorithm. Furthermore, an economic analysis of the proposed reliquefaction processes was conducted. The result shows that the conventional process has 46% and 8% lower exergy destruction compared to the proposed one for the nitrogen expander and mixed refrigerant cycle respectively. However, the proposed process outperforms the conventional process in terms of investment costs. The proposed process has 36% and 27% lower investment costs for the nitrogen and mixed refrigerant cycle compared to the conventional one. This leads to higher profit for the novel process and performs better in the face of uncertainty of liquified natural gas price.