Optimisation of liquefied natural gas production: genetic algorithm and custom-developed method

Abstract

This study comprehensively analyses various optimisation techniques applied to Liquefied Natural Gas (LNG) production. Two datasets were used to assess the performance of these techniques, with a focus on improving LNG output. The results revealed that the genetic algorithm exhibited the highest average percentage improvement in the first dataset, achieving a 12% optimisation, followed closely by a custom-developed optimisation method at 11%. Bayesian optimisation showed an average of 4%, while gradient descent demonstrated the lowest optimisation with -2%. Notably, the second dataset displayed even more significant improvements, with the custom optimisation algorithm leading at an average of 32%, surpassing the genetic optimization method's 30%. This study underscores the efficacy of the custom algorithm and its potential for enhancing LNG production, positioning it as a promising alternative to traditional optimisation approaches.