Optimisation of liquefied natural gas production: genetic algorithm and custom-developed method
1 PhD Student, Information System Engineering, Centre for Information and Telecommunication Engineering (CITE), University of Port Harcourt, Rivers State, Nigeria.
2 Former Director, NLNG Centre for Gas, Refining & Petrochemicals, University of Port Harcourt, Rivers State, Nigeria.
3 Assistant Director, Centre for Information and Telecommunication Engineering (CITE), University of Port Harcourt, Rivers State, Nigeria.
4 Director, Centre for Information and Telecommunication Engineering (CITE), University of Port Harcourt, Rivers State, Nigeria.
Research Article
Global Journal of Engineering and Technology Advances, 2023, 17(02), 031–039.
Article DOI: 10.30574/gjeta.2023.17.2.0223
Publication history:
Received on 27 September 2023; revised on 06 November 2023; accepted on 09 November 2023
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.
Keywords:
LNG Production; Optimisation Techniques; Custom Algorithm; Genetic Algorithm and Bayesian Optimisation
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0