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Reducing the CO<sub>2</sub> footprint at an LNG asset with replicate trains using operational data-driven analysis. A case study on end flash vessels

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  • Rakesh Paleja
  • Ekhorutomwen Osemwinyen
  • Matthew Jones
  • John Ayoola
  • Raghuraman Pitchumani
  • Philip Jonathan
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Article numbere1
<mark>Journal publication date</mark>31/01/2025
<mark>Journal</mark>Data-Centric Engineering
Volume6
Publication StatusPublished
Early online date8/01/25
<mark>Original language</mark>English

Abstract

A liquefied natural gas (LNG) facility often incorporates replicate liquefaction trains. The performance of equivalent units across trains, designed using common numerical models, might be expected to be similar. In this article, we discuss statistical analysis of real plant data to validate this assumption. Analysis of operational data for end flash vessels from a pair of replicate trains at an LNG facility indicates that one train produces 2.8%–6.4% more end flash gas than the other. We then develop statistical models for train operation, facilitating reduced flaring and hence a reduction of up to 45% in CO2 equivalent flaring emissions, noting that flaring emissions for a typical LNG facility account for ~4%–8% of the overall facility emissions. We recommend that operational data-driven models be considered generally to improve the performance of LNG facilities and reduce their CO2 footprint, particularly when replica units are present.