Enhancing Liquefied Natural Gas supply chain robustness through digital twin-driven machine learning models: A special case of cryogenic heat exchanger

<p>The Liquefied Natural Gas (LNG) supply chain plays a critical role in the global energy sector but remains vulnerable to technical disruptions that compromise operational stability. Equipment failures pose significant risks, leading to production halts and quality degradation. This paper pr...

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Bibliographic Details
Main Author:	Mariem Mhiri (17991544) (author)
Other Authors:	Hajer Mkacher (21837887) (author), Maryam Al-Khatib (19561909) (author), Mohamed Kharbeche (6579296) (author), Ahmed AlNouss (9872265) (author), Mohamed Haouari (10340697) (author)
Published:	2025
Subjects:	Commerce, management, tourism and services Transportation, logistics and supply chains Engineering Chemical engineering Manufacturing engineering Information and computing sciences Artificial intelligence LNG supply chain Digital twins Machine learning Predictive maintenance Supply chain robustness Supply chain resilience Cryogenic heat exchanger
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Enhancing Liquefied Natural Gas supply chain robustness through digital twin-driven machine learning models: A special case of cryogenic heat exchanger

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