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The future of HAZOPs – Comparing AI and human approaches in gas treatment systems.
Number: 64
Author(s) : NUEZ J. G. de la, FARIA A. H.
Summary
The liquefied natural gas (LNG) industry, in line with other industries that process hazardous substances, relies on rigorous Hazard and Operability (HAZOP) studies to ensure process safety and operability, yet traditional human-led workshops are time-and resource-intensive and susceptible to oversight and inconsistencies, while Artificial Intelligence (AI)-driven alternatives are rising with different levels of success as a solution for these issues.
This paper presents a comparative analysis of three HAZOP studies conducted on a small generic solvent purifier system: one using Generative AI (GenAI), one employing a Deterministic AI-based tool, HAZOP Assistant developed by Vysus Group and Kairos, and one conducted via a traditional
human workshop.
The GenAI approach, leveraging probabilistic models, effortlessly identified hazards but introduced inconsistencies as well as proved non-compliant with the European Union (EU) AI Act’s (Regulation (EU) 2024/1689) transparency requirements for high-risk applications. In contrast, the HAZOP Assistant, with its algorithm-based logic, achieved a faster analysis than the human workshop. It only generated the deviations Flow, Pressure, Temperature and Level, but managed to fill in all the results from the Traditional workshop and highlighted some missed scenarios from it. This milestone makes it possible to ensure that the outputs are aligned with standards and are auditable. The human workshop, while thorough, required more time and personnel, and missed some of the hazards identified by the deterministic AI. This study demonstrates that Deterministic AI has the ability to enhance efficiency and quality in safety studies, offering a scalable solution for LNG process safety while meeting stringent regulatory demands.
Available documents
Details
- Original title: The future of HAZOPs – Comparing AI and human approaches in gas treatment systems.
- Record ID : 30034677
- Languages: English
- Subject: Technology
- Source: 21st International Conference & Exhibition on Liquefied Natural Gas (LNG2026)
- Publication date: 2026/02/05
Links
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Indexing
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Themes:
Cryology, gas processing: general information;
LNG and LPG - Keywords: Artificial intelligence (AI); Gas; LNG; Natural gas; Efficiency; Quality; Safety; Comparison
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