CSE-Institut | CSE Center of Safety Excellence gGmbH


MetA HAZOP – Methodology for Automated HAZOP Studies

CSE Institute: MetA HAZOP Project

Development of an AI-supported Methodology for conducting automated or semi-automated HAZOP Studies

dashboard Objectives

As part of the research project, a comprehensive methodology for conducting automated or partially automated HAZOP studies is being developed. With the help of knowledge representations and inference engines, the steps of hazard identification, risk assessment and risk-based definition of safeguards are carried out.

The knowledge representations are to be created or expanded on the basis of existing HAZOP studies. Using artificial intelligence methods such as natural language processing systems, HAZOP tables and other necessary documents are to be correctly recorded and semantically evaluated so that they can then be converted into knowledge representations using suitable methods such as ontology learning.

AI-supported HAZOPs will not only save time and costs in the future, but above all make risk analyses more objective – for greater safety in processes and plants.
CSE Institute: MetA HAZOP Project

Figure: Screening of HAZOPs using Natural Language Processing as a basis for the AI-supported creation of ontologies.

dashboard MILESTONES


Concept for automated HAZOP studies


Digitization of existing HAZOPs through natural language processing


Automated generation of knowledge representations


Structuring of ontology modules as well as test and validation methodology


Input objects for automated HAZOPs

dashboard  Overview


Automated HAZOPs


Natural Language Processing


Ontology Learning

dashboard Motivation

Technical risk analyses are carried out in industry in order to systematically identify the hazards of technical systems for people and the environment, assess possible consequences and define suitable safeguards. In practice, risk analyses, such as HAZOP studies, are carried out by a team of experts at great expense of time. The risk posed by a plant must be regularly reviewed during its life cycle and may need to be reassessed several times in case of modifications or changed boundary conditions. The results of the risk analyses are significantly influenced by the composition of the team and the experience of the participants.

The time, personnel and costs involved can be significantly reduced by using automated concepts to carry out risk analyses. In addition to supporting the teams, automated concepts can help to expand and harmonize the knowledge base and reduce subjective assessments and evaluations. Documentation processes can also be automated.


Publications in preparation.

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Cover photo source "Tube Geometry", RiIM project: Harald Hoyer from Schwerin, Germany, CC BY-SA 2.0, via Wikimedia Commons

Cover photo source "Winstainforth", SafeDDT project, CC BY-SA 3.0, via Wikimedia Commons