Automation of the Hazard and Operability Method Using Ontology-based Scenario Causation Models

  • The hazard and operability (HAZOP) method is widely used in chemical and process industries to identify and evaluate hazards. Due to its human-centered nature, it is time-consuming, and the results depend on the team composition. In addition, the factors time pressure, type of implementation, experience of the participants, and participant involvement affect the results. This research aims to digitize the HAZOP method. The investigation shows that knowledge-based systems with ontologies for knowledge representation are suitable to achieve the objective. Complex interdisciplinary knowledge regarding facility, process, substance, and site information must be represented to perform the task. A result of this work is a plant part taxonomy and a developed object-oriented equipment entity library. During ontology development, typical HAZOP scenarios, as well as their structure, components, and underlying causal model, were investigated. Based on these observations, semantic relationships between the scenario components were identified. The likelihood of causes and severity of consequences were determined as part of an automatic risk assessment using a risk matrix to determine safeguards reliably. An inference algorithm based on semantic reasoners and case-based reasoning was developed to exploit the ontology and evaluate the input data object containing the plant representation. With consideration given to topology, aspects like the propagation of sub-scenarios through plant parts were considered. The results of the developed knowledge-based system were automatically generated HAZOP worksheets. Evaluation of the achieved results was based on representative case studies in which the relevance, comprehensibility, and completeness of the automatically identified scenarios were considered. The achieved results were compared with conventionally prepared HAZOP tables for benchmark purposes. By paying particular attention to the causal relationships between scenario components, the risk assessment, and with consideration of safeguards, the quality of the automatically generated results was comparable to conventional HAZOP worksheets. This research shows that formal ontologies are suitable for representing complex interdisciplinary knowledge in the field of process and plant safety. The results contribute to the use of knowledge-based systems for digitizing the HAZOP method. When used correctly, knowledge-based systems can help decrease the preparation time and repetitious nature of HAZOP studies and standardize results.

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Author:Johannes Imanuel SingleORCiD
Advisor:Jürgen Schmidt
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2022/02/09
Date of Publication:2022/02/09
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2022/02/08
Date of the Publication (Server):2022/02/10
Tag:Automatische Gefahrenanalyse; Automatische Risikobewertung; HAZOP Automatisierung; Ontologiebasierte Kausalmodelle; Semantische Reasoner
Automatic risk assessment; HAZOP Automation; HAZOP Digitalization; Ontology-based causation model
GND-Keyword:HAZOP-Verfahren; Formale Ontologie; Kausalmodell; Automation; Sicherheitstechnik; Risikoanalyse; Wissensbasiertes System
Number of page:XX, 270
Faculties / Organisational entities:Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik
DDC-Cassification:6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Licence (German):Creative Commons 4.0 - Namensnennung, nicht kommerziell, keine Bearbeitung (CC BY-NC-ND 4.0)