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Hazard and Operability (HAZOP) studies are conducted to identify and assess potential hazards which originate from processes, equipment, and process plants. These studies are human-centered processes that are time and labor-intensive. Also, extensive expertsise and experience in the field of process safety engineering are required. In the past, there have been several attempts by different research groups to (semi-)automate HAZOP studies. Within this research, a knowledge-based framework for the automatic generation of HAZOP worksheets was developed. Compared to other approaches, the focus is on representing semantic relationships between HAZOP relevant concepts under consideration of the degree of abstraction. In the course of this, expert knowledge from the process and plant safety (PPS) domain is embedded within the ontological model. Based on that, a reasoning algorithm based on semantic reasoners is developed to identify hazards and operability issues in a HAZOP similar manner. An advantage of the proposed method is that by modeling causal relationships between HAZOP concepts, automatically generated, but meaningless scenarios can be avoided. The results of the enhanced causation model are high quality extended HAZOP worksheets. The developed methodology is applied within a case study that involves a hexane storage tank. The quality and quantity of the automatically generated results agree with the original worksheets. Thus the ontology-based reasoning algorithm is well-suited to identify hazardous scenarios and operability issues. Node-based analyses, involving multiple process units, can also be carried out by slightly adapting the method. The presented method can help to support HAZOP study participants and non-experts in conducting HAZOP studies.

Safety assessments are conducted to identify and assess the risks that arise from processes, process plants or technical systems in general. This includes the identification of potential hazards posed by plants. One recognized and generally accepted method for this is the hazard and operability (HAZOP) method. It is a human-centered process that is time-and labor-intensive. In the presented research approach, the structure of a computer-aided HAZOP system is described. The identification of hazards and malfunctions within technical systems is knowledge-intensive. Within this research approach, it transpired that the semantically correct and detailed modeling of deviation cause and effect relationships in the form of ontologies are of particular importance to draw correct conclusions. Thus, the guiding principles of a knowledge representation framework are described from a process safety perspective, and serve as a basis for the automatic identification of hazards. An integral understanding of the process, process plant and involved substances requires extensive knowledge. The way in which this knowledge is used and the search for hazards is conducted has an influence on the completeness of the results. Within this approach, the hazard/malfunction identification is conducted on different layers of abstraction to improve the efficiency of the search algorithm. The proposed methodology is applied within a case study to a technical system that consists of a compressor, vessel and valve. The first results demonstrate that the proposed method is well-suited to understand and identify the context of hazards and malfunctions. Thus, a system for computer-aided HAZOP studies can be used to assist HAZOP conductors in performing hazard analysis while increasing the speed of safety assessments and serving as a decision support system.

Ontologies have been used in safety engineering for the representation of knowledge in the form of concepts, facts, and procedures. In this work, an ontology for Hazard and Operability (HAZOP), risk assessment, and related process and plant concepts, is proposed. It is intended to support participants during HAZOP studies. The ontology is designed based on competency questions which can be answered using ontology queries. The developed ontology is applied within a case study to provide knowledge to fill a HAZOP worksheet, including the risk assessment, of a gasoline storage tank system. The results demonstrate that the ontology is well-suited to answer the formulated competency questions correctly. Additionally, metrics have been used to assess the quality of the ontology and to be able to compare ontologies on a quantitative basis. Within future research, the proposed ontology could be enhanced and integrated with an upper ontology.