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Knowledge-based support of HAZOP studies using a CAEX model

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... Park (2017) developed a method for hazard identification based on extracted P&ID information called DA-HAZID. (Schmidberger et al., 2009) proposed the usage of CAEX data for knowledge-based support of HAZOP studies. and Kang et al. (1999) introduced AHA, a multi-model approach using an expert system shell and a rule base for hazard identification. ...
... Risk assessment is not considered, which is needed to propose adequate safeguards. Schmidberger et al. (2009) proposed a knowledge-based approach for HAZOP studies based on a CAEX plant model. The idea of using a CAEX model of the plant is useful, as information regarding material flow and information flow (process control) between plant components can be used directly. ...
Thesis
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.
... Originally defined by Schmitz et al. [SSE09] as the automated execution of tasks supporting the engineering process of automation systems, the scope of automation of automation has been progressively extended in the last years towards further automation and control areas. To state some examples, the applicability of the concept has been proven in theoretical use cases including fault detection and diagnosis [Chr15,DAIT11,Iyu11,LKJJ14], HAZOP studies [FSS09], alarm management [SCTF13], and derivation of simulation models [BF13,HFB15]. ...
Thesis
Confronted with the competitive challenges brought by the ongoing digitization of the industrial sector --Industry 4.0, plant owners and contractors in the process industry are called to create and exploit digital plant models enabling the efficient execution and integration of different activities across the entire plant lifecycle. In the case of existing process facilities, an important part of the information required for this task exists already in form of legacy engineering documents, such as scanned diagrams and schematics in elementary digital formats. However, current engineering and enterprise tools cannot fully exploit this source of knowledge due to the non-computer-interpretable nature and heterogeneity of such data sources. As a consequence, process experts and engineers must often retrieve and consolidate information manually when executing daily activities; a practice that is not only error prone but also time-intensive, and costly. In an effort to cope with that problem, this thesis presents a functional methodology for the capture, formalization, integration, and subsequent exploitation of legacy design documents, specifically Piping and Instrumentation Diagrams, and Control Logic Diagrams. Motivated by the concept Automation of Automation, the proposed methodology aims to serve as a basis for the automatic execution of required steps within automation-related plant modernization and operational tasks. Experimental results demonstrate the effective applicability of the approach in two use cases of industrial relevance: (a) automatic generation of plant simulation models for the validation of basic control functions during plant modernization projects, and (b) fault propagation analysis for supporting alarm management and fault diagnosis during plant operation.
... Previous research has shown that the use of P&IDs can support different tasks along the plant's life-cycle such as automated HAZOP (hazard and operability) studies [3], detection of design patterns [4], derivation of simulation models [5] and fault detection and diagnosis [6]. Existing approaches are all based on the prerequisite that P&IDs must be described in OO formats (eg, IEC 62424 CAEX/ AutomationML [7,8] or ISO 15926 [9]). ...
Article
Full-text available
Documentation in process industries can rise to bewildering dimensions. Furthermore, the difficulties of accessing it can be compounded by the range in formats used, including traditional paper and elementary digital representations. When a plant owner decides to consolidate this information – for example, to prepare a modernization process – bringing this eclectic documentation into a single, accessible and up-to-date format can require a gargantuan effort. ABB has explored means of efficiently extracting models from engineering documents in an automated and consistent manner. A joint research project by ABB and the Helmut Schmidt University (HSU) in Hamburg, Germany, resulted in a method based on optical recognition and semantic analysis that converts piping and instrumentation diagrams (P&IDs) into object-oriented models.
... Hierdurch würde die benötigte Zeit für die Informationsbeschaffung drastisch sinken. Wie ein im Rechner vorliegendes Anlagen-Strukturmodell sinnvoll und effizient ausgewertet werden kann, wurde in der Literatur bereits umfangreich dargestellt: Beispiele dafür sind Generierung von Simulationsmodellen [4,5], die automatisierte Störungsanalyse [6,7], die Unterstützung von HAZOP-Studien [8] und das Alarmmanagement [9]. Moderne Anlagenplanungs-Werkzeugumgebungen arbeiten bereits mit einem objektorientierten Anlagen-Strukturmodell, für die große Zahl bestehender Altanlagen liegt ein solches aber noch nicht vor und muss für die Optimierung des Betriebs und für die Vorbereitung von Modernisierungen bisher mühsam von Hand erstellt werden. ...
Article
Zusammenfassung Graphische Engineering-Dokumente beinhalten substanzielle Informationen für diverse Planungs- sowie Betriebsaktivitäten in der verfahrenstechnischen Industrie. Auch wenn diese Dokumente seit vielen Jahren mit dem Computer erstellt werden, werden sie in vielen Fällen jedoch als Papierausdruck oder in primitiven digitalen Formaten gespeichert, wodurch eine rechnergestützte und effiziente Auswertung dieser Dokumente erschwert wird. Um dieses Problem zu bewältigen, präsentiert dieser Beitrag eine neue Methodik für die automatische Analyse und computer-interpretierbare Beschreibung von Engineering-Dokumenten, welche als Scalable Vector Graphics (SVG) vorliegen.
... Motivation [2], die Generierung von Verriegelungslogik [3], die Generierung von Simulationsmodellen [4] sowie die Fehlererkennung und Diagnose [5]. Diese Methoden haben eine gemeinsame Grundvoraussetzung; sie beruhen darauf, dass R&I-Fließbilder in objektorientierter Form beschrieben werden (z.B. mittels IEC 62424 CAEX / AutomationML [6] oder ISO 15926 [7]). ...
Conference Paper
Die Modernisierung bestehender Anlagen („Brownfield“) hat hohe Bedeutung für den Erhalt der Wettbewerbsfähigkeit und die Erhöhung der Ressourceneffizienz produzierender Unternehmen. Voraussetzung dafür ist ein Verständnis der vorhandenen technischen Anlage. Eine typische Situation am Beginn solcher „Brownfield“-Projekte ist, dass ein Großteil der bereits vorhandenen Engineering-Dokumentation in papierbasierter Form vorliegt. Diese Dokumente werden üblicherweise durch Scannen als Rasterbild digitalisiert, wodurch Speicherung und Zugriff erleichtert werden. Gescannte Dokumente können nur manuell ausgewertet werden. Für ein effizientes Engineering von Erweiterungen oder Modernisierungen wird aber ein digitales Anlagenmodell („Digital Plant“) benötigt, welches die Objekte der Anlage und ihre Zusammenhänge beinhaltet, z.B. gemäß IEC 62424 (CAEX/AutomationML) oder ISO 15926. Auf Basis dieser digitalen Modelle sind vielfältige Auswertungen möglich, z.B. Fehlerdiagnose, Simulation und HAZOP-Analyse. Der Aufwand für eine manuelle Erstellung eines digitalen Anlagenmodells ist jedoch erheblich. Um die Vorteile des „Digital Plant“ auch in „Brownfield“-Projekten zu ermöglichen, wird in diesem Beitrag eine Methode beschrieben, mit der ein digitales Anlagenmodell größtenteils automatisch aus den Papierdokumenten generiert werden kann. Die Methode basiert auf optischer Erkennung symbolischer Formen inkl. der beschreibenden Texten und Verbindungen, sowie einer semantischen Analyse ihrer domänenspezifischen Konnotation. Der Ansatz ist insbesondere für die Digitalisierung von vorhandenen Rohrleitungs- und Instrumentenfließbildern geeignet, welche in vielen verfahrenstechnisch geprägten Industriezweigen zentrale Dokumente für das Verständnis von Anlage und Prozess sind.
Chapter
Industrial plant topology models can potentially automate many automation engineering tasks that are today carried out manually. Information on plant topologies is today mostly available in informal CAD drawings, but not formal models that transformations could easily process. The upcoming DEXPI/ISO15926 standard may enable turning CAD drawings into such models, but was so far mainly used for data exchange. This paper proposes extensions to the CAYENNE method for control logic and process graphics generation to utilize DEXPI models and demonstrates the supported model transformation chain prototypically in two case studies involving industrial plants. The results indicate that the model expressiveness and mappings were adequate for the addressed use cases and the model processing could be executed in the range of minutes.
Article
Software development for the automation of industrial facilities (e.g., oil platforms, chemical plants, power plants, etc.) involves implementing control logic, often in IEC 61131-3 programming languages. Developing safe and efficient program code is expensive and today still requires substantial manual effort. Researchers have thus proposed numerous approaches for automatic control logic generation in the last two decades, but a systematic, in-depth analysis of their capabilities and assumptions is missing. This paper proposes a novel classification framework for control logic generation approaches defining criteria derived from industry best practices. The framework is applied to compare and analyze 13 different control logic generation approaches. Prominent findings include different categories of control logic generation approaches, the challenge of dealing with iterative engineering processes, and the need for more experimental validations in larger case studies.
Article
Confronted with the need of plant modernization, facility owners and contractors in the process industry invest significant efforts to create digital plant models allowing for simulation and thereby validation of new engineering solutions. Although an important part of the information required for this task already exists in form of legacy engineering documentation, current computer-aided methods for generating digital plant models cannot exploit this source of knowledge owing to the non-computer-interpretable nature of the available information sources. In an effort to bridge the existing gap, this contribution presents a method based on optical recognition and semantic analysis, which is capable of automatically converting legacy engineering documents, specifically piping and instrumentation diagrams, into object-oriented plant descriptions and ultimately into qualitative plant simulation models. Resulting simulation models can serve as a basis to support engineering tasks requiring low-fidelity simulation, such as the validation of base control functions during the factory acceptance test (FAT).
Conference Paper
Throughout the operation of an automation system component failures are unavoidable and inevitably result in changes of the physical automation system structure (which are usually compensated soon after by maintenance or repair). This is in this work referred to as: temporal change of physical structure (TeCoPS). Simultaneously, due to dependencies between the physical and the functional structures, the functional automation system structure is also affected (which can negatively influence physical plant processes). Analyses of the effects of TeCoPS must be performed as part of the automation system engineering. This task can be supported by automated analyses, e.g. with the help of knowledge-based systems (KBS). In the focus of this paper is the formal modeling, i.e. with unambiguous and complete syntax and semantics, of TeCoPS, which utilizes the Process Specification Language (PSL). Utilization of PSL enables the integration of TeCoPS as knowledge into KBS. In addition, the paper shortly introduces the underlying metamodel for the modeling of automation systems.
Conference Paper
Full-text available
This paper presents an approach for using process knowledge in addition to structural knowledge for plant-wide diagnosis. System diagnosis based on fault-detection and fault-diagnosis often uses structural knowledge (e.g. hierarchies of sub-systems and components) to identify the root cause of faults and malfunctions. The structural knowledge describes the internal relationships of a plant and is particularly suitable for diagnosis if an assembly or component breaks down, which affects other parts of the system. However, the root cause of faults cannot always be identified based on structural knowledge alone, but information about the processes (e.g. production processes) is required in addition. Based on an integration of the VDI/VDE-guideline 3682 “Formalized process description” the authors present a method to consider process knowledge for an improved system diagnosis.
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