The identification of possible hazards in chemical plants is a very important part of the design process. This is because of the potential danger that large chemical installations pose to the public. One possible route for speeding up the identification of hazards in chemical plants is to use computers to identify hazards automatically. This will facilitate safe plant design and will avoid late design changes which can be very costly to implement. Previous research at Loughborough has concentrated on developing a model-based approach and an analysis algorithm for automating hazard identification. The results generated have demonstrated the technical feasibility of the approach. This approach requires a knowledge-base of unit models. This library of models describes how different plant equipment behaves in qualitative terms. The research described in this thesis develops a method for creating and testing the equipment models. The model library was previously achieved by an expert writing the models in a format that could be directly used by the system described above. An engineer unfamililar with the system would find this difficult. An alternative method would have been to use an intermediary (a knowledge engineer) to gather information from the engineer and convert it into the system format. This would be expensive. Both methods would take up a lot of the engineer's time. An engineer should be able to enter information personally in order to maintain efficiency and avoid information loss through the intermediary. A front end interface has been built to the system which enables an expert to enter information directly without needing to understand details of the application system. This interface incorporates ideas from the knowledge acquisition field in order to produce a tool that is simple to use. Unit-based qualitative modelling can lead to incorrect or ambiguous inference. The method developed here identifies situations where ambiguities may arise. A new modular approach is presented to overcome this type of problem. This method also presents a technique to verify that the models created are both complete and correct.

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There are increasing safety concerns in chemical and petrochemical process industry. The huge explosion of Nowruz oil Field platform that happened in Persian gulf-IRAN at 1983, along with other disastrous events have effected chemical industrial renaissance and led to high demand to enhance safety. Oil and chemical Industries involve complex processes and handle hazardous materials that may potentially cause catastrophic consequences in terms of human losses, injuries, asset lost and environmental stresses. One main reason of such catastrophic events is the lack of effective control and monitoring approaches that are required to achieve successful fault diagnosis and accurate hazard identification. Currently, there are aggressive worldwide efforts to propose an effective, robust, and high accuracy fault propagation analysis and monitoring techniques to prevent undesired events at early stages prior to their occurrence. Among these requirements is the development of an intelligent and automated control and monitoring system to first diagnose faulty equipment and process variable deviations, and then identify hazards associated with faults and deviations. Research into safety and control issues become high priority in all aspects. To support these needs, predictive control and intelligent monitoring system is under study and development at the Energy Safety and Control Laboratory (ESCL) – University of Ontario Institute of Technology (UOIT). The purpose of this research is to present a real time fault propagation analysis method for chemical / petrochemical process industry through fault semantic network (FSN) using accurate process variable interactions (PV-PV interactions). The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman (TE) chemical process. Unlike most existing probabilistic approaches, fault propagation analysis module classifies faults and identifies faulty equipment and deviations according to obtained data from the underlying processes. It is an expert system that identifies corresponding causes and consequences and links them together. FSN is an integrated framework that is used to link fault propagation scenarios qualitatively and quantitatively. Probability and fuzzy rules are used for reasoning causes and consequences and tuning FSN. / UOIT

This thesis investigates the proposition that use must be made of quantitative information to control the reporting of hazard scenarios in automatically generated HAZOP reports. HAZOP is a successful and widely accepted technique for identification of process hazards. However, it requires an expensive commitment of time and personnel near the end of a project. Use of a HAZOP emulation tool before conventional HAZOP could speed up the examination of routine hazards, or identify deficiencies I in the design of a plant. Qualitative models of process equipment can efficiently model fault propagation in chemical plants. However, purely qualitative models lack the representational power to model many constraints in real plants, resulting in indiscriminate reporting of failure scenarios. In the AutoHAZID computer program, qualitative reasoning is used to emulate HAZOP. Signed-directed graph (SDG) models of equipment are used to build a graph model of the plant. This graph is searched to find links between faults and consequences, which are reported as hazardous scenarios associated with process variable deviations. However, factors not represented in the SDG, such as the fluids in the plant, often affect the feasibility of scenarios. Support for the qualitative model system, in the form of quantitative judgements to assess the feasibility of certain hazards, was investigated and is reported here. This thesis also describes the novel "Fluid Modelling System" (FMS) which now provides this quantitative support mechanism in AutoHAZID. The FMS allows the attachment of conditions to SDG arcs. Fault paths are validated by testing the conditions along their arcs. Infeasible scenarios are removed. In the FMS, numerical limits on process variable deviations have been used to assess the sufficiency of a given fault to cause any linked consequence. In a number of case studies, use of the FMS in AutoHAZID has improved the focus of the automatically generated HAZOP results. This thesis describes qualitative model-based methods for identifying process hazards by computer, in particular AutoHAZID. It identifies a range of problems where the purely qualitative approach is inadequate and demonstrates how such problems can be tackled by selective use of quantitative information about the plant or the fluids in it. The conclusion is that quantitative knowledge is' required to support the qualitative reasoning in hazard identification by computer.

In safety-critical, networked embedded systems, it is important that the way in which a fault(s) in one component of the system can propagate throughout the system to other components is analyzed correctly. Many real-world systems, such as modern aircrafts and automobiles, use large-scale networked embedded systems with complex behavior. In this work, we have developed techniques and a software tool, FauPA, that uses those techniques to automate fault-propagation analysis of large-scale, networked embedded systems such as those used in modern aircraft. This work makes three main contributions. 1. Fault propagation analyses. We developed algorithms for two types of analyses: forward analysis and backward analysis. For backward analysis, we developed two techniques: a naive algorithm and an algorithm that uses Datalog. 2. A system description language. We developed a language that we call Communication System Markup Language (CSML) based on XML. A system can be specified concisely and at a high-level in CSML. 3. A GUI-based display of the system and analysis results. We developed a GUI to visualize the system that is specified in CSML. The GUI also lets the user visualize the results of fault-propagation analyses.

Les modèles cinématiques de plissement permettent de prévoir l'architecture des anticlinaux en profondeur à partir de leurs géométries de surface. Bien que les modèles classiques de type « kink-band » soient faciles à utiliser, ils ne peuvent expliquer que des géométries de plis limitées. Le modèle trishear surmonte cet inconvénient, mais son application reste difficile à mettre en oeuvre notamment en raison de nombreux paramètres mal contraints tels que le rapport P/S et l'angle de trishear. Dans cette thèse, nous avons développé un modèle cinématique de plissement à angle d'inter-flancs variable qui permet de rendre compte de toutes les variétés géométriques de plis sur propagation de faille. Ce modèle est basé sur la géométrie « kink-band » et son application nécessite de connaître uniquement les paramètres de surface du pli (plongements des flancs, amplitude et longueur d'onde du pli). Le modèle permet de quantifier le rapport P/S du pli durant sa croissance, ainsi que son évolution tardive en pli de rupture. Le modèle a été appliqué à la chaîne plissée et faillée de l'Atlas tunisien pour estimer la profondeur du niveau de décollement des plis sur propagation de faille. Les résultats se corrèlent bien avec les données de subsurface, au contraire des autres modèles classiques et du modèle de trishear où il est difficile d’établir une relation entre la géométrie de surface des anticlinaux et leur architecture profonde. / Kinematic models of folding allow deducing the deep architecture of folds from their surface geometries. Classical models are easy to use because of their kink-band geometry but they are only able to explain some particular fold shapes. The trishear model overcomes this drawback, but its application remains limited because it necessitates the knowledge of several unconstrained parameters such as the P/S ratio and the trishear angle. In this work, we developed a kinematic model based on the variation of the inter-limb angle during the folding. The model allows accounting for all surface geometries of fault-propagation folds. Since this model is based on the kink-band geometry, its application only requires the knowledge of the fold surface parameters, i.e. the dip of both limbs and the fold amplitude and wavelength. The model is also able to quantify the P/S ratio during the fold growth, together with its latest evolutional stage as a breakthrough fold. We applied the model in the Tunisian Atlas fold-and-thrust belt to estimate the depth of the decollement level associated to fault-propagation anticlines. Results are in good agreement with subsurface data. In contrast, both the other classical and trishear models are unable to explain the relationship between the surface geometry of anticlines and their deep architecture.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-28T16:50:59Z No. of bitstreams: 1 WAGNER DE SOUZA PORTO - DISSERTAÇÃO PPGCC 2009..pdf: 3676431 bytes, checksum: 91bede14a64447aa4598ba5c6b3365a4 (MD5) / Made available in DSpace on 2018-08-28T16:50:59Z (GMT). No. of bitstreams: 1 WAGNER DE SOUZA PORTO - DISSERTAÇÃO PPGCC 2009..pdf: 3676431 bytes, checksum: 91bede14a64447aa4598ba5c6b3365a4 (MD5) Previous issue date: 2009-09-16 / Este trabalho apresenta o Auto-FDI (Automatic Fault Detection and Isolation), uma ferramenta de detecção e isolamento de falhas em sistemas. A ferramenta usa o conceito de redundância analítica, onde sinais obtidos do sistema (possivelmente com falha) são comparados com sinais esperados, obtidos de um modelo. O isolamento de falhas emprega uma técnica desenvolvida neste trabalho, chamada isolamento automático. A técnica usa uma abordagem baseada em grafos que considera a propagação de falhas e a falta de informação sobre determinados componentes do sistema. Falhas são localizadas de forma mais precisa possível, dado o nível de detalhe do modelo. No escopo deste trabalho foi abordado todo o processo de especificação, projeto, implementação e validação da ferramenta, utilizada como prova de conceito para a técnica desenvolvida. A validação da ferramenta foi feita através da realização de um estudo de caso por potenciais usuários, o que permitiu demonstrar a aplicabilidade da ferramenta e a da técnica desenvolvida. / This work presents Auto-FDI (Automatic Fault Detection and Isolation), a software tool for detection and diagnosis of faults in systems. The tool uses the analytical redundancy concept, where signals from the (possibly faulty) system are compared with expected signals from a model. The fault isolation employs a technique developed on this work, called automatic isolation. This technique uses a graph-based approach which considers the fault propagation and the lack of information about certain components of the system. Faults are pinpointed as accurately as possible given the level of detail in the model. In the scope of this work was addressed the whole process of specification, design, implementation and validation of the tool - used as proof of concept for the developed technique. The validation of the tool was made by conducting a case study for potential users, that has demonstrated the applicability of the tool and the technique developed.