Reduction of uncertainty in risk assessment frequencies through fuzzy logic
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Tutor / directorDarbra Roman, Rosa Maria
Document typeMaster thesis
Rights accessRestricted access - author's decision
Safety in the chemical industry is a very complex subject; all the factors that may affect the safety in a chemical plant, process or even an equipment are numerous. To establish how safe a chemical plant or process is a parameter called risk is used. The risk can be quantified by the frequency of an accident and the consequences of this one. There are several methodologies which allow to assess the risk in order to then manage it and make decisions to decrease it. One of the most used in the chemical industry is the QRA (Quantitative Risk Analysis). This methodology allows, through a sequence of steps, to reach the value of the risk. However, during this process there exists uncertainty, which is associated to many variables involved in the process. One of the best ways to deal this uncertainty is through a methodology called fuzzy logic. Therefore in the first part of this report, the QRA methodology is going to be explained; all the steps in the sequence of this methodology will be introduced highlighting the importance of each one of them. Then, the second methodology called fuzzy logic is going to be presented, describing how this theory functions and how can be related with the safety of a chemical plant in a risk assessment process, especially in a QRA. The principal scope of this research is to combine these two methodologies in order to obtain better and more reliable results. The QRA methodology relies on many variables that can have uncertainty associated. The application of fuzzy logic in the safety of chemical plants through QRA is a new topic that will try to reduce uncertainty in this field and gives place to a future PhD thesis. The first attempt to combine these two methodologies will be done in a case study in ports, in which fuzzy logic is going to be applied in one of the most important steps of the QRA, the quantification of frequency. Using a modifier of the frequency through fuzzy logic, the uncertainty associated will be reduced in order to obtain more reliable results.
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