Browsing by Subject "LOPA"
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Item Bayesian-lopa methodology for risk assessment of an LNG importation terminal(2009-05-15) Yun, Geun-WoongLNG (Liquefied Natural Gas) is one of the fastest growing energy sources in the U.S. to fulfill the increasing energy demands. In order to meet the LNG demand, many LNG facilities including LNG importation terminals are operating currently. Therefore, it is important to estimate the potential risks in LNG terminals to ensure their safety. One of the best ways to estimate the risk is LOPA (Layer of Protection Analysis) because it can provide quantified risk results with less time and efforts than other methods. For LOPA application, failure data are essential to compute risk frequencies. However, the failure data from the LNG industry are very sparse. Bayesian estimation is identified as one method to compensate for its weaknesses. It can update the generic data with plant specific data. Based on Bayesian estimation, the frequencies of initiating events were obtained using a conjugate gamma prior distribution such as OREDA (Offshore Reliability Data) database and Poisson likelihood distribution. If there is no prior information, Jeffreys noninformative prior may be used. The LNG plant failure database was used as plant specific likelihood information. The PFDs (Probability of Failure on Demand) of IPLs (Independent Protection Layers) were estimated with the conjugate beta prior such as EIReDA (European Industry Reliability Data Bank) database and binomial likelihood distribution. In some cases EIReDA did not provide failure data, so the newly developed Frequency-PFD conversion method was used instead. By the combination of Bayesian estimation and LOPA procedures, the Bayesian-LOPA methodology was developed and was applied to an LNG importation terminal. The found risk values were compared to the tolerable risk criteria to make risk decisions. Finally, the risk values of seven incident scenarios were compared to each other to make a risk ranking. In conclusion, the newly developed Bayesian-LOPA methodology really does work well in an LNG importation terminal and it can be applied in other industries including refineries and petrochemicals. Moreover, it can be used with other frequency analysis methods such as Fault Tree Analysis (FTA).Item Layer of protection analysis applied to ammonia refrigeration systems(2009-05-15) Zuniga, Gerald AlexanderAmmonia refrigeration systems are widely used in industry. Demand of these systems is expected to increase due to the advantages of ammonia as refrigerant and because ammonia is considered a green refrigerant. Therefore, it is important to evaluate the risks in existing and future ammonia refrigeration systems to ensure their safety. LOPA (Layer of Protection Analysis) is one of the best ways to estimate the risk. It provides quantified risk results with less effort and time than other methods. LOPA analyses one cause-consequence scenario per time. It requires failure data and PFD (Probability of Failure on Demand) of the independent protection layers available to prevent the scenario. Complete application of LOPA requires the estimation of the severity of the consequences and the mitigated frequency of the initiating event for risk calculations. Especially in existing ammonia refrigeration systems, information to develop LOPA is sometimes scarce and uncertain. In these cases, the analysis relies on expert opinion to determine the values of the variables required for risk estimation. Fuzzy Logic has demonstrated to be useful in this situation allowing the construction of expert systems. Based on fuzzy logic, the LOPA method was adapted to represent the knowledge available in standards and good industry practices for ammonia refrigeration. Fuzzy inference systems were developed for severity and risk calculation. Severity fuzzy inference system uses the number of life threatening injuries or deaths, number of injuries and type of medical attention required to calculate the severity risk index. Frequency of the mitigated scenario is calculated using generic data for the initiating event frequency and PFD of the independent protection layers. Finally, the risk fuzzy inference system uses the frequency and severity values obtained to determine the risk of the scenario. The methodology was applied to four scenarios. Risk indexes were calculated and compared with the traditional approach and risk decisions were made. In conclusion, the fuzzy logic LOPA method provides good approximations of the risk for ammonia refrigeration systems. The technique can be useful for risk assessment of existing ammonia refrigeration systems.