LNG (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).
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2460 |
| Date | 15 May 2009 |
| Creators | Yun, Geun-Woong |
| Contributors | Mannan, M. Sam |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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