LOPA (Layer of Protection Analysis) estimates the risk because it can provide quantified risk results with less time and efforts than other methods. However, the failure data from the industry are very sparse and have statistically shaky grounds due to insufficient population of sample data and short-term operational history. Bayesian estimation is identified as a better method to use to compensate for the weaknesses found in the LNG industry's failure data. It can update the generic data with plant specific data.

The PFDs (Probability of Failure on Demand) of IPLs (Independent Protection Layers) were estimated with the conjugate beta prior distribution and binomial likelihood distribution. EIReDA (European Industry Reliability Data Bank) was used as prior information because it provided the failure data made from beta distribution. By the combination of Bayesian estimation and LOPA procedures, the Bayesian-LOPA methodology was developed. The method was applied to an LNG importation terminal. For seven incident scenarios, it produced valid risk values. The posterior values of every initiating event or IPLs are located between prior and likelihood values. This means that the posterior values are valid and well-updated. The found risk values were compared to the tolerable risk criteria given by CCPS (Center for the Chemical Process Safety) to make risk decisions and compared to each other to make a risk ranking in view of probabilistic risk analysis.

It can be generally concluded that the LNG terminal has good safety protections to prevent dangerous events. The newly developed Bayesian-LOPA methodology as one of the risk assessment methods really does work well in an LNG importation terminal and it can be applied in other industries including refineries, petrochemicals, nuclear plants, and aerospace industries. Moreover, it can be used with other frequency analysis methods such as Fault Tree Analysis (FTA) and Event Tree Analysis (ETA).