Improving Accuracy of Frequency Estimation of Major Vapor Cloud Explosions for Evaluating Control Room Location Through Quantitative Risk Assessment

Abstract

Frequency estimation of vapor cloud explosions (VCE) are traditionally based on sparse VCE incidents data analysis while process conditions that generate VCE’s have a wide spectrum giving VCE’s with different strengths. The present paper is focused on developing a systematic methodology to estimate the frequency of VCEs based on process and plant conditions in order to find optimal location of a control room using quantitative risk assessment (QRA). In the presented approach, after classification of plant areas according to variables likely to generate different release conditions, a comprehensive release study is carried out to determine individual probable leakages. Consequently, only those leakages that meet the criteria to launch a strong enough VCE are fed to an event tree in order to estimate the final VCE frequency. Compared to traditional methods, the new proposed approach has the advantages of both being supported by a more populated database of leakage frequencies not explosion incidents, and that it features a multi-variable functionality of process/plant conditions. The first advantage guarantees the accuracy and the latter lets one to use the methodology to differentiate between process units with regard to VCE frequency.