Improved quantitative risk model for integrity management of liquefied petroleum gas storage tanks: Mathematical basis, and case study

Abstract

Chemical, petrochemical, and refinery sectors have been facing tougher safety, environmental and mechanical integrity regulations as well as challenges associated with the need for cost reduction to improve competitiveness. Risk‐based Inspection (RBI) is a cost‐effective approach to manage operational risks by making an informed decision on inspection frequency, extent of inspections, and types of non‐destructive testing. This paper presents a comprehensive practical risk model that can simulate all components of liquefied petroleum gas (LPG) tanks, that is, roof, shell, nozzles, and bottom plates. The probability of failure (PoF) model was developed based on failure frequency data from four different sources to determine the adequate general failure frequencies, moreover, it addresses the fatigue effect due to cyclic loading. Reviewing past losses of containment incidents revealed that fires and explosion are the most likely outcomes, therefore, this risk model introduced area affected consequence of failure (CoF) simulation. The CoF model addresses all the main components of the LPG tank and deploys a practical gas dispersion model to estimate the vapor cloud explosion (VCE) and flash fire consequence affected areas. Furthermore, modified mass release equations are presented to account for the decline in the release rate with time to determine the pool fire area and leak duration. Consequence areas are determined based on serious personnel injuries and component damage from thermal radiation and explosions. To validate the risk model, a proof of concept assessment was performed on seven tanks.

Reliability of quantitative risk models: a case study from offshore gas production platform

“…Main part of level 2 calculation is dispersion modelling; however, the API methodology do not provide any specific dispersion model or specify certain requirements need to be addressed in the dispersion modelling. [17] introduced gas dispersion model can be used in the consequence calculations, however, that model is adequate only for light hydrocarbon gases (e.g., C 1 -C 2 ).…”

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

“…[ 10,12 ] Integrated risk models were developed to determine the causes, consequences, safety barriers, and scenario simulations of risks associated with LNG storage tanks, capable of quantifying the affected area in the event of a failure. [ 13,14 ] But the model‐driven methods mentioned above are more concerned with forecasting than they are with precisely describing the storage tank's present condition. Characterizing storage tanks using several techniques, such as optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), etc., is another popular analytical method.…”