(24d) Consequence Modeling Lack of Accuracy and Its Effects on Quantitative Risk Analysis Results | AIChE

(24d) Consequence Modeling Lack of Accuracy and Its Effects on Quantitative Risk Analysis Results


For the last three decades, Quantitative Risk Analysis has played a substantial role as an acceptable industry tool to determine new Projects' feasibilities as a function of their calculated risk, as well as in assessing existing facilities' risks. Various government agencies in many countries published their own individual and societal risk acceptance criteria, which may only be assessed via QRA techniques. Such techniques involve massive amount of consequence modeling and probability calculations. Similar to others, QRA tools have taken advantage of advances in computing power to make calculations quicker & easier to perform by users. This has resulted in QRA tools becoming "black boxes" where users provide simple inputs, then out pops the QRA results.

With a lack of industry standardization on minimum requirements of how consequence modeling calculations should be performed ?? QRA results for a single facility or project may vary to a great extent, at times by many orders of magnitude, from one study to another depending on the consequence analysis features and limitations. A common misconception is that making a probability error, or using different probability data sets, has the same effect on the final QRA results as making the same order of magnitude error in the consequence modeling portion of the QRA calculations. Nothing can be far from the truth. This paper illustrates the importance of choosing the proper consequence modeling tools in shaping QRA results: in essence the difference between a project "Go" or "No Go", and of millions of dollars at stake.


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