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Pressure Vessel Thickness Monitoring and Analyzing Using Linear Regression and Control Chart

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Metal loss due to corrosion is a universal phenomenon in refineries which could in turn cause leakage or explosion if not well monitored. There are several units in a refinery such as crude distillation unit, hydro-processing unit, acid alkylation unit, etc. In each unit, there are hundreds of pressure vessels which have different potential damage mechanisms. Hence, it’s critical to establish an effective and efficient way to monitor thickness changing behavior.

Product Number: 51323-18821-SG
Author: Chen Shao-Chi, Hsieh Cheng-Hsuan
Publication Date: 2023
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$20.00
$20.00

In API 510, it offers two ways for corrosion rate determination, one is using” point-to-point” method, in
which long-term and short-term corrosion rates can be compared so that the corrosion rate which best reflects the current process can be chosen.
The other way is “statistical analysis method”, in which owner can establish a representative and suitable model to monitor and analyze pressure vessel corrosiveness behavior in order to maintain its mechanical integrity. This paper, based on the experience of thousands of pressure vessels, will
present the model using linear regression which has been used successfully to monitor corrosion rate. And to maintain the quality of thickness measurements, statistical approaches like control chart have been applied effectively to reduce inspection errors.

In API 510, it offers two ways for corrosion rate determination, one is using” point-to-point” method, in
which long-term and short-term corrosion rates can be compared so that the corrosion rate which best reflects the current process can be chosen.
The other way is “statistical analysis method”, in which owner can establish a representative and suitable model to monitor and analyze pressure vessel corrosiveness behavior in order to maintain its mechanical integrity. This paper, based on the experience of thousands of pressure vessels, will
present the model using linear regression which has been used successfully to monitor corrosion rate. And to maintain the quality of thickness measurements, statistical approaches like control chart have been applied effectively to reduce inspection errors.

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