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Overview
A refinery in North America was experiencing varying amounts of corrosion in certain areas of piping across three different units. Due to the high corrosion in parts of some units, these stretches of piping were replaced at their respective three or four-year turnaround cycles, and as a result, the thinning data being captured had a short effective period with which to do predictive modeling. To reduce the risk of corrosion and optimize inspection costs, the company operating the refinery initiated a process improvement initiative. As part of this initiative, multiple approaches were considered to pilot different methodologies to improve the effectiveness of the data collected, overall reliability, and mitigate risks associated with equipment failure. Among those, they selected Pinnacle’s process to prioritize condition monitoring locations (CMLs) and frequencies using data science coupled with corrosion/materials & inspection engineering to better identify areas of vulnerability, characterize degradation attributes, ensure the right CMLs are inspected at the right time, and reduce inspection spend on ineffective CMLs.
CMLs have long served to be at the whim of linear approximations of previous thinning history, with little emphasis on projecting past CML performance from historical data onto piping being replaced and the new CML, a lack of industry experience specific to the service, or a valid corrosion or damage mechanisms review. CML prioritization by the methodology presented in this case study is a new CML optimization methodology that can predict future thinning based on past performance from that same CML across repairs and replacements, and rank CMLs by the risk posed to safety and production prior to the next scheduled action (repair/replacement/inspection/turnaround) taken on that CML.
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