A fuzzy-based model to determine CUI corrosion rate for carbon steel piping systems
One of the most common external corrosion failures in petroleum and power industry is due to corrosion under insulation (CUI). Despite being external, ironically the challenges are in the prevention and detection. The difficulty in corrosion monitoring has contributed to the scarcity of corrosion ra...
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| Main Authors: | , , |
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| Format: | Article |
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Asian Research Publishing Network
2016
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007193568&partnerID=40&md5=bb31699f49d17f7c4f35a77232850e00 http://eprints.utp.edu.my/25384/ |
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| Summary: | One of the most common external corrosion failures in petroleum and power industry is due to corrosion under insulation (CUI). Despite being external, ironically the challenges are in the prevention and detection. The difficulty in corrosion monitoring has contributed to the scarcity of corrosion rate data to be used in Risk-Based Inspection (RBI) analysis for degradation mechanism due to CUI. Limited data for CUI presented in American Petroleum Institute standard, (API 581) reflected some uncertainty for both stainless steels and carbon steels which limits the use of the data for quantitative RBI analysis. The objective of this paper is to present a fuzzy-based model to estimate CUI corrosion rate of carbon steel based on the API data. The fuzzy model has five inputs, which are operating temperature, type of environment, type of insulation, pipe complexity and insulation condition while the output in terms of CUI corrosion rate. The membership functions for both inputs and output will be discussed in details. A number of rules were used to perform defuzzification. After development of this fuzzy logic model, its root mean square error value (RMSE) and mean absolute deviation value (MAD) against API 581 data has also been checked, which revealed quite satisfactory results. The results from this model would provide corrosion engineers enough information about CUI corrosion rates concern to their plant so that they will be able to do necessary inferences in a more quantitative approach. © 2006-2016 Asian Research Publishing Network (ARPN). |
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