Available Prediction Methods for Corrosion under Insulation (CUI): A Review

Abstract

Abstract.Corrosion under insulation (CUI) is an increasingly important issue for the piping in industries especially petrochemical and chemical plants due to its unexpected catastrophic disaster. Therefore, attention towards the maintenance and prediction of CUI occurrence, particularly in the corrosion rates, has grown in recent years. In this study, a literature review in determining the corrosion rates by using various prediction models and method of the corrosion occurrence between the external surface piping and its insulation was carried out. The results, prediction models and methods available were presented for future research references. However, most of the prediction methods available are based on each local industrial data only which might be different based on the plant location, environment, temperature and many other factors which may contribute to the difference and reliability of the model developed. Thus, it is more reliable if those models or method supported by laboratory testing or simulation which includes the factors promoting CUI such as environment temperature, insulation types, operating temperatures, and other factors.

A Review of Corrosion under Insulation: A Critical Issue in the Oil and Gas Industry

“…It was reported from the case study that prediction accuracy was enhanced when using combined data sources from lab testing and field testing. Other commonly used models proposed by researchers include logistic regression [100], Bayesian networks [101], and hygrothermal models [102], etc. The details of each model have been thoroughly reviewed by Hou et al [103].…”

Combined Experimental and Field Data Sources in a Prediction Model for Corrosion Rate under Insulation

“…Although this type of data input is well established for enabling the development of different places of corrosion under the insulation model to be used as a local maintenance guide, the data input effects on the available model have not been extensively studied. Some research has been reported without the changing behavior of the corrosion phase, as the historical field data was only collected after decades of operation [11,12]. This issue affects the practicality and the quality of the prediction model developed.…”