Thesis
Investigation of corrosion of carbon steel under insulation
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2024
- Thesis identifier
- T16842
- Person Identifier (Local)
- 201876168
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Corrosion of metals under insulation is a serious concern for industries due to the fact that the insulation hides the metal from view which increases the likelihood of sudden failure. Carbon steel is one of the metal alloys frequently used in industries due to economic and technical reasons. However, it is quite susceptible to corrosion under insulation (CUI). The factors affecting corrosion of carbon steel under mineral wool insulation such as temperature, effectiveness of inhibitor, quantity and distribution of electrolyte in the insulation have not been extensively studied in the literature. In fact, studies on corrosion of metals under insulation are quite sparse compared to immersion (uninsulated) conditions. Therefore, the objectives of this study were to assess the effect of temperature (60 oC to 130 oC) on corrosion of carbon steel under insulation, effectiveness of a new commercial inhibitor (VpCI 619) in mitigating CUI of carbon steel, quantity and distribution of electrolyte (1wt. % NaCl) in mineral wool insulation as well as investigation of the drying times of the insulation using galvanic current and electrochemical impedance measurements. In addition, the prediction of CUI rate using Artificial Neural Network (ANN) was carried out with the aim of assessing the accuracy of prediction of different network parameters such as number of hidden layers, number of input parameters and choice of activation function. Prior to CUI studies, the water absorption capacity of mineral wool insulation was determined using standard procedures (ASTM C1511). This was carried out to assess the time it will take for the insulation to be saturated with water, the variability of repeated measurements as well as the total water content in the insulation. The CUI studies were carried out using a test rig that was based on ASTM G189-07 standard. The corrosion rates were estimated using weight loss technique and the effects of temperature, vapour phase inhibitor consisting primarily of sodium molybdate, quantity of electrolyte in insulation were investigated. The drying out profile of the insulation was assessed using galvanic current and electrochemical impedance measurements. Furthermore, the prediction of CUI rate was carried out using Artificial Neural Network and the effect of single and double hidden layers, sigmoid and hyperbolic tangent activation functions, as well as number of input parameters on accuracy of prediction of CUI rate were assessed. The results of the water absorption studies indicated continuous absorption of water even after immersion for 22 days. The water absorption capacity was greater for thermally treated insulation compared to untreated insulation samples due to thermal degradation of the oily additives and polymeric binders. The effect of temperature on CUI indicated an increase in corrosion rate from 60 oC to 80 oC. Further increase in temperature up to 130 oC resulted in a decrease in corrosion rate. The existence of a maximum point in the curve was attributed to the competing effects of two factors which include increased diffusivity of oxygen which dominates at low temperature and decreasing solubility of oxygen and insulation dry-out which dominates at temperatures exceeding 80 oC. The new commercial inhibitor was observed to mitigate the corrosion rate at the temperatures investigated in this study. The inhibition efficiency indicated an average of 89% when a dosage of 5.2 g/m2 of the inhibitor was used. The effectiveness was also observed to be dosage dependent with lower doses having less inhibition efficiency. The drying times of the insulation assessed using galvanic current and impedance methods were observed to decrease as temperature increased. The galvanic current was observed to decrease to zero while the impedance increased to high values as the insulation dries out. However, the drying times obtained from galvanic current method showed a higher variability compared to impedance method. The result of prediction of CUI rate using Artificial Neural Network indicated an increase in accuracy as the number of input parameters increased. Surprisingly, the accuracy of the predicted output from the four input parameters (temperature, dosage of inhibitor, quantity of electrolyte in insulation and sample position) was higher than the accuracy of the most influential parameters (temperature and dosage of inhibitor). This suggests that incorporation of more input parameters having some relationship with the output is more important in achieving a higher accuracy compared to using the most influential parameters only. In conclusion, this study indicated that mineral wool insulation absorbs water for a long period without saturation which increases the risk of CUI. Also, CUI rate increased with temperature up to 80 oC but decreased on further increase up to 130 oC. The new commercial inhibitor was effective in mitigating CUI at the temperatures investigated. Also, more test solution was observed at the lower part of the insulation compared to the upper part when installed on the CUI test rig which increases the risk of severe corrosion at the lower section of the insulation. The prediction of CUI rate using ANN indicated that inclusion of more input parameters could improve prediction accuracy. Moreover, the choice of activation functions also has effect on the accuracy of the predicted output.
- Advisor / supervisor
- Roy, Sudipta
- Green, Todd A.
- Resource Type
- DOI
- Date Created
- 2023
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PDF of thesis T16842 | 2024-03-07 | 公开 | 下载 |