CO2 corrosion rate determination mechanism implementing de Waard-Milliams model for oil & gas pipeline - Zubir, W.M.A.M. (Institute of Electrical and Electronics Engineers Inc., 2016)
Billions of ringgits are consistently being invested by the oil and gas companies to ensure pipeline integrity, especially to address problems caused by pipelines. However, the existing solutions in predicting corrosion occurrence often lead to an extremely high cost to be implemented. Worse, the current implementations require manual data entry, which lengthen the time taken for processing in order to obtain a reliable corrosion prediction. If a corroded pipeline failed to be predicted much earlier, the pipes might burst and will cause a significant chain of events that will affect the aqua eco-system. The companies will suffer huge financial losses to mitigate the catastrophic situation. Hence, a better pipeline corrosion determination mechanism needs to be developed to address the issues found in this dissertation. Through early prediction on the corrosion occurence, it will provide the user with the knowledge for a better decision making. This paper will deliberate on a system that predicts corrosion rate using the deWaard-Milliams model. Arduino UNO R3 was used in this project, in predicting the corrosion rate. As a proof of concept and performance, simulations were carried out using synthetic dataset to test the efficiency of the proposed system. Synthetic data was generated through the Gaussian function in Java. The proposed prototype was tested. Three types of testing were conducted which are data quality test, accuracy test and corrosion rate analysis. Data quality test is to ensure the synthetic data generated are closely matched with real data. Accuracy test evaluates the predicted corrosion rate against the real field data obtained from literature studies. Corrosion rate analysis provides an overview and comparison between the effects of the parameters determining the corrosion rate and the predicted corrosion rate. The results exhibit the capability of the mechanism to accurately predict the corrosion rate. Â© 2016 IEEE.