Prediction of Corrosion in Pipeline by using Deep Learning - BAHARUDIN, NUR FARAHIN (IRC, 2020)
The inspection of corrosion in the pipeline need to be implemented and maintained by the oil and gas company in order to transport various type of crude oil or natural gas over short and long distance. This is because, the corrosion rate could give significant impact on inside and outside of the pipeline surfaces which then leads to high cost of damage expenses. Therefore, the purpose of this research paper is to perform the prediction of corrosion in pipeline by using the deep learning method. This paper includes literature review and comparisons technique on the analytical and visualization tools. In addition, the accuracy of data will be validated by using the Cross-Validation technique in order to choose the lowest RMSE and best suited of LSTM model. Hence, the results based on the model prediction of corrosion rate will be visualized in Power BI dashboards so that the results could be shared, analyzed and discussed the solution to a better business decision.