Pipeline Corrosion Prediction Due To Naphthenic Acid Using Auto Regressive Integrated Moving Average Method (Arima) - ALI, NURUL FATIHAH AZWANI (IRC, 2019)
Corrosion presents a significant danger for many industries such as pipelines, storage tanks, heat exchangers, boilers, and other machinery and systems. The occurrence of acidic oil is one of the major cause that attack the oil and gas industry because of the Naphthenic acid. The Naphthenic acid corrosion is mainly the result of the acidity of crude products that are processed in order to meet business requirements in the crude Distillation Unit (CDU) and Vacuum Distillation Unit (VDU). The corrosion procedure, which was conducted longitudinally and circumferentially on the interior and exterior surfaces of the tube wall, was affected and caused. Thus, engineers are required to monitor the pipelines continuously. The corrosion rate information for a specific material-environment scheme must be identified to assess the design life or remaining life of an industrial component. The corrosion management pipeline inspection has to be regularly organized to ensure the smooth process and continuous flow in a steel pipeline. This study is focusing on developing prediction model and visualization of the prediction result. The modern technology world today had produce many useful software that we can use to produce innovation product that can help human through cut cost for corrosion prediction part. In this project, the dataset is revised and set to training and testing with the prediction model. Artificial Neural Network (ANN) and ARIMA model is used as algorithm for development of prediction model. Validation of prediction model is then conducted with the use of industrial data. The result from the model are then will be visualized in dashboard