Forecasting Corrosion Rate on Pipeline using Artificial Neural Network

• Main Author: Zolkifli, Nur Anissa
• Format: Final Year Project
• Language: English
• Published: 2022
• Subjects: T Technology (General)
• Online Access: http://utpedia.utp.edu.my/id/eprint/24539/1/Forecasting%20Corrosion%20Rate%20on%20Pipeline%20using%20Artificial%20Neural%20Network.pdf; http://utpedia.utp.edu.my/id/eprint/24539/

Large amounts of oil and gas are transported every day and distributed throughout the world through pipelines. They are considered the safest method of transporting oil and gas because they rarely fail. Nevertheless, pipelines could lead to deterioration and degradation. Therefore, it is crucial to frequently monitor pipelines in order to improve performance and decrease their failure rates down to a safe level. There are various models have been created to forecast pipeline conditions. Therefore, this paper describes the development of models that forecast the corrosion rate on pipeline using artificial neural network (ANN) for oil and gas refinery.