Prediction of Corrosion in Pipeline by using Deep Learning
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 t...
| Main Author: | BAHARUDIN, NUR FARAHIN |
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| Format: | Final Year Project |
| Language: | English |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-utpedia.21765 / |
| Published: |
IRC
2020
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| Subjects: | |
| Online Access: |
http://utpedia.utp.edu.my/21765/1/23227_Nur%20Farahin%20Baharudin.pdf http://utpedia.utp.edu.my/21765/ |
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| Summary: |
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. |
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