Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects
Natural gas, known for its cleanliness and cost-effectiveness, is transported across vast distances through pipelines. However, the safety concerns that arise from potential ruptures or leaks in these pipelines pose serious threats to the environment and human safety. This paper assesses the reliabi...
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Ram Arti Publishers
2025-04-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
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| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/16-IJMEMS-24-0500-10-2-285-299-2025.pdf |
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| author | Ajinkya Zalkikar Bimal Nepal Mani Venkata Rakesh Mutyala Anika Varshney Lianne Dsouza Hazlina Husin Om Prakash Yadav |
| author_facet | Ajinkya Zalkikar Bimal Nepal Mani Venkata Rakesh Mutyala Anika Varshney Lianne Dsouza Hazlina Husin Om Prakash Yadav |
| author_sort | Ajinkya Zalkikar |
| collection | DOAJ |
| description | Natural gas, known for its cleanliness and cost-effectiveness, is transported across vast distances through pipelines. However, the safety concerns that arise from potential ruptures or leaks in these pipelines pose serious threats to the environment and human safety. This paper assesses the reliability of pipelines that have undergone corrosion, compounded by the fluid hammer effect observed in the liquefied gas flow. Machine learning models including support vector machines, linear discriminant analysis, random forest bagging, and Artificial Neural Networks have been meticulously crafted to forecast the safety status of pipelines, considering variables such as the pipe dimensions, material characteristics, fluid velocity, and flow rate. The design of the experiment methodology plays a pivotal role in calculating the pressure surge in pipelines corroded over time due to ongoing corrosion effects. The proposed machine learning models based on simulated data aim to predict the safety status of corroded pipelines with an accuracy rate of up to 97% in controlled environments. Integrating the proposed machine learning models for reliability analysis and pressure surge detection in corroded pipelines, in conjunction with the fluid hammer effect, offers an innovative approach to identifying risks and hazards. |
| format | Article |
| id | doaj-art-04b5275cdfbf43e5a6e6f344d30db245 |
| institution | Kabale University |
| issn | 2455-7749 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj-art-04b5275cdfbf43e5a6e6f344d30db2452025-02-07T15:37:16ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492025-04-01102285299https://doi.org/10.33889/IJMEMS.2025.10.2.016Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer EffectsAjinkya Zalkikar0Bimal Nepal1Mani Venkata Rakesh Mutyala2Anika Varshney3Lianne Dsouza4Hazlina Husin5Om Prakash Yadav6Department of Industrial and System Engineering, Texas A&M University in College Station, TX, USA.Department of Engineering Technology and Industrial Distribution, Texas A&M University in College Station, TX, USA.Department of Industrial and Systems Engineering, Texas A&M University in College Station, TX, USA.Anika VarshneyDepartment of Petroleum Engineering, Texas A&M University, College Station, TX, USA.Department of Petroleum Engineering, University Technology-PETRONAS (UTP), Malaysia.Department of Industrial and Manufacturing Engineering, North Carolina A&T University, Greensboro, NC, USA.Natural gas, known for its cleanliness and cost-effectiveness, is transported across vast distances through pipelines. However, the safety concerns that arise from potential ruptures or leaks in these pipelines pose serious threats to the environment and human safety. This paper assesses the reliability of pipelines that have undergone corrosion, compounded by the fluid hammer effect observed in the liquefied gas flow. Machine learning models including support vector machines, linear discriminant analysis, random forest bagging, and Artificial Neural Networks have been meticulously crafted to forecast the safety status of pipelines, considering variables such as the pipe dimensions, material characteristics, fluid velocity, and flow rate. The design of the experiment methodology plays a pivotal role in calculating the pressure surge in pipelines corroded over time due to ongoing corrosion effects. The proposed machine learning models based on simulated data aim to predict the safety status of corroded pipelines with an accuracy rate of up to 97% in controlled environments. Integrating the proposed machine learning models for reliability analysis and pressure surge detection in corroded pipelines, in conjunction with the fluid hammer effect, offers an innovative approach to identifying risks and hazards.https://www.ijmems.in/cms/storage/app/public/uploads/volumes/16-IJMEMS-24-0500-10-2-285-299-2025.pdffluid hammer effectmachine learningartificial neural networksdesign of experimentspipeline reliability |
| spellingShingle | Ajinkya Zalkikar Bimal Nepal Mani Venkata Rakesh Mutyala Anika Varshney Lianne Dsouza Hazlina Husin Om Prakash Yadav Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects International Journal of Mathematical, Engineering and Management Sciences fluid hammer effect machine learning artificial neural networks design of experiments pipeline reliability |
| title | Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects |
| title_full | Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects |
| title_fullStr | Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects |
| title_full_unstemmed | Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects |
| title_short | Enhancing Pipeline Reliability Analysis through Machine Learning: A Focus on Corrosion and Fluid Hammer Effects |
| title_sort | enhancing pipeline reliability analysis through machine learning a focus on corrosion and fluid hammer effects |
| topic | fluid hammer effect machine learning artificial neural networks design of experiments pipeline reliability |
| url | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/16-IJMEMS-24-0500-10-2-285-299-2025.pdf |
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