Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines
Transporting oil and gas via pipelines has been creating countless challenges; sand particles deposit and erode the pipelines wall. Generally, conventional erosion rate prediction models are conservative due to numerous generalizations and theories. Finite Element Analysis via computational fluid dy...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024015421 |
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| author | Nur Tantiyani Ali Othman Zulfan ADI Putra Shahrul Azman Zainal Abidin Fadzrul Izwan M Ali |
| author_facet | Nur Tantiyani Ali Othman Zulfan ADI Putra Shahrul Azman Zainal Abidin Fadzrul Izwan M Ali |
| author_sort | Nur Tantiyani Ali Othman |
| collection | DOAJ |
| description | Transporting oil and gas via pipelines has been creating countless challenges; sand particles deposit and erode the pipelines wall. Generally, conventional erosion rate prediction models are conservative due to numerous generalizations and theories. Finite Element Analysis via computational fluid dynamics (CFD) approach has proven to be useful to solve sand deposition problems and to estimate the eroded pipe due to its capability to precisely determine erosion deficiencies and make predictions with great precision. In this study, a CFD model was developed to calculate sand erosion rates in a 2-inch, 90° elbow pipe. Effects of particle sizes, sand flowrates, fluids velocities and pipe diameters on erosion rates were studied. The model was validated against literature data, and it was then used to generate data via sensitivity analysis simulations. The data became the basis for developing artificial neural network (ANN) models, which were then deployed in the environment of a process simulation software called Symmetry. Based on this approach, a variety of pipelines can be modelled, and maximum erosion rates of pipelines are calculated using deployed ANN models. Hence, a comprehensive study on the fluid flow dynamics and erosion rates of the pipelines can be evaluated simultaneously. |
| format | Article |
| id | doaj-art-f98cbdf9692a4f459b01d8ea3c7fa994 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-f98cbdf9692a4f459b01d8ea3c7fa9942025-08-20T01:58:16ZengElsevierResults in Engineering2590-12302024-12-012410328810.1016/j.rineng.2024.103288Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelinesNur Tantiyani Ali Othman0Zulfan ADI Putra1Shahrul Azman Zainal Abidin2Fadzrul Izwan M Ali3Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi UKM, Selangor, Malaysia; Corresponding author.Group Technical Solutions, Project Delivery & Technology Division, PETRONAS Sdn. Bhd. Kuala Lumpur, MalaysiaGroup Technical Solutions, Project Delivery & Technology Division, PETRONAS Sdn. Bhd. Kuala Lumpur, MalaysiaGroup Technical Solutions, Project Delivery & Technology Division, PETRONAS Sdn. Bhd. Kuala Lumpur, MalaysiaTransporting oil and gas via pipelines has been creating countless challenges; sand particles deposit and erode the pipelines wall. Generally, conventional erosion rate prediction models are conservative due to numerous generalizations and theories. Finite Element Analysis via computational fluid dynamics (CFD) approach has proven to be useful to solve sand deposition problems and to estimate the eroded pipe due to its capability to precisely determine erosion deficiencies and make predictions with great precision. In this study, a CFD model was developed to calculate sand erosion rates in a 2-inch, 90° elbow pipe. Effects of particle sizes, sand flowrates, fluids velocities and pipe diameters on erosion rates were studied. The model was validated against literature data, and it was then used to generate data via sensitivity analysis simulations. The data became the basis for developing artificial neural network (ANN) models, which were then deployed in the environment of a process simulation software called Symmetry. Based on this approach, a variety of pipelines can be modelled, and maximum erosion rates of pipelines are calculated using deployed ANN models. Hence, a comprehensive study on the fluid flow dynamics and erosion rates of the pipelines can be evaluated simultaneously.http://www.sciencedirect.com/science/article/pii/S2590123024015421CFDCOMSOLANNErosionSymmetryOil and gas Industry |
| spellingShingle | Nur Tantiyani Ali Othman Zulfan ADI Putra Shahrul Azman Zainal Abidin Fadzrul Izwan M Ali Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines Results in Engineering CFD COMSOL ANN Erosion Symmetry Oil and gas Industry |
| title | Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines |
| title_full | Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines |
| title_fullStr | Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines |
| title_full_unstemmed | Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines |
| title_short | Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines |
| title_sort | integration of cfd analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines |
| topic | CFD COMSOL ANN Erosion Symmetry Oil and gas Industry |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024015421 |
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