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A decision tree model for accurate prediction of sand erosion in elbow geometry

Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been app...

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Main Authors: Alakbari, F.S., Mohyaldinn, M.E., Ayoub, M.A., Salih, A.A., Abbas, A.H.
Format: Article
Published: Elsevier Ltd 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37510/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163417889&doi=10.1016%2fj.heliyon.2023.e17639&partnerID=40&md5=c91dc0fa95b56115595cb128be7c8406
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spelling oai:scholars.utp.edu.my:375102023-10-04T13:30:26Z http://scholars.utp.edu.my/id/eprint/37510/ A decision tree model for accurate prediction of sand erosion in elbow geometry Alakbari, F.S. Mohyaldinn, M.E. Ayoub, M.A. Salih, A.A. Abbas, A.H. Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0, 6.27, 6.26, and 5.5, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industry's cost and time. © 2023 Elsevier Ltd 2023 Article NonPeerReviewed Alakbari, F.S. and Mohyaldinn, M.E. and Ayoub, M.A. and Salih, A.A. and Abbas, A.H. (2023) A decision tree model for accurate prediction of sand erosion in elbow geometry. Heliyon, 9 (7). ISSN 24058440 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163417889&doi=10.1016%2fj.heliyon.2023.e17639&partnerID=40&md5=c91dc0fa95b56115595cb128be7c8406 10.1016/j.heliyon.2023.e17639 10.1016/j.heliyon.2023.e17639 10.1016/j.heliyon.2023.e17639
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0, 6.27, 6.26, and 5.5, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industry's cost and time. © 2023
format Article
author Alakbari, F.S.
Mohyaldinn, M.E.
Ayoub, M.A.
Salih, A.A.
Abbas, A.H.
spellingShingle Alakbari, F.S.
Mohyaldinn, M.E.
Ayoub, M.A.
Salih, A.A.
Abbas, A.H.
A decision tree model for accurate prediction of sand erosion in elbow geometry
author_facet Alakbari, F.S.
Mohyaldinn, M.E.
Ayoub, M.A.
Salih, A.A.
Abbas, A.H.
author_sort Alakbari, F.S.
title A decision tree model for accurate prediction of sand erosion in elbow geometry
title_short A decision tree model for accurate prediction of sand erosion in elbow geometry
title_full A decision tree model for accurate prediction of sand erosion in elbow geometry
title_fullStr A decision tree model for accurate prediction of sand erosion in elbow geometry
title_full_unstemmed A decision tree model for accurate prediction of sand erosion in elbow geometry
title_sort decision tree model for accurate prediction of sand erosion in elbow geometry
publisher Elsevier Ltd
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37510/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163417889&doi=10.1016%2fj.heliyon.2023.e17639&partnerID=40&md5=c91dc0fa95b56115595cb128be7c8406
_version_ 1779441394601951232
score 13.222552

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