Comparative analysis on the prediction of leak on gas pipeline using physical models and machine learning regression models
Chikwe, A., Aniyom, E., Nwanwe, O., Odo, J. (2024). Comparative analysis on the prediction of leak on gas pipeline using physical models and machine learning regression models. EKB Journal Management System, 26(1), 1-6. doi: 10.21608/jpme.2024.238425.1177
Anthony Chikwe; Ebenezer Ananiyom Aniyom; Onyebuchi Ivan Nwanwe; Jude Emeka Odo. "Comparative analysis on the prediction of leak on gas pipeline using physical models and machine learning regression models". EKB Journal Management System, 26, 1, 2024, 1-6. doi: 10.21608/jpme.2024.238425.1177
Chikwe, A., Aniyom, E., Nwanwe, O., Odo, J. (2024). 'Comparative analysis on the prediction of leak on gas pipeline using physical models and machine learning regression models', EKB Journal Management System, 26(1), pp. 1-6. doi: 10.21608/jpme.2024.238425.1177
Chikwe, A., Aniyom, E., Nwanwe, O., Odo, J. Comparative analysis on the prediction of leak on gas pipeline using physical models and machine learning regression models. EKB Journal Management System, 2024; 26(1): 1-6. doi: 10.21608/jpme.2024.238425.1177

Comparative analysis on the prediction of leak on gas pipeline using physical models and machine learning regression models

Journal of Petroleum and Mining Engineering
Volume 26, Issue 1 - Serial Number 10, July 2024, Page 1-6  XML PDF (1.14 MB)
Document Type: Original Article
DOI: 10.21608/jpme.2024.238425.1177
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Authors
Anthony Chikweorcid ; Ebenezer Ananiyom Aniyomorcid ; Onyebuchi Ivan Nwanwe email orcid ; Jude Emeka Odoorcid
Department Of Petroleum Engineering Federal University Of Technology Owerri P.M.B. 1526 Owerri
Abstract
Pipeline leaks in the natural gas industry present multifaceted challenges, encompassing not only diminished product volume but also environmental degradation and potential catastrophic events such as explosions. Addressing these challenges requires a comprehensive approach, including the development and implementation of effective detection systems. Previous efforts have focused on physical surveys and the utilization of acoustic systems and pressure sensors to detect leaks promptly. However, recent advancements in technology have spurred interest in mathematical and machine learning models as potential solutions. This study delves into the comparative analysis of mathematical and machine learning models for leak prediction in gas pipelines, aiming to discern the most effective approach. Specifically, an existing mathematical model, derived from the Weymouth equation, is plotted against a machine learning algorithm—a random forest regressor, to be precise. Through rigorous evaluation, encompassing statistical error metrics, sensitivity analysis, and economic considerations, the study sheds light on the relative efficacy of these models. Ultimately, the findings not only contribute to enhancing leak detection capabilities but also underscore the transformative potential of machine learning in addressing complex industrial challenges.
Keywords
Machine Learning; Leak Detection; Gas pipeline; Mathematical Models; Prediction
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