Development of an Internal Corrosion Maintenance Management System to Enhance the Operational Availability of Natural Gas Midstream Pipeline Systems: A Case Study of Gasco's Mtwara to Dar Es Salaam Pipeline System
Abstract
Internal corrosion significantly threatens midstream natural gas pipelines' reliability, safety, and efficiency, notably Tanzania's GASCO-operated 542 km Mtwara-Dar es Salaam pipeline transporting gas from Mnazi Bay since 2015. This study identifies key corrosion drivers, develops a predictive model for operational availability, and proposes a validated Internal Corrosion Management System (ICMS) to enhance performance. Using a mixed-methods approach involving surveys from 46 of 64 professionals at GASCO, TPDC, and EWURA, chemical analyses of 2,615 SCADA records, and machine learning modeling via XGBoost, Random Forest, and Support Vector Machine algorithms, the research highlighted major factors through Relative Importance Index analysis: solid/liquid contaminants (RII=0.932), moisture (RII=0.924), infrequent pigging (RII=0.852), design/material issues (RII=0.800), microbiological activity (RII=0.752), operational conditions (RII=0.728), and inhibitors (RII=0.708). The XGBoost model achieved superior performance with 87.43% accuracy, 87.21% precision, 87.32% recall, and 87.27% F1-score, outperforming Random Forest and SVM. Feature importance analysis identified the Corrosive Index (0.23), Fe₂O₃ percent (0.19), and Cl percent (0.15) as the most influential predictors. Model validation demonstrated robust performance with 91.67% severe case detection and 86.9% temporal F1-score. The developed ICMS, integrated with SCADA systems, provides real-time monitoring, automated severity classification, and maintenance prioritisation, supporting data-driven decisions to lower costs and ensure sustainable gas supply under EWURA regulations aligned with NACE SP0110, ASME B31.8S, and API 1160 standards
Downloads
References
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD, 785-794.
Deus, I. (2024). Natural gas utilization and domestic energy development in Tanzania. NTNU.
Ekechukwu, D.E., & Simpa, P. (2024). Trends, findings, and future prospects of renewable energy integration within the oil and gas sector operations. World Journal of Advanced Engineering Technology and Sciences, 12(1), 152-167.
Elboujdaini, M. (2006). Hydrogen-induced cracking and sulfide stress cracking. Corrosion Engineering Handbook, 199-234.
Fessler, R.R. (2008). Pipeline corrosion. U.S. Department of Transportation, PHMSA.
Fjeldstad, O.-H., Katera, L., & Rakner, L. (2019). Governing petroleum resources: Prospects and challenges for Tanzania. CMI and REPOA.
Frydenberg, H., & Torp, M.P. (2020). Impact on polymer-coated pipelines. NTNU.
Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38-50.
Hassan, N., et al. (2021). Machine learning applications in corrosion prediction. Materials Science and Engineering, 1-15.
Lam, C., & Zhou, W. (2016). Statistical analyses of incidents on onshore gas transmission pipelines based on PHMSA database. International Journal of Pressure Vessels and Piping, 145, 29-40.
Little, B.J., Lee, J.S., & Ray, R.I. (2020). The influence of marine biofilms on corrosion: A concise review. Electrochimica Acta, 54(1), 2-7.
NACE International. (2016). International measures of prevention, application, and economics of corrosion technologies study. NACE.
Nelson, V.V., Sridhar, N., & Dunn, D. (2017). Microbiologically influenced corrosion in aluminium alloys. IntechOpen.
Nesic, S. (2007). Key issues related to modelling of internal corrosion of oil and gas pipelines. Corrosion Science, 49(12), 4308-4338.
Papavinasam, S. (2013). Corrosion control in the oil and gas industry. Gulf Professional Publishing.
PHMSA. (2000). Advisory bulletin: Internal corrosion in gas transmission pipelines. U.S. Department of Transportation.
Poncian, J., & Jose, J. (2019). National resource ownership and community engagement in Tanzania's natural gas governance. Energy Policy, 133, 110903.
Raihan, A., Begum, R.A., & Said, M.N.M. (2024). An overview of the recent development and prospects of renewable energy in Italy. Renewable and Sustainable Energy, 2(2), 0008.
Sharma, A., et al. (2021). IoT-enabled monitoring systems in gas pipelines. Journal of Pipeline Engineering, 45-58.
Shin, D.-H., Kim, Y., & Lee, J. (2022). Evaluation of commercial corrosion sensors for real-time monitoring of pipe wall thickness. Sensors, 22(19), 7562.
Shirazi, S.A., McLaury, B.S., Shadley, J.R., & Rybicki, E.F. (1995). Generalization of the API RP 14E guideline for erosive services. Journal of Petroleum Technology, 47(8), 693-698.
Sun, Y., et al. (2022). AI-based predictive maintenance frameworks for pipeline systems. Industrial AI Applications, 234-249.
Tumbu, L. (2024). Tanzania Petroleum Development Corporation (TPDC) and its role in self-reliance and sustainability. Syracuse University.
Vapnik, V. (1998). Statistical learning theory. Wiley.
Wasim, M., & Djukic, M.B. (2022). External corrosion of oil and gas pipelines: A review of failure mechanisms and predictive preventions. Journal of Natural Gas Science and Engineering, 100, 104467.
Yu, B., Li, D.Y., & Grochev, A. (2019). Effects of water on corrosion behavior. Corrosion Reviews, 37(1), 23-45.
Zhou, Y., et al. (2020). Random forest applications in pipeline integrity management. Engineering Applications of AI, 67-82.
Zhou, W., et al. (2021). Pipeline integrity management using inline inspection tools. Journal of Pipeline Systems Engineering and Practice, 12(3), 04021015.
Copyright (c) 2025 Jumanne Kongoka, Esebi Nyari
This work is licensed under a Creative Commons Attribution 4.0 International License.
