Fuzzy Inference System Modeling for Corrosion Risk Management of Natural Gas Pipelines
الموضوعات : Fuzzy Optimization and Modeling Journal
Nazila Adabavazeh
1
,
Mehrdad Nikbakht
2
,
Atefeh Amindoust
3
,
Sayed Ali Hassanzadeh-Tabrizi
4
1 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 - Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
4 - Advanced Materials Research Center, Department of Materials Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
الکلمات المفتاحية: Modeling, Fuzzy Inference System, Natural Gas Pipelines, Risk Management, Corrosion Risk, Corrosion Management.,
ملخص المقالة :
Due to the complexity of operating conditions, predicting corrosion in natural gas pipelines is a challenge, and therefore its use in the gas industry is problematic. So, the present study employs corrosion risk management in control systems and decision prediction with fuzzy inference systems to address the uncertain phenomena of critical factors affecting natural gas pipelines. In the study, 84 factors were derived and 14 critical factors were identified using the Lawshe approach. The Boehm risk management framework was used and the factors were analysed in pairs in a fuzzy inference system. The probability and consequences of each factor were determined according to the API 581 standard and the weight coefficient of each factor was determined using the FUCOM method. In this study, a fuzzy inference system with two fuzzy inference subsystems was developed to comprehensively address both aspects of evaluation and response. First, the system uses the API 581 standard risk matrix to identify the risk level, followed by the risk response strategy determination through the sensitivity risk priority index chart. The results show that at the first level of the system, the highest risk was classified as severe and harsh, while at the second level of the system, the maximum output behavior occurred during the wear-out phase of the pipeline. The proposed fuzzy inference system has the potential to significantly contribute to the effective management of pipeline corrosion risk and the economic productivity of the gas industry.
1. Adabavazeh, N., Nikbakht, M., Amindoust, A., & Hassanzadeh-Tabrizi ,S. A. (2024). The identification and analysis of pivotal factors influencing the corrosion of natural gas pipelines using fuzzy cognitive map. Engineering Failure Analysis, 166, 108806.
2. Agarwal, R., & Nishad, A.. (2023). A Fuzzy Mathematical Modeling for Evaluation and Selection of a Best Sustainable and Resilient Supplier by Using EDAS Technique. Process Integration and Optimization for Sustainability. 8. 1-10.
3. Ahmed, S., A., Akmar, M., A., Candra, K., J., Lashari, N., Sarwar, U., Jameel, S. M., Inayat, M., & Oladosu, T. L. (2022). A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion. International Journal of Pressure Vessels and Piping, 200, 104841.
4. Aloqaily, A. (2018). Cross-Country Pipeline Risk Assessments and Mitigation Strategies. Gulf Professional Publishing, v, ISBN 9780128160077..
5. API Recommended Practice 581 (2019). Risk-Based Inspection Methodology, American Petroleum Institute, USA.
6. Azoor, R.M., Deo, R.N., Birbilis, N., & Kodikara, J. (2019). On the optimum soil moisture for underground corrosion in different soil types. Corrosion Science, 159, 108116.
7. Bai, Y., Wu, J., Ren, Q., Jiang, Y., Cai, J. (2023). A BN-based risk assessment model of natural gas pipelines integrating knowledge graph and DEMATEL. Process Safety and Environmental Protection, 171, 640-654.
8. Batbayar, K., Takács, M., & Kozlovszky, M. (2016).Medical device software risk assessment using FMEA and fuzzy linguistic approach: Case study, IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI). Timisoara, Romania. 197-202.
9. Boehm, B. W. (1991). Software risk management: principles and practices. IEEE Software, 8, 1, 32-41.
10. Cox, W. M. (2014). A Strategic Approach to Corrosion Monitoring and Corrosion Management. Procedia Engineering, 86, 567-575.
11. Fang, J., Cheng, X., Gai, H., Lin, S., & Lou, H. (2023). Development of machine learning algorithms for predicting internal corrosion of crude oil and natural gas pipelines. Computers & Chemical Engineering, 177, 108358.
12. Fink, S. (2021). Untersuchung zur Wasserstoffversprödung von Gasrohrleitungen; Technische Universität Graz, Graz, Austria.
13. Hagarová, M., Cervova, J, & Jaš, F. (2015). Selected types of corrosion degradation of pipelines. Koroze a Ochrana Materialu. 59. 30-36.
14. Huang, Y., Qin, G., & Yang, M. (2023). A risk-based approach to inspection planning for pipelines considering the coupling effect of corrosion and dents. Process Safety and Environmental Protection, 180, 588-600.
15. Jamshidi, A., Abbasgholizadeh, R, Samira , Ait-Kadi, D., & Ruiz, A. (2015). A comprehensive fuzzy risk-based maintenance framework for prioritization of medical devices. Applied Soft Computing. 32, 322-334.
16. Jiang, F., & Dong, Sh. (2024). Probabilistic-based burst failure mechanism analysis and risk assessment of pipelines with random non-uniform corrosion defects, considering the interacting effects. Reliability Engineering & System Safety, 242, 10978.
17. Jianxing, Y., Chen, Y., Yang, Y. Zh. (2019). A weakest t-norm based fuzzy fault tree approach for leakage risk assessment of submarine pipeline. Journal of Loss Prevention in the Process Industries, 62, 103968.
18. Li, C, & Xi, Z. (2019). Social Stability Risk Assessment of Land Expropriation: Lessons from the Chinese Case. International Journal of Environmental Research and Public Health. 16(20),3952.
19. Li, X., Wang, J., Abbassi, R., Chen, G. (2022). A risk assessment framework considering uncertainty for corrosion-induced natural gas pipeline accidents. Journal of Loss Prevention in the Process Industries, 75, 104718.
20. Liang, X., Ma, W., Ren, J., Dang, W., Wang, K., Nie, H., Cao, J., & Yao, T. (2022). An integrated risk assessment methodology based on fuzzy TOPSIS and cloud inference for urban polyethylene gas pipelines. Journal of Cleaner Production, 376, 134332.
21. Ma, D., Mao, W., Zhang, G., Liu, Ch., Han,Y., Zhang, X., Wang, H., Cen, K., Lu, W., Li, D., & Zhang, H. (2024). Dynamic risk assessment of gas pipeline operation process by fusing visual and olfactory monitoring. Journal of Safety Science and Resilience, 5(2), 156-166.
22. Mahmood, Y., Chen, J., Yodo, N., & Huang, Y. (2024). Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies. Gas Science and Engineering, 125, 205283
23. Muhammad, H., Zhang, T. & Nasser, M. (2023). Adoption of big data analytics for energy pipeline condition assessment. International Journal of Pressure Vessels and Piping, 105061.
24. Muhlbauer, W. K. (2006). Enhanced Pipeline Risk Assessment. WKM Consultasy.
25. Pamučar, D., Stević, Ž., & Sremac, S. (2018). A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM). Symmetry, 10, 393.
26. Prasanta, K, D, (2010). Managing project risk using combined analytic hierarchy process and risk map. Applied Soft Computing, 10(4), 990-1000.
27. Qin, M., Liao, K., He, G., He, T., Leng, J., & Zhang, Sh. (2023). Quantitative risk assessment of static equipment in petroleum and natural gas processing station based on corrosion-thinning failure degree. Process Safety and Environmental Protection, 172, 144-156.
28. Raeihagh, H., Behbahaninia, A., & Aleagha, M. M. (2020). Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field. Journal of Loss Prevention in the Process Industries, 68, 104238.
29. Speight, J. G. (2015). Subsea and Deepwater Oil and Gas Science and Technology. Gulf Professional Publishing, 213-256.
30. Taghipour, M., Lashkaripour, G.R., & Ghafoori, M. et al. (2016). Evaluating the soil corrosion of Bushehr, Iran, based on a new classification system for corrosive soils. Anti-Corros. Methods Mater. 63 (5), 347–354.
31. Velázquez, J.C., Valor, A., & Caleyo, F. (2024). Statistical study of localized internal corrosion defects in oil and gas pipelines through sampling inspection. Process Safety and Environmental Protection, 186, 566-576.
32. Wang, F. , Xu, J., Xu, Y., Jiang, L., & Ma, G. (2020). A comparative investigation on cathodic protections of three sacrificial anodes on chloride-contaminated reinforced concrete. Construction and Building Materials. 246. 118476.
33. Wang, W., Shen, K., Wang, B., Dong, Ch., Khan, F., & Wang, Q. (2017). Failure probability analysis of the urban buried gas pipelines using Bayesian networks. Process Safety and Environmental Protection, 111, 678-686.
34. Woldesellasse, H., & Tesfamariam, Solomon (2023). Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline. Reliability Engineering & System Safety, 240, 109573.
35. Xu, W., Yuan, K., Li ,W. , & Ding, W. (2023). An Emerging Fuzzy Feature Selection Method Using Composite Entropy-Based Uncertainty Measure and Data Distribution. IEEE Transactions on Emerging Topics in Computational Intelligence, 7, 1, 76-88.
36. Yang, Y., Faisal, Kh., Thodi, P., & Abbassi, R., (2017). Corrosion induced failure analysis of subsea pipelines. Reliability Engineering & System Safety, 159, 214-222.
37. Zhang, Y., Chen, L., Qin, H. Jiang, Z., Du, Y., & Lu, M. (2024). Research on fluctuation characteristics of AC interference parameters and corrosion behavior of buried gas pipelines. International Journal of Electrochemical Science, 19(1), 100396.
38. Zhao, Lei, Qi, Gang, Dai, Yong, Ou, Hongxiang, Xing, Zh., Long, Zhao, & Yan,Y. (2023). Integrated dynamic risk assessment of buried gas pipeline leakages in urban areas. Journal of Loss Prevention in the Process Industries, Volume 83, 105049.
