Fuzzy Inference System Modeling for Corrosion Risk Management of Natural Gas Pipelines

Subject Areas : 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

Received: 2024-08-05 Accepted : 2024-09-30 Published : 2024-10-12

Keywords: Modeling, Fuzzy Inference System, Natural Gas Pipelines, Risk Management, Corrosion Risk, Corrosion Management.,

Abstract :

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.

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