Pipeline risk assessment using artificial intelligence: A case from the colombian oil network

Abstract

Currently, in order to make decisions regarding the safety of pipelines, the risk values and risk targets are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve a large number of variables and deal with high amounts of uncertainty. Therefore, there is a strong need for a powerful tool to cope with that uncertainty, and one of the best tools dealing with uncertainty is the implementation of artificial intelligence methods using fuzzy logic. Hence, this study aims to present an artificial intelligence inference system that minimizes the uncertainty of traditional approaches of risk assessment in pipelines. Also, in order to show the applicability of the model developed, this study presents a case from the Colombian oil transportation network. Besides that, this study presents an uncertainty analysis for the risk values, comparing the results of the inference system with traditional approach. The results show that the inference system performs better since the magnitude of the average error and its standard deviation are less than the traditional approach. © 2017 American Institute of Chemical Engineers Process Process Saf Prog 37:110–116, 2018

Prediction Of Chemical Plants Operating Performances: A Machine Learning Approach

“…Considering the prediction/modeling challenges, in [2] the authors implement AI techniques to predict NO X emissions from coal-powder power plants, in [1] the AI was used to evaluate the operation of a wet scrubber system for air pollution management and in [11] the collection efficiency of Venturi scrubbers was evaluated by using different AI techniques; the work in [12] present an artificial intelligence inference system that minimizes the uncertainty of traditional approaches of risk assessment in pipelines by using case study from the Colombian oil transportation network while in [10] an AI technique was implemented to address the numerical solutions of a adsorption fixed-bed column where a monoclonal antibody is purified.…”

Application of artificial intelligence (AI) in the area of corrosion protection

“…At present, AI is playing a more and more important role in the field of scientific research. In terms of corrosion science research, the application of AI mainly focuses on corrosion protection materials and methods (Belayadi et al , 2019; Coşkun and Karahan, 2018; Feiler et al , 2020; Jafari et al , 2011; Jiménez-Come et al , 2012; Mousavifard et al , 2015; Wen et al , 2009; Zadeh Shirazi and Mohammadi, 2017), corrosion image recognition (Ahuja and Shukla, 2018; Hoang and Tran, 2019; Petricca et al , 2016; Tian et al , 2019; Yao et al , 2019) and corrosion life prediction (Al-Shehri, 2019; Alani and Faramarzi, 2014; Chae et al , 2020; Guzman Urbina and Aoyama, 2018; Zhi et al , 2020). Compared with traditional methods, AI shows unique advantages (Boucherit et al , 2022; Boucherit et al , 2019; Boucherit and Arbaoui, 2021).…”

A Narrative of Urban Underground Pipeline System Disasters in China in 2021: Spatial and Temporal Distribution, Causal Analysis, and Response Strategies

“…Soft computing techniques based on fuzzy inference systems have also been developed to deal with data imprecision, uncertainty, and fuzziness in risk analysis and modeling of buried pipeline systems [44]. In a case study on Colombia's oil transportation network, an artificial intelligence inference system has also been proposed to reduce the uncertainty of traditional risk assessment methods in pipelines [45].…”