000			01771nab a2200205 4500
005			20260520004608.0
008			260224s2018 xxu ing
041			_aInglés
245	0	0	_aPipeline Risk Assessment Using Artificial Intelligence: A Case from the Colombian Oil Network
260			_a _b _cmar. 2018
270			_a09/10/2020 ; 09/10/2020
300			_a7 p. ; 110-116
520			_aCurrently, 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.
581			_a1
773	0		_tProcess safety progress _g
942			_cARTICULO
100	1		_aGuzman Urbina, Alexander _959767
100	1		_aAoyama, Atsushi _959768
999			_c193608 _d193608