Real‐Time Leak Isolation Based on State Estimation with Fitting Loss Coefficient Calibration in a Plastic Pipeline

Navarro, A.E.; Begovich, O.; Sánchez, Juan Diego; Besançon, Gildas

doi:10.1002/asjc.1362

Cited by 21 publications

(14 citation statements)

References 11 publications

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“…There was some uncertainty associated with the kinematic viscosity value, which in Garcia et al (2009) is described as v = 1.02 × 10 −6 (m 2 s −1 ), while in Navarro et al (2017) this value is given as v = 2 × 10 −6 (m 2 s −1 ) for apparently the same pipe. Thus, because the temperature of the experiment is unknown, and viscosity can not be determined unambiguously, the possible estimation error is depicted in the graph (figure 12) as a shaded zone (band).…”

Section: Methodsmentioning

confidence: 97%

Improved model of isothermal and incompressible fluid flow in pipelines versus the Darcy–Weisbach equation and the issue of friction factor

Kowalczuk

Tatara

2020

J. Fluid Mech.

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Section: Methodsmentioning

confidence: 97%

Improved model of isothermal and incompressible fluid flow in pipelines versus the Darcy–Weisbach equation and the issue of friction factor

Kowalczuk

Tatara

2020

J. Fluid Mech.

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“…Once the leak is detected, an algebraic observer allows for estimating the leak position from the ESL fixed by the extended Kalman filter. Since leak isolation is performed in equivalent length coordinates, a transformation to the original coordinates is necessary [31].…”

Section: Brief Historymentioning

confidence: 99%

Kalman Filters for Leak Diagnosis in Pipelines: Brief History and Future Research

Torres

Jiménez-Cabas

González

et al. 2020

JMSE

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The purpose of this paper is to provide a structural review of the progress made on the detection and localization of leaks in pipelines by using approaches based on the Kalman filter. To the best of the author’s knowledge, this is the first review on the topic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, the paper gathers the results from different research groups such that these are presented in a unified fashion. For this reason, a classification of the current approaches based on the Kalman filter is proposed. For each of the existing approaches within this classification, the basic concepts, theoretical results, and relations with the other procedures are discussed in detail. The review starts with a short summary of essential ideas about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, brief discussions of some emerging research problems, such as the leak detection in pipelines transporting heavy oils; the main challenges; and some open issues are addressed.

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“…where the constant λ is function of the orifice area and the discharge coefficient (for simplicity, the λ coefficient is referred as "leak magnitude" from now on); Q l is the flow through the leak; and H l is the head pressure at the leak point Navarro et al [21].…”

Section: Pipeline Mathematical Modelmentioning

confidence: 99%

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

et al. 2019

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This work deals with the Leak Detection and Isolation (LDI) problem in water pipelines based on some heuristic method and assuming only flow rate and pressure head measurements at both ends of the duct. By considering the single leak case at an interior node of the pipeline, it has been shown that observability is indeed satisfied in this case, which allows designing an observer for the unmeasurable state variables, i.e., the pressure head at leak position. Relying on the fact that the origin of the observation error is exponentially stable if all parameters (including the leak coefficients) are known and uniformly ultimately bounded otherwise, the authors propose a bank of observers as follows: taking into account that the physical pipeline parameters are well-known, and there is only uncertainty about leak coefficients (position and magnitude), a pair of such coefficients is taken from a search space and is assigned to an observer. Then, a Genetic Algorithm (GA) is exploited to minimize the integration of the square observation error. The minimum integral observation error will be reached in the observer where the estimated leak parameters match the real ones. Finally, some results are presented by using real-noisy databases coming from a test bed plant built at Cinvestav-Guadalajara, aiming to show the potentiality of this method.

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Real‐Time Leak Isolation Based on State Estimation with Fitting Loss Coefficient Calibration in a Plastic Pipeline

Cited by 21 publications

References 11 publications

Improved model of isothermal and incompressible fluid flow in pipelines versus the Darcy–Weisbach equation and the issue of friction factor

Improved model of isothermal and incompressible fluid flow in pipelines versus the Darcy–Weisbach equation and the issue of friction factor

Kalman Filters for Leak Diagnosis in Pipelines: Brief History and Future Research

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

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