A study on a real-time leak detection method for pressurized liquid refrigerant pipeline based on pressure and flow rate

Tian, Shen; Du, Juanli; Shao, Shuangquan; Xu, Hongbo; Tian, Changqing

doi:10.1016/j.applthermaleng.2015.11.067

Cited by 41 publications

(15 citation statements)

References 27 publications

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“…Figure 6a depicts the measured pressure head at inlet (u 1 = H in ) and outlet (u 2 = H out ) of the pipeline (i.e., the observation input). Figure 6b shows the measured flow upstream (x 1 = Q in ) and downstream (u 1 = H in ) of the pipe together with their respective synthetic data (Q in andQ out ), generated by (8). As it can be seen, the mathematical model follows the real data in a proper way despite the measurement noise.…”

Section: Leak Case In Valvementioning

confidence: 94%

“…The main parameters of the pipeline system are shown in Table 1. The sampling rate was 300 Hz satisfying the Courant's condition for system (8).…”

Section: Methodsmentioning

confidence: 99%

“…The scientific community has paid attention to that problem and has proposed several methodologies for monitoring and supervision purposes in order to avoid losses and accidents (see, e.g, [2][3][4][5][6][7][8][9][10][11]). In particular, in Begovich et al [2], a LDI algorithm based on Billman and Isermann [3] has been implemented and tested with accurate results and based on steady-state conditions, which increase the convergence time in the leak parameter estimation process.…”

Section: Introductionmentioning

confidence: 99%

“…In the last reference, the authors argued that the most likely future development in pipeline leak detection and location tends to be the use of two or more different methods. Finally, Tian et al [8] proposed an algorithm to locate leaks based on the pressure difference profile along the pipeline. It considers the effect of the static pressure increases at the leakage point.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Leak Case In Valvementioning

confidence: 94%

“…The main parameters of the pipeline system are shown in Table 1. The sampling rate was 300 Hz satisfying the Courant's condition for system (8).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

et al. 2019

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“…A parameterized transient model was established to locate multiple leaks in a pipeline based on changes in fluid flow and the operating pressure (Verde et al 2014). Major efforts have also been made to develop techniques for leakage location in pipeline systems (Sandberg et al 1989;Fukushima et al 2000;Tian et al 2016;Ge et al 2008). Particularly, model-based leak Edited by Xiu-Qin Zhu detection methods have been regarded as the most appropriate and applicable for analyzing the amount of oil leakage.…”

Section: Introductionmentioning

confidence: 99%

Modeling and analysis of a catastrophic oil spill and vapor cloud explosion in a confined space upon oil pipeline leaking

et al. 2019

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Oil spill-induced vapor cloud explosions in a confined space can cause catastrophic consequences. In this work, investigation was conducted on the catastrophic pipeline leak, oil spill, and the resulting vapor cloud explosion accident occurring in China in 2013 by modeling analysis, field surveys, and numerical simulations. The total amount of the spilled oil was up to 2044.4 m 3 due to improper disposal. The long residence time of the oil remaining in a confined space permitted the formation of explosive mixtures and caused the vapor cloud explosion. A numerical model was developed to estimate the consequence of the explosion based on volatilization testing results. The results show that the death-leading zone and the glass-breaking zone could be 18 m and 92 m, respectively, which are consistent with the field investigation. The severity of the explosion is related to the amount of the oil spill, properties of oil, and volatilization time. It is recommended that a comprehensive risk assessment be conducted to analyze the possible consequences upon oil spilling into a confined space. Prompt collection and ventilation measures should be taken immediately after the spill occurs to reduce the time for oil volatilization and prevent the mixture from reaching its explosive limit.

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In‐situ leakage detection of single bag filter in baghouse based on the resistance change of conductive polymer fibers

Guo

Shen

et al. 2023

J of Applied Polymer Sci

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Baghouse is effective for controlling the particles emitted from industrial production, and the in‐situ leakage detection of a single bag filter is essential for maintaining the high filtration performance of the whole baghouse. In the present study, the changes of PM emission concentration induced by the leakage of the bag filter materials was studied. The results showed that the concentration of particles in the size range of 20–1000 nm significantly increased when the bag filter sample had three holes with a diameter of ~500 μm. In order to prevent the loss induced by the leakage of bag filter, a resistance sensing method was developed to detect the early leakage of single bag filter. The effectiveness of proposed sensing method was verified by detecting the leakage of polymethyl methacrylate (PMMA) bag filter. A sensing grid made of PMMA conductive threads was embedded in the PMMA bag filter, and the resistance change (ΔR) induced by the broken of sensing grid was used as sensing signal. The ΔR of sensing grid was 3.24, 3.31, and 3.05 Ω, respectively, when the bag filter failed due to the mechanical stress, chemical corrosion, and thermal erosion. Moreover, a model was proposed to predict the numbers of broken conductive PMMA threads that could indicate the damage area of the failed bag filter.

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A study on a real-time leak detection method for pressurized liquid refrigerant pipeline based on pressure and flow rate

Cited by 41 publications

References 27 publications

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

Evolutionary Observer Ensemble for Leak Diagnosis in Water Pipelines

Modeling and analysis of a catastrophic oil spill and vapor cloud explosion in a confined space upon oil pipeline leaking

In‐situ leakage detection of single bag filter in baghouse based on the resistance change of conductive polymer fibers

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