Design and construction of a prototype gamma-ray densitometer for petroleum products monitoring applications

Khorsandi, M.; Feghhi, S.A.H.

doi:10.1016/j.measurement.2011.06.001

Cited by 42 publications

(13 citation statements)

References 5 publications

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“…At the first step, simulated data for the annular regime were benchmarked toward experimental measurements. For the sake of simplicity in evaluation of the data, it is better to normalize both simulated and experimental results to unit [16]. As shown in Fig.…”

Section: -Simulationmentioning

confidence: 99%

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

2015

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Section: -Simulationmentioning

confidence: 99%

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

2015

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“…To quantify the reconstruction error, Root Mean Square (RMS) Error was employed using Eq. 2: (2) where the N p represents the total number of image pixels and µ j is linear attenuation coefficient value in j th pixel. The RMS error values have been calculated 0.0586 and 0.0597 for DSGA and ART algorithms, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…[1][2][3]. Gamma-ray scanning technique has been already used for troubleshooting analysis of the columns in industry [4].…”

Section: Introductionmentioning

confidence: 99%

Gamma-ray CT as a complementary technique for structural inspection of tray-type distillation columns

Khorsandi

Feghhi

2016

Measurement

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“…In this regard, the flow metering method, called gamma ray densitometry, which is based on gamma ray attenuation, has become very popular recently. Gamma ray densitometry has been used successfully in the application of multiphase flow measurement and well monitor in oil and gas industry (Salgado, Brandao, Conti, & Salgado, 2016;Bishop & James, 1993;Salgado et al, 2007;Tjugum, Frieling, & Johansen, 2002;Khorsandi & Feghhi, 2011;Khorsandi, Feghhi, Salehizadeh, & Roshani, 2013). The degree of gamma ray attenuation in multiphase flows depends on density and absorption coefficients, as inherent characteristics of material through which gamma ray passes, the material thickness and gamma ray wavelength (Bishop & James, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…According to their results, by using their approach, the density of materials used in their study can be predicted without any prior knowledge of the material composition. Another investigation on the application of artificial neural networks in gamma ray densitometry and multiphase flow metering can be found in the literature (Bishop et al, 1993;Khorsandi & Feghhi, 2011;Roshani et al, 2017;Abro, Khoryakov, Johansen, & Kocbach, 1999). Karami, Roshani, Salehizadeh, and Nazemi (2017) studied the applicability of fuzzy logic in the prediction of phase volume fractions in annular flow.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of multilayer perceptron, radial basis function, fuzzy inference system, and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

Khayat

Afarideh

2019

Expert Systems

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Application of predictive models in industrial multiphase flow metering has attracted an increasing attention recently. Void fraction (VF), water–liquid ratio (WLR), and flow regime are key parameters, measured by oil/water/gas multiphase flow metres (MPFM) in petroleum industry. Artificial neural networks and fuzzy inference systems (FIS) are reliable and efficient computational models, which can be simply implemented on microprocessors of MPFMs, having the advantages of trainability, adaptability, and capability to model non‐linear functions. In this paper, a wavelet‐based adaptive neuro‐FIS (WANFIS) is introduced and validated by the prediction of multiphase flow measurement critical parameters including flow regime, VF, and WLR. The performance of the proposed WANFIS model is then compared with multilayer perceptron (MLP), radial basis function (RBF) network, and an FIS trained by fuzzy c‐means and a subtractive clustering method in the prediction of flow parameters in a customized designed structure of oil/water/gas MPFM. Structural parameters of all predictive models are first optimized to yield the most efficient structure for the available dataset. Comparison is then made between the optimized predictive models, in terms of mean squared error of parameter prediction, computation time, and repeatability of the prediction process. According to the obtained results, MLP model using Levenberg–Marquardt training algorithm and WANFIS model using gradient‐based back propagation dynamical iterative learning algorithm are the most efficient models, which give the best performance compared with other used models. All predictive models can predict the flow regime with 100% accuracy, whereas the highest inaccuracy is related to the prediction of WLR. The results of this study can be used to select and develop the most appropriate predictive model applicable in predicting and identifying flow measurement parameters in industrial MPFMs.

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Design and construction of a prototype gamma-ray densitometer for petroleum products monitoring applications

Cited by 42 publications

References 5 publications

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

Gamma-ray CT as a complementary technique for structural inspection of tray-type distillation columns

Performance evaluation of multilayer perceptron, radial basis function, fuzzy inference system, and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

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