Developing a gamma ray fluid densitometer in petroleum products monitoring applications using Artificial Neural Network

Khorsandi, M.; Feghhi, S.A.H.; Salehizadeh, A.; Roshani, Gholam Hossein

doi:10.1016/j.radmeas.2013.06.007

Cited by 42 publications

(17 citation statements)

References 14 publications

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“…Many researchers have used the different types of ANNs in gamma densitometry [3][4][5][6][7][8][9][10][11][12][13] in order to classification, clustering and prediction. Cong et al [14] reviewed applications of ANNs in flow and heat transfer problems in nuclear engineering.…”

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confidence: 99%

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

2015

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confidence: 99%

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

2015

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“…Recent works show use of machine learning methods as e.g. artificial neural networks (ANN) [9][10][11][12][13][14][15][16][17][18] for this purpose. The flow structures classification with ANN is comprised of three steps: data acquisition and pre-processing, feature extraction, and structures classification.…”

Section: Introductionmentioning

confidence: 99%

Application of ANN and PCA to two-phase flow evaluation using radioisotopes

et al. 2017

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Abstract. In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug -bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN) to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA) was used. It was found that the reduction of signals features allows for building a network with better performance.

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“…A quantitative description of flow regimes can be obtained by the analysis of the signals registered in radioisotope measurements. According to [22][23][24][25][26][27][28][29][30][31] …”

Section: Introductionmentioning

confidence: 99%

Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 1: time domain

Hanus

Zych

Petryka

et al. 2016

EPJ Web of Conferences

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Abstract. The paper presents an application of the gamma-absorption method to study a gas-liquid two-phase flow in a horizontal pipeline. In the tests on laboratory installation two 241 Am radioactive sources and scintillation probes with NaI(Tl) crystals have been used. The experimental set-up allows recording of stochastic signals, which describe instantaneous content of the stream in the particular cross-section of the flow mixture. The analyses of these signals by statistical methods allow to determine the mean velocity of the gas phase. Meanwhile, the selected features of signals provided by the absorption set, can be applied to recognition of the structure of the flow. In this work such three structures of air-water flow as: plug, bubble, and transitional plug -bubble one were considered. The recorded raw signals were analyzed in time domain and several features were extracted. It was found that following features of signals as the mean, standard deviation, root mean square (RMS), variance and 4 th moment are most useful to recognize the structure of the flow.

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Developing a gamma ray fluid densitometer in petroleum products monitoring applications using Artificial Neural Network

Cited by 42 publications

References 14 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

Application of ANN and PCA to two-phase flow evaluation using radioisotopes

Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 1: time domain

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