Precise calculation of natural gas sound speed using neural networks: An application in flow meter calibration

Farzaneh–Gord, Mahmood; Mohseni-Gharyehsafa, Behnam; Ebrahimi‐Moghadam, Amir; Jabari-Moghadam, Ali; Тойкка, А. М.; Zvereva, Irina A.

doi:10.1016/j.flowmeasinst.2018.10.013

Cited by 25 publications

(5 citation statements)

References 40 publications

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“…The SOS has drawn considerable attention in several optimization fields as compared to differential evolution (DE) and particle swarm optimization (PSO) due to its simple procedure and consistency in accurate predictions. 110–113…”

Section: Progressions In Ann Framework For Optimizing Metal Adsorptio...mentioning

confidence: 99%

“…The SOS has drawn considerable attention in several optimization elds as compared to differential evolution (DE) and particle swarm optimization (PSO) due to its simple procedure and consistency in accurate predictions. [110][111][112][113] Moradi et al, 2020 used a hybrid of Bayesian regularization (BR) and Grey wolf optimizer (GWO) with ANN to model Pb(II) and Co(II) adsorption on pistachio shells. 101 The ANN space was initially optimized using the BR algorithm, using principles of probability distributions to prevent overtting of the ANN.…”

Section: Ensemble Ann Frameworkmentioning

confidence: 99%

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Application of neural network in metal adsorption using biomaterials (BMs): a review

Nighojkar

Zimmermann

Ateia

et al. 2023

Environ. Sci.: Adv.

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Section: Progressions In Ann Framework For Optimizing Metal Adsorptio...mentioning

confidence: 99%

Section: Ensemble Ann Frameworkmentioning

confidence: 99%

Application of neural network in metal adsorption using biomaterials (BMs): a review

Nighojkar

Zimmermann

Ateia

et al. 2023

Environ. Sci.: Adv.

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“…In order to obtain higher accuracy, several authors have utilized ANN to predict important multiphase flow parameters including flow pattern (Ternyik et al, 1995;Osman, 2004;Zhao et al, 2013;Al-Naser et al, 2016;Figueiredo et al, 2016;Hamidi et al, 2018;Hanus et al, 2018), liquid holdup (Ternyik et al, 1995;Shippen and Scott, 2004;Mohammadi, 2006;Azizi et al, 2016a;Zhao et al, 2019), heat transfer (Boostani et al, 2017;Hamidi et al, 2018), gas void fraction (Azizi et al, 2016a;Parrales et al, 2018), pressure gradient (Bar et al, 2010;Azizi and Karimi, 2015) under different flow conditions using varied gas-liquid mixtures and pipe dimensions and geometries. Other researchers have employed this method to determine gas and liquid properties (El-Sebakhy, 2009;Kamyab et al, 2010;Ahmadi et al, 2016;Roshani and Nazemi, 2017;Farzaneh-Gord et al, 2018).…”

Section: Comparison With An Artificial Neural Network Modelmentioning

confidence: 99%

Upward interfacial friction factor in gas and high-viscosity liquid flows in vertical pipes

Ribeiro

Liao

Aliyu

et al. 2019

Chemical Engineering Communications

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In this study, experiments were carried out in a vertical 60-mm internal diameter pipe with air and oil (viscosities 100-330 mPa s) constituting the gas and liquid phases. Superficial air and oil velocity ranges used were 9.81-59.06 m/s and 0.024-0.165 m/s respectively. Visual observations and change in slope of pressure drop-plot were used to identify flow pattern transition to annular flow. Using the experimental data as well as other reported data, a new correlation to predict interfacial friction factor in upward gas-viscous liquid annular flow regime was developed.Compared to the performance of 16 existing correlations using higher viscosity liquids, that of the new correlation was better. The performance of another correlation we derived for predictions at both low and higher low viscous showed good agreement with measurements. In addition, a neural network model to predict the interfacial friction factor involving both low and high viscous liquids was developed and it excellently described the experimental data.

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“…In the present study, the speed of sound for natural gas was calculated for the first time. For application to a natural gas flowmeter calibration, the ANN outputs were employed to determine the critical mass flux of the sonic nozzle at different temperatures and pressures [ 16 ]. The various deep-learning technologies improve the accuracy of the flow rate measurement and allows improvement in perception capability.…”

Section: Introductionmentioning

confidence: 99%

Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM

Jin

Chung

Park

2020

Sensors

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(1) Background: This study is aimed at the development of a precise and inexpensive device for flow information measurement for external flow. This novel flowmeter uses an LSTM (long short-term memory) neural network algorithm to analyze the vibration responses of the gauge plate. (2) Methods: A signal processing method using an LSTM neural network is proposed for the development of mass flow rate estimation by sensing the vibration responses of a gauge plate. An FFT (fast Fourier transform) and an STFT (short-time Fourier transform) were used to analyze the vibration characteristics of the gauge plate depending on the mass flow rate. For precise measurements, the vibration level and roughness were computed and used as input features. The actual mass flow rate measured by using a weight transducer was employed as the output features for the LSTM prediction model. (3) Results: The estimated flow rate matched the actual measured mass flow rate very closely. The deviations in measurements for the total mass flow were less than 6%. (4) Conclusions: The estimation of the mass flow rate for external flow through the proposed flowmeter by use of vibration responses analyzed by the LSTM neural network was proposed and verified.

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Precise calculation of natural gas sound speed using neural networks: An application in flow meter calibration

Cited by 25 publications

References 40 publications

Application of neural network in metal adsorption using biomaterials (BMs): a review

Application of neural network in metal adsorption using biomaterials (BMs): a review

Upward interfacial friction factor in gas and high-viscosity liquid flows in vertical pipes

Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM

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