Enhancing the performance of a dual-energy gamma ray based three-phase flow meter with the help of grey wolf optimization algorithm

Karami, Alimohammad; Roshani, Gholam Hossein; Nazemi, Ehsan; Roshani, Sobhan

doi:10.1016/j.flowmeasinst.2018.10.015

Cited by 65 publications

(34 citation statements)

References 37 publications

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“…where, ε 0 is the free space dielectric constant and ε r is the relative dielectric constant of the medium filling the cylinder. Substituting equation (5) into (6) yields;…”

Section: The Analytical Solutionmentioning

confidence: 99%

“…But they are often limited by the intrusive techniques used to measure the temperature and pressure within the pipeline. Recent electrical techniques used to study material properties include correlative flow meter [3], [4], optical sensors [5], reactance measurements [6], [7], and multi-electrode imaging systems [8]. These methods monitor the electrical characteristics of a single [9] or composite material [10], [11] to identify changes in dielectric properties.…”

Section: Introductionmentioning

confidence: 99%

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Novel Solution for Multi-Phase Semi-Cylindrical Capacitive Sensors

et al. 2019

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An accurate theoretical formulation is presented for a semi-cylindrical parallel plate capacitor, independent of the electrode dimensions. An approximate solution is also presented to relate the changes in capacitance with changing volume-ratio of the multi-phase dielectric filling of the capacitor. Two applications are considered to verify the proposed techniques. In the first application, the calculated capacitance is compared with the measured capacitance of a healthy tree trunk to identify the initial stage of pest infestation. In the second application, an approximate capacitance of a three-phase mixture, consisting of gas, oil and brine layers, are used to identify the phase-fraction of a petroleum carrying pipeline with a steady-state flow. The advantage and limitations of the proposed approximate method are studied. In addition to logging and monitoring the oil supply, this simple method can electronically control multi-phase pipes with intravenous injections to collect postoperative fluids from the human body. INDEX TERMS Cylindrical-capacitor, multi-phase flow, dielectric sensor, material properties.

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“…where, ε 0 is the free space dielectric constant and ε r is the relative dielectric constant of the medium filling the cylinder. Substituting equation (5) into (6) yields;…”

Section: The Analytical Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Solution for Multi-Phase Semi-Cylindrical Capacitive Sensors

et al. 2019

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“…In particular, [10] applied a methodology based on the recognition of pulse height distribution patterns of an artificial neural network (ANN), proposing an approach for the independent prediction of the holdup in multiphase flows, as well as [11], which innovated in the recognition of PHD patterns through the neural network. In [12] they introduced a hybrid network in which an optimization algorithm (GWO) is used to train the neural network and obtain mean error values, as well as the determination of fluid velocities [13].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Holdup in Horizontal Wateroil Flow Using a Neural Network Approach

Díaz¹,

González‐Estrada²,

Cely³

2021

14th WCCM-ECCOMAS Congress

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In this work, the application of an artificial neural network (ANN) is proposed to develop a predicting model for the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe. For this, the surface velocities of each fluid and the differential pressure in the pipeline are used as input parameters of the multilayer artificial neural network with backpropagation, while the holdup of the fluids is used as the output parameter for the training. A set of 56 experimental data was obtained in the LabPetro-CEPETRO-UNICAMP laboratory. The best performing results for the predictive model show a mean absolute error (AAPE) of 3.01% and a coefficient of determination 2 of 0.9964 using 15 neurons in the hidden layer of the network and the TanSig transfer function.

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“…Nazemi et al also used two detectors and one radioactive source to predict the void fraction independent of flow regime with this difference that both of detectors registered transmitted photons (Nazemi et al 2016a). More studies about radiation-based nuclear gauges and also some applications of ANN in nuclear engineering can be found in references El Abd (2014), Jing et al (2006), Salgado et al (2014), Nazemi et al (2014), Hanus et al (2014a, b), Mosorov et al (2016), Zych et al (2014), Jung et al (2009), Nazemi et al (2015Nazemi et al ( , 2016b Karami et al (2018), Roshani and Nazemi (2018) and Roshani et al (2013Roshani et al ( , 2014Roshani et al ( , 2016Roshani et al ( , 2017aRoshani et al ( , 2018a.…”

Section: Introductionmentioning

confidence: 99%

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

2019

Self Cite

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The problem of how to accurately measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the key challenges in the petroleum industry. The current research highlights the capability of a hybrid system of the Jaya optimization algorithm and the adaptive neuro-fuzzy inference system (ANFIS), to model the stratified three-phase flow of gas-oil-water. As a matter of fact, the present study devotes to forecast the volume fractions in the stratified three-phase flow, on the basis of a dual-energy metering system, including the 152 Eu and 137 Cs and one NaI detector, using the aforementioned hybrid model. Since the summation of volume fractions are constant (equal to 100%), a constraint modelling problem exists, meaning that the hybrid model must forecast only two volume fractions. In this paper, three main hybrid models are employed. The first network is applied to forecast the gas and water volume fractions, the next one to forecast the water and oil volume fractions, and the last one to forecast the oil and gas volume fractions. For the next step, the hybrid models are trained based on numerically obtained data from the MCNP-X code.

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Enhancing the performance of a dual-energy gamma ray based three-phase flow meter with the help of grey wolf optimization algorithm

Cited by 65 publications

References 37 publications

Novel Solution for Multi-Phase Semi-Cylindrical Capacitive Sensors

Novel Solution for Multi-Phase Semi-Cylindrical Capacitive Sensors

Predictive Modeling of Holdup in Horizontal Wateroil Flow Using a Neural Network Approach

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

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