Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

Alanazi, Abdullah K.; Alizadeh, Seyed Mehdi Seyed; Nurgalieva, Karina Shamilyevna; Nesic, Slavko; Guerrero, John William Grimaldo; Abo‐Dief, Hala M.; Eftekhari-Zadeh, Ehsan; Nazemi, Ehsan; Narozhnyy, Igor M.

doi:10.3390/app12031336

Cited by 45 publications

(16 citation statements)

References 101 publications

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“…In the past few decades, a variety of mathematical computational approaches have been implemented in different research fields such as fluid mechanic engineering [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], chemical engineering , electrical engineering , robotics and automation , urban planning engineering [73][74][75][76], petroleum engineering [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], energy engineering [95,96], mathematics [97][98][99][100][101][102][103][104], environmental engineering [105][106]…”

Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

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Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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“…In the past few decades, various advanced computational methods have been applied in various fields of study such as chemical engineering [32][33][34][35][36][37], electrical and computer engineering [38][39][40][41], civil engineering [42][43][44], mechanical engineering [45][46][47][48][49][50][51], petroleum engineering [52][53][54][55][56][57][58][59][60][61][62][63], and environmental engineering [64,65], etc. The ANN has been demonstrated to be the most potent technique for classification and prediction among the aforementioned computational methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

et al. 2022

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In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

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“…A total of 126 simulations were reported in this examination. It is noteworthy that numerous studies on approximating multiphase flows have been reported recently [17][18][19][20][21][22][23][24][25][26]. Figure 2 presents the placement of the pipe between the detector and the gamma source so that after being emitted from the source, the photons pass through the pipe and its contents and are absorbed by the detector.…”

Section: Simulation Structurementioning

confidence: 99%

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

et al. 2022

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In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a very good error compared to the detection systems presented in previous studies. Moreover, this performance is indicative of the utility of our GMDH neural network extraction process and its potential applications in determining parameters such as type of flow regime, volume percentage, etc. in multiphase flows and across other areas of the oil and gas industry.

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Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

Cited by 45 publications

References 101 publications

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

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