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

Iliyasu, Abdullah M.; Mayet, Abdulilah Mohammad; Hanus, Robert; El-Latif, Ahmed A. Abd; Salama, Ahmed S.

doi:10.3390/en15124500

Cited by 14 publications

(5 citation statements)

References 36 publications

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“…However, the big gap in the literature is that no suitable method has been provided to determine the most optimal ones. In this research, we have attempted to introduce the most optimal combination using a PSO-based characteristic selection method [23][24][25][26].…”

Section: Feature Selectionmentioning

confidence: 99%

Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows

et al. 2023

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What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.

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Section: Feature Selectionmentioning

confidence: 99%

Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows

et al. 2023

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“…Nuclear and atomic data tabulated for this energy range is often used to predict the effects of low-energy interactions between projectile particles (such as neutrons, photons, and light ions) and target nuclei. Academics have shown an ongoing fascination with modelling X-ray or gamma-radiation-using structures using the MCNP method [15][16][17][18]. The methodology presented in the current research was simulated using the MCNP code [19].…”

Section: Simulation Setupmentioning

confidence: 99%

Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System

et al. 2023

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The scaling of oil pipelines over time leads to issues including diminished flow rates, wasted energy, and decreased efficiency. To take appropriate action promptly and avoid the aforementioned issues, it is crucial to determine the precise value of the scale within the pipe. Non-invasive gamma attenuation systems are one of the most accurate detection methods. To accomplish this goal, the Monte Carlo N Particle (MCNP) algorithm was used to simulate a scale thickness measurement system, which included two sodium iodide detectors, a dual-energy gamma source (241 Am and 133 Ba radioisotopes), and a test pipe. Water, gas, and oil were all used to mimic a three-phase flow in the test pipe, with the volume percentages ranging from 10% to 80%. Moreover, a scale ranging in thickness from 0 to 3 cm was inserted into the pipe, gamma rays were shone on the pipe, and on the opposite side of the pipe, photon intensity was measured by detectors. There were 252 simulations run. Fifteen time and frequency characteristics were derived from the signals collected by the detectors. The ant colony optimisation (ACO)-based approach is used to pick the ideal inputs from among the extracted characteristics for determining the thickness of the scale within the pipe. This technique led to the introduction of thirteen features that represented the ideal combination. The features introduced by ACO were introduced as inputs to a multi-layer perceptron (MLP) neural network to predict the scale thickness inside the oil pipe in centimetres. The maximum error found in calculating scale thickness was 0.017 as RMSE, which is a minor error compared to earlier studies. The accuracy of the present study in detecting scale thickness has been greatly improved by using the ACO to choose the optimal features.

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“…Numerous academic works [13][14][15][16] attest to researchers' growing interest in using the MCNP algorithm for the simulation of structures employing X-ray or gamma radiation. MCNP code was used as the simulation platform to simulate the framework suggested in this research [17].…”

Section: Simulation Setupmentioning

confidence: 99%

An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions

et al. 2023

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Over time, the accumulation of scale within the transmission pipeline results in a decrease in the internal diameter of the pipe, leading to a decline in efficiency and energy waste. The employment of a gamma ray attenuation system that is non-invasive has been found to be a highly precise diagnostic technique for identifying volumetric percentages across various states. The most appropriate setup for simulating a volume percentage detection system through Monte Carlo N particle (MCNP) simulations involves a system consisting of two NaI detectors and dual-energy gamma sources, namely 241Am and 133Ba radioisotopes. A three-phase flow consisting of oil, water, and gas exhibits symmetrical homogenous flow characteristics across varying volume percentages as it traverses through scaled pipes of varying thicknesses. It is worth mentioning that there is an axial symmetry of flow inside the pipe that creates a homogenous flow pattern. In this study, the experiment involved the emission of gamma rays from one end of a pipe, with photons being absorbed by two detectors located at the other end. The resulting data included three distinct features, namely the counts under the photopeaks of 241Am and 133Ba from the first detector as well as the total count from the second detector. Through the implementation of a two-output MLP neural network utilising the aforementioned inputs, it is possible to accurately forecast the volumetric percentages with an RMSE of under 1.22, regardless of the thickness of the scale. The minimal error value ensures the efficacy of the proposed technique and the practicality of its implementation in the domains of petroleum and petrochemicals.

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

Cited by 14 publications

References 36 publications

Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows

Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows

Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System

An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions

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