Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products

Roshani, Gholam Hossein; Ali, Peshawa J. Muhammad; Mohammed, Shivan; Hanus, Robert; Abdulkareem, Lokman A.; Alanezi, Adnan Alhathal; Sattari, Mohammad; Amiri, Sara; Nazemi, Ehsan; Eftekhari-Zadeh, Ehsan; Kalmoun, El Mostafa

doi:10.3390/pr9050828

Cited by 28 publications

(27 citation statements)

References 30 publications

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“…After training, MLP neural networks can conjecture the volume ratio of each petroleum product with an average absolute error of 2.72. Sattari et Roshani and his colleagues in [4] attempted to use the obtained absorption spectra to train and test the multilayer perceptron network (MLP) using an X-ray source, a detector, and other routine procedures. After training, MLP neural networks can conjecture the volume ratio of each petroleum product with an average absolute error of 2.72.…”

Section: Introductionmentioning

confidence: 99%

Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

et al. 2022

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When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent.

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Section: Introductionmentioning

confidence: 99%

Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

et al. 2022

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“…For example, time-domain [19,20], frequency domain [21], and timefrequency domain [22,23] are some momentous feature derivation techniques used. In the study report by Roshani et al, the volume ratio of petroleum products was predicted with significant accuracy using a designed fluid control system [24]. The distinguishing point in the research was the fact that it did not use feature extraction methods, which have led to the presentation of differing work.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

et al. 2022

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Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry.

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“…Three RBF neural networks were trained with the tasks of determining volume percentages and detecting the type of flow regimes using frequency characteristics. In [ 14 ], X-ray tubes were used to implement a diagnostic system, but the lack of characteristic extraction techniques was a disadvantage of this system. In another study [ 15 ], wavelet transform characteristics were applied to develop previous work ([ 14 ]).…”

Section: Introductionmentioning

confidence: 99%

“…In [ 14 ], X-ray tubes were used to implement a diagnostic system, but the lack of characteristic extraction techniques was a disadvantage of this system. In another study [ 15 ], wavelet transform characteristics were applied to develop previous work ([ 14 ]). In [ 15 ], the characteristics of the fifth stage approximation and the details of the first to fifth stages were extracted using wavelet transform and introduced as the inputs for the MLP neural network.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

et al. 2022

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Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

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Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products

Cited by 28 publications

References 30 publications

Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

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