Monitoring system of oil by-products interface in pipelines using the gamma radiation attenuation

Salgado, William Luna; Dam, Roos Sophia de Freitas; Barbosa, Caroline Mattos; Silva, Ademir X. da; Salgado, César Marques

doi:10.1016/j.apradiso.2020.109125

Cited by 20 publications

(3 citation statements)

References 15 publications

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“…Different volume percentages were simulated for both oil and water fluids. The volume percentages could be detected with an accuracy of 1% [2]. In other studies, researchers simulated two-phase [3][4][5] and three-phase [6][7][8] compositions at different volumetric percentages and different flow regimes.…”

Section: Introductionmentioning

confidence: 99%

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

et al. 2021

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This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

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Section: Introductionmentioning

confidence: 99%

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

et al. 2021

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“…The group of C. Salgado proposed, in 2016, a technique based on a broad beam detection system and artificial neural network (ANN) to determine the density of petroleum and its derivatives [1]. A cesium-137 source and one sodium iodide crystal detector were modeled using MCNPX code, and the system was tested on a range of petroleum products with densities varying from 0.55 to 1.26 g/cm 3 . The ANN was fed with the photon energy from the detector: the resulting density of petroleum products could be estimated independent of fluid composition.…”

Section: Introductionmentioning

confidence: 99%

“…The ANN was fed with the photon energy from the detector: the resulting density of petroleum products could be estimated independent of fluid composition. In 2020, W. L. Salgado et al designed an experimental setup to validate the Monte Carlo simulation [3]. The equipment was composed of a cesium-137 radioisotope source, a glass pipe, and one NaI scintillation detector.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.

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Development of a non-invasive method for monitoring variations in salt concentrations of seawater using nuclear technique and Monte Carlo simulation

Barbosa

Kenup-Hernandes

Raitz

et al. 2021

Applied Radiation and Isotopes

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Monitoring system of oil by-products interface in pipelines using the gamma radiation attenuation

Cited by 20 publications

References 15 publications

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

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

Development of a non-invasive method for monitoring variations in salt concentrations of seawater using nuclear technique and Monte Carlo simulation

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