应用傅里叶变换提高不依赖尺度厚度的伽马体积百分比检测系统的精度

IF 2.5 4区 工程技术 Q3 CHEMISTRY, ANALYTICAL Separations Pub Date : 2023-10-07 DOI:10.3390/separations10100534
Abdulilah Mohammad Mayet, John William Grimaldo Guerrero, Thafasal Ijyas, Javed Khan Bhutto, Neeraj Kumar Shukla, Ehsan Eftekhari-Zadeh, Hala H. Alhashim
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引用次数: 0

摘要

随着时间的推移,石油管道内部逐渐形成水垢。所制备的刻度具有高密度,对光子有强烈的衰减,降低了基于伽马辐射的三相流量计的测量精度。值得一提的是,当需要或希望测量进口分离和/或混合上游的井流时,就需要多相流计量。在本研究中,提出了一种基于人工智能的新技术来克服上述问题。最初,一个探测系统由两个NaI探测器和一个双能伽玛源(241 Am和133 Ba放射性同位素)组成,使用蒙特卡罗N粒子(MCNP)代码。在包含不同厚度的结垢层的管道内,模拟了油、水和气的不同体积百分比的分层流动状态。两个探测器记录了可能通过管道的衰减光子。两个检测器分别从接收到的信号中提取了四个特征,其名称分别为第一和第二主导信号频率的幅值。利用上述获得的特征来训练两个径向基函数(RBF)神经网络来预测每个成分的体积百分比。油气预测神经网络的RMSE值分别为0.27和0.29。通过测量管道中的两相流体,可以通过从管道的总体积中减去两相的体积来计算第三相的体积。提取和引入合适的特征来确定体积百分比,减少检测系统的计算负担,考虑管道的尺度值厚度,提高确定石油管道体积百分比的准确性是当前研究的一些优点,这增加了所提出的系统作为石油和石化行业可靠的测量系统的可用性。
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Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness
With the passage of time, scale gradually forms inside the oil pipeline. The produced scale, which has a high density, strongly attenuates photons, which lowers the measurement accuracy of three-phase flow meters based on gamma radiation. It is worth mentioning that the need for multiphase flow metering arises when it is necessary or desirable to meter well stream(s) upstream of inlet separation and/or commingling. In this investigation, a novel technique based on artificial intelligence is presented to overcome the issue mentioned earlier. Initially, a detection system was comprised of two NaI detectors and a dual-energy gamma source (241 Am and 133 Ba radioisotopes) using Monte Carlo N particle (MCNP) code. A stratified flow regime with varying volume percentages of oil, water, and gas was modeled inside a pipe that included a scale layer with varying thicknesses. Two detectors record the attenuated photons that could travel through the pipe. Four characteristics with the names of the amplitude of the first and second dominant signal frequencies were extracted from the received signals by both detectors. The aforementioned obtained characteristics were used to train two Radial Basis Function (RBF) neural networks to forecast the volumetric percentages of each component. The RMSE value of the gas and oil prediction neural networks are equal to 0.27 and 0.29, respectively. By measuring two phases of fluids in the pipe, the volume of the third phase can be calculated by subtracting the volume of two phases from the total volume of the pipe. Extraction and introduction of suitable characteristics to determine the volume percentages, reducing the computational burden of the detection system, considering the scale value thickness the pipe, and increasing the accuracy in determining the volume percentages of oil pipes are some of the advantages of the current research, which has increased the usability of the proposed system as a reliable measuring system in the oil and petrochemical industry.
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来源期刊
Separations
Separations Chemistry-Analytical Chemistry
CiteScore
3.00
自引率
15.40%
发文量
342
审稿时长
12 weeks
期刊介绍: Separations (formerly Chromatography, ISSN 2227-9075, CODEN: CHROBV) provides an advanced forum for separation and purification science and technology in all areas of chemical, biological and physical science. It publishes reviews, regular research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, unique features of this journal: Manuscripts regarding research proposals and research ideas will be particularly welcomed. Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. Manuscripts concerning summaries and surveys on research cooperation and projects (that are funded by national governments) to give information for a broad field of users. The scope of the journal includes but is not limited to: Theory and methodology (theory of separation methods, sample preparation, instrumental and column developments, new separation methodologies, etc.) Equipment and techniques, novel hyphenated analytical solutions (significantly extended by their combination with spectroscopic methods and in particular, mass spectrometry) Novel analysis approaches and applications to solve analytical challenges which utilize chromatographic separations as a key step in the overall solution Computational modelling of separations for the purpose of fundamental understanding and/or chromatographic optimization
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