Abdulilah Mohammad Mayet, John William Grimaldo Guerrero, Thafasal Ijyas, Javed Khan Bhutto, Neeraj Kumar Shukla, Ehsan Eftekhari-Zadeh, Hala H. Alhashim
{"title":"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","authors":"Abdulilah Mohammad Mayet, John William Grimaldo Guerrero, Thafasal Ijyas, Javed Khan Bhutto, Neeraj Kumar Shukla, Ehsan Eftekhari-Zadeh, Hala H. Alhashim","doi":"10.3390/separations10100534","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":21833,"journal":{"name":"Separations","volume":"4 1","pages":"0"},"PeriodicalIF":2.5000,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness\",\"authors\":\"Abdulilah Mohammad Mayet, John William Grimaldo Guerrero, Thafasal Ijyas, Javed Khan Bhutto, Neeraj Kumar Shukla, Ehsan Eftekhari-Zadeh, Hala H. Alhashim\",\"doi\":\"10.3390/separations10100534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":21833,\"journal\":{\"name\":\"Separations\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Separations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/separations10100534\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Separations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/separations10100534","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
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.
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