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Efficient processing algorithms of radiotracer signals for optimizing flowrate measurements based on residence time distribution.

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Title: Efficient processing algorithms of radiotracer signals for optimizing flowrate measurements based on residence time distribution.
Authors: Ali, Elsayed H.1 (AUTHOR), Arafa, Horeya A.1 (AUTHOR), Kasban, H.1 (AUTHOR), El Tokhy, Mohamed S.1 (AUTHOR) engtokhy@gmail.com
Source: Journal of Radioanalytical & Nuclear Chemistry. Feb2026, Vol. 335 Issue 2, p1431-1452. 22p.
Subjects: Flow measurement, Machine learning, Scintillation counters, Digital signal processing, Support vector machines, Uncertainty (Information theory), Signal processing, Ensemble learning
Abstract: This study proposes an integrated, uncertainty-aware framework for estimating pipeline flowrate from radiotracer residence-time-distribution (RTD) signals and evaluates it on industrial data. The novel contributions are threefold: a unified workflow that begins with digital signal processing, extracts RTD features, and then applies machine-learning (ML) prediction while reporting both point estimates and quantified uncertainty (bootstrap and Bayesian credible intervals); a systematic mean-residence-time (MRT)-centric analysis showing greater stability than peak-time methods across detector pairs; and a comparative assessment of two field data-acquisition systems (Ludlum and Ashtar/ALTIX) using Tc-99 m injections (5–10 mCi) recorded by scintillation detectors. RTD features (MRT, FWHM, peak amplitude, spectral descriptors) are mapped to flowrate using support-vector regression, Random Forest, and gradient-boosted trees. The machine-learning layer captures nonlinear relations between RTD features and flowrate that are not recovered by peak-based calculations, while the uncertainty layer assigns calibrated confidence to each estimate. Overall, an MRT-centric, ensemble-learning approach improves the reliability and interpretability of RTD-based flow estimation and is suitable for on-line investigations using radiotracer technology. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Radioanalytical & Nuclear Chemistry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192418295
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Efficient processing algorithms of radiotracer signals for optimizing flowrate measurements based on residence time distribution.
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  Data: <searchLink fieldCode="AR" term="%22Ali%2C+Elsayed+H%2E%22">Ali, Elsayed H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Arafa%2C+Horeya+A%2E%22">Arafa, Horeya A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kasban%2C+H%2E%22">Kasban, H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22El+Tokhy%2C+Mohamed+S%2E%22">El Tokhy, Mohamed S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> engtokhy@gmail.com</i>
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  Data: <searchLink fieldCode="DE" term="%22Flow+measurement%22">Flow measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Scintillation+counters%22">Scintillation counters</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+signal+processing%22">Digital signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink>
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  Label: Abstract
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  Data: This study proposes an integrated, uncertainty-aware framework for estimating pipeline flowrate from radiotracer residence-time-distribution (RTD) signals and evaluates it on industrial data. The novel contributions are threefold: a unified workflow that begins with digital signal processing, extracts RTD features, and then applies machine-learning (ML) prediction while reporting both point estimates and quantified uncertainty (bootstrap and Bayesian credible intervals); a systematic mean-residence-time (MRT)-centric analysis showing greater stability than peak-time methods across detector pairs; and a comparative assessment of two field data-acquisition systems (Ludlum and Ashtar/ALTIX) using Tc-99 m injections (5–10 mCi) recorded by scintillation detectors. RTD features (MRT, FWHM, peak amplitude, spectral descriptors) are mapped to flowrate using support-vector regression, Random Forest, and gradient-boosted trees. The machine-learning layer captures nonlinear relations between RTD features and flowrate that are not recovered by peak-based calculations, while the uncertainty layer assigns calibrated confidence to each estimate. Overall, an MRT-centric, ensemble-learning approach improves the reliability and interpretability of RTD-based flow estimation and is suitable for on-line investigations using radiotracer technology. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Radioanalytical & Nuclear Chemistry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10967-025-10714-x
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 1431
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      – SubjectFull: Flow measurement
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Scintillation counters
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      – SubjectFull: Digital signal processing
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      – SubjectFull: Support vector machines
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      – SubjectFull: Uncertainty (Information theory)
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      – SubjectFull: Signal processing
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      – SubjectFull: Ensemble learning
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    Titles:
      – TitleFull: Efficient processing algorithms of radiotracer signals for optimizing flowrate measurements based on residence time distribution.
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            NameFull: Ali, Elsayed H.
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            – D: 01
              M: 02
              Text: Feb2026
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              Y: 2026
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