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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]
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Database: Engineering Source
Description
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]
ISSN:02365731
DOI:10.1007/s10967-025-10714-x