Browsing by Author "Shunashu, Ishigita Lucas"

Now showing 1 - 4 of 4
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    Enhanced Liquid Detection in Wet Gas Metering Via Microwave Sensing and Random Forest Regression
    (ASME, 2026-03-01) Shunashu, Ishigita Lucas; Kaunde,Osmund; Mwakipesile,Duncan
    This study explored the integration of machine learning regression models with a microwave transmission line sensor for estimating liquid volume fraction and liquid flowrate in wet gas flows. Under low liquid loading conditions (gas volume fraction 95–99.9%), four models: Bruggeman, support vector regression, Gaussian process regression, and random forest regression were evaluated. Random forest regression delivered the best tradeoff between accuracy, robustness, and computational efficiency, achieving a relative absolute error of 2.23% for liquid volume fraction and approximately 5% for liquid flowrate, with a Durbin–Watson statistic of 2.02 indicating minimal residual autocorrelation. Feature importance analysis identified the mixture dielectric constant as the dominant predictor (approximately 97% contribution), while other dimensionless parameters had a limited impact. Support vector regression failed to generalize, and although Gaussian process regression showed slightly higher accuracy, its computational cost limited real-time applicability. Overall, random forest regression combined with microwave sensing offers a scalable, nonintrusive solution for wet gas metering, with future validation needed under industrial hydrocarbon–water conditions and liquid loading flow regimes
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    Integrated Ultrasonic Flow Meter and Microwave Sensing Technology for Wet Gas Measurement: Development and Validation of Over-Reading Correction Models
    (John Wiley & Sons, 2025-11-20) Shunashu, Ishigita Lucas; Kaunde, Osmund; Mwakipesile, Duncan
    Accurate wet gas flow measurement is essential for production optimisation, custody transfer, and regulatory compliance in theenergy and chemical industries. Conventional ultrasonic flow meters often overestimate gas flow rates due to liquid entrainment,while microwave sensors alone struggle with phase discrimination under dynamic conditions. This study introduces a hybrid metering system, Ultrasonic Flow Meters andMicrowave Sensing Measurement ofWet Gas (USMMW), that integrates transit-time ultrasonic flow measurement with microwave dielectric sensing to correct over-reading errors. Experimental data were collected from a controlled multiphase flow loop using a 2-inch pipeline equipped with an ultrasonic meter and a 2.7 GHz microwave sensor. A data-driven over-reading correction model (OR) was developed using detected liquid volume fraction (LVF) and eight dimensionless parameters derived via the Buckingham Pi theorem.Multiple regression andmachine learning techniques, including multilinear regression (MLR) and random forest regression (RFR), were applied to optimise model performance. Validation results showed that the USMMW system achieved corrected gas flow rates with an average relative absolute error (RAE) of 3.02%, outperforming conventional differential pressure models. The findings demonstrate that USMMW offers a robust, non-intrusive solution for real-time wet gas metering under mist and stratified flow regimes, with potential for scalable industrial deployment.
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    Modelling Over-Reading Correction Factors for Ultrasonic Flow Meters in Wet Gas Measurement Using Advanced Regression and Machine Learning Techniques
    (Elsevier, 2025-10-08) Shunashu, Ishigita Lucas; Kaunde, Osmund
    Accurate wet gas measurement is essential for optimizing production, transmission, and reservoir management in oil and gas operations. Ultrasonic flow meters, though non-intrusive and versatile, often overestimate flow rates due to the presence of liquid phases, leading to significant operational and economic errors. To address this, a data-driven correction model was developed using computational techniques to predict and compensate for overreading. This study evaluates the performance of several advanced regression and machine learning approaches, including polynomial regression, random forest regression, nonlinear curve fitting, neural networks, multiple linear regression, ridge regression, and lasso regression, using an extensive experimental dataset. Key input variables include liquid volume fraction, Lockhart–Martinelli parameter, Froude number, Weber number, slip ratio, and density ratio. Among the models tested, random forest regression and multiple linear regression achieved the highest accuracy, with average relative absolute errors of 3.02% and 3.20 % respectively. These findings demonstrate the potential of data-driven modeling to enhance the reliability of ultrasonic flow meters in complex wet gas environments.
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    Modelling over-reading correction factors for ultrasonic flow meters in wet gas measurement using advanced regression and machine learning techniques
    (Elsevier Ltd., 2025-10-08) Shunashu, Ishigita Lucas; Kaunde, Osmund
    Accurate wet gas measurement is essential for optimizing production, transmission, and reservoir management in oil and gas operations. Ultrasonic flow meters, though non-intrusive and versatile, often overestimate flow rates due to the presence of liquid phases, leading to significant operational and economic errors. To address this, a data-driven correction model was developed using computational techniques to predict and compensate for over-reading. This study evaluates the performance of several advanced regression and machine learning approaches, including polynomial regression, random forest regression, nonlinear curve fitting, neural networks, multiple linear regression, ridge regression, and lasso regression, using an extensive experimental dataset. Key input variables include liquid volume fraction, Lockhart–Martinelli parameter, Froude number, Weber number, slip ratio, and density ratio. Among the models tested, random forest regression and multiple linear regression achieved the highest accuracy, with average relative absolute errors of 3.02% and 3.20 % respectively. These findings demonstrate the potential of data-driven modeling to enhance the reliability of ultrasonic flow meters in complex wet gas environments.