This paper reviews the use of non-intrusive optical infrared sensing for gas-liquid flow characterisation in pipes. The application of signal analysis techniques to infrared-derived temporal signal outputs enables the objective determination of flow characteristics such as flow regimes, phase fractions and total pressure drops. Key considerations for improving the performance of infrared sensors are discussed. These include global and local measurements, ray divergence, effects of ambient light and temperature variations. Most experimental studies have reported consistent and excellent results for flow regime identifications and phase fraction estimation but with a few validating total pressures drop from correlations and direct pressure measurements. Other gaps in research were highlighted; these include the use of pipes sizes greater than 0.005 m for experimentation under high superficial velocities conditions greater than 10 m/s. The capabilities of infrared sensing as a standalone measurement for flow metering were considered a possibility via an inferential approach for phase volumetric rates. More so, the derived infrared sensing flow characteristics could be combined with available pressure-volume-temperature correlations in estimating mass flow rates of each phase. As a future development, a conceptual modification to surface installations using a transparent opaque coupling is suggested to overcome the accessibility limitation of infrared light penetration for opaque pipes.

The accurate identification of gas–liquid flow regimes in pipes remains a challenge for the chemical process industries. This paper proposes a method for flow regime identification that combines responses from a nonintrusive optical sensor with linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) for vertical upward gas–liquid flow of air and water. A total of 165 flow conditions make up the dataset, collected from an experimental air–water flow loop with a transparent test section (TS) of 27.3 mm inner diameter and 5 m length. Selected features extracted from the sensor response are categorized into feature group 1, average sensor response and standard deviation, and feature group 2 that also includes percentage counts of the calibrated responses for water and air. The selected features are used to train, cross validate, and test four model cases (LDA1, LDA2, QDA1, and QDA2). The LDA models produce higher average test classification accuracies (both 95%) than the QDA models (80% QDA1 and 45% QDA2) due to misclassification associated with the slug and churn flow regimes. Results suggest that the LDA1 model case is the most stable with the lowest average performance loss and is therefore considered superior for flow regime identification. In future studies, a larger dataset may improve stability and accuracy of the QDA models, and an extension of the conditions and parameters would be a useful test of applicability.

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The application of real - time monitoring technologies presents a means to harnessing proactive or reactive controls in minimizing severity effects of slugging in the production system. This paper presents the development of a non-intrusive optical infrared sensing (NIOIRS) setup, for slug monitoring in pipes. The flow characteristics monitored were the development of slug flows and average phase fractions of gas and liquid in a vertical test section (0.018m by 1m) for superficial velocities of 0-0.131 m/s for water and 0 – 0.216 m/s for air. The measurement principle was based on the disparities in refractive indices of each phase in the sensing area. The sensing component of the sensor consisted of two pairs of IR emitters and photodiodes operated at wavelengths of 880 nm specifications. A circuit, for signal conditioning, amplification and data acquisition was set up to convert infrared light detected into voltage signals. Development of slug flow regimes was monitored from signal distributions binned under reference voltages. The transitions from bubble to slug flow, were observed at 10 percent count of the signal distributions around typical sensor response for air. Validation from photos showed good agreements with the sensor response. A single peaked distribution around the response for water indicated bubble flow regimes, with the development of two peaks indicated increasing gas slugs for increasing superficial gas velocities compared to liquid slug in the pipe. Phase fraction results were interpreted from a derived calibration models, which were based on the average observed response and reference responses of water and air over time. This model was compared with swell level changes, photographs and homogenous and drift flux correlation with agreement within maximum error bands +/− 0.5 % based on the swell level method and +/− 0.3% based on photographs. The Real-time application was carried out via the execution of an algorithm which incorporated the calibration information from the NIOIRS. The derived signals were processed and analyzed onto a display to identify slug flow development and phase fractions in real-time. A cheap and accurate sensing setup has been developed with the potential of real time monitoring of flow regimes and phase fraction determination.