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Positron emission particle tracking (PEPT) is a non‐invasive technique that can be used for following the trajectories of particles in fluidized beds, so increasing understanding of solids motion in fluidized bed processes. We describe how PEPT is applied, how its performance is optimized, and how trajectory information can be built up into instantaneous and time‐averaged measures of particle movement. Choices and pitfalls in data processing are explained and illustrated by reference to the travelling fluidized bed (TFB) collaboration initiated by Professor John Grace. [ABSTRACT FROM AUTHOR]

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A dense particle suspension, also called an upflow bubbling fluidized bed, is an innovative alternative to the heat transfer fluids commonly used in concentrated solar power plants. An additional advantage of this technology is that it allows for direct thermal storage due to the large heat capacity and maximum temperature of the particle suspension. The key to the proposed process is the effective heat transfer from the solar heated surfaces to the heat transfer fluid, i.e. the circulating solid suspension. In order to better understand the process and to optimise the design of the solar receiver, it is of paramount importance to know how particles behave inside the bundle of small tubes. To access to the particle motion in the solar receiver, two different techniques are carried out: experimental using positron emission particle tracking (PEPT) and 3D numerical simulation via an Eulerian n-fluid approach with NEPTUNE_CFD code. Both numerical predictions and PEPT measurements describe an upward flow at the centre of the transport tube with a back-mixing flow near the wall which influences the heat transfer mechanism. Comparisons between experiment and computation were carried out for the radial profiles of the solid volume fraction, and vertical and radial time-averaged and variance velocities of solid, and demonstrating the capability of NEPTUNE_CFD code to simulate this peculiar upflow bubbling fluidized bed. [ABSTRACT FROM AUTHOR]

A transportable fluidization column, operating under identical conditions at three different locations, was employed to compare three experimental solids flux measurement techniques for hydrodynamic characterization of gas-fluidized beds. This paper compares measurements of solids mass and momentum flux obtained by radioactive particle tracking at the Ecole Polytechnique, positron emission particle tracking at University of Birmingham, and borescopic high speed particle image velocimetry at PSRI, carried out with FCC particles of mean diameter 107 μm. These techniques provided broadly similar time-average solids flux profiles, but there were significant quantitative differences. Analysis of the results, focusing on the fundamentals of each measurement technique, provides valuable insights into the reasons for the discrepancies. The results also add to a unique hydrodynamic database for validation of CFD and other models. [ABSTRACT FROM AUTHOR]

Positron emission particle tracking (PEPT) is used to study a wide range of scientific, industrial, and biomedical systems, typically those inaccessible through conventional optical particle tracking techniques. However, in dense or thick-walled systems a fraction of the coincident gamma-rays emitted from a PEPT tracer, called Lines-of-Response (LoRs), are attenuated via Compton scattering. Additionally, at high source activity, random LoRs may be formed by two unrelated events. The incorporation of scattered or random LoRs decreases PEPT spatial accuracy and can distort the trajectory. In this work, we use validation experiments and simulations to investigate the spatial accuracy of the Birmingham Method (BM) PEPT algorithm when two key free parameters are changed: the total number of LoRs in the sample and the fraction of LoRs in the sample used to locate the tracer. Our results show that the default algorithm parameters are not suitable for all cases, however, Monte Carlo simulations of PEPT experiments can be used to estimate the optimal parameter values. Ultimately a variant of the BM, called Dynamic-BM, is demonstrated in a virtual PEPT experiment. Dynamic-BM uses the optimal parameters on a sample-by-sample basis improving PEPT accuracy in this case by 4.03% over the best constant parameters and 76.5% over the default parameters. These improvements make PEPT a more accurate and thus more useful tool. • Two key free parameters a PEPT algorithm are optimised for spatial accuracy. • Optimal parameter values are discovered through experiments and simulations of PEPT. • The Birmingham Method is extended to dynamically change parameter values. • Using optimal values, spatial errors decrease by 76.5% compared to default values. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we examine the movement of particles within a continuous powder mixer using PEPT (Positron Emission Particle Tracking). The benefit of the approach is that the particle movement along the vessel can be measured non-invasively. The effect of impeller rotation rate, powder flow rate, and powder cohesion on the particle trajectory, dispersive axial transport coefficient, and residence time is examined. Increase in the impeller rotation rate decreased the residence time, increased the axial dispersion coefficient, and resulted in longer total path length. Effect of flow rate was different at two different rotation rates. At lower rotation rate, increase in flow rate increased the residence time, decreased the axial dispersion, and resulted in longer total path length. At higher rotation rate, increase in flow rate decreased the residence time, increased the total path length and showed a complex dependence on the axial dispersion coefficient. Increasing cohesion (measured using the flow index, dilation, and the Hausner ratio) did not affect the axial dispersion coefficient significantly, but had significant effects on the total particle path length traveled and the residence time. These results, relevant to pharmaceutical powders, provide better physical understanding of the influence of operating parameters on the flow behavior in the continuous mixer. In addition, one of the main obstacles of modeling continuous mixing of particles is to know the appropriate values for the modeling parameters as well as validate modeling approaches. One example is the dispersion coefficient which leads to an analytical solution for the axial dispersion model of a continuous blending process. [ABSTRACT FROM AUTHOR]