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Solids flow dynamics in gas-solid risers is inherently complex. Model refinement through experimental validation requires the acquisition of detailed nonintrusive measurements. In this study, noninvasive computer-automated radioactive particle tracking (CARPT) is employed to visualize and quantify in a three-dimensional domain the solids dynamics and mixing in gassolid risers. This technique has the added advantage that, along with the derived Eulerian solids flow field (time-average velocity map and various turbulence parameters such as the Reynolds stresses, turbulent kinetic energy), it also provides directly the Lagrangian description of the solids motion. The solids velocity field data are obtained in two different risers at low and high solids fluxes at varying superficial gas velocity to span both the fast-fluidized (FF) and dilute phase transport (DPT) regimes. The effect of operating conditions on solids flow and mixing is studied. Comparative analysis of the results is presented to provide insights into the complex solids flow patterns characteristic of gas-solid risers.

Circulating fluidized-bed (CFB) risers with Geldart group B particles have found significant application in combustion reactions. The present work attempts to study the solids flow dynamics in a CFB riser that is operated with group B particles, using computational fluid dynamics (CFD) techniques. The key feature in the present study is that the various closure schemes in the CFD model have been evaluated against data from non-invasive experimental techniques:  computer automated radioactive particle tracking (CARPT) for solids velocity field and computed tomography (CT) for solids holdup. Since solids flow in a riser is multiscale in character, in addition to the measured averaged solids velocity profiles and solids fraction profiles in the experimental section, mean granular temperature profiles have also been compared. Two flow regimes (viz., fast fluidization and dilute phase transport) have been considered in this study.

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Lange and Carson (1984 J. Comput. Assist. Tomogr. 8 306–16) defined image reconstruction for transmission tomography as a maximum likelihood estimation problem and derived an expectation maximization (EM) algorithm to obtain the maximum likelihood image estimate. However, in the maximization step or M-step of the EM algorithm, an approximation is made in the solution which can affect the image quality, particularly in the case of domains with high attenuating material. O'Sullivan and Benac (2007 IEEE Trans. Med. Imaging 26 283–97) reformulated the maximum likelihood problem as a double minimization of an I-divergence to obtain a family of image reconstruction algorithms, called the alternating minimization (AM) algorithm. The AM algorithm increases the log-likelihood function while minimizing the I-divergence. In this work, we implement the AM algorithm for image reconstruction in gamma ray tomography for industrial applications. Experimental gamma ray transmission data obtained with a fan beam geometry gamma ray scanner, and simulated transmission data based on a synthetic phantom, with two phases (water and air) were considered in this study. Image reconstruction was carried out with these data using the AM and the EM algorithms to determine and quantitatively compare the holdup distribution images of the two phases in the phantoms. When compared to the EM algorithm, the AM algorithm shows qualitative and quantitative improvement in the holdup distribution images of the two phases for both the experimental and the simulated gamma ray transmission data.