Recent advances in in-process characterization of suspensions and slurries

Characterizing Flocculated Mineral Sediments with Acoustic Backscatter, Using Solid and Hybrid Scattering Models

This study investigated the performance of an acoustic backscatter system (ABS) for the in situ particle characterization of complex wastes. Two sediments were used: a fine, milled calcite that was flocculated with anionic polyacrylamide and naturally flocculated pond sludge. Particles were initially measured independently by light-based techniques to gain size, the coefficient of variation (COV), and fractal dimensions. For acoustic experiments, a bespoke, high-fidelity ABS was employed with 1, 2.25, and 5 MHz probes and a recirculating mixing tank. Initially, the concentration independent attenuation and backscatter coefficients were measured for each system using a robust calibration procedure at multiple concentrations. Comparisons of the total scattering cross-section (χ) and form function (f) were made between the experimental data and two semiempirical models: a Solid Scattering model and a Hybrid model (where the effects of bound fluid are incorporated). Experimental data compared more closely to the Solid Scattering model, as it was assumed scattering was dominated by small, bound "flocculi" rather than the macroscopic structure. However, if the COV was used as a fit parameter, the hybrid model could give equally accurate fits for a range of input aggregate sizes, highlighting that important size and structure information can be gained from the acoustic models if there is some a priori system data. Additionally, dual-frequency inversions were undertaken to measure concentration profiles for various frequency pairs. Here, the lowest frequency pair gave the best performance (with accurate measurements in the range of 2-35 g·L-1) as interparticle scattering was lowest.

Online 3D Characterization of Micrometer-Sized Cuboidal Particles in Suspension

Characterization of particle size and shape is central to the study of particulate matter in its broadest sense. Whilst 1D characterization defines the state of the art, the development of 2D and 3D characterization methods has attracted increasing attention, due to a common need to measure particle shape alongside size. Herein, ensembles of micrometer-sized cuboidal particles are studied, for which reliable sizing techniques are currently missing. Such particles must be characterized using three orthogonal dimensions to completely describe their size and shape. To this end, the utility of an online and in-flow multiprojection imaging tool coupled with machine learning is experimentally assessed. Central to this activity, a methodology is outlined to produce micrometer-sized, non-spherical analytical standards. Such analytical standards are fabricated using photolithography, and consist of monodisperse micro-cuboidal particles of user-defined size and shape. The aforementioned activities are addressed through an experimental framework that fabricates analytical standards and subsequently uses them to validate the performance of our multiprojection imaging tool. Significantly, it is shown that the same set of data collected for particle sizing can also be used to estimate particle orientation in flow, thus defining a rapid and robust protocol to investigate the behavior of dilute particle-laden flows.

Monitoring and Visualization of Crystallization Processes Using Electrical Resistance Tomography: CaCO3 and Sucrose Crystallization Case Studies

In the current research work, electrical resistance tomography (ERT) was employed for monitoring and visualization of crystallization processes. A first-of-its-kind MATLAB-based interactive GUI application “ERT-Vis” is presented. Two case studies involving varied crystallization methods were undertaken. The experiments were designed and performed involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media, and the cooling crystallization of sucrose representing the lower conductivity solution–solute combination. The software successfully provided key insights regarding the process in both crystallization systems. It could detect and separate the solid concentration distributions in the low as well as high conductivity solutions using the visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps by reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data.