(690b) Computational Prediction of the Just-Suspended Speed, Njs, in Stirred Vessels Using the Lattice Boltzmann Method (LBM) | AIChE

(690b) Computational Prediction of the Just-Suspended Speed, Njs, in Stirred Vessels Using the Lattice Boltzmann Method (LBM)


Sirasitthichoke, C. - Presenter, New Jersey Institute of Technology
Armenante, P., New Jersey Institute of Technology
Industrial processes such as chemical reactions, dissolution of solids, and crystallization that involve finely dispersed solid particles suspended in a liquid in stirred vessels are widely used in the chemical and pharmaceutical process industries. Suspending all solids in the surrounding liquid is a crucial process objective in process optimization. The minimum agitation speed required to achieve off-bottom solid suspension, Njs, is a critical parameter used to ensure solid suspension in stirred vessels. Previous work has been extensively focused on the experimental determination of Njsusing approaches that include solids sampling, visual observation, pressure gauge technique, steady cone radius measurements, and, more recently, electrical resistance tomography (ERT). Although each technique has different advantages and disadvantages such as objectivity, unobtrusiveness, visual observation, and applicability to industrial stirred vessels, it is still difficult and impractical for a wide range of industrial applications. Recently, Computational Fluid Dynamics (CFD) has been used to attempt to predict Njs, mainly using the Reynolds Average Navier Stokes (RANS) to simulate turbulence. However, solid suspension is three-dimensional and highly dependent on velocity fluctuations that are space and time dependent. It is thereby essential to introduce a more suitable CFD approach so as to predict Njs more reliably. This could additionally alleviate the need for extensive and expensive experimentation. Therefore, the objective of this study is to quantify Njs in a stirred tank provided with different axial and radial impellers using the Lattice Boltzmann Method (LBM). To do so, the mass fraction of suspended solid particles in a control volume near the bottom of the vessel was computationally predicted over a wide range of agitation speeds. Logistic regression analysis was combined with the computational approach to interpret the measurement data and hence predict Njs using a mathematical approach recently developed by our group to extract Njs from experimental ERT data. We validated the our CFD approach by comparing the predicted Njsresults with available experimental data. The predicted Njsresults were found in good agreement with those previously published, indicating that this CFD model combined with the logistic regression analysis proposed here are suitable for the determination of Njs. In turn, this work could help facilitate the solid suspension design and scale-up of industrial stirred vessels.