Heat transfer of dry granular materials in a bladed mixer: Effect of thermal properties and agitation rate

Abstract

Agitated drying of pharmaceuticals remains a challenging manufacturing step due to the simultaneous heat transfer, mass transfer, and physicochemical changes occurring during the process. This work focuses on the heat transfer component by implementing the discrete element method to model dry particles in a heated bladed mixer. Simulations varying material conductivities and impeller agitation rates were conducted to evaluate the influence on the mean bed temperature and distribution. The results indicated that increasing the agitation rate generally improved heat transfer up until a critical agitation rate where the rate of heat transfer plateaued. The magnitude of this improvement in heat transfer depended on the material's thermal properties. We observed three regimes: a conduction‐dominated regime where particles heated quickly but with an annular temperature gradient, a granular convection‐dominated regime where particles heated slowly but uniformly, and an intermediate regime. The results were nondimensionalized to enable predictions and help inform drying protocols.

Infrared temperature measurements and DEM simulations of heat transfer in a bladed mixer

“…The heat transfer equations were added to EDEM ® to allow for both the position and temperature of particles to be tracked. Relevant parameter values for the model were taken from previous study 28,29 as shown in Table 3. The chosen values for the material properties correspond to the physical properties of glass beads, except for the Young's modulus.…”

“…The advantage of the model we have implemented is it has only one unknown thermal parameter (the thermal conductivity), is not computationally expensive, and shows simple scaling behavior in terms of particle and process parameters. 28,29 As understanding improves, we hope to incorporate additional elements of heat transfer into the model. An additional motivation for not including additional complexities into the model is that additional complexities lead to additional parameters and in many cases these parameter values are not known.…”

“…This phenomenon was also observed and discussed in previous study. 29 Hot particles heated by the bottom wall were lifted to the surface by the impeller blades. At the same time,…”

“…One way of accounting for disparities in the starting temperature of each experiment is to normalize the temperature of the bed by the initial temperature difference between the material and the jacket. To analyze the data, it is useful to carry out a heat balance 36 between the wall and the bed of particles 29 :…”

“…The input value of the thermal conductivity, k for our simulations was calibrated from one experiment (the case with the agitation rate of 25 rpm) by using a dimensionless scaling developed in previous study. 29 Figure 10 illustrates the procedure to calibrate k. Previously, in Hartmanshenn et al, 29 we had identified a dimensionless scaling based on the heating time of the bed, the agitation rate, and the thermal properties of the material, that enabled us to collapse simulation data together and obtain an equation to relate these variables. We can use the same approach in this work and define three timescales used in the dimensionless scaling.…”