(418p) Experiments and Numerical Modeling of An Aqueous Film Coating Process In a Pan Coater

To better understand the influence of processing parameters on coating variability by experimentally validated first principles model. Lactose non-pareils (3.3-3.5 mm) are used as the model compound to perform coating experiments at previously determined optimal mixing conditions. The non-pareils are coated upto 6% (~250 µm) theoretical weight gain using Black Opadry II. Discrete element based numerical model is developed to study the effect of granular mixing and coating in a pan coater where the post processing data from simulations is used to calculate the residence time distribution which in turn predicts the effect of process variables on coating variability. Moreover, numerical modeling of spray dynamics is performed for the same operational parameter set and similar spray characteristics (center and the radius of the spray zone) used in the experiments.  The coating uniformity is estimated for three levels of pan (tilt, rotational speed and fill load) and spray (flow rate and coating fluid concentration) variables by calculating the change in diameter of the non-pareils using a vernier caliper. A balance between the particle motion and the rate of spray application is maintained to achieve a minimum variability in coating. The mass distribution of the coated particles is quantified in the numerical model by the total number of particles passing through the spray zone and the frequency distribution of the residence time of the particles. The coating variability decreased with the increase in pan tilt and an optimum pan speed. Pan fill level does not seem to have much effect under the conditions observed. Further, increase in the spray concentration and flow rate at high tilt and optimum speed decreased the variability. Simulations show good agreement with the experiments regarding the effect of orientation (tilt) of the pan coater on coating variability. However, the simulations over predicted the effect of speed as compared to the experiments to obtain the minimum coating variability. The coating variability is calculated for various processing parameters. A priori performance predictive model is developed which provides a better understanding of the particle dynamics and hence the factors affecting coating uniformity.