(333a) Development of Multi-Dimensional Population Balance Using a Breakage Mode Classification Kernel for the Prediction of Milled Granule Quality Attributes
AIChE Annual Meeting
Tuesday, November 15, 2022 - 12:30pm to 12:50pm
Breakage modes describe the granule breakage behavior and are dependent on the granule properties and the force applied to it. Particle breakage in a milling process such as the Comil is known to undergo impact and attrition mode. Each mode can occur individually or simultaneously during the mill operation, which is attributed to the process conditions and material properties. Materials with lower porosity undergo impact-attrition mode throughout the breakage process, whereas materials with higher porosity undergo impact mode in the beginning and transition into impact-attrition breakage mode as the mill approached steady-state . Current PBMs simulate the milling process by calculating breakage kernels using only one type of breakage mode which limits their applicability. Different breakage modes within the mill can alter the final granule quality attributes. Thus, it is essential for the model to be able to incorporate different breakage modes and calculate breakage kernels based on the classification between these modes based on the process conditions and material properties.
In this study, a novel multi-component population balance model (PBM) is being developed that incorporates the classification of breakage modes into its breakage kernel. The model can predict PSD, porosity, and content uniformity. The breakage kernels in the PBM account for interactions between multiple solid components (e.g., API and Excipient) and gas volume. The kernel equations are a function of impeller speed and material properties, such as particle size and porosity. A classification kernel is being used to distinguish between the different breakage modes that the mill undergoes, giving the model the ability to simulate different CQAs produced at different breakage modes. Experimental data collected will be used to optimize the estimated parameters in the proposed kernels and validate the model results. The proposed PBM model will accurately predict an increase in the average porosity within each size class and an improved content uniformity post mill, where super-potency with respect to the API in the fines region is diminished. The model also accurately predicts a bimodal PSD for materials with lower porosity and a unimodal distribution for materials with higher porosity.
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