This paper describes the development of a multidimensional population balance model (PBM) which can account for the dynamics of a continuous powder mixing/blending process. The PBM can incorporate the important design and process conditions and determine their effects on the various critical quality attributes (CQAs) accordingly. The important parameters considered in this study are blender dimensions and presence of noise in the inlet streams. The blender dynamics have been captured in terms of composition of the ingredients, (relative standard deviation) RSD, and (residence time distribution) RTD. PBM interacts with discrete element modeling (DEM) via one-way coupling which forms a basic framework for hybrid modeling. The results thus obtained have been compared against a full DEM simulation which is a more fundamental particle-level model that elucidates the dynamics of the mixing process. Results show good qualitative agreement which lends credence to the use of coupled PBM as an effective tool in control and optimization of mixing process due to its relatively fewer computational requirements compared to DEM. 1. Introduction and Background Although the pharmaceutical industries must satisfy strict production specification norms imposed by regulatory authorities, mainly due to inefficient control strategies [1, 2] and the nonpredictive effects of input parameters, the final products obtained are often nonuniform with a high level of variability with respect to product quality [3]. Moreover, the behavior of powder processing units are not well characterized as compared to the fluid processing units due to the absence of set of governing equations derived from the first principles which can describe granular flow under specific conditions. The interactions of the particles with surrounding particles, fluid, or equipment wall is quite complex to understand, model and manage. Bulk material behavior is decided by the interactions among individual particles at microscale, which is chaotic. Hence often the pharmaceutical industries have to follow a univariate trial and error approach for their process development. However efforts are being made in order to introduce science-based holistic development of process and product by using Quality by Design (QbD) and Process Analytical Technology (PAT) tools [4, 5]. Continuous manufacturing offers many advantages such as better process understanding and control. Several other chemical industries (e.g., Petroleum Refineries, Petrochemicals and Food) have adapted state of the art simulation techniques and satisfy
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