Abstract
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.
Highlights
Introduction and Background the pharmaceutical industries must satisfy strict production speci cation norms imposed by regulatory authorities, mainly due to inefficient control strategies [1, 2] and the nonpredictive effects of input parameters, the nal products obtained are o en nonuniform with a high level of variability with respect to product quality [3]
population balance model (PBM) interacts with discrete element modeling (DEM) via one-way coupling which forms a basic framework for hybrid modeling. e results 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
In a DEM simulation, each particle is assigned a unique number known as the particle ID. ese data were used to obtain the relative standard deviation (RSD) as a function of blender length and time, rate of out ow, component A composition at discharge, and residence time distribution (RTD)
Summary
The pharmaceutical industries must satisfy strict production speci cation norms imposed by regulatory authorities, mainly due to inefficient control strategies [1, 2] and the nonpredictive effects of input parameters, the nal products obtained are o en nonuniform with a high level of variability with respect to product quality [3]. Ese particle level information can be fed to the PBM from which the macroscopic variables (RSD, RTD, blend composition, etc.) affecting the entire unit operation (mixing) can be extracted In this way the model incorporates multiscale information and illustrates one-way coupling where DEM provides the velocity information and is combined with the PBM which simulates key blend attributes as a function of time and applies the microscopic properties from particle level in order to capture macroscopic properties which characterize the mixing performance of the entire blender. GPROMs (tm) is a robust and fast equation-oriented [38, 39] so ware package which allows both steady state and dynamic simulation runs. e process model can be built by developing fundamental mathematical expressions relating various physical and chemical variables/parameters without specifying the order in which these equations need to be solved. e motivation behind this work is to present a more dynamic system which updates the particle properties at regular interval of time and generates the CQAs taking information from DEM, that can be simulated in a realistic time period to facilitate design, control, and optimization
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