(205c) A Model-Based Design of Experiment (MB-DOE) Approach Towards Scale-up of High Shear Wet Granulation Operation
In this work, a predictive data driven hybrid process model of the HSWG operation has been developed by combining all the available correlations among dimensionless numbers and relationships between other relevant geometric, quality and operational parameters, such that the effect of all the CPPs on the granule attributes can be quantified.
This process model has been further implemented within an optimization framework for designing experiments such that the space covered by the output variables (i.e., granule quality attributes) is maximized, while obtaining an orthogonal bracketing study in the process input parameters, within the desired design space. The resulting problem is a multi-objective optimization that seeks to fulfill the lower level problem, which solves the hybrid process model, while maximizing the optimality criterion written in the upper problem .
This work will illustrate a model-based DOE approach where appropriate experiments have been designed for the initial ranging study at the pilot scale granulator based on the observations made at the small-scale granulator. This also aided in identifying the range of operating parameters at the pilot scale such that the granulation has properties in the desired space.
In addition, the hybrid process model, implemented within the optimizer has been further used to check that the proposed operating conditions for the very first commercial scale study will yield granulation with property in the desired space, prior to the actual experimental run.
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