Bellinghausen, S., Gavi, E., Jerke, L. et al. (3 more authors) (2022) Model-driven design using population balance modelling for high-shear wet granulation. Powder Technology, 396 (Part A). pp. 578-595. ISSN 0032-5910
Abstract
Model-driven design approaches have great potential to improve current engineering workflows for wet granulation and other particulate processes. The key to model-driven design is a predictive process model. In this paper, a novel predictive model is proposed for high-shear wet granulation using a one-dimensional population balance modelling framework. The wet granulation mechanisms are represented by rate expressions which are based on mechanistic understanding. Material characterisation tests and granulation experiments are designed to verify critical modelling assumptions and determine the modelling parameters. Based on the Sobol’ indices results from a parameter sensitivity analysis, the impactful parameters to estimate are identified: critical pore saturation, and coefficients for consolidation, collision and breakage. Only impactful parameters that cannot be measured are estimated to reduce the experimental effort and improve the model's predictive power. Lab-scale experiments are designed to estimate parameters individually before fine-tuning the results. The model is assessed using a novel model validation workflow, which is based on predictions of experiments at four different scales from lab scale to pilot plant: 2 L to 70 L.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier B.V |
Keywords: | Model-driven design; Predictive modelling; Scale-up; High-shear wet granulation; Population balance modelling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Nov 2022 14:22 |
Last Modified: | 15 Nov 2022 14:22 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.powtec.2021.10.028 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193290 |