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Twin-Screw Extrusion Process to Produce Renewable Fiberboards
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Model driven design for twin screw granulation using mechanistic-based population balance model.

Li Ge Wang1, John P Morrissey2, Dana Barrasso3

  • 1Department of Chemical and Biological Engineering, University of Sheffield, UK.

International Journal of Pharmaceutics
|July 26, 2021
PubMed
Summary

This study introduces a Model Driven Design (MDD) framework for twin screw granulation using a population balance model (PBM). The approach simplifies parameter fitting, reducing experiments needed for model calibration and validation.

Keywords:
Model driven designModel validationParameter estimationPopulation balance modelSensitivity analysisTwin screw granulation

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Area of Science:

  • Chemical Engineering
  • Process Modeling
  • Particulate Technology

Background:

  • Population Balance Models (PBMs) are crucial for simulating granulation processes.
  • Mechanistic-based PBMs require accurate parameterization, which can be complex.
  • Model Driven Design (MDD) offers a structured approach to process modeling.

Purpose of the Study:

  • To present a generic Model Driven Design (MDD) framework for particulate processes.
  • To apply this framework to a twin screw granulation process using a mechanistic PBM.
  • To streamline the parameter estimation and validation of granulation models.

Main Methods:

  • Developed a mechanistic-based Population Balance Model (PBM) incorporating nucleation, breakage, layering, and consolidation kernels.
  • Performed sensitivity analysis on PBM parameters to identify significant factors like liquid to solid ratio (L/S ratio).
  • Utilized the MDD framework for model calibration, verification, and validation with a reduced number of experiments.

Main Results:

  • Sensitivity analysis identified key parameters, significantly reducing the number of fitting parameters for the PBM.
  • Only nine granulation experiments were necessary for model calibration and validation.
  • A model validation flowchart was proposed to track kinetic rate parameter evolution.

Conclusions:

  • The MDD framework effectively simplifies the application and validation of mechanistic PBMs for granulation.
  • The methodology reduces experimental burden for model development.
  • The presented framework is generalizable to various particulate processes.