Dataset as a basis for process modeling of twin-screw wet granulation: A parametric study of residence time distributions and granulation kinetics

  • Timo Plath (Creator)
  • Rakulan Sivanesapillai (Research team member)
  • Carolin Korte (Research team member)
  • Thomas Weinhart (Supervisor)

Dataset

Description

Twin-screw wet granulation is a crucial unit operation in shifting from pharmaceutical batch to continuous processes, but granulation kinetics as well as residence times are yet poorly understood. Experimental findings are highly dependent on screw configuration as well as formulation and therefore have limited universal validity. In the study underlying this dataset, an experimental design with a repetitive screw setup was conducted to measure the effect of specific feed load (SFL), liquid-to-solid ratio (L/S) and inclusion of a distributive feed screw on particle size distribution (PSD) and shape as well as residence time distribution of a hydrophilic Lactose/MCC based formulation. An intermediate sampling point was obtained by changing inlet ports along the screw axis. Camera-based particle size analysis (QICPIC) indicated no significant change of PSD between the first and second kneading section except for low L/S and low SFL where fines increase. Mean residence time was approximated as a bilinear fit of L/S and SFL. Moreover, large mass flow pulsations were observed by continuous camera measurements of residence time distribution and correlated to hold-up of the twin-screw granulator. These findings indicate fast granulation kinetics and process instabilities for high mean residence times, questioning current standards of two kneading compartments for wet granulation. The present study further underlines the necessity of developing a multiscale simulation approach including particle dynamics in the future. This dataset should function as a basis for process modeling of twin-screw wet granulation as fully resolved residence time distributions and PSDs can be directly extracted and applied for validation and calibration. Furthermore it contains fully working copies of python tools to analyze the data, verify integrity and make this dataset reusable according to the FAIR principles.
Date made available23 Apr 2021
Publisher4TU.Centre for Research Data
Date of data production15 Jul 2020 - 8 Dec 2020

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