Literature DB >> 30339868

Computational Fluid Dynamics-Discrete Element Method Modeling of an Industrial-Scale Wurster Coater.

Peter Böhling1, Johannes G Khinast2, Dalibor Jajcevic3, Conrad Davies4, Alan Carmody4, Pankaj Doshi5, Mary T Am Ende5, Avik Sarkar5.   

Abstract

Large-scale fluid bed coating operations using Wurster coaters are common in the pharmaceutical industry. Experimental measurements of the coating thickness are usually analyzed for just few particles. To better predict the coating uniformity of the entire batch, computational techniques can be applied for process understanding of the key process parameters that influence the quality attributes. Recent advances in computational hardware, such as graphics processing unit, have enabled simulations of large industrial-scale systems. In this work, we perform coupled computational fluid dynamics-discrete element method simulations of a large-scale coater that model the actual particle sizes. The influence of process parameters, inlet air flow rate, atomizing air flow rate, bead size distribution, and Wurster gap height is studied. The focus of this study is to characterize the flow inside the coater; eventually, this information will be used to predict the coating uniformity of the beads. We report the residence time distribution of the beads inside the Wurster column, that is, the active coating zone, which serves as a proxy for the amount of coating received by the beads per pass. The residence time provides qualitative and quantitative measurements of the particle-coating uniformity. We find that inlet air flow rate has the largest impact on the flow behavior and, hence, the coating uniformity.
Copyright © 2019 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Keywords:  coating; fluid bed; mathematical model(s); powder technology(s); residence time(s)

Mesh:

Year:  2018        PMID: 30339868     DOI: 10.1016/j.xphs.2018.10.016

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  4 in total

Review 1.  Developing HME-Based Drug Products Using Emerging Science: a Fast-Track Roadmap from Concept to Clinical Batch.

Authors:  Josip Matić; Amrit Paudel; Hannes Bauer; Raymar Andreina Lara Garcia; Kinga Biedrzycka; Johannes G Khinast
Journal:  AAPS PharmSciTech       Date:  2020-06-22       Impact factor: 3.246

2.  Particle-level residence time data in a twin-screw feeder.

Authors:  Peter Toson; Johannes G Khinast
Journal:  Data Brief       Date:  2019-10-18

3.  Validating a Numerical Simulation of the ConsiGma(R) Coater.

Authors:  Peter Boehling; Dalibor Jacevic; Frederik Detobel; James Holman; Laura Wareham; Matthew Metzger; Johannes G Khinast
Journal:  AAPS PharmSciTech       Date:  2020-11-26       Impact factor: 3.246

Review 4.  Direct Compaction Drug Product Process Modeling.

Authors:  Alexander Russell; John Strong; Sean Garner; William Ketterhagen; Michelle Long; Maxx Capece
Journal:  AAPS PharmSciTech       Date:  2022-01-31       Impact factor: 3.246

  4 in total

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