Validation of fluid bed granulation utilizing artificial neural network

Behzadi, Sharareh Salar; Klocker, Johanna; Hüttlin, h. c. Herbert; Wolschann, Peter; Viernstein, Helmut

doi:10.1016/j.ijpharm.2004.07.051

Cited by 34 publications

(5 citation statements)

References 20 publications

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“…Larger granules were obtained when lower atomization pressure was used in the process. The same results are recorded in earlier studies [9–12]. But, unexpectedly, granules flowability was not improved with decrease in the atomization pressure.…”

Section: Resultssupporting

confidence: 89%

“…It has been shown that increase in the atomization pressure leads to decrease in the binder droplet size [7, 10]. In most studies [9–12], granules size increased with decrease in atomization pressure. Small-size granules were obtained when high air flow rate was used, because of the more intensive breakage and faster evaporation of binder solution [7, 11–13].…”

Section: Introductionmentioning

confidence: 99%

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Design Space Approach in Optimization of Fluid Bed Granulation and Tablets Compression Process

Djuriš

Medarević

Krstić

et al. 2012

The Scientific World Journal

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The aim of this study was to optimize fluid bed granulation and tablets compression processes using design space approach. Type of diluent, binder concentration, temperature during mixing, granulation and drying, spray rate, and atomization pressure were recognized as critical formulation and process parameters. They were varied in the first set of experiments in order to estimate their influences on critical quality attributes, that is, granules characteristics (size distribution, flowability, bulk density, tapped density, Carr's index, Hausner's ratio, and moisture content) using Plackett-Burman experimental design. Type of diluent and atomization pressure were selected as the most important parameters. In the second set of experiments, design space for process parameters (atomization pressure and compression force) and its influence on tablets characteristics was developed. Percent of paracetamol released and tablets hardness were determined as critical quality attributes. Artificial neural networks (ANNs) were applied in order to determine design space. ANNs models showed that atomization pressure influences mostly on the dissolution profile, whereas compression force affects mainly the tablets hardness. Based on the obtained ANNs models, it is possible to predict tablet hardness and paracetamol release profile for any combination of analyzed factors.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Design Space Approach in Optimization of Fluid Bed Granulation and Tablets Compression Process

Djuriš

Medarević

Krstić

et al. 2012

The Scientific World Journal

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“…Increasing the fluidising air velocity can result in more breakage of the agglomerates which manifests as reduction in the growth rate [9,10] or a reduction in average granule size [38][39][40]. It has been reported in literature that breakage of particles during the granulation process promotes material exchange between the granules which improves product homogeneity [15,16].…”

Section: Effect Of Process Variables On Granule Homogeneitymentioning

confidence: 99%

Investigation of influence of process variables on mechanical strength, size and homogeneity of pharmaceutical granules produced by fluidised hot melt granulation

Mangwandi¹,

Zainal

Liu³

et al. 2015

Powder Technology

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Publisher rights This is the author's version of a work that was accepted for publication in Powder Technology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Powder Technology, vol 272, issue March 2015 doi: 10.1016/j.powtec.2014.11.042. General rightsCopyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact openaccess@qub.ac.uk. The overall aim of the project was to study the influence of process variables on the 36 distribution of a model active pharmaceutical ingredient (API) during fluidised melt 37 granulation of pharmaceutical granules with a view of optimising product characteristics. 38Granules were produced using common pharmaceutical excipients; lactose monohydrate 39 using poly ethylene glycol (PEG1500) as a meltable binder. Methylene blue was used as a

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“…These agglomerates may have a size, porosity and liquid distribution that allow them to be utilised within the pharmaceutical and food industries, among others [1]. The granulation process is typically modelled mechanistically using population balance models (PBM) [2] (though non-mechanistic pure neural network approaches have also been investigated [3,4]). Using PBMs, the particle ensemble is transformed through processes such as nucleation, coagulation, breakage, consolidation, layering and wetting.…”

Section: Introductionmentioning

confidence: 99%

A high-dimensional, stochastic model for twin-screw granulation Part 2: Numerical methodology

McGuire

Mosbach

Lee

et al. 2018

Chemical Engineering Science

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In the second part of this study, we present the stochastic weighted particle population balance framework used to solve the twin-screw granulation model detailed in the first part of this study. Each stochastic jump process is presented in detail, including a new nucleation jump event capable of capturing the immersion nucleation processes in twin-screw granulation. A variable weighted inception algorithm is presented and demonstrated to reduce the computational cost of simulations by up to two orders of magnitude over traditional approaches. The relationship between the performance of the simulation algorithm and key numerical parameters within the nucleation jump process are explored and optimum are operating conditions identified. Finally, convergence studies on the complete simulation algorithm demonstrate that the algorithm is very robust against changes in the number of stochastic particles used, provided that the number of particles exceeds a minimum required for numerical stability.

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Validation of fluid bed granulation utilizing artificial neural network

Cited by 34 publications

References 20 publications

Design Space Approach in Optimization of Fluid Bed Granulation and Tablets Compression Process

Design Space Approach in Optimization of Fluid Bed Granulation and Tablets Compression Process

Investigation of influence of process variables on mechanical strength, size and homogeneity of pharmaceutical granules produced by fluidised hot melt granulation

A high-dimensional, stochastic model for twin-screw granulation Part 2: Numerical methodology

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