A multi-scale, mechanistic model of a wet granulation process using a novel bi-directional PBM–DEM coupling algorithm

Barrasso, Dana; Eppinger, Thomas; Pereira, Frances E.; Aglave, Ravindra; Debus, Kristian; Bermingham, Sean K.; Ramachandran, Rohit

doi:10.1016/j.ces.2014.11.011

Cited by 70 publications

(34 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the DEM simulation is performed for an industrial blender, the complex shape boundary must be modeled precisely. In previous studies [28,29], meshes were employed to create the wall boundary of the complexly shaped device. The meshes have commonly been used in simulations employing the finite element method and finite volume method.…”

Section: Introductionmentioning

confidence: 99%

Discrete element simulation for the evaluation of solid mixing in an industrial blender

Sakai

Shigeto

Basinskas

et al. 2015

Chemical Engineering Journal

105

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Discrete element simulation for the evaluation of solid mixing in an industrial blender

Sakai

Shigeto

Basinskas

et al. 2015

Chemical Engineering Journal

105

View full text Add to dashboard Cite

“…In previous TSG modelling studies, compartmental residence times have been estimated through the use of DEM [48] and chemical imaging techniques [49,50,15]. In the case where DEM has been used, the PBM and DEM solvers are coupled, allowing collision statistics [51] and residence time estimation to be made, however, the significant computational In order to create a framework for the prediction of residence times, information from a number of existing twin-screw material flow studies must be 255 considered. A number of experimental studies have examined the mass distribution over the twin-screw system, through the use of Positron Emission Particle Tracking (PEPT) [53,54].…”

Section: Residence Time Estimationmentioning

confidence: 99%

“…This has generally been attempted through the use of compartmental population balance models (PBM) [12]. Several examples of compartmental twin-screw PBMs exist in the literature [13,14,15, 16,17]. In these examples, the screw barrel domain is modelled as a number of connected 40 compartments that permit process conditions and thus particle morphology to vary along the length of the simulation domain.…”

mentioning

confidence: 99%

Analysing the effect of screw configuration using a stochastic twin-screw granulation model

McGuire

Mosbach

Reynolds

et al. 2019

Chemical Engineering Science

View full text Add to dashboard Cite

In this work, a framework for modelling twin-screw granulation processes with variable screw configurations using a high-dimensional stochastic population balance method is presented. A modular compartmental approach is presented and a method for estimating residence times for model compartments based on screw element geometry is introduced. The model includes particle mechanisms for nucleation, primary particle layering, coalescence, breakage, and consolidation. A new twin-screw breakage model is introduced, which takes into account the differing breakage dynamics between two types of screw element.Additionally, a new sub-model for the layering of primary particles onto larger agglomerates is presented. The resulting model is used to simulate a twin-screw system with a number of different screw configurations and the predictive power of the model is assessed through comparison with an existing experimental data set in the literature. For most of the screw configurations simulated, the model predicts the product particle size distribution at large particle sizes with a reasonable degree of accuracy. However, the model has a tendency to over-predict the amount of fines in the final product. Nevertheless, the model qualitatively captures the reduction in fines associated with an increase in the number of kneading elements, as observed experimentally. Based on model results, a number of key areas for future model development are identified and discussed. 35 may be quickly assessed without the usage of excipient/API or the need to set-up the device etc. This has generally been attempted through the use of compartmental population balance models (PBM) [12]. Several examples of compartmental twin-screw PBMs exist in the literature [13,14,15, 16,17]. In these examples, the screw barrel domain is modelled as a number of connected 40 compartments that permit process conditions and thus particle morphology to vary along the length of the simulation domain. These examples have used a sectional solution approach [18,19,20] which allows the compartmental PBM to be approximated and solved as a system of ordinary differential equations.This numerical approach generally limits the particle representation to taken 45 3 on three dimensions at most. The Stochastic particle method [21,22,23, 24,25,26,27,28] is alternative approach that has been employed to solve PBMs for batch granulation systems [29, 30,31,32,33,34,35,36, 37], silica [38] and TiO 2 [39] nano-particle synthesis, soot formation [40,41], and more recently twin-screw granulation [42, 43]. Unlike sectional methods, stochastic particle 50 methods permit much more complex particle representations, which can then be leveraged within the process model description, whilst still yielding a numerical problem that can be solved with acceptable levels of computational effort.The main aims of this paper are:1. Improve the stochastic TSG model in McGuire et al. [42, 43] based on the 55 areas identified for improvement. 2. Construct a modelling framework that allows for the compar...

show abstract

“…In hot-melt extrusion, influences between some modeled material properties of this section can be observed, and recent works study a coupling PBM-DEM to create an accurate modeling [119,121,[131][132][133][134][135]. Figure 23 presents an example (see [136]) of a good combination between these two methods to determine particle physical characteristics.…”

Section: Population Balance Modeling and Discrete Element Modelingmentioning

confidence: 99%

“…PBM are used in various areas, such as crystallization [113], powder mixing [114], powder milling [115] and wet granulation [116,117]. In extrusion processes, this method can be employed to compute different material properties, such as the active pharmaceutical ingredient (API) or excipient component concentration [118], the RTD [119], the particle size distribution, the liquid content and the porosity [120,121]. DEM is based on a different approach where each particle is considered as an individual entity [122][123][124][125][126][127].…”

Section: Population Balance Modeling and Discrete Element Modelingmentioning

confidence: 99%

A Review of Dynamic Models of Hot-Melt Extrusion

2016

View full text Add to dashboard Cite

Hot-melt extrusion is commonly applied for forming products, ranging from metals to plastics, rubber and clay composites. It is also increasingly used for the production of pharmaceuticals, such as granules, pellets and tablets. In this context, mathematical modeling plays an important role to determine the best process operating conditions, but also to possibly develop software sensors or controllers. The early models were essentially black-box and relied on the measurement of the residence time distribution. Current models involve mass, energy and momentum balances and consists of (partial) differential equations. This paper presents a literature review of a range of existing models. A common case study is considered to illustrate the predictive capability of the main candidate models, programmed in a simulation environment (e.g., MATLAB). Finally, a comprehensive distributed parameter model capturing the main phenomena is proposed.

show abstract

A multi-scale, mechanistic model of a wet granulation process using a novel bi-directional PBM–DEM coupling algorithm

Cited by 70 publications

References 39 publications

Discrete element simulation for the evaluation of solid mixing in an industrial blender

Discrete element simulation for the evaluation of solid mixing in an industrial blender

Analysing the effect of screw configuration using a stochastic twin-screw granulation model

A Review of Dynamic Models of Hot-Melt Extrusion

Contact Info

Product

Resources

About