During twin screw granulation (TSG), small particles, which generally have irregular shapes, agglomerate together to form larger granules with improved properties. However, how particle shape impacts the conveying characteristics during TSG is not explored nor well understood. In this study, a graphic processor units (GPUs) enhanced discrete element method (DEM) is adopted to examine the effect of particle shape on the conveying characteristics in a full scale twin screw granulator for the first time. It is found that TSG with spherical particles has the smallest particle retention number, mean residence time, and power consumption; while for TSG with hexagonal prism (Hexp) shaped particles the largest particle retention number is obtained, and TSG with cubic particles requires the highest power consumption. Furthermore, spherical particles exhibit a flow pattern closer to an ideal plug flow, while cubic particles present a flow pattern approaching a perfect mixing. It is demonstrated that the GPU-enhanced DEM is capable of simulating the complex TSG process in a full-scale twin screw granulator with non-spherical particles.
Rotary tabletting presses are widely used to produce tablets in the pharmaceutical industry. In the tabletting process using a rotary press, rotary die filling is one of critical process steps, as powder behaviour during die filling dictates the quality of final products, such as dosage and weight variations. It is hence of importance to understand powder flow behaviour during rotary die filling. The purpose of this study is to identify the critical process parameters and material attributes that determine the die filling performance. For this purpose, a model rotary die filling system with a paddle feeder was constructed to mimic the powder feeding process in a typical rotary press. Using this model system, the effects of turret speed and paddle speed on die filling behaviour were investigated. Three grades of microcrystalline cellulose powders were considered. It was found that the turret speed has a more pivotal role in controlling the die filling performance than the paddle speed. In addition, it is demonstrated that powder flowability has a great impact on the fill weight variation, and a higher weight variation is induced for the powders with poorer flowability.
The presence of liquids in particulate materials can have a significant effect on their bulk behaviour during processing and handling. It is well recognised that the bulk behaviour of particulate materials is dominated by the interactions between particles. Therefore, a thorough understanding of particle-particle interaction with the presence of liquids is critical in unravelling complex mechanics and physics of wet particulate materials. In the current study, a discrete element method for wet particulate systems was developed, in which a contact model for interactions with pendular liquid bridges between particles of different sizes was implemented. In order to evaluate the accuracy and robustness of the developed DEM, normal elastic impacts of wet particles with a wall were systematically analysed. It was shown that the DEM simulations can accurately reproduce the experimental observations reported in the literature. In addition, the DEM analysis was also in good agreement with the elastohydrodynamic model. It was further demonstrated that the rebound behaviour of wet particles is dominated by the Stokes number. There was a critical Stokes number, below which the particle will stick with the wall. For impacts with a Stokes number higher than the critical Stokes number, the coefficient of restitution increases as the stokes number increases for elastic particles. It was also found that the contact angle and surface tension played an insignificant role in the normal impact of wet particles, while the viscosity of the liquid has a dominant effect on the rebound behaviour.
Diffusion-induced swelling or shrinkage of particles is ubiquitous in many industrial processes and nature. Aiming to rigorously model these deformable particles, a microscopic model that considers the microstructural evolution of individual particles is developed for the first time and implemented into the discrete element method (DEM), which is experimentally validated. The robustness of this model is also evaluated by comparing its performance with a macroscopic diffusion-induced swelling model and a phenomenological swelling model. The swelling behaviours of a single particle and particle beds of various configurations in water are then analysed. It is shown that the microscopic swelling model and the phenomenological swelling model can better describe the swellings of single particle and particle beds than the macroscopic swelling model. Moreover, the microscopic swelling model can not only reproduce the phenomena of volume expansion of particles but also well predict the microstructural evolution of individual particles, as observed experimentally. Furthermore, the microscopic swelling model is capable of describing the shrinkage processes and the particle-particle diffusions of swellable particles. It is hence demonstrated that DEM with the microscopic swelling model, which captures the microscopic physical mechanisms of particle swelling and the microstructural change of swelling particles, could be a useful tool for modelling swellable granular materials in various industrial processes.
As one of critical process steps during pharmaceutical tabletting, rotary die filling is still not well understood. To address this issue, a model rotary die filling system with a paddle feeder was developed to closely mimic the industrial process. Using this model system, the performance of various pharmaceutical powders at different turret and paddle speeds was evaluated, and the dependence of fill variation on process conditions and material properties was examined. A comprehensive dataset was created and reported here to show the effects of material and process parameters on the die filling performance and the filling consistency. It is believed that the data can also be used for data-driven process modelling and for developing robust machine learning models for pharmaceutical manufacturing.
Twin screw granulation (TSG) is increasingly used to produce granules in various industries, such as food, pharmaceutical, and fine chemicals. However, there is a large parametric space in terms of screw designs, formulation properties and operating conditions, so how to maximise the production throughput while maintaining consistent product quality is not a trivial task and still needs further investigation. In this study, the TSG process was systematically analysed using a discrete element method (DEM) based on the graphics processor unit (GPU) architectures that can provide not only macroscopic information but also microscopic insights into the complicated TSG process. In particular, the particle flow profiles and residence time distributions were obtained from the simulations and analysed in details. The effects of particle size and screw speed on flow behaviour of particles in TSG were also explored. It was shown that the mean residence time and its variance in the granulator decreased with increasing particle size and screw speed. The E-curves of the residence time with larger particle size at higher screw speed had a narrower spread, implying that particles with a larger size had similar residence time in the twin screw granulator. In addition, the cumulative distribution function, the F-curves, showed a higher increasing rate for larger particles and higher screw speeds, indicating a faster conveying efficiency. (C) 2020 Elsevier B.V. All rights reserved.