An experimentally validated DEM study of powder mixing in a paddle blade mixer

Advances in numerical simulation of unit operations for tablet preparation

Recently, there has been an increase in the use of numerical simulation technology in pharmaceutical preparation processes. Numerical simulation can contribute to a better understanding of processes, reduce experimental costs, optimize preparation processes, and improve product quality. The intermediate material of most dosage forms is powder or granules, especially in the case of solid preparations. The macroscopic behavior of particle materials is controlled by the interactions of individual particles with each other and surrounding fluids. Therefore, it is very important to analyze and control the microscopic details of the preparation process for solid preparations. Since tablets are one of the most widely used oral solid preparations, and the preparation process is relatively complex and involves numerous units of operation, it is especially important to analyze and control the tablet production process. The present paper discusses recent advances in numerical simulation technology for the preparation of tablets, including drying, mixing, granulation, tableting, and coating. It covers computational fluid dynamics (CFD), discrete element method (DEM), population balance model (PBM), finite element method (FEM), Lattice-Boltzmann model (LBM), and Monte Carlo model (MC). The application and deficiencies of these models in tablet preparation unit operations are discussed. Furthermore, the paper provides a systematic reference for the control and analysis of the tablet preparation process and provides insight into the future direction of numerical simulation technology in the pharmaceutical industry.

Parametric Analysis of a Double Shaft, Batch-Type Paddle Mixer Using the Discrete Element Method (DEM)

To improve the understanding of the mixing performance of double shaft, batch-type paddle mixers, the discrete element method (DEM) in combination with a Plackett–Burman design of experiments simulation plan is used to identify factor significance on the system’s mixing performance. Effects of several factors, including three material properties (particle size, particle density and composition), three operational conditions (initial filling pattern, fill level and impeller rotational speed) and three geometric parameters (paddle size, paddle angle and paddle number), were quantitatively investigated using the relative standard deviation (RSD). Four key performance indicators (KPIs), namely the mixing quality, mixing time, average mixing power and energy required to reach a steady state, were defined to evaluate the performance of the double paddle mixer. The results show that the material property effects are not as significant as those of the operational conditions and geometric parameters. In particular, the geometric parameters were observed to significantly influence the energy consumption, while not affecting the mixing quality and mixing time, showing their potential towards designing more sustainable mixers. Furthermore, the analysis of granular temperature revealed that the centre area between the two paddles has a high diffusivity, which can be correlated to the mixing time.

Effect of the Mixer Design Parameters on the Performance of a Twin Paddle Blender: A DEM Study

The design parameters of a mixing system have a major impact on the quality of the final product. Therefore, identifying the optimum parameters of mixing systems is highly relevant to various industrial processes dealing with particulate flows. However, the studies on the influences of the mixer’s design features are still insufficient. In this study, the Discrete Element Method (DEM) is used to examine the impact of paddle angle, width, and gap on the mixing performance of a twin paddle blender. The mixing performance and particle flow are assessed using the relative standard deviation (RSD) mixing index, velocity field, diffusivity coefficient, granular temperature, the force acting on particles, and the mixer’s power consumption. The mixing performance is highest for a paddle angle of 0° at the cost of the highest forces acting on particles. The paddle width is indicated as a critical factor for achieving better mixing quality. In contrast, the powder mixing efficiency and the mixer’s power consumption are not significantly affected by the paddle gap. The results regarding the power consumption denote that the mixer using the paddle angle of 60° has the minimum power consumption. Moreover, increasing the paddle width results in the enhancement of the mixer’s power consumption.

Control Strategy for Excipient Variability in the Quality by Design Approach Using Statistical Analysis and Predictive Model: Effect of Microcrystalline Cellulose Variability on Design Space

Although various quality by design (QbD) approaches have been used to establish a design space to obtain robust drug formulation and process parameters, the effect of excipient variability on the design space and drug product quality is unclear. In this study, the effect of microcrystalline cellulose (MCC) variability on drug product quality was examined using a design space for immediate-release tablets of amlodipine besylate. MCC variability was assessed by altering the manufacturer and grade. The formulation was developed by employing the QbD approach, which was optimized using a D-optimal mixture design. Using 36 different MCCs, the effect of MCC variability on the design space was assessed. The design space was shifted by different manufacturers and grades of MCC, which resulted in associations between the physicochemical properties of MCC and critical quality attributes (CQAs). The correlation between the physicochemical properties of MCCs and CQAs was assessed through a statistical analysis. A predictive model correlating the physicochemical properties of MCCs with dissolution was established using an artificial neural network (ANN). The ANN model accurately predicted dissolution with low absolute and relative errors. The present study described a comprehensive QbD approach, statistical analysis, and ANN to comprehend and manage the effect of excipient variability on the design space.

Understanding Powder Behavior in an Additive Manufacturing Process Using DEM

The handling of bulk solids in the form of powders is a fundamental process in a wide range of manufacturing industries, such as the automotive, aerospace, food, and healthcare sectors. All these sectors employ additive manufacturing (AM), as it enables the production of complex parts in a short amount of time. Thus, it is considered an established method for developing an agile manufacturing environment that can drastically reduce the lead time from conception to the production stage. At the same time, powder is a unique material sensitive to environmental and machine conditions; hence, establishing an optimal configuration is not straight-forward. This work presents a discrete element method (DEM) simulation of an experimental dosing system used in AM. We introduce a robust workflow that correlates suitable experimental data with simulation results, establishing models of real powders with different flowability. The results showed an excellent agreement between the experimental data and the simulation results and provided a better understanding of the material behavior. Furthermore, we employed a coarse-grained approach to extract continuum fields from the discrete data. The results showed that the cohesion level in the system was enough to create agglomerates that hindered the transport of the material and produced nonuniform distribution.

A comprehensive review of the application of DEM in the investigation of batch solid mixers

Powder mixing is a vital operation in a wide range of industries, such as food, pharmaceutical, and cosmetics. Despite the common use of mixing systems in various industries, often due to the complex nature of mixing systems, the effects of operating and design parameters on the mixers’ performance and final blend are not fully known, and therefore optimal parameters are selected through experience or trial and error. Experimental and numerical techniques have been widely used to analyze mixing systems and to gain a detailed understanding of mixing processes. The limitations associated with experimental techniques, however, have made discrete element method (DEM) a valuable complementary tool to obtain comprehensive particle level information about mixing systems. In the present study, the fundamentals of solid-solid mixing, segregation, and characteristics of different types of batch solid mixers are briefly reviewed. Previously published papers related to the application of DEM in studying mixing quality and assessing the influence of operating and design parameters on the mixing performance of various batch mixing systems are summarized in detail. The challenges with regards to the DEM simulation of mixing systems, the available solutions to address those challenges and our recommendations for future simulations of solid mixing are also presented and discussed.

Conceptualisation of an Efficient Particle-Based Simulation of a Twin-Screw Granulator

Discrete Element Method (DEM) simulations have the potential to provide particle-scale understanding of twin-screw granulators. This is difficult to obtain experimentally because of the closed, tightly confined geometry. An essential prerequisite for successful DEM modelling of a twin-screw granulator is making the simulations tractable, i.e., reducing the significant computational cost while retaining the key physics. Four methods are evaluated in this paper to achieve this goal: (i) develop reduced-scale periodic simulations to reduce the number of particles; (ii) further reduce this number by scaling particle sizes appropriately; (iii) adopt an adhesive, elasto-plastic contact model to capture the effect of the liquid binder rather than fluid coupling; (iv) identify the subset of model parameters that are influential for calibration. All DEM simulations considered a GEA ConsiGma™ 1 twin-screw granulator with a 60° rearward configuration for kneading elements. Periodic simulations yielded similar results to a full-scale simulation at significantly reduced computational cost. If the level of cohesion in the contact model is calibrated using laboratory testing, valid results can be obtained without fluid coupling. Friction between granules and the internal surfaces of the granulator is a very influential parameter because the response of this system is dominated by interactions with the geometry.

Model-Based Scale-Up Methodologies for Pharmaceutical Granulation

In the pharmaceutical industry, it is a major challenge to maintain consistent quality of drug products when the batch scale of a process is changed from a laboratory scale to a pilot or commercial scale. Generally, a pharmaceutical manufacturing process involves various unit operations, such as blending, granulation, milling, tableting and coating and the process parameters of a unit operation have significant effects on the quality of the drug product. Depending on the change in batch scale, various process parameters should be strategically controlled to ensure consistent quality attributes of a drug product. In particular, the granulation may be significantly influenced by scale variation as a result of changes in various process parameters and equipment geometry. In this study, model-based scale-up methodologies for pharmaceutical granulation are presented, along with data from various related reports. The first is an engineering-based modeling method that uses dimensionless numbers based on process similarity. The second is a process analytical technology-based modeling method that maintains the desired quality attributes through flexible adjustment of process parameters by monitoring the quality attributes of process products in real time. The third is a physics-based modeling method that involves a process simulation that understands and predicts drug quality through calculation of the behavior of the process using physics related to the process. The applications of these three scale-up methods are summarized according to granulation mechanisms, such as wet granulation and dry granulation. This review shows that these model-based scale-up methodologies provide a systematic process strategy that can ensure the quality of drug products in the pharmaceutical industry.

Discrete Element Simulation and Validation of a Mixing Process of Granular Materials

The mixing processes of granular materials have gained wide interest among various fields of science and engineering. In this study, our focus is a mixing process for offshore mining. We conducted numerical simulations using the discrete element method (DEM) in comparison with experimental works on mixing color sand. Careful calibration of initial packing densities has been performed for the simulations. For validation, the steady-state torques on the mixer head, the maximal increase of surface height after mixing, and the surface mixing patterns have been compared. The effect of particle size on the simulation results has been clarified. With the particle size approaching the actual particle size, consistent torques and mixing patterns indicate the capability of the DEM code for studying the particular mixing process, while the results for the maximal increase of surface height should be interpreted with more caution.

Numerical Modeling for Simulation of Compaction of Refractory Materials for Secondary Steelmaking

The purpose of this work is to simulate the powder compaction of refractory materials, using the discrete element method (DEM). The capability of two cohesive contact models, implemented in different DEM packages, to simulate the compaction of a mixture of two refractory materials (dead burnt magnesia (MgO) and calcined alumina (Al₂O₃)) was analyzed, and the simulation results were compared with experimental data. The maximum force applied by the punch and the porosity and final shape quality of the compact were examined. As a starting point, the influence of Young's modulus (E), the cohesion energy density (CED), and the diameter of the Al₂O₃ particles (D) on the results was analyzed. This analysis allowed to distinguish that E and CED were the most influential factors. Therefore, a more extensive examination of these two factors was performed afterward, using a fixed value of D. The analysis of the combined effect of these factors made it possible to calibrate the DEM models, and consequently, after this calibration, the compacts had an adequate final shape quality and the maximum force applied in the simulations matched with the experimental one. However, the porosity of the simulated compacts was higher than that of the real ones. To reduce the porosity of the compacts, lower values of D were also modeled. Consequently, the relative deviation of the porosity was reduced from 40-50% to 20%, using a value of D equal to 0.15 mm.

Validation of a Discrete Element Method (DEM) Model of the Grinding Media Dynamics within an Attritor Mill Using Positron Emission Particle Tracking (PEPT) Measurements

Positron emission particle tracking (PEPT) was used to investigate the grinding media dynamics in a laboratory-scale attritor mill in the absence of powder. The grinding media motion was analysed as a function of the equipment’s typical operating parameters: impeller speed, impeller clearance and bead fill level. It was observed that the impeller speed had the strongest influence on the media motion. An increase of the impeller speed from 300 rpm to 600 rpm led to a change in the bead recirculation patterns with the increasing formation of well segregated upper and lower recirculation loops that fully developed at the maximum speed of 600 rpm. For a constant impeller speed, an increase of the bead loading did not majorly affect the bead velocity as remarked by minor changes on the flow field. For all the impeller clearance values, the occupancy plots revealed an inefficient dead region at the bottom of the attritor where the beads were moving at very low velocity. In this region the beads were tightly packed under their own weight and, furthermore, there was an absence of direct contact with the impeller arms. The depth of this region increased proportionally to the distance between the bottom of the impeller and the vessel base indicating that a minimum value of clearance should be set to optimise the lower recirculation pattern. For two experimental conditions, the data generated by PEPT measurements were utilised to set-up a friction-adjusted discrete element method (DEM) model. Here, the simulation results were qualitatively and quantitatively compared against the PEPT data by assessing the averaged velocity flow fields and the average velocity profiles at different radial locations inside the vessel. Given the intrinsic uncertainty of the PEPT measurements, the DEM model results were in considerably good agreement with the experimental results. The major discrepancy was observed close to the vessel wall where the simulations overpredicted the velocity by about 10%.

Application of the Discrete Element Method for Manufacturing Process Simulation in the Pharmaceutical Industry

Process simulation using mathematical modeling tools is becoming more common in the pharmaceutical industry. A mechanistic model is a mathematical modeling tool that can enhance process understanding, reduce experimentation cost and improve product quality. A commonly used mechanistic modeling approach for powder is the discrete element method (DEM). Most pharmaceutical materials have powder or granular material. Therefore, DEM might be widely applied in the pharmaceutical industry. This review focused on the basic elements of DEM and its implementations in pharmaceutical manufacturing simulation. Contact models and input parameters are essential elements in DEM simulation. Contact models computed contact forces acting on the particle-particle and particle-geometry interactions. Input parameters were divided into two types-material properties and interaction parameters. Various calibration methods were presented to define the interaction parameters of pharmaceutical materials. Several applications of DEM simulation in pharmaceutical manufacturing processes, such as milling, blending, granulation and coating, were categorized and summarized. Based on this review, DEM simulation might provide a systematic process understanding and process control to ensure the quality of a drug product.

Study of the capsule-filling dosator process via calibrated DEM simulations

Capsule filling is frequently accomplished via the dosator process. Controlling the main quality attributes, i.e., the fill weight and the fill weight variability, requires excellent process understanding. For the investigation of critical process parameters of low-dose capsule filling, DEM simulations were used. Two contact models (Hertz model and Luding model) were calibrated and validated to represent the lactose powder Lactohale 100. Both models gave good results, yet the Luding model resulted in better predictions. Since the dosator process is a volume-based approach, the dosator geometry is an important factor. We provide evidence that not only the volume, but also the proportions of the dosator affect the capsule fill weight. Slim nozzle chambers cause lower mass due to increased wall friction impact. The process is not volumetric, as a fill weight to volume correlation does not exist. Furthermore, the fill weight increases with higher powder beds and smaller gaps between the nozzle tip and the container bottom due to higher densification of the powder. Fill weight variations due to bed inhomogeneity were investigated by varying the bulk density of the initial powder bed.

Scale-Up Strategy in Quality by Design Approach for Pharmaceutical Blending Process with Discrete Element Method Simulation

An approach combining quality by design (QbD) and the discrete element method (DEM) is proposed to establish an effective scale-up strategy for the blending process of an amlodipine formulation prepared by the direct compression method. Critical process parameters (CPPs) for intermediate critical quality attributes (IQAs) were identified using risk assessment (RA) in the QbD approach. A Box–Behnken design was applied to obtain the operating space for a laboratory-scale. A DEM model was developed by the input parameters for the amlodipine formulation; blending was simulated on a laboratory-scale V-blender (3 L) at optimal settings. The efficacy and reliability of the DEM model was validated through a comparison of simulation and experimental results. Change of operating space was evaluated using the validated DEM model when scaled-up to pilot-scale (10 L). Pilot-scale blending was simulated on a V-blender and double-cone blender at the optimal settings derived from the laboratory-scale operating space. Both pilot-scale simulation results suggest that blending time should be lower than the laboratory-scale optimized blending time to meet target values. These results confirm the change of operating space during the scale-up process. Therefore, this study suggests that a QbD-integrated DEM simulation can be a desirable approach for an effective scale-up strategy.

A Mixing Behavior Study of Biomass Particles and Sands in Fluidized Bed Based on CFD-DEM Simulation

The present paper studied the mixing characteristics of biomass and sands in a fluidized bed. A three dimensional model is calculated on the basis of computational fluid dynamics (CFD) and the discrete element method (DEM), while the lab-scale experiments under similar conditions are conducted. To investigate the mixing behavior of biomass and sands, particle distribution, particles time averaged kinetic motion and the Lacey index are analyzed and the effects of gas velocity and biomass size are discussed. Gas velocity provides the basic motion for particle movement and biomass particles gain a lot more kinetic motion than sands due to their large size. The biomass mixing process in a horizontal direction is more sensitive to gas velocity than in a vertical direction. Biomass size could slightly affect the mixing quality and a well mixing in fluidized bed could be reached if the size of biomass to sands is smaller than 4 times.

Linking attractive interactions and confinement to the rheological response of suspended particles close to jamming

We study the response to simple shear start-up of an overdamped, athermal assembly of particles with tuneable attractive interactions. We focus on volume fractions close to the jamming point, where such systems can become disordered elastoplastic solids. By systematically varying the strength of the particle-particle attraction and the volume fraction, we demonstrate how cohesion and confinement individually contribute to the shear modulus and yield strain of the material. The results provide evidence for the influence of binding agents on the rheology of dense, athermal suspensions and describe a set of handles with which the macroscopic properties of such materials can be engineered.