Fully-3D DEM simulation of fluidised bed spray granulation using an exploratory surface energy-based spray zone concept

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.

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.

Computational Modeling of Fluidized Beds with a Focus on Pharmaceutical Applications: A Review

The fluidized bed is an essential and standard equipment in the field of process development. It has a wide application in various areas and has been extensively studied. This review paper aims to discuss computational modeling of a fluidized bed with a focus on pharmaceutical applications. Eulerian, Lagrangian, and combined Eulerian-Lagrangian models have been studied for fluid bed applications with the rise of modeling capabilities. Such models assist in optimizing the process parameters and expedite the process development cycle. This paper discusses the background of modeling and then summarizes research papers relevant to pharmaceutical unit operations.

CFD-DEM -DDM Model for Spray Coating Process in a Wurster Coater

A multiscale model by coupling computational fluid dynamics (CFD) with a discrete element model (DEM) and discrete droplet model (DDM) is developed to simulate a lab-scale Wurster coater. Two case studies are conducted to study the effect of particle shape in the system. In the first case study, 45,000 spherical particles are coated for 5 s while for the second case study, a mixture of 22,500 spherical particles and 22,500 cylindrical particles is simulated. The residence time distributions (RTD) of particles in different spray zones are compared, and the best spray zone is derived by analysing the positions of spray droplet-particle contacts. The simulation results show that the RTD of the particles within an accurate spray zone can provide valuable information on the final product's particles size distribution. Furthermore, the coefficient of variation (COV) for the coating mass received by the particles is studied for both case studies.

Empirical prediction model based process optimization for droplet size and spraying angle during pharmaceutical fluidized bed granulation

The objective of this study was to predict the droplet size and the spraying angle during the process of binder atomization in pharmaceutical fluidized bed granulation using an empirical model. The effects of the binder viscosity, the atomization pressure, and the spray rate on the droplet size and the spraying angle were investigated using a response surface central composite design and analysis of variance. Prediction models for droplet size and spraying angle were then established using stepwise regression analysis and were validated by comparing the measured and predicted values. The results showed that the droplet size model and the spraying angle model were well established, with an R² of 0.93 (p < 0.0001) and a root mean square error (RMSE) of 10.10, and an R² of 0.82 (p < 0.0001) and an RMSE of 3.69, respectively. The error between the measured and predicted values of the droplet size and the spraying angle were less than 10%, indicating that the established models were accurate. The results of the present study were significant in predicting the droplet size and spraying angle in the process of pharmaceutical fluidized bed granulation.

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.