Dynamics of fine grinding in a fluidized bed opposed jet mill

Numerical Simulation and Analysis of the Airflow Field in the Crushing Chamber of the Hammer Mill

The airflow dynamics within hammer mills' crushing chambers significantly affect material crushing and screening. Understanding the crushing mechanism necessitates studying the airflow distribution. Using a self-built crushing test platform and computational fluid dynamics (CFD) simulations, we investigated the impact of screen aperture size, rotor speed, hammer-screen clearance, hammer quantity, and mass flow rate on airflow distribution within the rotor region, circulation layer, and screen apertures. Results indicated generally uniform axial static pressure distribution within the rotor region, with radial gradients. Increased rotor speed improved radial static pressure gradients, while higher mass flow rates reduced them. The highest airflow velocity within the circulation layer reached approximately 83.46% of the hammer tip's tangential velocity. Greater rotor speed and hammer quantity intensified circulation airflow, whereas increased mass flow rate decreased it. Eddies formed within screen apertures with higher rotor speeds and hammer quantities but diminished with larger apertures and higher mass flow rates. Static pressure differences across screen apertures increased with mass flow rate and rotor speed but decreased significantly with larger apertures. This systematic examination provides insights into airflow distribution within hammer mill crushing chambers, offering a theoretical foundation for improving and designing hammer mills.

Eulerian Multiphase Simulation of the Particle Dynamics in a Fluidized Bed Opposed Gas Jet Mill

The compressible and turbulent gas–solid multiphase flow inside a fluidized bed opposed jet mill was systematically investigated through numerical simulations using the Euler–Euler approach along with the kinetic theory of granular flow and frictional models. The solid holdup and nozzle inlet air velocity effects on the gas–solid dynamics were assessed through a detailed analysis of the time-averaged volume fraction, the time-averaged velocity, the time-averaged streamlines, and the time-averaged vector field distributions of both phases. The simulated results were compared with the experimental observations available in the literature. The numerical simulations contributed to a better understanding of the particle–flow dynamics in a fluidized bed opposed gas jet mill which are of fundamental importance for the milling process performance.

Fluidized Bed Jet Milling Process Optimized for Mass and Particle Size with a Fuzzy Logic Approach

The milling process is a complex phenomenon dependent on various technological and material parameters. The development of a fluidized bed jet milling model is of high practical significance, since milling is utilized in many industries, and its complexity is still not sufficiently recognized. Therefore, this research aims to optimize fluidized bed jet milling with the use of fuzzy logic (FL) based approach as one of the primary artificial intelligence (AI) methods. The developed fuzzy logic model (FLMill) of the investigated process allows it to be described as a non-iterative procedure, over a wide range of operating conditions. Working air pressure, rotational speed of the classifier rotor, and time of conducting the test are considered as inputs, while mass and mean Sauter diameter of the product are defined as outputs. Several triangular and constant linguistic terms are used in the developed FLMill model, which was validated against the experimental data. The optimum working air pressure and the test's conducting time are 500 kPa and 3000 s, respectively. The optimum rotational speed of the classifier is equal to 50 s^-1, considering the mass of the grinding product, and 250 s^-1 for the mean Sauter diameter of the product. Such operating parameters allow obtaining 243.3 g of grinding product with the mean Sauter diameter of 11 µm. The research proved that the use of fuzzy logic modeling as a computer-based technique of solving mechanical engineering problems allows effective optimization of the fluidized bed jet milling process.

Application of Transformation Matrices to the Solution of Population Balance Equations

The development of algorithms and methods for modelling flowsheets in the field of granular materials has a number of challenges. The difficulties are mainly related to the inhomogeneity of solid materials, requiring a description of granular materials using distributed parameters. To overcome some of these problems, an approach with transformation matrices can be used. This allows one to quantitatively describe the material transitions between different classes in a multidimensional distributed set of parameters, making it possible to properly handle dependent distributions. This contribution proposes a new method for formulating transformation matrices using population balance equations (PBE) for agglomeration and milling processes. The finite volume method for spatial discretization and the second-order Runge–Kutta method were used to obtain the complete discretized form of the PBE and to calculate the transformation matrices. The proposed method was implemented in the flowsheet modelling framework Dyssol to demonstrate and prove its applicability. Hence, it was revealed that this new approach allows the modelling of complex processes involving materials described by several interconnected distributed parameters, correctly taking into consideration their interdependency.