Grinding in an air classifier mill — Part I: Characterisation of the one-phase flow

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.

Development of a Model for the Separation Characteristics of a Deflector Wheel Classifier Including Particle Collision and Rebound Behavior

Deflector wheel classifiers are widespread in industry for the separation of powders into fine and coarse powders. Even though this separation process has been known for quite some time, it is not yet fully understood, and existing models fail to precisely predict the separation characteristics. Due to the high throughput of deflector wheel classifiers, it is greatly beneficial to estimate the separation characteristics before the experiment. Here, the developed model critically examines the usual assumptions, such as ideal airflow, neglection of particle–wall and particle–particle interactions, or spherically-shaped particles. First, the investigation of the air flow using a Particle Image Velocimetry (PIV) system showed significant differences to the assumed ideal flow field, then particle sphericity and its influence on the interaction between the particles and the paddles of the deflector wheel was investigated and compared with particle rebound behavior on a static wall. Surprisingly, comminuted glass behaves similarly to comminuted limestone in multiple aspects and not like glass beads. To determine the number of particle–particle collisions, Discrete Element Method (DEM) simulations were performed. The aforementioned aspects found application in the model and the separation behavior was well-estimated.

Development of Prediction Models for Pressure Loss and Classification Efficiency in Classifiers

This paper presents the development of prediction models for pressure loss and classification efficiency in classifiers. Classifiers belong to one of the most important classification devices in gas particle processing and a fast and accurate determination of pressure loss and cut size is of great interest. The first model developed in this work allows the calculation of pressure loss as a function of geometric and operational parameters. It is based on a number of measured values that are obtained from previous numerical simulations (CFD). The maximum deviation of the model is less than 20% and the model operates in real time. However, the model requires calibration for each type of classifier. The second model for classification efficiency is based on a simplified two-dimensional approach in which the flow profile and particle trajectories are determined exclusively for the area between two classifier blades. The model is applicable for all geometrical and operational parameters and calculates the desired parameters within a few minutes, with a maximum error rate of 25%. In combination, the two models allow for the process optimization of classifiers in complete systems.

Effects of Flow Baffles on Flow Profile, Pressure Drop and Classification Performance in Classifiers

This paper presents a study of the use of flow baffles inside a centrifugal air classifier. An air classifier belongs to the most widely used classification devices in mills in the mineral industry, which is why there is a great interest in optimizing the process flow and pressure loss. Using Computational Fluid Dynamics (CFD), the flow profile in a classifier without and with flow baffles is systematically compared. In the simulations, turbulence effects are modeled with the realizable k–ε model, and the Multiple Reference Frame approach (MRF) is used to represent the rotation of the classifier wheel. The discrete phase model is used to predict the collection efficiency. The effects on the pressure loss and the classification efficiency of the classifier are considered for two operating conditions. In addition, a comparison with experimental data is performed. Firstly, the simulations and experiments show good agreement. Furthermore, the investigations show that the use of flow baffles is suitable for optimizing the flow behavior in the classifier, especially in reducing the pressure loss and therefore energy costs. Moreover, the flow baffles have an impact on the classification performance. The impact depends on the operation conditions, especially the classifier speed. At low classifier speeds, the classifier without flow baffles separates more efficiently; as the speed increases, the classification performance of the classifier with flow baffles improves.