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

Extraction of Mathematical Correlations Applied in the Aerodynamic Separation of Solid Particles

This article describes the methodology used to identify the mathematical equation that describes the correlations between the input and output parameters of an experiment. As a technological process, aerodynamic separation was chosen to represent the behavior of a solid particle within an ascending vertical airflow. The experimental data were used to identify two parameters, namely the average linear velocity and the angular velocity. The Table Curve 3D program was used to develop a mathematical equation describing the dependence between the input parameters (the shape and size of the solid particle, as well as the velocity of the airflow) and the monitored parameters. A pyramid-type analysis (following a filtering system, a general equation was determined from a large number of equations that characterize an experimental set mathematically) was designed in order to determine a single mathematical equation that describes the correlation between the input variables and those obtained as accurately as possible. The determination of the mathematical equation started with the number of equations generated by the Table Curve 3D program; then, the equations with a correlation coefficient greater than 0.85 were chosen; and finally, the common equations were identified. Respecting the working methodology, one equation was identified, which has for the average linear velocity, a correlation coefficient r2 of between 0.88–0.99 and 0.86–0.99 for the angular velocity.

Effects of a Guide Cone on the Flow Field and Performance of a New Dynamic Air Classifier

A new dynamic air classifier was designed to address the problems of uneven material dispersion and high dust concentration in industrial applications of turbo air classifiers. This paper presents a study on the use of guide cones in the new dynamic air classifier. The ANSYS-Fluent 19.2 software was implemented to simulate the airflow in the dynamic air classifier, and the impact of the guide cone size on the flow field and classification performance of the dynamic air classifier was investigated. The simulation results indicated that with the increase in the guide cone height, the flow field distribution becomes reasonable and the velocity distributions become uniform. When the guide cone height is greater than twice the distance between the guide cone and the bottom of the rotor cage, there is no discernible change in the flow field distribution and classification efficiency. When the guide cone diameter is approximately 0.9 times the diameter of the rotor cage, the airflow pathline is more reasonable, and the flow field and velocity distributions are more uniform. An improper guide cone diameter and height will worsen the classification environment, resulting in a significant decline in classification performance. The material experimental and discrete phase simulation (DPM) showed that DPM can anticipate the changing trends of the cut size and classification accuracy. This study provides theoretical assistance for the structural design and optimization of an air classifier.

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.

Airflow Velocity Designing for Air Classifier of Manufactured Sand Based on CPFD Method

Airflow classification is the key technology for the dry separation of manufactured sand. To solve the problem of low separation accuracy and poor gradation grade, the classification process of manufactured sand under different inlet and outlet airflow velocities changes in the multi-air inlet classifier is simulated by using Barracuda based on Computational Particle Fluid Dynamics (CPFD) method. The influence of various airflow velocity in air inlets and outlet on the sand classification is analyzed. The optimal combination of airflow velocity that meets the design goals is obtained. The results show that the airflow velocity and location of the air inlet and outlet have a significant impact on medium-grained (0.15~1.18 mm) and fine-grained (0.075~0.3 mm) sand. Adjusting the airflow velocity at air inlet 2 and air outlet can most effectively change the overall sand separation effect, while 41 m/s (inlet 2) and 6 m/s (outlet) would be the best velocity combination.