Dynamic Modeling of Fine Coal Separations in a Hindered-bed Classifier

Abstract Hindered-bed classifiers have recently received significant attention for the potential of achieving effective density-based separations. Although the fundamental principles of hindered-bed classifiers are relatively simple, the complex interactions between the operating parameters provide a degree of difficulty that hinders the ability for on-line plant optimization. Thus, a dynamic population balance model was developed to evaluate and optimize the separation performance achievable on the basis of density by a hindered-bed classifier for the cleaning of fine coal. The steady-state results of the model include partition curves as a function of particle size and density as well as metallurgical data. A statistical comparison of the simulation results with those obtained from industrial units reveal a high level of accuracy with adjusted R2 values greater than 0.95. Simulation results indicate the potential for efficient density-based separations over a particle size range of 3:1.