## 1 Introduction
The classification environment could be degraded by an inadequate guide cone, and this can lead to significantly reduced classification performance. Some previous studies have investigated the effects of the guide cone on the flow field and classification performance of new rotor-type dynamic classifiers for CaCO\({}_{3}\) classification materials [9; 16]. Lab-scale models of these classifiers were used for steady simulations using the one-way coupling method. For a new dynamic classifier with a simple cylindrical rotor cage, when the ratio of the guide cone diameter to the diameter of the rotor cage was approximately 0.9, the distributions of the flow field and velocity were more uniform, which enhanced the classification efficiency. Additionally, an increase in the guide cone height contributes to improving the flow field distribution, but almost no noticeable change occurs when the guide cone height is greater than twice the distance from the rotor bottom to the guide cone [16]. Meanwhile, both the diameter and height of the diversion cone were also investigated for the new rotor-type dynamic classifier. The mentioned study found that the best classification efficiency could be achieved with a ratio of 1.0:1.0 or 1.0:0.9 between the diameter of the diversion cone and that of the rotor cage chassis, while the flow field distribution was almost unaffected by the diversion cone height [9]. To date, numerous projects have been carried out, providing significant discoveries that establish a basis for such classifiers' optimization. However, there is a lack of research on the guide cone in a real-sized industrial-scale micron air classifier used to classify quartz particles. As mentioned above, several studies have been conducted on guide cones. However, the structures and functions of these guide cones are entirely different from the guide cones used in real-sized industrial-scale micron air classifiers. It should be noted that, in the new rotor-type dynamic classifiers reviewed above [9; 16], the guide cone uses its outer surface to direct the flow within the classifier. Furthermore, the structural configurations of the devices and the classification materials considered in these studiesdiffer from those of the micron air classifier and quartz material, resulting in significant differences in the dynamic characteristics of the multiple-phase flows within these devices. The simulation of an industrial classifier may face challenges associated with its accuracy and the convergence of the solution and would incur high costs related to the calculation resources and time. Therefore, many past studies have used lab-scale classifiers for steady-state simulations, with one-way coupling between the gas and solid phases [9; 16; 17]. However, it is very difficult for a miniature model to accurately capture the complex flow in a real-sized model. The reason for this is that a large amount of time and effort is required to conduct a thorough investigation to guarantee both geometric and dynamic similarity [17]. Significant advances in numerical research on air classifiers have been realized with the development of computer science. Recently, it has been demonstrated that computational fluid dynamics (CFD) coupled with the discrete phase method (DPM) is effective in simulating multiple-phase flows [18; 19; 20; 21; 22; 23; 24]. Numerous scholars have attempted to extend the CFD-DEM approach to capture complex multiple-phase flows [25; 26; 27; 28; 29; 30; 31; 32]. To reduce the computational resources and time consumed, simple models such as the standard k-\(\epsilon\) or RNG k-\(\epsilon\) model were often used as turbulence models in the past. Currently, the RSM provides the best accuracy in the modeling of highly swirling flows and anisotropic turbulence in air classifiers [15; 20; 33; 34; 35; 36]. However, the RSM carries high costs related to the calculation resources and time, as well as risks due to the difficulties in achieving the convergence of the solution.