Segregation is a significant factor that can affect the uniformity of a mixture. In order to address and mitigate segregation, accurate data is necessary to characterize particles throughout the process. However, obtaining data in a manner that does not impact the mixture can be challenging, particularly in non-laboratory settings where conditions may not be as controlled.
In laboratory settings, stable lighting and coloured particles can be used to aid in differentiation. However, this approach may not be feasible for all materials. Therefore, the development of a tool that can identify particles without the need for colouring or consistent lighting is highly valuable.
This study focuses on a common mixture found in blast furnaces, consisting of coke, pellet, and sinter. The similarities in colour schemes and overlapping sizes of coke and sinter present challenges for particle recognition. Unstable lighting further complicates the differentiation process.
The proposed method is applied to a mixture containing all three components to demonstrate its capabilities. This method effectively distinguishes between the various particles, providing information on both the material and area of each particle. The measurement data is utilized to assess material and size segregation within the mixture. Material segregation is evaluated using a square grid and ring configuration, while size segregation is determined by categorizing particles into three groups based on diameter, including Feret diameter and area-equivalent diameter.

The Discrete Element Method (DEM) in combination with an One Factor At a Time (OFAT) experimental simulation plan were employed to investigate the effect of a selection of factors on the mixing performance of a double shaft, batch-type paddle mixer. Most influential factors on the mixing performance of the paddle mixer are desired for optimization purposes. Selected factors are the three material characteristics (particle size, particle density, composition), three operational conditions (initial filling pattern, fill level, impeller rotational speed) and three geometric characteristics (paddle size, paddle angle and paddle number).

A 175L paddle mixer is converted into a simulation model and the material model is adopted from literature. The material model comprises of free-flowing, perfectly spherical glass beads. The Hertz-Mindlin contact model is used to simulate the granular material. Furthermore, the simulation strategy consists of the filling process and mixing process. In the former, all input settings are specified. The latter process uses the filling process results to mix the 2-component mixture for a total real-time of 30 seconds. To ensure robust, stable and fit-for-purpose DEM simulations, a 'worst-case scenario' simulation is built for the filling process. By variations in shear modulus and time step, the simulation time is being reduced without compromising the stability or realistic behavior of the material. The output is then used in both processes for all simulations in the experimental simulation plan.

The mixture quality is assessed by the mixing index Relative Standard Deviation (RSD), where both the mixing effectiveness (mixture quality after 30 seconds) and the mixing efficiency (the time it takes to reach a RSD lower than or equal to 0.2) are evaluated. The particle size and particle density have a significant effect on the final mixture quality after 30 seconds of mixing (KPI 1). Moreover, screenshots in x, y and z directions at predefined time steps are generated to serve as visual feedback to understand the mixing mechanisms and flow patterns qualitatively. And, the RSD only stipulates the global mixing performance of the system. To be able to observe local differences in mixture quality, heat maps are generated. Additional local grid systems derived from the global 14x14x9 are designed such that the mass concentration of one of the components can be visualized in x, y or z direction.

The particle size and particle density have a significant effect on the final mixture quality after 30 seconds of mixing (KPI 1). Moreover, the composition, initial filling pattern, fill level, impeller rotational speed and paddle size have a significant effect on how fast a steady-state mixture quality is reached (KPI 2). Finally, the paddle angle and paddle number have a significant effect on both KPIs and therefore holds most potential with respect to optimization purposes.