## 2 Materials and Methods
### Numerical Calibration  
Calibration, or parameter identification as mentioned in many sources, involves determining a suitable set of parameters that yields a simulated bulk material behaviour closely resembling that observed in real-world experiments. Industrially significant powders often comprise various components, each exhibiting a particle size distribution (PSD) ranging from microns to millimetres. Simulating these powder processing systems using the Discrete Element Method (DEM) and contemporary computing technology is obstructed by the small particle sizes. Establishing a direct and precise correspondence between a DEM particle and an actual particle is very rarely achievable within the scope of numerous industrial applications. In the framework of our research, we implemented a well-established material calibration process. It was observed that the particles did not exhibit significant cohesive behaviour. Hence, we implemented a calibration procedure specifically designed for free-flowing, non-cohesive materials. The procedural details are explained in the flowchart presented in Figure 4, as outlined in the work of [PERSON] and [PERSON] [39]. This flowchart functions as a comprehensive guide, providing references to sections explaining the impact of specific parameters on bulk behaviour, along with suggested experiments for calibrating each parameter. Our study follows this established procedure, ensuring a comprehensive and informed approach to material calibration within the field of non-cohesive materials. [PERSON] et al. [40] explored the impact of stiffness reduction on bulk behaviour through an analysis of multiple case studies. Their findings indicated that maintaining the particle shear modulus above 1 \(\times\) 10\({}^{8}\) Pa, resulting in overlaps of 0.3% or less did not significantly change resistance. Conversely, lower stiffness values, leading to overlaps exceeding 0.3%, resulted in smoother interaction and resistance. Due to computational limitations, the modulus was reduced, a decision supported by findings in articles [41; 42], where a similar analysis was conducted with erosive sand of comparable sizes. Calibration based on these considerations provided a satisfactory approximation for entry data. Moreover, throughout all simulations, contact overlap was consistently monitored during simulations. We investigated sliding friction between powder and steel by employing an inclined plate (Figure 4), where particles were adhered to the contact surface of a suitably large object. In our examination of rolling and sliding friction between powder particles and steel, we adopted a method involving a steel ball. By compacting the powder and observing the ball's rolling behaviour, we addressed the complexities associated with fine powders. While this method didn't yield superior results, it provided results fine-tuned enough for our numerical simulations. The material properties utilized in the simulation are presented in Table 1. Calibration also involved conducting a direct shear test using a 60 \(\times\) 60 mm shear box. Previous findings indicated that differences in shear results are unclear when the shear velocity is below 0.1 m/s. This velocity significantly differs from the experimental test, where particle contacts might dissipate kinetic energy due to damping. Consequently, the lower part of the shear box was moving at a constant speed of 0.001 m/s, while the upper part remained stationary. All other procedures were adhered to as outlined in [43, 44, 45]. Figure 4: Visual representation of the main calibration scheme for obtaining numerical simulation data using modified partial solutions from the Rocky Calibration Suite. Developed in the 1920s, Design of Experiments (DOE) is a statistical theory that gained prominence in engineering and psychology since the 1950s. It efficiently examines the impact of factors on outcomes [47]. In this study, we applied the DOE approach to systematically investigate the performance of a screw conveyor.