PARTICLES 2023

Tailored Characterization Testing: A DEM Case Study for Bin Blending

  • Mostafaei, Fatemeh (Research Center Pharmaceutical Engineering)
  • Khinast, Johannes (Institute for Process and Particle Technology)
  • Forgber, Thomas (Research Center Pharmaceutical Engineering)

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In recent years, the Discrete Element Method (DEM) has become an important tool for predicting the behavior of granular materials in many industries. However, the predictive power of such a simulation is only valuable if the simulated powder accurately represents the real material. Clearly, the process of selecting suitable contact model parameters, also called calibration, has attracted the attention of various research groups [1], [2]. Usually, a workflow is presented in which the simulated behavior of powder matches the macroscopically observable behavior of one or multiple characterization experiments. If the error between the experiment and the simulation is sufficiently small, the powder is said to be calibrated. Despite the common agreement that the “stress experienced” by a powder should be the same in the characterization test and the actual process, no rigorous approach to tailor the characterization tests to specific processes has been established until now. The current contribution addresses the abovementioned shortcomings and identifies the most suitable characterization tests for a bin blending process. To that end, we select several pharmaceutical powders in the DEM model. We execute frequently used bulk characterization tests for all powders while evaluating the process performance in the mixing device. This allows us to correlate key performance indicators, e.g., RSD, mixing time, and surface velocity, to the result from the bulk characterization test. We show that the powder compressibility and dynamic angle of repose are more closely correlated with the key performance indicators. In contrast, the shear test and static angle of repose play a less critical role. The results can be used to save time and material during characterization.