Scientific papers
Calibrating particle contact model parameters is crucial for DEM simulations. However, there is currently no universal calibration technique that provides a generalized set of parameters for a given powder. Additionally, parameters are obtained for a specific coarse-graining ratio. This study presents a method to decouple coarse-graining and calibration by using resolved (experimental size) particles for calibration. This was accomplished by selecting an appropriate surrogate system (with a manageable number of particles) for calibration and utilizing simulation techniques like periodic boundary conditions. We employed a bulk calibration approach using Artificial Neural Networks to calibrate the DEM contact model parameters for lactose fast flo 316, a key excipient in the pharmaceutical industry. Parameters were obtained using a wedge-shaped hopper as a small-surrogate system and validated with other systems (a wedge-hopper with a rotating cylinder near the discharge area, and a tablet-press feeder) to demonstrate that the parameters are not system-dependent. Furthermore, a maximum limit for the coarse-grain ratio was established, up to which the resolved calibrated parameters could be applied. Finally, the parameter set was validated using a Korsch XL100 tablet press feeder experiment and corresponding DEM simulation with multilevel and fixed coarse-graining.
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