(103f) Development of Discrete Element Method Calibration Approach for Pharmaceutical Applications | AIChE

(103f) Development of Discrete Element Method Calibration Approach for Pharmaceutical Applications


Bhalode, P. - Presenter, Rutgers University
Tao, Y., Rutgers University
Muzzio, F., Rutgers, The State University of New Jersey
Ierapetritou, M., University of Delaware
Discrete element modeling (DEM) has been extensively used for detailed simulation of particulate physics[1]across various industries. In pharmaceutical manufacturing, DEM modeling has also been widely applied for various applications[2,3] like material transport and storage, feeding, blending, granulation, milling, compression and film coating, to obtain a detailed insight into the powder flow and powder dynamics. Although DEM has immense advantages for simulating particulate flow, there exists significant limitations for such applications. One of the critical limitations is the accurate calibration of the powder system in DEM. This corresponds to evaluating the DEM parameters which best represent the powder in DEM space, as observed in an experimental setting. Calibration is a widely studied problem in DEM literature, and researchers have shown different approaches using bulk properties measurements to handle these issues [4-6]. However, most of these studies[5,7] available in the literature are limited to specific powders or specific processing conditions and thus, the calibration process needs to be repeated every time a powder or processing condition is encountered. Furthermore, the literature lacks in addressing the problem of solution multiplicity for the problem of DEM calibration[8,9], which states that there can be multiple possible solutions that replicate the bulk behavior, which makes it difficult to identify a unique set of DEM parameters.

In the proposed work, the authors aim to address the problem of DEM calibration for pharmaceutical manufacturing using a unique strategy utilizing a combination of multiple bulk measurement tests. The combination of bulk measurement tests includes shear cell test, FT4 flow energy test and an instrumented rotating drum to account for static and consolidated state, dynamic flow regime and non-consolidated state of powder flow respectively. These tests aim to span the majority regime of observed powder flow behavior and processing conditions in different applications for pharmaceutical manufacturing. Following the demonstration of material calibration, the proposed work aims at extending the idea to develop a novel concept of DEM calibration database. The DEM calibration database, equivalent to the material characterization library, includes the calibrated DEM parameters of commonly used pharmaceutical powders, selected from different clusters of the material library[10]. Multivariate analysis techniques like principal component analysis and clustering analysis are implemented to explore the knowledge space of the database. Lastly, the DEM calibration database is validated for new pharmaceutical powders based on predictive models constructed using surrogate modeling. The novelty of the proposed work is that the developed DEM calibration space can then be used as a lookup guide for quick access to calibrated DEM parameters of known pharmaceutical powders.


[1] P.A. Cundall, O.D.L. Strack, A discrete numerical model for granular assemblies, Géotechnique. 30 (1980) 331–336. doi:10.1680/geot.1980.30.3.331.

[2] W.R. Ketterhagen, M.T.A. Ende, B.C. Hancock, Process modeling in the pharmaceutical industry using the discrete element method, Journal of Pharmaceutical Sciences. 98 (2009) 442–470. doi:10.1002/jps.21466.

[3] S. Bin Yeom, E.-S. Ha, M.-S. Kim, S.H. Jeong, S.-J. Hwang, Du Hyung Choi, Application of the Discrete Element Method for Manufacturing Process Simulation in the Pharmaceutical Industry, Pharmaceutics 2020, Vol. 12, Page 235. 11 (2019) 414. doi:10.3390/pharmaceutics11080414.

[4] C. Coetzee, Calibration of the discrete element method: Strategies for spherical and non-spherical particles, Powder Technology. 364 (2020) 851–878. doi:10.1016/j.powtec.2020.01.076.

[5] C.J. Coetzee, Review: Calibration of the discrete element method, Powder Technology. 310 (2017) 104–142. doi:10.1016/j.powtec.2017.01.015.

[6] M.W. Johnstone, Calibration of DEM models for granular materials using bulk physical tests, The University of Edinburgh, 2010.

[7] M. Marigo, E.H. Stitt, Discrete Element Method (DEM) for Industrial Applications: Comments on Calibration and Validation for the Modelling of Cylindrical Pellets, KONA Powder and Particle Journal. 32 (2015) 236–252. doi:10.14356/kona.2015016.

[8] T. Roessler, C. Richter, A. Katterfeld, F. Will, Development of a standard calibration procedure for the DEM parameters of cohesionless bulk materials – part I: Solving the problem of ambiguous parameter combinations, Powder Technology. 343 (2019) 803–812. doi:10.1016/j.powtec.2018.11.034.

[9] C. Richter, T. Robler, G. Kunze, A. Katterfeld, F. Will, Development of a standard calibration procedure for the DEM parameters of cohesionless bulk materials – Part II: Efficient optimization-based calibration, Powder Technology. 360 (2020) 967–976. doi:10.1016/j.powtec.2019.10.052.

[10] M.S. Escotet-Espinoza, S. Moghtadernejad, J. Scicolone, Y. Wang, G. Pereira, E. Schafer, et al., Using a material property library to find surrogate materials for pharmaceutical process development, Powder Technology. 339 (2018) 659–676. doi:10.1016/j.powtec.2018.08.042.