A Computationally Efficient Surrogate-Based Reduction of a Multiscale Comill Process Model

Nirupaplava Metta, Rohit Ramachandran, Marianthi Ierapetritou

Research output: Contribution to journalArticle

1 Scopus citations


Purpose: Particle breakage in milling operations is often modeled using population balance models (PBMs). A discrete element method (DEM) model can be coupled with a PBM in order to explicitly identify the effect of material properties on breakage rate. However, the DEM-PBM framework is computationally expensive to evaluate due to high-fidelity DEM simulations. This limits its application in continuous process modeling for dynamic simulation, optimization, or control purposes. Methods: The current work proposes the use of surrogate modeling (SM) techniques to map mechanistic data obtained from DEM simulations as a function of processing conditions. To demonstrate the benefit of the SM-PBM approach in developing integrated process models for continuous pharmaceutical manufacturing, a comill-tablet press model integration utilizing the proposed framework is presented. Results: The SM-PBM approach is in excellent agreement with the DEM-PBM approach to predict particle size distributions (PSDs) and dynamic holdup, with a maximum sum of square errors of 0.0012 for PSD in volume fraction and 0.93 for holdup in grams. In addition, the time taken to run a DEM simulation is in the order of days whereas the proposed hybrid model takes few seconds. The SM-PBM approach also enables comill-tablet press model integration to predict tablet properties such as weight and hardness. Conclusions: The proposed hybrid framework compares well with a DEM-PBM framework and addresses limitations on computational expense, thus enabling its use in continuous process modeling.

Original languageEnglish (US)
JournalJournal of Pharmaceutical Innovation
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Pharmaceutical Science
  • Drug Discovery


  • Discrete element method
  • Milling
  • Model integration
  • Population balance model
  • Reduced model
  • Surrogate model

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