Sparse identification of physically plausible aggregation kernels for wet granulation processes

Journal of Process Control, vol. 166, pp. 103804

Abstract

Population balance models provide a mathematical framework for describing the dynamics of particulate systems. For aggregation processes, such as twin-screw wet granulation, population balance models critically depend on accurate aggregation kernels, yet first-principles derivation is often impractical, and data-driven methods can lack physical interpretability. This work presents a sparse identification framework for learning physically plausible aggregation kernels directly from data. The approach enforces physical constraints, exploits structural properties, applies a systematic scaling strategy, and extends naturally to actuated systems. Validation on experimental data from a continuous twin-screw wet granulation process confirms the method’s robustness and its ability to recover physically meaningful aggregation kernels from real-world measurement data. The identified population balance model was able to predict the median particle size with a mean absolute error of 134.8 µm (10 %).

Authors 7

1. Stefan Ruben Tölle corresponding Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

2. Lorenz Dörschel Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

3. Stefan Klinken-Uth

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany

4. Alana Delvos

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany

5. Jörg Breitkreutz

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany

6. Heike Vallery Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University · Delft University of Technology

   Affiliation as printed

   Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

7. Sebastian Stemmler Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany