Sparse identification of physically plausible aggregation kernels for wet granulation processes

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 %).