Crystallization is critical in pharmaceutical manufacturing, influencing active pharmaceutical
ingredient (API) purity and processability. This study models the cooling crystallization
of resveratrol in a water‐ethanol solvent using a two‐dimensional population balance
model (2D‐PBM). Experimental data from Focused Beam Reflectance Measurement (FBRM),
UV/Vis spectroscopy, and microscopy supported model calibration via design of experiments.
The well‐calibrated model enabled multi‐objective optimization (MOO) to (1) maximize
yield and minimize batch time, and (2) explore the relationship between aspect ratio
and median crystal size. While the first scenario showed minimal trade‐offs, the second
revealed a balance between aspect ratio and size/yield. A hybrid approach combining
mechanistic modeling with machine learning drastically accelerated simulations and
enabled efficient prediction of Pareto‐optimal solutions. This integration offers
a scalable and accurate optimization strategy for complex crystallization processes.
