<scp>2D</scp> population balance modeling and <scp>ML</scp> ‐based multi‐objective optimization for the crystallization process of resveratrol

Abstract 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.