Neural Network-Based Kinetic Model for Antisolvent Crystallization of Benzophenone: Construction, Validation, and Mechanistic Interpretation

The emergence of artificial neural networks and the widespread application of process analytical technology (PAT) have founded a robust foundation for neural network applications in crystallization research. This study investigated benzophenone antisolvent crystallization kinetics through online monitoring of the crystallization process via PAT tools and kinetics fitting via neural networks. The antisolvent crystallization process was simulated by integrating network-based kinetics with population balance. The findings suggest that benzophenone exhibits size-independent growth in water–methanol systems. The neural network-based model demonstrates improved performance (a consistent 50 ± 5% enhancement in prediction accuracy (R2) over empirical kinetic models) in predicting crystallization kinetics. Furthermore, the network-based process model achieved remarkable agreement with the experimental crystal size distribution, showing smaller deviation (1.1%), less than that of traditional empirical models (5.29%). This work proposed a robust crystallization process model combining PAT tools and artificial neural networks, enabling rapid crystallization kinetics determination and accurate process simulations.