Data-Driven Prediction of Crystal Size Metrics Using LSTM Networks and In Situ Microscopy in Seeded Cooling Crystallization

Abstract

This work presents a data-driven modeling framework for predicting image-derived crystal size metrics in seeded cooling crystallization using Long Short-Term Memory (LSTM) neural networks. The model leverages in situ microscopy data to predict square-weighted D10, D50, D90, and particle counts based solely on seed loading and temperature profiles, without requiring real-time supersaturation measurements. To enhance predictive power, engineered process descriptors—including temperature derivatives and integrals—were incorporated as dynamic features. Experimental validation was performed using creatine monohydrate crystallization from aqueous solution, with LSTM models trained on a diverse dataset encompassing variable seed loadings and cooling profiles. The feature-engineered LSTM model consistently outperformed its non-engineered counterpart, particularly under nonlinear cooling conditions where crystallization dynamics were the most complex. This approach offers a practical alternative to mechanistic models and spectroscopic process analytical technology (PAT) tools by enabling accurate prediction of chord length distribution (CLD) metrics from routinely collected data. The framework is easily transferable to other crystallization systems and provides a low-complexity, high-accuracy tool for accelerating lab-scale crystallization development.