Recent advances in the digital transformation of crystallization process development and operation: Synergy between model- and AI-driven strategies

Crystallization plays a critical role across multiple industries, determining key particulate product attributes such as purity, particle size distribution, morphology, and polymorphic form. The multiscale nature of this process, encompassing molecular interactions, phase transitions, and transport phenomena, imposes high levels complexity on the design and control of systems in which particle characteristics govern performance. Digital strategies, including emerging AI-driven approaches, are increasingly recognized as powerful tools for managing multiscale complexity, reducing inherent uncertainties, and enhancing process development. This review aims to explore recent advances and critically analyze how digital methods can be applied at each stage of process development. The discussion begins with data acquisition and augmentation, including synthetic data generation, model-based experimental design, and rigorous data preprocessing and validation. This is followed by the modeling strategies tailored to specific design and operation objectives, including mechanistic, data-driven, and hybrid approaches for predicting crystallization dynamics and particulate properties. Finally, recent control and optimization solutions are discussed, focusing on model-based and adaptive algorithms for open and closed-loop strategies. The review concludes with a forward-looking perspective on emerging trends, highlighting the integration of digital twins, real-time optimization, and sustainability metrics which together are expected to enable intelligent, resilient, and sustainability-aligned crystallization systems capable of meeting future industrial and regulatory requirements.