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
indus-trial and regulatory requirements.
