Recent advances in the digital transformation of crystallization process development and operation: Synergy between model- and AI-driven strategies (Open Access)--颗粒学报PARTICUOLOGY

Ma, Y., Yuan, X., Yewale, A., & Benyahia, B. (2026). Recent advances in the digital transformation of crystallization process development and operation: Synergy between model- and AI-driven strategies. Particuology, 110, 223-253. https://doi.org/10.1016/j.partic.2026.01.014

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

Yiming Ma, Xuming Yuan, Ashish Yewale, Brahim Benyahia^*

Department of Chemical Engineering, Loughborough University, Leicestershire, LE113TU, United Kingdom

10.1016/j.partic.2026.01.014

Volume 110, March 2026, Pages 223-253

Received 15 October 2025, Revised 16 January 2026, Accepted 17 January 2026, Available online 23 January 2026, Version of Record 3 February 2026.

E-mail: b.benyahia@lboro.ac.uk

Highlights

• Reviews digital strategies for managing the multiscale complexity of crystallization processes.

• Outlines a sequential workflow from data acquisition to modeling and closed-loop control.

• Compare mechanistic, data-driven, and hybrid modeling approaches for modeling crystallization dynamics and process behavior.

• Highlights emerging trends in digital twins, process design, and real-time optimization.

Abstract

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.

Graphical abstract

Keywords

Digital design; Crystallization; Data-driven; Machine learning; Process control; Sustainability

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