Algorithm-Driven Design and Control of Complex Fluid Processing

Dear Colleagues,

Complex fluids are central to a wide range of products used in everyday life, particularly in cosmetics, pharmaceuticals, and personal care, as well as in related sectors such as food processing, biotechnology, and specialty materials. The final quality of products such as creams, gels, suspensions, and emulsions is intricately linked to the way these materials are processed. Even minor variations in mixing protocols, temperature profiles, or applied shear can profoundly impact their microstructure, stability, and overall performance. Consequently, relying on traditional trial-and-error methods has become increasingly expensive, inefficient, and impractical for scaling production.

This Special Issue seeks contributions from researchers and industry professionals who are developing and employing algorithm-based strategies to enhance the design, processing, scale-up, and control of complex fluids. We are particularly interested in interdisciplinary work that integrates rheology, transport phenomena, data science, machine learning, and advanced process control. Our aim is to foster the development of more predictive, reliable, and reproducible approaches to manufacturing.

We welcome submissions ranging from fundamental research to practical industrial case studies, with a special focus on methodologies that establish clear connections between process parameters, material structure, and end-product performance.

Topics of interest include, but are not limited to, the following:

• Algorithmic design and optimization of processes for emulsions, gels, creams, suspensions, and slurries;
• Rheology-informed modeling and control of non-Newtonian and viscoelastic fluids;
• Computational Fluid Dynamics (CFD) modeling of non-Newtonian fluids, including shear-thinning, shear-thickening, yield-stress, and viscoelastic behaviors;
• CFD studies of multiphase systems, including two-phase flows (e.g., liquid–liquid emulsions, gas–liquid systems) and three-phase flows (e.g., gas–liquid–solid or liquid–liquid–solid systems) with Newtonian and non-Newtonian phases;
• Coupled CFD–rheology approaches to predicting mixing efficiency, phase dispersion, breakup, coalescence, and residence-time distributions;
• Machine learning and hybrid (physics-based + data-driven) models for complex fluid behavior and process prediction;
• Digital twins and soft sensors integrating CFD, rheology, and real-time process data for monitoring and control;
• Scale-up and scale-down strategies for mixers, reactors, extruders, and filling operations, supported by CFD and dimensionless analysis;
• Inline and at-line analytics linked with CFD and rheological models to ensure product consistency and quality;
• Process robustness, reproducibility, and manufacturability in regulated environments;
• Applications in cosmetics, pharmaceuticals, food, biotechnology, and soft materials processing.

Dr. Argang Kazemzadeh
Prof. Dr. Farhad Ein-Mozaffari
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

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