Farhad Ein-Mozaffari

This study develops a physics-informed surrogate for predicting three-dimensional gas–liquid hydrodynamics in an aerated double-impeller stirred tank. Reference fields were generated using steady-state computational fluid dynamics (CFD) simulations in ANSYS Fluent at impeller speeds of 200–500 rpm. Three models with identical network architectures were examined: a supervised neural network (NN) trained on CFD data, a boundary-informed neural network (BINN) incorporating boundary constraints, and a physics-informed neural network (PINN) incorporating boundary and reduced physics constraints. At 400 and 500 rpm, the PINN outperformed the NN and BINN, achieving normalized liquid- and gas velocity errors below 10% and pressure errors of approximately 3–4%. The NN and BINN showed greater attenuation of impeller-induced gradients in high-shear regions. At the unseen intermediate speeds of 350 and 450 rpm, the PINN reproduced the principal CFD flow structures. Normalized gas velocity errors were approximately 11–14%, while normalized pressure errors ranged from 4.48% to 9.22%, depending on the operating speed and error metric. Each CFD simulation required approximately 100–140 h, whereas PINN inference at 200,000 spatial locations required approximately 2–3 h per operating condition. The initial cost of generating the CFD training data must be considered when assessing the benefit of repeated surrogate evaluations.

Rheology is central to the design and industrial manufacture of cosmetic products, which are multicomponent soft materials whose performance depends on formulation, microstructure, processing history, temperature, shear, and time. Although apparent viscosity is widely used for quality control, a single-point measurement cannot fully describe yield behavior, viscoelasticity, thixotropic recovery, wall slip, extensional response, crystallization, or sensory performance. This review presents an industrial framework for applying rheology across the cosmetic-product lifecycle, from formulation and raw-material selection to scale-up, manufacturing, filling, release, stability, quality assurance, and consumer experience. Steady-shear, oscillatory, transient, nonlinear, extensional, tribological, powder-flow, and slurry measurements are discussed together with key methodological limitations. Particular attention is given to emulsification, polymer hydration, pigment and powder incorporation, deaeration, cooling, crystallization, and scale-up criteria, including tip speed, power input, circulation, heat transfer, and energy history. Rheological fingerprints are also examined for batch comparability, process analytical technology, statistical process control, computational fluid dynamics, machine learning, and digital twins. The review highlights links among formulation, microstructure, rheology, processing, stability, package performance, sensory response, and consumer outcomes and proposes a stage-based testing framework and research priorities for more robust cosmetic manufacturing.

Gas–liquid–solid (G-L-S) mechanically agitated reactors are commonly used in chemical, pharmaceutical and bioprocessing applications due to their low operating costs and controlled and effective mixing. Computational Fluid Dynamics (CFD) is a powerful tool that enhances the understanding of flow dynamics, phase interactions and reactor performance. However, the CFD modeling of G-L-S mechanically agitated reactors is not extensively studied in the literature due to complex multiphase interactions, along with reactor design variations. This paper provides a critical synthesis of the literature, offering an overview not only of G-L-S stirred tank CFD modeling approaches but also of practical guidance on their selection and validation. Emerging high-resolution experimental techniques such as Electrical Resistance Tomography (ERT) coupled with pressure transducers, and Machine Learning (ML) models combined with experimental data, look promising to overcome current three-phase validation limitations. Future work to enhance predictive capabilities and reactor design and operation includes developing real-time digital twins, physics-based ML models and/or hybrid CFD-ML models.

Gas-inducing reactors (GIRs) are widely used in applications where external gas recycling is unsafe or operationally restricted, yet quantitative design guidelines for impeller–stator geometry remain scarce, despite its strong influence on gas dispersion and retention. This study investigates the effects of stator blade number and blade thickness on gas holdup in a double-impeller GIR using a three-dimensional Euler–Euler CFD framework. Stator configurations with 12–48 blades and blade thicknesses of 1.5–45 mm were examined and validated against experimental data, with gas holdup predictions agreeing within 5–10%. The results show that the stator open-area fraction (ϕ_A) is the dominant geometric parameter governing the balance between radial dispersion and axial confinement. High-ϕ_A stators (fewer, thinner blades) enhance bulk recirculation and bubble residence time, increasing gas holdup by up to ~20% relative to dense stator designs, whereas low-ϕ_A stators suppress macro-circulation, promote axial gas transport, and reduce holdup despite higher local dissipation near the rotor–stator gap. A modified gas-holdup correlation incorporating ϕ_A is proposed, yielding strong agreement with CFD and experimental data (R² = 0.96). Torque analysis further reveals competing effects between impeller gassing, which lowers hydraulic loading, and increased flow resistance at low ϕ_A, which elevates torque. Overall, the results provide quantitative guidance on how stator blade number and thickness influence gas holdup, enabling informed stator design and optimization in GIRs to improve gas dispersion through rational geometric selection rather than trial and error approaches.

Polyhydroxyalkanoates (PHAs) are emerging, promising sustainable biobased, biodegradable polymers with strong potential to replace conventional plastics in packaging, agricultural, cosmetics, and biomedical applications. In this study, we investigate the crystallization behavior of two key PHA typespolyhydroxybutyrate (PHB) and poly­(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)under both isothermal and nonisothermal conditions using controlled hyperthermal cycles. Isothermal analyses were performed following rapid hypercooling at 500 °C/min to isolate crystallization kinetics, effectively minimizing interference from the cooling stage. The isothermal data revealed activation energies of 91 kJ/mol for PHB and 139 kJ/mol for PHBV. Hypercooling cycles were also employed to examine nonisothermal crystallization kinetics at cooling rates up to 500 °C/min, mimicking industrial processing speeds. The nonisothermal analysis of PHB and PHBV showed a pronounced decrease in crystallinity with increasing cooling rates. Specifically, PHB’s crystallinity dropped from 48.6 to 10.9%, while that of PHBV fell from 45.9% to near zero, accompanied by the disappearance of exothermic peaks. The isothermal and nonisothermal crystallization behaviors were analyzed using the commonly used modeling, revealing the limited capability of these models in terms of the prediction of the nonisothermal crystallization kinetics from isothermal crystallization data. This study provides novel insights into the thermally driven crystallization mechanisms of PHAs and underscores their sensitivity to processing conditionscritical knowledge for optimizing manufacturing techniques in sustainable polymer applications.