Juan Pablo Gallo Molina
Prof. dr. ir. Ingmar Nopens - Ghent University
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Organic Solvent Nanofiltration (OSN): The efficient separation of organic molecules is highly valuable for the chemical, food and pharmaceutical industries. The use of membranes (ceramic or polymeric) allows for significant energy and cost savings in comparison with traditional technologies such as distillation. Predictive models are required to better understand and optimize OSN operations.
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Rheology of concentrated suspensions: The flow behaviour of highly concentrated suspensions (e.g., cement, food materials, biological systems, etc.) is dependent on their microstructures and on the (multiscale) phenomena affecting them. Models capable of predicting this behaviour from first principles can therefore play an important role in a wide range of applications.
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Process modelling of food-grade emulsions: The manufacturing of highly consumed food emulsions such as margarine has significant room for improvement. This preordains the development of models capable of describing this complex process, in which thermodynamic, microstructural and viscous effects combine to produce the final product properties.
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Population balance models applied to water technology: Suspended matter play an important role in several water processes such as coagulation-flocculation. Population balance models can offer valuable insight in this regard and can potentially be used to obtain a better description of full scale operations.
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Population Balance models (PBM): This method can be used to track the time-dependent evolution of distributed properties (e.g., particle size). It is valuable in systems where these distributions play an important role such as suspensions, emulsions, etc.
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Molecular Dynamics (MD): By tracking the trajectories and momenta of individual atoms, MD allows for the deduction of multiple properties of interest (e.g., interaction forces, transport mechanisms and coefficients, etc.) with a very high resolution regarding a system’s chemical composition and other conditions.
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Dynamic process models: Systems of differential equations can be implemented to predict the transient response of a process considering the present physical phenomena.
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Hybrid models: Data driven approaches (e.g., PLS, random forests, neural networks) can be coupled with physics-based models in order to capture non-modelled dynamics and improve predictive power.
