Aligned with digital transformation goals, this thematic line includes mathematical modeling and Process Systems Engineering (PSE) approaches to support decision-making during the creation and operation of chemical supply chains, in a variety of problems including plant, process and product design, process monitoring and control, and production and distribution planning. Several modeling paradigms are considered, including molecular modeling, continuum (fluid dynamics) modeling, multiphase/dispersed systems, and discrete/network models. Data-based models and process analytics tools are also explored, as well as a combination of these with first principles models (hybrid modeling), thus following the new paradigm of PSE where both deductive (based on chemical engineering sciences) and inductive (data-centric) approaches are considered and merged, taking full advantage of the available knowledge and information to design, control and optimize chemical products, processes, and systems.
Specific projects may be more fundamental, developing methodologies applicable to a welldefined typology of problems, or more focused on a particular application. The following are representative examples: simulation of nanoscale systems (e.g., noble metal nanoparticles functionalized with antimicrobial peptides, silica-based aerogels), CFD modeling of multiphase/dispersed systems in biotechnology and pharmaceutical processes, non-linear model-predictive control applications (cement grinding unit, microalgae bioreactor), PAT development & machine learning in pharmaceutical or environmental processes, model-based optimal design of experiments (e.g., applied to multiple chemical and biological processes or to the design of clinical trials), causal network discovery applied to chemical reaction systems and industrial processes, rigorous modeling of process alternatives in the area of energy and decarbonization.