The thematic line Model-Based Engineering, in its broad sense, includes computational applications for the development of holistic solutions, innovative single-scale and multiscale methods and technologies for the supervision, diagnosis, optimization and/or design of processes, products and systems.
Molecular modeling and simulation (MM&S) provides critical insights at fundamental level which are often inaccessible to experimental procedures alone, thus being crucial in the bottom-up design rationale. State-of-art MM&S methods, from quantum mechanics to advanced classical/ab initio molecular dynamics, are targeted to study nanoscale systems, such as e.g.: investigation of noble metal nanoparticles functionalized with antimicrobial peptides; multiscale simulation of silica-based aerogels; mechanistic insights into prebiotics/biomenbrane interactions.
On the subject of continuum modeling and simulation, up-to-date computational fluid dynamic based (CFD) approaches are applied in addition to the more traditional approaches in the realm of engineering. Examples of this include:
- CFD modeling approaches to describe different processes including multiphase processes;
- Combination of Discrete Element Methods with CFD to more accurately describe disperse systems, including predictive population balance models;
- CFD-based mixing and hydrodynamic studies for multiphase reactive systems design.
With regards to the field of Process Systems Engineering, the formulation of mathematical models can be based on first principles, data-driven or a combination of both, following a hybrid modeling approach. The systemic view of problems is an integral part of the methods and solutions to be developed. Representative examples of these approaches include: the optimization and supervision of systems such as biodiesel
fedbatch and continuous process units, bioreactors, cement grinding process; data-driven based surrogate models for process optimization, process troubleshooting analysis, tuning of PID controllers and of linear and nonlinear Model Predictive Control.