The intensive one-week course is aimed at providing fundamental elements about advanced techniques for (bio)chemical reactor modelling to course students by means of an active participation learning approach based on a combination of theoretical (lectures) and practical (case studies, exercises) activities. In detail, the course is built up around hands-on practical activities relying on a few currently relevant engineering systems that are addressed with a multi-disciplinary perspective and that cover a wide range of industrial applications. Most relevant established and innovative modelling techniques for (bio)chemical reactors are introduced, discussed and applied using dedicated computational tools (e.g. Matlab and Simulink, ANSYS Fluent), so as to transfer skills to course students that could be effectively applied to cross-sectorial case studies. The students are provided with handout slides, covering the topics presented during the lectures, and code scripts and start-up materials, allowing them to practice autonomously on the techniques presented in the course. Moreover, some additional literature is suggested based on specific needs, to further master the topics covered by the course.
Detailed program (to be detailed soon in the view of the first course edition)
> LECTURE 1: FUNDAMENTALS
(i) Fundamentals of (bio)chemical reactor engineering
(ii) Overview of (bio)chemical reactor modelling techniques
> LECTURE 2: DETERMINISTIC MODELLING OF MULTI-SCALE AND MULTI-PHYSICAL PROCESSES
> LECTURE 3: BLACK-BOX AND STOCHASTIC MODELLING
> LECTURE 4: INTEGRATING REACTOR MODELLING IN ENGINEERING SYSTEMS
(i) Integration of (bio)chemical reactors in engineering systems
(ii) Elements of process monitoring, modelling and control
> CLASSWORK 1: REACTOR ANALYSIS AND APPLICATION OF COMPARTMENT MODELLING
> CLASSWORK 2: MODELLING OF MULTI-SCALE PROCESSES - Modelling of combined suspended and granular biomass reactors
> CLASSWORK 3: DETAILED MODELLING OF MULTI-PHYSICAL PROCESSES - CFD modelling of UV-driven processes
> CLASSWORK 4: ENGINEERING SYSTEMS - Plant/full-process simulation, control and assessment
Expected learning outcomes
Expected learning outcomes are related to the successful application of advanced modelling techniques to relevant engineering systems, including the following tasks: (1) analysis of (bio)chemical reactors and identification of the most appropriate modelling approach and related computational tools, (2) effective application of computational tools, (3) use of modelling outcomes for process assessment and optimization.