Chemical engineering model analysis

• To develop a mathematical model for description of stationary, as well as non-stationary processes, involving transport of matter and energy by convection and diffusion, as well as chemical reactions;
• To formulate initial and boundary conditions for a mathematical problem applying relevant physical considerations;
• To learn and to be able of working with the population balances. To set mathematical problem formulations for the systems involving the population balances. To select a solution method for such systems;
• To apply the different discretization methods for solutions of the ordinary and partial differential equations;
• To apply the methods for qualitative analysis and numerical solution of the coupled first order differential equations, including stiff differential equations and differential-algebraic equations;
• To learn the analytical solution methods for the systems described by the first order hyperbolic partial differential equations;
• To apply numerical tools for solutions of the parabolic partial differential equations; analyze steady states and stability;
• To be capable of applying a modern programming language (presumably, Matlab or Python) for numerical solution of the practically relevant statements and for graphical analysis of the solutions.

Teaching is built around a number of model examples, which broadly describe the statements of the problems with the chemical- and biotechnical contents. The problems are solved numerically, applying MATLAB as a tool. The possible typical model examples are (the choice may be different in the different years):

Modelling of the enzymatic hydrolyses of cellulose.

Modelling of the combustion processes.

Modelling of particle coating, with agglomeration.

Chromatografical separation. Linear and nonlinear isotherms

Multiphase flows in porous media, including deep bed filtration and carbon dioxide underground storage.

01035, 28160, 28864, or similar courses. Initial knowledge of programming (preferably, Matlab or Python).

“Learn by doing”. Short lectures. Group work on the course assignments