IN/0240/EN - PROCESS MODELING AND SIMULATION
Academic Year 2021/2022
Free text for the University
ROBERTO BARATTI (Tit.)
- Teaching style
- Lingua Insegnamento
|[70/88] CHEMICAL AND BIOTECHNOLOGICAL PROCESS ENGINEERING||[88/00 - Ord. 2020] PERCORSO COMUNE||9||90|
Attending classes, the student will learn:
- Knowledge and understanding
Knowledge and understanding of the needed steps to develop a mathematical model for a system. Knowledge and understanding of numerical method to solve a mathematical model.
- Applying knowledge and understanding
Understanding the problems related to the modeling and simulation of the main units present in process industry.
- Making judgments
Skills in judge the need to model and simulate a real process.
- Communication skills
Communication skills acquired in-group work.
- Learning skills
Ability to independent study and analysis of technical books on the course arguments.
Knowledge: An adequate knowledge of the fundamental methodological aspects of the basic sciences (calculus, geometry, physics), applied science (thermodynamics, reaction system) and operation units (distillation column and reactors).
Skills: Be able to solve systems of algebraic equations, how to derive and integrate functions. Knowing how to formulate a process model.
Course overview. Model definition. Error types (3 hours).
Programming (6 hours of practice).
Steady state mono-dimensional nonlinear models. Numerical solution methods: bisection, Newton and secant (9 hours + 6 hours of practice).
Steady state multi-dimensional nonlinear models, Numerical solution of nonlinear systems (9 hours + 6 hours of practice).
Integral system models. Numerical solution methods: trapeze, Simpson and Gauss (9 hours + 6 hours of practice).
Nonlinear dynamic systems: mono- and multi-dimensional. Numerical solution methods: Eulero (explicit and implicit), Heun and Runge-Kutta (12 hours + 3 hours of practice).
System described by second order differential equations, Numerical solution methods: finite difference, orthogonal polynomials (9 hours + 6 hours of practice).
System described by partial differential equations. Numerical solution methods: finite difference, orthogonal polynomials (9 hours + 6 hours of practice).
Lectures will be prevalently held in classrooms, also integrated with online teaching resources, by using specific online platforms managed by the University of Cagliari.
Theoretical lessons on numerical calculus will be held by prof. Grosso (30 hours) while the model development lessons and practice will be held by prof. Baratti (60 hours).
60 hours of class and 30 hours of practice, the student is invited to work with the colleagues for finding the solutions of the assigned problems. The teachers is in the class to give support.
Verification of learning
The final exam consists in delivering written reports for the assigned homeworks, analogous to the one carried out in class, and a final discussion (in-presence or remotely using computer aids).
The student, to pass the exam, should be able to:
- derive a mathematical model of chemical processes and able to numerically solve it;
- analyze the process and choose the appropriate model to simulate it;
- able to understand the scientific literature in order to acquire the needed knowledge to derive a mathematical model;
- write a brief report on the obtained results.
The student is evaluated according to the following criteria:
- demonstrated knowledge and understanding: 18/30-21/30;
-demonstrated knowledge and understanding and ability of applying knowledge: 23/30-26/30;
- demonstrated knowledge and understanding, ability of applying knowledge and ability to deliver exhaustive reports: 27/30-30/30.
Smith – Chemical Process Design and Integration – J. Wiley
Coulson-Richardson - Chemical Engineering - vol. 2, vol. 6
H. Scott Fogler, “Elements of Chemical Reaction Engineering”, Prentice Hall, 1999
S. Carrà e M. Morbidelli, “Chimica Fisica Applicata”, Hoepli, 1983
“Fenomeni di Trasporto”, R. Byron Bird, Warren E. Stewart and Edwin N. Lightfoot, Casa Editrice Ambrosiana, Milano
V. Comincioli, Analisi Numerica, McGraw-Hill Italia.
"Numerical recipes: The Art of Scientific Computing ", W.H. Press, B.P. Flannery, S.A. Teukolsky, W.T. Vetterling, University Press, Cambridge
Course notes are available on Moodle..