Mathematics for chemical engineers - AM413007
Title: Mathematics for chemical engineers
Guaranteed by: Department of Mathematics (413)
Actual: from 2019
Semester: winter
Points: winter s.:5
E-Credits: winter s.:5
Examination process: winter s.:
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
For type: Master's (post-Bachelor)
Guarantor: Axmann Šimon Mgr. Ph.D.
Janovská Drahoslava prof. RNDr. CSc.
Kočí Petr doc. Ing. Ph.D.
Class: Předměty pro matematiku
Interchangeability : M413007, N413032, S413032
Z//Is interchangeable with: M413007
Examination dates   Schedule   
This subject contains the following additional online materials
Annotation -
Last update: Kubová Petra Ing. (22.01.2018)
The course builds on students' knowledge acquired in undergraduate studies. Its main focus is the study of differential equations and their systems, dynamical systems (qualitative theory), as well as a brief introduction to vector analysis and theory of partial differential equations. An integral part of this course is to practice the theoretical mathematical knowledge on specific examples from chemical engineering using advanced software.
Aim of the course -
Last update: Kubová Petra Ing. (22.01.2018)

The aim of the course is to enable students to brush up on and deepen the knowledge acquired in undergraduate mathematics courses of study. Although students will work in the future in various fields of chemistry, they should be able to use in the formulation, analysis, and simulation results of its rigorous mathematical tools, including most advanced software available.

Literature -
Last update: Kubová Petra Ing. (19.08.2019)

R: M. Kubíček, M. Dubcová, D. Janovská, Numerical Methods and Algorithms,

R: J. F. Epperson: An Introduction to Numerical Methods and Analysis,Wiley, New York, 2002, ISBN 0-471-31647-4

R: B. Bowerman, R.T. O'Counel: Applied Statistics (1997, IRWIN Inc Company)

A: R.A. Horn, C.R. Johnson: Matrix Analzsis. Cambridge Universitz Press, 1999. ISBN 0-521-38632-2

Learning resources -
Last update: Kubová Petra Ing. (22.01.2018)

Teaching methods -
Last update: Kubová Petra Ing. (22.01.2018)

Lectures take place according to the syllabus. The theoretical mathematical knowledge is applied to specific tasks in chemical engineering. Matlab (namely „pplane“) is used for simulations of the behavior of dynamic systems.

Syllabus -
Last update: Kubová Petra Ing. (19.08.2019)

1. Matrix equations, inverse matrix. Eigenvalues and eigenvectors of matrices, generalized eigenvectors. Solving a system of linear algebraic equations.

2. Singular values, singular value decomposition. Least squares solution of a system of linear algebraic equations. Normal equations.

3. Linear and nonlinear regression.

4. Solving systems of nonlinear equations, Newton method. Newton method for solving systems of nonlinear equations.

5. Implicit function of one or more variables, general theorem for implicit functions.

6. Numerical solution of ordinary differential equations, initial value problem: Euler's method, Runge-Kutta methods, multistep methods.

7. Numerical solution of ordinary differential equations, boundary value problem, method of shooting.

8. Vector field, the trajectory of the system, equilibrium conditions, phase portrait. Invariant set, ω-limit sets of trajectories.

9. Systems of linear ODEs with constant coefficients: Solving linear systems using eigenvalues, eigenvectors and generalized eigenvectors. Phase

portraits of linear systems in R^1, R^2.

10. Systems of nonlinear equations: classification of equilibrium states of nonlinear systems. Principles of construction of phase portraits in the

plane. Homoclinics and heteroclinics.

11. Basics of vector and tensor calculus. Nabla operator algebra. Grenn’s, Gauss's-Ostrogradski's theorems.

12. Curves. Line integral of scalar and vector fields.

13. Surface integrals of scalar and vector fields. Gauss's and Stokes's theorems.

14. Classification of PDE for two independent variables. Diffusion equation and wave equation in 1-D. Fourier methods for their solution.

Fourier’s series.

Entry requirements -
Last update: Janovská Drahoslava prof. RNDr. CSc. (31.05.2019)

Students are expected to have either completed the prerequisite courses Mathematics A and Mathematics B or possess the equivalent knowledge prior to enrolling in the course. Students are recommended to complete the course Numerical methods prior to enrolling in the course.

Registration requirements -
Last update: Borská Lucie RNDr. Ph.D. (06.05.2019)

No requirements.

Course completion requirements -
Last update: Janovská Drahoslava prof. RNDr. CSc. (31.05.2019)

During the semester, students develop several miniprojects (their number depends on the difficulty of the task). On the basis of their preparation, students will gain an assessment. Without the assessment student can’t take the examination. The exam consists of a written and an oral part. For admission to the oral exam, it is necessary to gain at least 50 points from the test. If a student writes the test for the sufficient number of points and fails in the oral part, the written test need not to be repeated.

Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 1 28
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 1,5 42
Účast na seminářích 0,5 14
5 / 5 140 / 140