Master of Eng. in Automation & IT
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Automation & IT   Course   Modules   Modelling and Simulation

Modelling and Simulation of Technical Processes


Qualification aims

Students will comprehend methods and technologies of modelling and simulation of continuous and discrete event systems and develop their own small simulators. In particular, they will intensively work with "state of the art“ continuous and discrete event simulation software.

Among other things, they will

  • analyse new tasks and problems,
  • choose suitable solution methods,
  • determine and evaluate correct solutions, and
  • present the results.

Courses

The module consists of 4 courses:

Numerical methods

Tutor

Prof. Kampmann

Credit points

2 CP

Term

Fall

Contents

  • Number representation and normalisation
  • Transcendent and implicit functions, continued fractions
  • Efficient procedures for solving linear equation systems, LU decomposition, procedures for sparse matrices, partitioning, recursive procedures (CG), iterative procedures (SOR)
  • Root finding in non-linear problems (single- and multi-dimensional)
  • Numerical integration of standard differential equation systems (linear, non-linear, formal procedures (Runge-Kutta etc.)
  • Time step control (stability, integration order)
  • Boundary value problems (linear, non-linear, time-dependant), FDM, FEM, BEM
  • Stochastic simulation
  • Design and organisation of a Monte Carlo simulator


Modelling and simulation of continuous systems

Tutor

Prof. Scheuring

Credit points

4 CP

Term

Spring

Contents

  • Modelling of process-engineering processes resp. unit operations (thermodynamics, data on chemical media, valves, pumps, reactors, distillation columns, etc.)
  • Design and organisation of a simulator
  • Sequential-modular simulation
  • Dynamic simulation
  • Introduction to UniSim
  • Process industry applications


Modelling and simulation of discrete event systems

Tutor

Prof. Scheuring + Prof. Westenberger

Credit points

2 CP

Term

Spring

Contents

  • Specifications of discrete event systems
  • Compositional modelling of discrete event systems
  • Object-oriented simulation of discrete event systems
  • Introduction to Arena and Plant Simulation (formerly Simple ++, eM-Plant)
  • Probability distribution
  • Queuing theory
  • Process and production industry application examples


Data-driven modelling and model optimization

Tutor

Prof. Bartz-Beielstein + Prof. Konen

Credit points

6 CP

Term

Spring

Contents

  • Data from real-world problems (industry, economy, science)
  • Data preparation
  • Design of experiments (DOE)
  • Design and analysis of computer experiments (DACE)
  • Treatment of missing values and huge data sets
  • Data visualization
  • Data analysis, computational statistics
  • Data mining, CRISP-DM Process
  • Analysis, especially classification and regression
  • Learning, especially advanced modelling techniques: Bootstrap, bagging, meta learner (e.g. random forests), empirical learning problems
  • Evaluation of modelling results (e.g., error measures, overfitting, cross validation, precision and recall)
  • Sequential parameter optimization (SPO)


Bibliography

  • Stoer, Burlisch, Einführung in die numerische Mathematik I + II, ISBN 3-540-09346-X and ISBN 3-540-08840-7
  • Schwedlick, Kretschmar, Numerische Verfahren für Naturwissenschaftler und Ingenieure, ISBN 3-343-00580-0
  • Vlach J., Singal K., Computer Methods for Circuit Analysis and Design, ISBN 0-442-28108-0
  • UniSim-Documentation, Honeywell 2006
  • Kelton, W.D., Sadowski, R.P., Sadowski, D.A.: Simulation with Arena. McCraw-Hill 2002
  • Banks, J.: Discrete-Event System Simulation, Prentice-Hall, 1996
  • Liebl: Simulation. 2nd revision, Munich. Oldenbourg 1995
  • Greasley A.: Simulation Modelling for Business. Ashgate Hants 2004.
  • Feldmann K., Reinhardt G. (Hrsg.): Simulationsbasierte Planungssysteme für Organisation und Produktion. Springer Berlin 1999.
  • Fishman G.S.: Discrete-Event Simulation. Springer Series in Operations Research. Springer 2001.
  • Kelton, W.D., Sadowski, R.P., Sadowski, D.A.: Simulation with Arena. McCraw-Hill 2002 McCraw-Hill 2002
  • Witten, Ian H., Frank, Eibe: Data Mining, Hanser, 2nd ed., 2005.
  • Hastie, Tibshirani, Friedeman: The Elements of Statistical Learning. Springer, 2001.
  • Law, A.M., Kelton, W.D., Simulation Modeling and Analysis. McGraw-Hill, Boston. 2000
  • Bartz-Beielstein, T.: Experimental Research in Evolutionary Computation. Springer, Berlin 2006