Simulation and Optimization
Content
The main objective of this course is the interdisciplinary cooperation of students of different specializations such as process engineering, regenerative energy systems, electrical engineering and information system technology. The course will highlight the following topics:
- Static and dynamic first-principle modeling and simulation approaches
- Data-driven modeling and Machine Learning
- Gray-box modeling, model identification and design of experiments
- Model-based engineering for conventional and modular plants
- Soft sensors and Model Predictive Control
During the exercises, the students will apply and consolidate the gained knowledge by programming simulations within MATLAB.
The core of the course is a joint, interdisciplinary project work. In small groups (3-6 people) under the supervision of the instructor, students work on a self-contained project on a topic from the list above.
Scope
(1 1 0) SWS Lecture and Exercise
(0 0 2) SWS Project work
Learning materials
The enrollment in the course is done via OPAL-website Simulation and Optimization. Please register yourself on OPAL before the beginning of the semester. Only via OPAL you will get current information about the course incl. the first familiarization event. The teaching materials will also be made available via OPAL website.
Teaching mode
Due to the on-going Corona conditions and the indisciplinary nature of the course, lectures and exercises take place digitally. Students are provided with lecture notes in digital form to encourage self-study. Depending on the topic, the teaching material for each lecture unit includes: lecture notes, Matlab live scripts, slides and/or videos, as well as exercises and sample solutions. Each lecture unit is followed by an online consultation during which students have the opportunity to ask the instructor questions about the course material. Project work is also scheduled with regular consultation.
Required Knowledge
Due to the interdisciplinary structure of the course, students are encouraged to combine their knowledge from the different field of studies. The content from the lectures listed below builds the knowledge base for this course:
- Process analysis and experimental design
- System process engineering
- Theoretical process analysis
- Experimental process analysis
- Automation technology
- Control engineering
Contact
In case of any question regarding the course please contact Dr. Valentin Khaydarov
© Michael Kretzschmar
Postdoc
NameMr Dr.-Ing. Jonathan Mädler
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Process Systems Engineering Group
Process Systems Engineering Group
Visiting address:
Merkelbau Helmholtzstraße 14
01069 Dresden
Postal address:
TUD Dresden University of Technology Arbeitsgruppe Systemverfahrenstechnik
01062 Dresden