Final Thesis

CES Project thesis - Machine learning and optimization

Key Info

Basic Information

Unit:
Process Systems Engineering
Type:
Projectthesis
Focus/Key Topic:
simulativ
Date:
2018

Contact

Description of the thesis:

In order to optimize complex chemical processes it is often necessary to develop detailed models of the process. The development of such models is often time-consuming and it can yield complex models, which are not useful for efficient optimization. Thus, the employment of machine learning surrogate-models such as artificial neural networks, Gaussian processes, and convex region surrogate models can be useful for optimization.

In the proposed work, you will extend an existing machine-learning algorithm for an industry application. This will include the implementation of a GUI as well as some code modifications. Finally, the algorithm will be applied to a current engineering problem.

What we expect from you:

  • You should be open-minded and enjoy working in a team (also enjoy programming)
  • You should be interested in machine learning, neural networks, chemical processes, and optimization
  • You should be very good at programming (C++)
  • You should be able to communicate in English

What we offer you:

  • A highly motivated team
  • Always a contact person for your work
  • Lots of challenging and interesting tasks
  • A possibility to gain in-depth knowledge about artificial neural networks, optimization, machine learning

Contact:

If you have any further question or if you are interested in the work, please feel free to contact me.

RWTH Aachen University

Aachener Verfahrenstechnik - Process Systems Engineering (AVT.SVT)

Forckenbeckstr. 51

Room A-203

52074 Aachen, Germany

Tel: +49 241 8097018

Email: artur.schweidtmann@avt.rwth-aachen.de

Web: http://www.avt.rwth-aachen.de