CES Project thesis - Machine learning and optimization
- Process Systems Engineering
- Focus/Key Topic:
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
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)
52074 Aachen, Germany
Tel: +49 241 8097018