Literature review about neural networks and machine learning in chemical engineering
- Process Systems Engineering
- Projectthesis / Forschungspraktikum for Biotechnologen / Studythesis for Umweltingenieure
- Focus/Key Topic:
- simulativ / other
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.
The proposed work is a literature review about neural networks and machine learning in chemical engineering.
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 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 by e-mail.
RWTH Aachen University
Aachener Verfahrenstechnik - Process Systems Engineering (AVT.SVT)
52074 Aachen, Germany
Tel: +49 241 8097018