Final Thesis

ANN Artificial Neural Networks for Chemical Process Optimization

Key Info

Basic Information

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, the first step will be a literature review about common machine learning techniques. Then, promising techniques should be implemented and applied to a chemical process. One of the most important steps in the machine learning is the actual “learning” or parameter fitting. Thus, a focus of this work will deal with different methods of learning and will compare their performance. Finally, a chemical process should be optimized using the developed methods.

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 have some basic knowledge about programming
  • It would be nice if you know already some Matlab, GAMS, gPROMS, and/or 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 everything works well a possibility to continue your work as a HIWI at the institute


If you have any further question or if you are interested in the work, please feel free to contact me. This work can possible be advised by a team three PhD students namely Adrian, Pascal, and Artur.

RWTH Aachen University

Aachener Verfahrenstechnik - Process Systems Engineering (AVT.SVT)

Forckenbeckstr. 51

Room A-203

52074 Aachen, Germany

Tel: +49 241 8097018