Final Thesis

Efficient Dynamic Optimization of Large-Scale Processes

Key Info

Basic Information

Unit:
Process Systems Engineering
Type:
Masterthesis / Bachelorthesis
Focus/Key Topic:
simulativ
Date:
21.11.2017

Contact

The amount of renewable energies in the portfolio of energy sources has been increasing significantly accompanied by the fluctuation of electricity production, resulting in several challenges to be addressed by process operations. Beside the idea of storing excess energy, there exists the idea of adjusting large energy consumers, such as chemical processes in accordance to these energy fluctuations. This challenge is summarized by the terminus technicus Demand Side Management.

Dynamic optimization enables large-scale processes to be operated optimally in an economical sense and at the same time fulfil further constraints such as product purities. This requires accurate and usually non-linear dynamic models, which are often modelled as structured systems. They must be reformulated as mathematical systems of equations by so-called model servers before they can be deployed in an optimization framework. Besides this model server, efficient algorithms for dynamic optimization are also required.

This work will focus on the integration, application, and extension of a model server with an existing algorithm for dynamic optimization.

What we expect from you:

  • You should be open-minded and enjoy working in a team (also enjoy programming)
  • You should be interested in numerical optimization
  • You should have some basic knowledge about programming
  • It would be nice if you know already some C++ and Modelica
  • 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
  • Chance to continue your work as a HIWI at the institute

Contact:

If you have any further question or if you are interested in the work, please feel free to contact me. This work may be advised by a team three PhD students.

RWTH Aachen University

Aachener Verfahrenstechnik - Process Systems Engineering (AVT.SVT)

Forckenbeckstr. 51

Room B-227

52074 Aachen, Germany

Tel: +49 241 80 97013

Email: adrian.caspari@avt.rwth-aachen.de

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