AEE - Institute for Sustainable Technologies (AEE INTEC) is a private research institute founded in 1988. AEE INTEC employs around 70 staff members from 10 different nations working as full-time employees, doctoral candidates, master's theses and internship students. AEE INTEC works in the fields of "Buildings", "Cities and Networks", "Technology Development" and "Industrial Systems" on basic and applied research. AEE INTEC cooperates with industries, universities, and other research institutes in national and international R&D projects.
DigitalEnergyTwin is an ongoing research project with the aim of supporting industry with a methodology and software tool to optimise the operation and design of the industrial energy demand and supply system. By applying the digital twin method, detailed models for selected energy-relevant processes and renewable technologies are developed, validated and simplified. The core of the project is the development of an optimisation approach based on both standardised examples and real applications in the manufacturing industry. This is the first time that a solution will be developed for managing in real-time the volatile renewable energy supply and its efficient use for fluctuating energy demand at the process level in industry.
- Literature review of the state of the art in optimisation methods
- Derivation and development of suitable methods for the optimisation of industrial systems on the basis of an evaluation matrix
- Definition of the "ideal" and suitable optimisation method (genetic algorithms for black box models, gradient-based approaches for physical models, etc.)
- Derive and define local key performance indicators (KPIs) as the basis for optimisation and combine them into global KPIs
- Application and integration of the optimisation framework into the software DigitalEnergyTwin
What we need
- Solution oriented, creative, independent and reliable way of working
- Educational background in industrial processes and renewable technologies
- Interest in thermodynamic based simulation and identification of the relationships between energy demand and sustainable supply
- Interest in linear and nonlinear optimisation will be an advantage
What we offer
- Paid master thesis under the framework of an ongoing research project
- Supervision by experienced staff and competent professional support.
- Time frame: Start date Autumn 2021; duration approx. 6 months
Jürgen Fluch, Tel 03112 5886-454, firstname.lastname@example.org