Background:

The synthesis of e-fuels is crucial to reduce emissions in the transportation sector, especially in areas where direct electrification is not possible. Ideally, renewable energy sources such as solar and wind power are used to minimize greenhouse gas emissions during production. However, this means that continuous stationary operation is not possible and a highly dynamic mode of operation is required instead.

As the components required for e-fuel systems are very capital-intensive, the system layout must be adapted to the local wind and solar radiation conditions. This dimensioning is done with the help of optimization algorithms that use the local wind and solar conditions over longer periods of time, usually a year, as input and determine the ideal system layout based on the technical boundary conditions. One problem here is that the optimizer has so-called "perfect foresight", while a real plant can only plan its operation on the basis of weather forecasts for the next few hours or is limited to seasonal forecasts.

Aim of the master's thesis:

The aim of this master's thesis is to develop a control strategy for the operation of e-fuel systems based on the results provided by the optimization. This strategy should take into account the real conditions and the unpredictability of weather conditions and thus ensure the most efficient and stable operation of the system.

Task definition:

Mapping the e-fuel system in the "AVEVA" process simulation software

Programming a Python-based tool for process control via the API

Development of a control strategy based on the real weather forecasts

Comparison of the developed strategy with the idealized "perfect foresight" strategy.

Documentation and evaluation of the results.

Prerequisites:

- Degree in engineering, process engineering or comparable.

- Knowledge of optimization methods and modeling.

- Programming skills, especially in Python

- Interest in renewable energies and sustainable technologies.