In order to reduce the fine dust pollution (primarily submicron particles) at metalworking workplaces, so-called dust collection systems are used.

For the ressource-efficient autonomous control of dust collections systems (e.g. wet scrubbers, bag house filters, …), the in-line characterization oft he particle lade gas flow (particle concentration (PC) and particle size distribution (PSD)) is of central importance. For this reason, cost-effective particle sensors are required which are able to reliably characterize the aerosol (PC, PSD) even under complex process conditions (high flow velocities, high relative humidities).

However, the current state of the art only provides laboratory measurement technology, which on the one hand is too expensive (~20000€) and on the other hand provides insufficient connectivity for further processing of the measurement data (post-processing).

For this reason, it will be investigated to what extent low-cost particle sensors (~40€ - 1000€), which were developed for the cost-effective measurement of air quality, are suitable for use in digitized dust collection systems. In addition, machine learning methods for post-processing (regression based models, gaussian processes, neurol networks, …) will be tested for their applicability.

Due to the diversity of the research project, I can offer you experimental (optimization of operational behavior, investigation of fundamental mechanisms), as well as theoretical (application and testing of AI approaches to optimize measurement data, flow simulations) and constructive theses.

I am also happy to offer you the opportunity to present your results during project meetings with my industry partner (SME in Germany).

If you are generally interested in the potential of applying artificial intelligence methods to digitalized processes in process engineering, I would be happy if you send me an email or give me a call. Afterwards, we can work together to align the thesis according to your interests and wishes.

What I can offer you:

• I maintain an intensive mentoring relationship with my students with regular appointments (if requested) and I’m always available to support with problems
• I give my students the freedom to contribute their own ideas to the final project
• The thesis is your work and not mine, which is why I prefer to be your advisor and not to interfere too much
• A permanent workplace (incl. IT equipment) in an office at the institute or at the test facility during the thesis (if available)
• Probably the best fresh grounded filter coffee at the KIT

Your tasks include:

• A short introduction to the basics of particle separation and particle measurement technology (low-cost PM sensors)
• Depending on the specific topic, familiarization with AI approaches, CFD simulations, design, at the test facility (laboratory activity) …
• Creation and tracking of a project plan for your thesis

What you should bring to the job:

• Having fun to learn and try new things
• High degree of independence
• Ability to communicate
• Goal oriented work
• Study field: CIW/VT, BIW, MACH, Informatics, Mathematics

Contact information:
Felix Reinke, M.Sc.
E-Mail: felix.reinke∂kit.edu
Tel.: +49 721 608-46575