Thesis’ Details
Topic
Development of self-learning optimization algorithms for pyrometallurgical process development using the case study of electronic scrap recycling
Description
Master Thesis
Duration
6 month(s)
Start
Immediately
Contact
Background
Development of intelligent, sequential algorithms for the synthesis and optimization of pyrometallurgical processes based on thermochemical data. The aim is to develop a self-learning algorithm that automatically determines process sequences under defined, process-related restrictions and an intelligent optimization search.
Job Definition
- Design of an efficient search strategy
- Literature review to identify technical limitations
- Implementation of the algorithm
- Evaluation through comparison with the state of the art
Requirements
• Good knowledge of Python and Scikit-Learn
• Ability to work independently and responsibly
• Interest in computational metallurgy





