Thesis’ Details
Topic
Development of self-learning optimization algorithms for pyrometallurgical process development using the case study of electronic scrap recycling
Description
Bachelor Thesis
Duration
3 month(s)
Start
Immediately
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
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