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

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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