Integrated Processes for Producing Fine Chemicals
 

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This project aims to create self-learning control concepts for multi-step catalytic processes of fine chemistry. As a benchmark problem conversion of olefins to α-amino acids and β-amino alcohols is considered.These processes will include integrated product crystallization. Additionally, the project will explore membrane separation techniques for the separation of catalysts and solvents.Online optimization will be performed at both the 'single batch' and 'batch-to-batch' levels. The operating conditions will be determined using available measurement data and hybrid mathematical models, combining fundamental physical chemistry knowledge with data-driven machine learning approaches. The gained knowledge will be integrated into a self-learning control framework and experimentally validated in cooperation with the project partners.

Cooperation

  • Prof. Beller and Dr. Kubis (LIKAT Rostock)
  • Prof. Kragl (University Rostock)
  • PSE group
  • PCF group
  • Prof. von Langermann (OvGU)
  • Prof. Kulak (University Potsdam)

Funding

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