Project Details
Projekt
Subproject SP7: Self-learning control of the catalytic conversion of olefins to α-amino acids and β-amino alcohols
Applicant
Professor Dr.-Ing. Achim Kienle
Subject Area
Chemical and Thermal Process Engineering
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 501735683
This proposal is part of the research unit FOR5538. In this subproject we aim at the development of self-learning control systems for online optimization of catalytic conversions of olefins to α-amino acids and β-amino alcohols. This subproject complements the integrated molecular, material and process design performed in SP6. It accounts for plant-model mismatch and unforeseen disturbances by repetitive online optimization on the single batch and/or the batch-to-batch level to automatically re-adjust the operational variables using available measurement information and so-called hybrid mathematical models. Hybrid modeling combines physical chemical knowledge obtained in the other subprojects with data-driven approaches from machine learning. Essential steps of this approach comprise the development of: (i) hybrid mathematical models of the individual process steps, (ii) suitable methods for online model adaption, (iii) efficient strategies for online optimization, (iv) the integration of the aforementioned methods into self-learning control concepts (v) systematic in silico testing of the developed methods, and finally (vi) their experimental validation in cooperation with the corresponding subprojects involved in this research unit. Besides nonlinear models also families of locally linearized process descriptions will be considered and compared to the nonlinear approach. To safely satisfy crucial operational constraints during the iterative optimization in the presence of uncertainties, a hierarchical approach with direct control of critical variables on the lower level is suggested. Focus in the first funding period will be on the enzymatic conversion of α-keto carboxylic acids to α-amino acids, especially homophenylalanine, and α-hydroxy ketones to β-amino alcohols, especially homophenylalaninol with integrated product crystallization as studied in SP3, and afterwards on membrane separation for catalyst recycle and solvent separation, as studied in SP4. Since the approach of this subproject relies also on a priori knowledge to be generated in SP3, during the startup phase, the methodology for the enzymatic reactive crystallization process will be first developed and tested for a model system which has been studied in the preparatory work of this research unit. Possible extensions for a second funding period comprise the integration of further reaction steps into an overall self-learning control strategy with special emphasis on ‘plantwide’ aspects, i.e. which steps have to be controlled in which way to achieve the overall process objectives. Further, we aim at an integration of the design approach in SP6 with the control approach considered in this subproject.
DFG Programme
Research Units
