Data-driven product-process optimization of N-isopropylacrylamide microgel flow-synthesis

Luise F. Kaven, Artur M. Schweidtmann, Jan Keil, Jana Israel, Nadja Wolter, Alexander Mitsos*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their small size allows them to pass human cell boundaries. For applications with specified requirements regarding size, producing tailored microgels in a continuous flow reactor is advantageous because the microgel properties can be controlled tightly. However, no fully-specified mechanistic models are available for continuous microgel synthesis, as the physical properties of the included components are only studied partly. To address this gap and accelerate tailor-made microgel development, we propose a data-driven optimization in a hardware-in-the-loop approach to efficiently synthesize microgels with defined sizes. We optimize the synthesis regarding conflicting objectives (maximum production efficiency, minimum energy consumption, and the desired microgel radius) by applying Bayesian optimization via the solver “Thompson sampling efficient multi-objective optimization” (TS-EMO). We validate the optimization using the deterministic global solver “McCormick-based Algorithm for mixed-integer Nonlinear Global Optimization” (MAiNGO) and verify three computed Pareto optimal solutions via experiments. The proposed framework can be applied to other desired microgel properties and reactor setups and has the potential of efficient development by minimizing number of experiments and modeling effort needed.

Original languageEnglish
Article number147567
Number of pages11
JournalChemical Engineering Journal
Publication statusPublished - 2024

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


This work was performed as a part of project B4 of the CRC 985 “Functional Microgels and Microgel Systems” funded by Deutsche Forschungsgemeinschaft (DFG) . The authors thank Jan Steinstraßen for support with conducting continuous microgel syntheses. The authors also thank Johannes M. M. Faust for fruitful discussions and Jannik Lüthje and Daniel Jungen for support with the software MAiNGO.


  • Bayesian optimization
  • Flow-chemistry
  • Microgel synthesis
  • Product-process optimization


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