activity
20162024
most citedGlobal Deterministic Optimization with Artificial Neural Networks Embedded

196 citations · 723 across the 27 of their papers we have counts for

collaborators

29 papers

eess.SY2024★ 17 cited

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study

Jan C. Schulze, Alexander Mitsos

We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. We propose generic model structures combining delay-coordinate encoding of measu…

math.OC2024

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response

Sonja H. M. Germscheid, Benedikt Nilges, Niklas von der Assen +2

This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity gen…

cs.CE2023

Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis

Luise F. Kaven, Artur M. Schweidtmann, Jan Keil +3

Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their size in the nanometer range allows…

math.OC2023

Optimal Design and Flexible Operation of a Fully Electrified Biodiesel Production Process

Mohammad El Wajeh, Adel Mhamdi, Alexander Mitsos

The flexible operation of electrified chemical processes, powered by renewable electricity, offers economic and ecological incentives, paving the way for a more sustainable chemica…

cs.LG2023★ 10 cited

End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control

Daniel Mayfrank, Alexander Mitsos, Manuel Dahmen

(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic models that are sufficiently accurate and computationally tractable. Data-driven surrogate models for mecha…

physics.chem-ph2023★ 60 cited

Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction

Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin +1

We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly…