29 citations · 33 across the 5 of their papers we have counts for
5 papers
Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
Jan C. Schulze, Danimir T. Doncevic, Nils Erwes +1
Achieving real-time capability is an essential prerequisite for the industrial implementation of nonlinear model predictive control (NMPC). Data-driven model reduction offers a way…
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…
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…
Cost-Optimal Power-to-Methanol: Flexible Operation or Intermediate Storage?
Simone Mucci, Alexander Mitsos, Dominik Bongartz
The synthesis of methanol from captured carbon dioxide and green hydrogen could be a promising replacement for the current fossil-based production. The major energy input and cost…
Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep Learning
Jan C. Schulze, Danimir T. Doncevic, Alexander Mitsos
We use Koopman theory to develop a data-driven nonlinear model reduction and identification strategy for multiple-input multiple-output (MIMO) input-affine dynamical systems. While…