3 papers
cs.LG2026
Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach
Mehmet Velioglu, Song Zhai, Alexander Mitsos +3
Separating liquid-liquid dispersions in gravity settlers is critical in chemical, pharmaceutical, and recycling processes. The dense-packed zone height is an important performance…
cs.LG2025
Sample-Efficient Reinforcement Learning of Koopman eNMPC
Daniel Mayfrank, Mehmet Velioglu, Alexander Mitsos +1
Reinforcement learning (RL) can be used to tune data-driven (economic) nonlinear model predictive controllers ((e)NMPCs) for optimal performance in a specific control task by optim…
cs.LG2024
Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
Mehmet Velioglu, Song Zhai, Sophia Rupprecht +3
In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) f…