10 papers
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…
Data-Driven Conditional Flexibility Index
Moritz Wedemeyer, Eike Cramer, Alexander Mitsos +1
With the increasing flexibilization of processes, determining robust scheduling decisions has become an important goal. Traditionally, the flexibility index has been used to identi…
End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit
Daniel Mayfrank, Kayra Dernek, Laura Lang +2
With our recently proposed method based on reinforcement learning (Mayfrank et al. (2024), Comput. Chem. Eng. 190), Koopman surrogate models can be trained for optimal performance…
DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
Jan Pavšek, Alexander Mitsos, Manuel Dahmen +2
We propose a machine learning (ML) architecture to better capture the dependency of thermodynamic properties on the independent states. When predicting state-dependent thermodynami…
Molecular Machine Learning in Chemical Process Design
Jan G. Rittig, Manuel Dahmen, Martin Grohe +2
We present a perspective on molecular machine learning (ML) in the field of chemical process engineering. Recently, molecular ML has demonstrated great potential in (i) providing h…
Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
Hongzhen Zhang, Tim Kerkenhoff, Neil Kichler +4
Spatial Branch and Bound (B&B) algorithms are widely used for solving nonconvex problems to global optimality, yet they remain computationally expensive. Though some works have bee…