2 citations · 4 across the 3 of their papers we have counts for
3 papers
Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge
Quirin Göttl, Jonathan Pirnay, Jakob Burger +1
Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learnin…
Convex Envelope Method for determining liquid multi-phase equilibria in systems with arbitrary number of components
Quirin Göttl, Jonathan Pirnay, Dominik G. Grimm +1
The determination of liquid phase equilibria plays an important role in chemical process simulation. This work presents a generalization of an approach called the convex envelope m…
Deep Anomaly Detection on Tennessee Eastman Process Data
Fabian Hartung, Billy Joe Franks, Tobias Michels +15
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tenn…