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KEEN Presentation Overview



E. Esche, J. Weigert, G. B. Rihm, J. Göbel, J.-U. Repke, Architectures for Neural Networks as Surrogates for Dynamic Systems in Chemical Engineering, Eng. Chem. Res. 2021.

J. Damay, F. Jirasek, M. Kloft, M. Bortz, H. Hasse, Ind. Eng. Chem. Res. 2021, 60 (40), 14564 – 14578.

B. J. Franks, M. Anders, M. Kloft, P. Schweitzer, Trainability for Universal GNNs Through Surgical Randomness 2021.

R. Gedda, L. Beilina, R. Tan, Interactive Change Point Detection using optimisation approach and Bayesian statistics applied to real world applications 2021.

G. Brand-Rihm, E. Esche, J.-U. Repke, Sampling Space Reduction for Data-driven Modelling of Batch Distillation - Introducing Expert Process Knowledge through Operation Recipes, in 31st European Symposium on Computer Aided Process Engineering, Vol. 50, Computer Aided Chemical Engineering, Elsevier 2021.

L. Lueg, D. Schack, E. Örs, R. Schmidt, P. Bickert, M. von Kurnatowski, P. O. Ludl, M. Bortz, in 31st European Symposium on Computer Aided Process Engineering, Vol. 50, Computer Aided Chemical Engineering, Elsevier 2021.


Chemie Ingenieur Technik Volume 93, Issue 12; Special Issue: Artificial Intelligence in Chemical Engineering


J. Winz, C. Nentwich, S. Engell, Surrogate modelling of thermodynamic equilibria: applications, sampling and optimization, Chemie Ingenieur Technik 2021, 93 (12), 1898–1906.

J. Oeing, L. M. Neuendorf, L. Bittorf, W. Krieger, N. Kockmann, Chemie Ingenieur Technik 2021, 93 (12), 1917–1929.

J. Oeing, F. Henke, N. Kockmann, Chemie Ingenieur Technik 2021, 93 (12), 1930 – 1936.

B. Schmidt, R. Tan, N. Li, M. Hollender, M. Gärtler, Chemie Ingenieur Technik 2021, 93 (12), 1955 –1967.

J. C. Schöneberger, B. Aker, A. Fricke, Chemie Ingenieur Technik 2021, 93 (12), 1998 – 2009.

D. Schack, L. Lueg, R. Schmidt, M. von Kurnatowski, P. O. Ludl, M. Bortz, Chemie Ingenieur Technik 2021, 93 (12), 2052 – 2062.

M. Gärtler, V. Khaydarov, B. Klöpper, L. Urbas, Chemie Ingenieur Technik 2021, 93 (12), 2063–2080.

M. Wiedau, G. Tolksdorf, J. Oeing, N. Kockmann, Chemie Ingenieur Technik 2021, 93 (12), 2105 –2115.



KEEN: BIG DATA/ LOW QUALITY/ TINY INFORMATION – Erklärbare KI für die Prozessindustrie?!
Lecture by Prof. Leon Urbas on 10.06.2020



J. Oeing, N. Kockmann (2021) Standardization of HAZOP-studies for application of Artificial Intelligence

V. Khaydarov (2021) Recognition of Flow Regime and Evaluation of KPI in Aerated Stirred Tanks

L. Sherpa, R. Jäkel, R. Müller-Pfefferkorn (2021) Making Data Shareable - the Keen Data Management Platform

J. Schöneberger, B. Aker, A. Fricke, M. Kawohl, Ch. Hiller, V. Khaydarov (2021) Broaden the Operation Window for a Batch Distillation

Z. Charania, M. Krawczyk-Becker, U. Enste, D. Wagner-Stürz, L. Urbas (2021) Efficient Integration Architecture For Enabling AI-Based Applications In Brownfield Plants

R. Tan, B. Kloepper (2021) An overview of plant data for machine learning: categories, availability, and common problems

A. Fricke, B. Aker, J. C. Schöneberger (2021) Interaction of Process Engineers and Software Tools in Machine Learning (ML)

B. Bordas, M. P. Galvis Cordoba, K. Kurt, A. Bamberg, P. Sander, D. Bleidorn, Ch. Sonntag (2021) AI-driven Optimization, Monitoring and Decision Support for Batch Plants

J. Oeing, W. Welscher, N. Kockmann (2021) Artificial Intelligence supported P&ID-development

V. Khaydarov, J. Schöneberger, S. Blumenschein, M. Schleehahn (2021) Operating window exploration of continuous reaction plant

F. Bähner, M. Gärtler, B. Klöpper, S. Maczey, S. Merkelbach, P. Sander, C. Song, C. Sonntag, R. Tan (2021) Towards Automatic Batch Phase Detection: First Results using Machine Learning

J. Winz, U. J. Ravali Theeda, B. Bordas, K. Kurt, A. Bamberg, S. Engell (2021) Hybrid modeling and control of a batch distillation process of polymer solutions

S. Lammers, A. Lasch (2021) Does AI have to be humanoid?

L. Neuendorf, Ch. Schlander, B. J. Franks (2021) Preselection of separation units and ML-supported operation of an extraction column

J. Oeing, F. Henke, N. Kockmann (2021) AI-supported prediction of separation processes

G. Brand Rihm, E. Esche, J.-U. Repke, C. Nentwich, Ch. Hiller, M. Kawohl (2021) Training of data-driven models for real-time optimization of operation trajectories of distillation processes


Preliminary Work

B. Beisheim, K. Rahimi-Adli, S. Krämer, S. Engell, Energy performance analysis of continuous processes using surrogate models. Energy 183, 776-787, 2019.  

R. Heese, M. Walczak, T. Seidel, N. Asprion, M. Bortz, Optimized data exploration applied to the simulation of a chemical process,Comp Chem Eng. 124, 326-342, 2019  

N. Asprion, R. Böttcher, R. Pack, M.-E. Stavrou, J. Höller, J. Schwientek, M. Bortz; Gray-Box Modeling for the Optimization of Chemical Processes; Chem. Ing. Tech. 2018  

F. Stenger, L. Urbas, L. Woppawa: 100% Digital in der Prozessindustrie: Smart Equipment wird ein essentieller Building Block in einer digitalisierten Prozesslandschaft. CITPlus 10/2018.

L. Schegner, L. Urbas, M. Krauss, J. Birk: Autonomie und Assistenz in der Prozessindustrie. Tagungsband Automation 2018, pp.

N. Kockmann, L. Bittorf, W. Krieger, F. Reichmann, M. Schmalenberg, S. Soboll, Smart Equipment – A Perspective Paper, Chem. Ing. Technik, 90 (11), 1806-1822, 2018

N. Kockmann, P. Thenée, C. Fleischer, G. Laudadio, T. Noel, Safety Assessment in Development and Operation of Modular Continuous-Flow Processes, Reaction Chemistry & Engineering, 2, 258-280, 2017

N. Bortz, J. Burger, N. Asprion, S. Blagov, R. Böttcher, U. Nowak, A. Scheithauer, R. Welke, K.-H. Küfer, H. Hasse; Multi-criteria optimization in chemical process design and decision support by navigation on Pareto sets; Comp. Chem. Eng. 60 (2014) 354