Bamberg, A., Urbas, L., Bröcker, S., Bortz, M., & Kockmann, N. (2021). The Digital Twin – Your Ingenious Companion for Process Engineering and Smart Production. Chemical Engineering & Technology, 44(6), 954–961.

Brand-Rihm, G., Esche, E., & Repke, J.-U. (2021). Sampling Space Reduction for Data-driven Modelling of Batch Distillation - Introducing Expert Process Knowledge through Operation Recipes. In Computer Aided Chemical Engineering. 31st European Symposium on Computer Aided Process Engineering (Vol. 50, pp. 611–616). Elsevier.

Damay, J., Jirasek, F., Kloft, M., Bortz, M., & Hasse, H. (2021). Predicting Activity Coefficients at Infinite Dilution for Varying Temperatures by Matrix Completion. Industrial & Engineering Chemistry Research, 60(40), 14564–14578.

Gärtler, M., Khaydarov, V., Klöpper, B., & Urbas, L. (2021). The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges. Chemie Ingenieur Technik, 93(12), 2063–2080.

Lueg, L., Schack, D., Örs, E., Schmidt, R., Bickert, P., Kurnatowski, M. von, Ludl, P. O., & Bortz, M. (2021). Data-driven Process Design Exemplified on the Steam Methane Reforming Process. In Computer Aided Chemical Engineering. 31st European Symposium on Computer Aided Process Engineering (Vol. 50, pp. 1013–1019). Elsevier.

Oeing, J., Henke, F., & Kockmann, N. (2021). Machine Learning Based Suggestions of Separation Units for Process Synthesis in Process Simulation. Chemie Ingenieur Technik, 93(12), 1930–1936.

Oeing, J., Neuendorf, L. M., Bittorf, L., Krieger, W., & Kockmann, N. (2021). Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches. Chemie Ingenieur Technik, 93(12), 1917–1929.

Schack, D., Lueg, L., Schmidt, R., von Kurnatowski, M., Ludl, P.O., Bortz, M.; Data-Driven Process Simulation Using Connected Surrogate Unit Models Exemplified on a Steam Methane Reforming Process; Chemie Ingenieur Technik 93, pp. 2052-2062, 2021, DOI:

Schmidt, B., Tan, R., Li, N., Hollender, M., & Gärtler, M. (2021). Efficient Process for Batch Analysis. Chemie Ingenieur Technik, 93(12), 1955–1967.

Schweidtmann, A. M., Esche, E., Fischer, A., Kloft, M., Repke, J.-U., Sager, S., & Mitsos, A. (2021). Machine Learning in Chemical Engineering: A Perspective. Chemie Ingenieur Technik, 93(12), 2029–2039.

Schöneberger, J. C., Aker, B., & Fricke, A. (2021). Explaining and Integrating Machine Learning Models with Rigorous Simulation. Chemie Ingenieur Technik, 93(12), 1998–2009.

Wiedau, M., Tolksdorf, G., Oeing, J., & Kockmann, N. (2021). Towards a Systematic Data Harmonization to Enable AI Application in the Process Industry. Chemie Ingenieur Technik, 93(12), 2105–2115.

Winz, J., Nentwich, C., & Engell, S. (2021). Surrogate Modeling of Thermodynamic Equilibria: Applications, Sampling and Optimization. Chemie Ingenieur Technik, 93(12), 1898–1906.

Winz, J., & Engell, S. (2021). Optimization based sampling for gray-box modeling using a modified upper confidence bound acquisition function. In Computer Aided Chemical Engineering. 31st European Symposium on Computer Aided Process Engineering (Vol. 50, pp. 953–958). Elsevier.

Varshneya, S., Ledent, A., Vandermeulen, R. A., Lei, Y., Enders, M., Borth, D., & Kloft, M. (2021, August 19–27). Learning Interpretable Concept Groups in CNNs. In M. Gini & Z.-H. Zhou (Eds.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp. 1061–1067). International Joint Conferences on Artificial Intelligence Organization.

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