2021

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. https://doi.org/10.1002/ceat.202000562

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. https://doi.org/10.1016/B978-0-323-88506-5.50097-8

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. https://doi.org/10.1021/acs.iecr.1c02039

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. https://doi.org/10.1002/cite.202100134

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. https://doi.org/10.1016/B978-0-323-88506-5.50156-X

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. https://doi.org/10.1002/cite.202100082

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. https://doi.org/10.1002/cite.202100051

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: https://doi.org/10.1002/cite.202100087

Schmidt, B., Tan, R., Li, N., Hollender, M., & Gärtler, M. (2021). Efficient Process for Batch Analysis. Chemie Ingenieur Technik, 93(12), 1955–1967. https://doi.org/10.1002/cite.202100081

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. https://doi.org/10.1002/cite.202100083

Schöneberger, J. C., Aker, B., & Fricke, A. (2021). Explaining and Integrating Machine Learning Models with Rigorous Simulation. Chemie Ingenieur Technik, 93(12), 1998–2009. https://doi.org/10.1002/cite.202100089

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. https://doi.org/10.1002/cite.202100203

Winz, J., Nentwich, C., & Engell, S. (2021). Surrogate Modeling of Thermodynamic Equilibria: Applications, Sampling and Optimization. Chemie Ingenieur Technik, 93(12), 1898–1906. https://doi.org/10.1002/cite.202100092

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. https://doi.org/10.1016/B978-0-323-88506-5.50147-9

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. https://doi.org/10.24963/ijcai.2021/147

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