Baldan, M., Ludl, P. O., Süss, P., Schack, D., Schmidt, R., & Bortz, M. (2023). Real‐Time Interactive Navigation on Input‐Output Data Sets in Chemical Processes. Chemie Ingenieur Technik, 95(7), 1028–1040. https://doi.org/10.1002/cite.202200240

Bordas, B., Kurt, K., Bamberg, A., & Engell, S. (2023). Gray‐Box Modeling of the Molecular Weight Distribution in a Batch Polymerization Reactor. Chemie Ingenieur Technik, 95(7), 1104–1113. https://doi.org/10.1002/cite.202200234

Bortz, M., Dadhe, K., Engell, S., Gepert, V., Kockmann, N., Müller-Pfefferkorn, R., Schindler, T., & Urbas, L. (2023). AI in Process Industries – Current Status and Future Prospects. Chemie Ingenieur Technik, 95(7), 975–988. https://doi.org/10.1002/cite.202200247

Brand-Rihm, G. B., Schueler, M., Nentwich, C., Esche, E., & Repke, J.-U. (2023). Adaptation of Dynamic Data‐Driven Models for Real‐Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning. Chemie Ingenieur Technik, 95(7), 1125–1133. https://doi.org/10.1002/cite.202200228

Brand-Rihm, G., Esche, E., & Repke, J.-U. (2023). Efficient dynamic sampling of batch processes through operation recipes. Computers & Chemical Engineering, 108433. https://doi.org/10.1016/j.compchemeng.2023.108433

Dathatri, A. G., Strem, N., & Klöpper, B. (2023). Combining Representation Learning and Active Learning for Applications in Process Manufacturing. Chemie Ingenieur Technik, 95(7), 1018–1027. https://doi.org/10.1002/cite.202200224

Damay, J., Ryzhakov, G., Jirasek, F., Hasse, H., Oseledets, I., & Bortz, M. (2023). Predicting Temperature‐Dependent Activity Coefficients at Infinite Dilution Using Tensor Completion. Chemie Ingenieur Technik, 95(7), 1061–1069. https://doi.org/10.1002/cite.202200230

Ehlhardt, J., Ahmad, A., Wolf, I., & Engell, S. (2023). Real‐Time Optimization Using Machine Learning Models Applied to the 4,4′‐Diphenylmethane Diisocyanate Production Process. Chemie Ingenieur Technik, 95(7), 1096–1103. https://doi.org/10.1002/cite.202200244

Elsheikh, M., Ortmanns, Y., Hecht, F., Roßmann, V., Krämer, S., & Engell, S. (2023). An Approach to Dependable Hybrid Modeling with Application to an Industrial Distillation Column. In Computer Aided Chemical Engineering. 33rd European Symposium on Computer Aided Process Engineering (Vol. 52, pp. 1753–1758). Elsevier. https://doi.org/10.1016/B978-0-443-15274-0.50278-X

Elsheikh, M., Ortmanns, Y., Hecht, F., Roßmann, V., Krämer, S. & Engell, S. (2023, July 9-14). Model Predictive Control of an Industrial Distillation Column Based on a Hybrid Model: Adapting the Domain of Validity. Proc. 22nd IFAC- World Congress, Yokohama.

Elsheikh, M., & Engell, S. (2023, August 22–25). Learning-Based Predictive Control Using a Hybrid Model with Adaptive Domain of Validity. In 2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR) (pp. 127–132). IEEE. https://doi.org/10.1109/MMAR58394.2023.10242519

Elsheikh, M., Ortmanns, Y., Hecht, F., Roßmann, V., Krämer, S. and Engell, S. (2023), Control of an Industrial Distillation Column Using a Hybrid Model with Adaptation of the Range of Validity and an ANN-based Soft Sensor. Chemie Ingenieur Technik, 95: 1114-1124. https://doi.org/10.1002/cite.202200232

Franks, B. J., Anders, M., Kloft, M., & Schweitzer, P. (2023). A Systematic Approach to Universal Random Features in Graph Neural Networks. Transactions on Machine Learning Research (TMLR). https://openreview.net/forum?id=AXUtAIX0Fn

Gedda, R., Beilina, L., & Tan, R. (2023). Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility. Computer Modeling in Engineering & Sciences, 134(3), 1737–1759. https://doi.org/10.32604/cmes.2022.019764

Gopa, K. R., Wenzel, T., Jena, S., Assawajaruwan, S., Khaydarov, V., & Urbas, L. (2023). Developing Cole-Cole model for Bacillus subtilis fermentation. In Computer Aided Chemical Engineering. 33rd European Symposium on Computer Aided Process Engineering (Vol. 52, pp. 2711–2716). Elsevier. https://doi.org/10.1016/B978-0-443-15274-0.50431-5

Gärtler, M., Hollender, M., Klöpper, B., Maczey, S., Tan, R., Song, C., Bähner, F. D., Krämer, S., Just, G., Khaydarov, V., Urbas, L., & Gedda, R. (2023). Machine Learning Approaches for Phase Identification Using Process Variables in Batch Processes. Chemie Ingenieur Technik, 95(7), 989–1002. https://doi.org/10.1002/cite.202200231

Hubert, S., Meintschel, J., Bleidorn, D., Ortmanns, Y., & Wallrath, R. (2023). Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation. Chemie Ingenieur Technik, 95(7), 1003–1011. https://doi.org/10.1002/cite.202200242

Hartung, F., Franks, B. J., Michels, T., Wagner, D., Liznerski, P., Reithermann, S., Fellenz, S., Jirasek, F., Rudolph, M., Neider, D., Leitte, H., Song, C., Kloepper, B., Mandt, S., Bortz, M., Burger, J., Hasse, H., & Kloft, M. (2023). Deep Anomaly Detection on Tennessee Eastman Process Data. Chemie Ingenieur Technik, 95(7), 1077–1082. https://doi.org/10.1002/cite.202200238

Jirasek, F., Hayer, N., Abbas, R., Schmid, B., & Hasse, H. (2023). Prediction of parameters of group contribution models of mixtures by matrix completion. Physical Chemistry Chemical Physics: PCCP, 25(2), 1054–1062. https://doi.org/10.1039/D2CP04478A

Jirasek, F., & Hasse, H. (2023). Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures. Annual Review of Chemical and Biomolecular Engineering, 14, 31–51. https://doi.org/10.1146/annurev-chembioeng-092220-025342

Khaydarov, V., Becker, M. P., & Urbas, L. (2023). Image‐Based Flow Regime Recognition in Aerated Stirred Tanks Using Deep Transfer Learning. Chemie Ingenieur Technik, 95(7), 1172–1179. https://doi.org/10.1002/cite.202200246

Klose, A., Wagner-Stürz, D., Neuendorf, L., Oeing, J., Khaydarov, V., Schleehahn, M., Kockmann, N., & Urbas, L. (2023). Automated Evaluation of Biochemical Plant KPIs based on DEXPI Information. Chemie Ingenieur Technik, 95(7), 1165–1171. https://doi.org/10.1002/cite.202200239

Kockmann, N., Schindler, T., & Urbas, L. (2023). AI in Process Industries – Incubator Labs and Use Cases. Chemie Ingenieur Technik, 95(7), 963. https://doi.org/10.1002/cite.202370702

Lammers, S., & Lasch, A. (2023). Linguistic Framing of Artificial Intelligence: What Language to Use When Talking about Artificial Intelligence. Chemie Ingenieur Technik, 95(7), 1012–1017. https://doi.org/10.1002/cite.202200226

Ledent, A., Alves, R., & Kloft, M. (2023). Orthogonal Inductive Matrix Completion. IEEE Transactions on Neural Networks and Learning Systems, 34(5), 2259–2270. https://doi.org/10.1109/TNNLS.2021.3106155

Neuendorf, L., Hammal, Z., Fricke, A., & Kockmann, N. (2023). AI‐Based Supervision for a Stirred Extraction Column Assisted with Population Balance‐Based Simulation. Chemie Ingenieur Technik, 95(7), 1134–1145. https://doi.org/10.1002/cite.202200241

Neuendorf, L., Höving, S., Bennemann, L., & Kockmann, N. (2023). Detecting Crystals in Suspensions: Convolutional Neural Networks vs. Gravity‐Based Approach for Size Distribution Detection. Chemie Ingenieur Technik, 95(7), 1146–1153. https://doi.org/10.1002/cite.202200235

Neuendorf, L. M., Khaydarov, V., Schlander, C., Kock, T., Fischer, J., Urbas, L., & Kockmann, N. (2023). Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry. Chemie Ingenieur Technik, Article cite.202300047. Advance online publication. https://doi.org/10.1002/cite.202300047

Sherpa, L., Müller-Pfefferkorn, R., Tolksdorf, G., Khaydarov, V., Wiedau, M., & Urbas, L. (2023). ProMetaS – A Metadata Schema for Process Engineering and Industry. Chemie Ingenieur Technik, 95(7), 1041–1048. https://doi.org/10.1002/cite.202200225

Sherpa, L., Müller-Pfefferkorn, R., Enste, U., Tolksdorf, G., Kawohl, M., & Wiedau, M. (2023). Tool Chain to Extract and Contextualize Process Data for AI Applications. Chemie Ingenieur Technik, 95(7), 1070–1076. https://doi.org/10.1002/cite.202300004

Wagner, D., Michels, T., Schulz, F., Nair, A., Rudolph, M., Kloft, M. (2023). TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. Transactions on Machine Learning Research (TMLR) https://openreview.net/forum?id=iMmsCI0JsS

Winz, J., Assawajaruwan, S., & Engell, S. (2023). Development of a Dynamic Gray‐Box Model of a Fermentation Process for Spore Production. Chemie Ingenieur Technik, 95(7), 1154–1164. https://doi.org/10.1002/cite.202200237

Winz, J., Fromme, F., & Engell, S. (2023). Overcoming the modeling bottleneck: A metho-dology for dynamic gray-box modeling with optimized training data. Journal of Process Control 130, Article 103089. https://doi.org/10.1016/j.jprocont.2023.103089

[2023] [2022] [2021] [2020