Ahmad, A., Song, C., Tan, R., Gartler, M., & Klöpper, B. (2022, September 6–9). Active Learning Application for Recognizing Steps in Chemical Batch Production. In 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–4). IEEE. https://doi.org/10.1109/ETFA52439.2022.9921701
Behr, A. S., Neuendorf, L. M., Sakthithasan, P., Boettcher, K. E., & Kockmann, N. (2022, August 11–12). Process control using AI on a digital twin of an extraction column in VR. In 2022 IEEE German Education Conference (GeCon) (pp. 1–6). IEEE. https://doi.org/10.1109/GeCon55699.2022.9942788
Bordas, B., Kurt, K., Bamberg, A., & Engell, S. (2022, May 2–5). Developing a digital twin of a polymerization reaction for process optimization. In 2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) (pp. 1–6). IEEE. https://doi.org/10.1109/ASMC54647.2022.9792518
Brand-Rihm, G., Esche, E., & Repke, J.-U. (2022). Data-driven modelling of full batch distillation cycles based on recurrent neuronal networks. In Computer Aided Chemical Engineering. 32nd European Symposium on Computer Aided Process Engineering (Vol. 51, pp. 385–390). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50065-5
Brand Rihm, G., Esche, E., & Repke, J.-U. (2022). Recipe optimization of batch distillation trajectories based on a data‐driven model. Chemie Ingenieur Technik, 94(9), 1313. https://doi.org/10.1002/cite.202255286
Diewald, F., Höller, J., Ludl, P. O., Schwartz, P., Heese, R., Asprion, N., & Bortz, M. (2022). Don't Let Your Constraints Constrain You – Towards Design Space Exploration with Flexible Constraints for Flow Sheet Simulations. Chemie Ingenieur Technik, 94(9), 1313–1314. https://doi.org/10.1002/cite.202255006
Großmann, O., Bellaire, D., Hayer, N., Jirasek, F., & Hasse, H. (2022). Database for liquid phase diffusion coefficients at infinite dilution at 298 K and matrix completion methods for their prediction. Digital Discovery, 1(6), 886–897. https://doi.org/10.1039/D2DD00073C
Hayer, N., Jirasek, F., & Hasse, H. (2022). Prediction of Henry's law constants by matrix completion. AIChE Journal, 68(9), Article e17753. https://doi.org/10.1002/aic.17753
Hoffmann, M. W., & Drath, R. (2022, March 28–31). How to survive a PhD – using Design Thinking methods and the Business Model Canvas. In 2022 IEEE Global Engineering Education Conference (EDUCON) (pp. 1652–1657). IEEE. https://doi.org/10.1109/EDUCON52537.2022.9766528
Jirasek, F., Bamler, R., Fellenz, S., Bortz, M., Kloft, M., Mandt, S., & Hasse, H. (2022). Making thermodynamic models of mixtures predictive by machine learning: Matrix completion of pair interactions. Chemical Science, 13(17), 4854–4862. https://doi.org/10.1039/D1SC07210B
Just, N., Song, C., Haffner, E. G., & Gärtler, M. (2022, December 9–11). Recognizing Phases in Batch Production via Interactive Feature Extraction. In 2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) (pp. 203–209). IEEE. https://doi.org/10.1109/RAAI56146.2022.10092982
Kröger, C., Khaydarov, V., & Urbas, L. (2022). Data-driven, Image-based Flow Regime Classification for Stirred Aerated Tanks. In Computer Aided Chemical Engineering. 32nd European Symposium on Computer Aided Process Engineering (Vol. 51, pp. 1363–1368). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50228-9
Lammers, S., Lasch, A., & Fuchs, A. (2022). Linguistisches Framing von Künstlicher Intelligenz. Handlungsempfehlungen für die Kommunikation des KEEN-Verbundprojekts. Zenodo. https://doi.org/10.5281/zenodo.7415550
Neuendorf, L. M., Z. Baygi, F., Kolloch, P., & Kockmann, N. (2022). Implementation of a Control Strategy for Hydrodynamics of a Stirred Liquid–Liquid Extraction Column Based on Convolutional Neural Networks. ACS Engineering Au, 2(4), 369–377. https://doi.org/10.1021/acsengineeringau.2c00014
Oeing, J., Welscher, W., Krink, N., Jansen, L., Henke, F., & Kockmann, N. (2022). Using artificial intelligence to support the drawing of piping and instrumentation diagrams using DEXPI standard. Digital Chemical Engineering, 4, 100038. https://doi.org/10.1016/j.dche.2022.100038
Sohns, J.-T., Schmitt, M., Jirasek, F., Hasse, H., & Leitte, H. (2022). Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics, 28(1), 540–550. https://doi.org/10.1109/TVCG.2021.3114870
Vicente, M. L., Granjo, J. F., Tan, R., & Bähner, F. D. (2022). A Benchmark Model to Generate Batch Process Data for Machine Learning Testing and Comparison. In Computer Aided Chemical Engineering. 32nd European Symposium on Computer Aided Process Engineering (Vol. 51, pp. 217–222). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50037-0
Winz, J., & Engell, S. (2022). A methodology for gray-box modeling of nonlinear ODE systems. In Computer Aided Chemical Engineering. 32nd European Symposium on Computer Aided Process Engineering (Vol. 51, pp. 1483–1488). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50248-4
Winz, J., & Engell, S. (2022). Reliable nonlinear dynamic gray-box modeling by regularized training data estimation and sensitivity analysis. IFAC-PapersOnLine, 55(7), 86–93. https://doi.org/10.1016/j.ifacol.2022.07.426
Winz, J., Fromme, F., & Engell, S. (2022). Supporting Hyperparameter Optimization in Adaptive Sampling Methods. In Computer Aided Chemical Engineering. 14th International Symposium on Process Systems Engineering (Vol. 49, pp. 835–840). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50139-1