Maschinelles Lernen in der Verfahrenstechnik

Wissenschaftliche Journals

Ausgewählte Artikel:

  • F. Jirasek, H. Hasse: Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures, Ann. Rev. Chem. Biomol. Eng. 14 (2023) 31-51. DOI: 10.1146/annurev-chembioeng-092220-025342.
  • T. Specht, K. Münnemann, H. Hasse, F. Jirasek: Rational Method for Defining and Quantifying Pseudo-components Based on NMR Spectroscopy, Phys. Chem. Chem. Phys. 25 (2023) 10288-10300. DOI: 10.1039/D3CP00509G.
  • T. Specht, J. Arweiler, J. Stüber, K. Münnemann, H. Hasse, F. Jirasek: Automated Nuclear Magnetic Resonance Fingerprinting of Mixtures, Magn. Reson. Chem. (2023) in press. DOI: 10.1002/mrc.5381.
  • N. Hayer, F. Jirasek, H. Hasse: Prediction of Henry's Law Constants by Matrix Completion, AlChE J. 68 (2022) e17753. DOI: 10.1002/aic.17753.
  • F. Jirasek, R. Bamler, S. Fellenz, M. Bortz, M. Kloft, S. Mandt, H. Hasse: Making Thermodynamic Models of Mixtures Predictive by Machine Learning: Matrix Completion of Pair Interactions, Chem. Sci. 13 (2022) 4854-4862. DOI: 10.1039/D1SC07210B.
  • F. Jirasek, H. Hasse: Perspective: Machine Learning of Thermophysical Properties, Fluid Phase Equilib. 549 (2021) 113206. DOI: 10.1016/j.fluid.2021.113206.
  • T. Specht, K. Münnemann, H. Hasse, F. Jirasek: Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification, J. Chem. Inf. Model. 61 (2021) 143-155. DOI: 10.1021/acs.jcim.0c01186.
  • F. Jirasek, R. A. S. Alves, J. Damay, R. A. Vandermeulen, R. Bamler, M. Bortz, S. Mandt, M. Kloft, H. Hasse: Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion, J. Phys. Chem. Lett. 11 (2020) 981-985. DOI: 10.1021/acs.jpclett.9b03657.
  • F. Jirasek, R. Bamler, S. Mandt: Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties, Chem. Commun. 56 (2020) 12407-12410. DOI: 10.1039/D0CC05258B.
     

Vollständige Publikationsliste: Google Scholar

Wissenschaftliche Konferenzen

Ausgewählte Vorträge:

  • F. Jirasek: Hybrid Thermodynamic Modeling of Mixtures (Invited Planary Lecture), Thermodynamik-Kolloquium, Hannover, 25.-27.09.2023.
  • F. Jirasek: Modeling Unknown Mixtures (Invited Lecture), Mathematical Methods in Process Engineering (MMiPE), Kaiserslautern, 05.-06.10.2023.
  • F. Jirasek, N. Hayer, J. Arweiler, D. Gond, T. Specht, H. Hasse: Hybridizing Machine Learning and Physical Modeling for Predicting Thermodynamic Properties of Mixtures, International Conference on Properties and Phase Equilibria for Product and Process Design (PPEPPD), Tarragona, Spain, 21.-25.05.2023.
  • F. Jirasek, T. Specht, K. Münnemann, H. Hasse: Thermodynamic Modeling of Unknown Mixtures with NMR Spectroscopy and Machine Learning (Invited Lecture), NMRPM Symposium, Kaiserslautern, 24.-26.05.2023.
  • F. Jirasek: Hybridizing Machine Learning and Physical Modeling of Mixtures (Invited Lecture), EFCE Spotlight Talks of Working Party on Thermodynamics and Transport Properties, Web-Conference, 25.11.2022.
  • F. Jirasek: Machine Learning meets Physical Knowledge: Hybrid Models for the Prediction of Fluid Properties (Invited Lecture), 18th joint UNIFAC Consortium and DDBST User Meeting 2021, Web-Conference, 14.09.2021.
     

Ausgewählte kommende Konferenzen, auf welchen wir vertreten sind:

  • 33rd European Symposium on Applied Thermodynamics (ESAT), Edinburgh, UK, 09.-12.06.2024. Link.
  • 22nd Symposium on Thermophysical Properties, Boulder, CO, USA. 23.-28.06.2024. Link.