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Fabian Jirasek
Fabian Jirasek
Laboratory of Engineering Themodynamics (LTD), University of Kaiserslautern
Bestätigte E-Mail-Adresse bei mv.uni-kl.de
Titel
Zitiert von
Zitiert von
Jahr
Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion
F Jirasek, RAS Alves, J Damay, RA Vandermeulen, R Bamler, M Bortz, ...
The Journal of Physical Chemistry Letters 11 (3), 981-985, 2020
302020
Crystal phase transformation of α into β phase poly (vinylidene fluoride) via particle formation caused by rapid expansion of supercritical solutions
S Wolff, F Jirasek, S Beuermann, M Türk
RSC advances 5 (82), 66644-66649, 2015
282015
Method for estimating activity coefficients of target components in poorly specified mixtures
F Jirasek, J Burger, H Hasse
Industrial & Engineering Chemistry Research 57 (21), 7310-7313, 2018
162018
Digitalization in thermodynamics
E Forte, F Jirasek, M Bortz, J Burger, J Vrabec, H Hasse
Chemie Ingenieur Technik 91 (3), 201-214, 2019
122019
NEAT—NMR Spectroscopy for the Estimation of Activity Coefficients of Target Components in Poorly Specified Mixtures
F Jirasek, J Burger, H Hasse
Industrial & Engineering Chemistry Research 58 (21), 9155-9165, 2019
82019
Prediction of the elution profiles of proteins in mixed salt systems in hydrophobic interaction chromatography
N Galeotti, E Hackemann, F Jirasek, H Hasse
Separation and Purification Technology 233, 116006, 2020
72020
Hybridizing physical and data-driven prediction methods for physicochemical properties
F Jirasek, R Bamler, S Mandt
Chemical Communications 56 (82), 12407-12410, 2020
72020
Recovery of furfural and acetic acid from wood hydrolysates in biotechnological downstream processing
N Galeotti, F Jirasek, J Burger, H Hasse
Chemical Engineering & Technology 41 (12), 2331-2336, 2018
52018
Predicting Activity Coefficients at Infinite Dilution for Varying Temperatures by Matrix Completion
J Damay, F Jirasek, M Kloft, M Bortz, H Hasse
Industrial & Engineering Chemistry Research 60 (40), 14564-14578, 2021
42021
Application of NEAT for the simulation of liquid–liquid extraction processes with poorly specified feeds
F Jirasek, J Burger, H Hasse
AIChE Journal 66 (2), e16826, 2020
42020
Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification
T Specht, K Münnemann, H Hasse, F Jirasek
Journal of Chemical Information and Modeling 61 (1), 143-155, 2021
32021
Application of NEAT for determining the composition dependence of activity coefficients in poorly specified mixtures
F Jirasek, J Burger, H Hasse
Chemical Engineering Science 208, 115161, 2019
32019
Solid-liquid equilibrium in the system 2-keto-L-gulonic acid+ sodium-2-keto-L-gulonate+ water
F Jirasek, J Burger, H Hasse
Fluid Phase Equilibria 473, 318-322, 2018
32018
Perspective: Machine Learning of Thermophysical Properties
F Jirasek, H Hasse
Fluid Phase Equilibria 549, 113206, 2021
22021
Estimating activity coefficients of target components in poorly specified mixtures with NMR spectroscopy and COSMO-RS
T Specht, K Münnemann, F Jirasek, H Hasse
Fluid Phase Equilibria 516, 112604, 2020
22020
Solid‐Liquid Equilibrium in the System 2‐Keto‐L‐Gulonic Acid + L‐Ascorbic Acid + Water
F Jirasek, N Galeotti, J Burger, H Hasse
Chemical engineering & technology 41 (12), 2306-2311, 2018
22018
Influence of pH value and salts on the adsorption of lysozyme in mixed‐mode chromatography
J Kreusser, F Jirasek, H Hasse
Engineering in life sciences 21 (11), 753-768, 2021
12021
Influence of salts on the adsorption of lysozyme on a mixed-mode resin
J Kreusser, F Jirasek, H Hasse
Adsorption Science & Technology 2021, 2021
12021
Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions
F Jirasek, R Bamler, S Fellenz, M Bortz, M Kloft, S Mandt, H Hasse
Chemical Science 13 (17), 4854-4862, 2022
2022
Conceptual design of a crystallization-based trioxane production process
CF Breitkreuz, M Dyga, E Forte, F Jirasek, J de Bont, J Wery, T Grützner, ...
Chemical Engineering and Processing-Process Intensification 171, 108710, 2022
2022
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