Prediction of Henry's law constants by matrix completion
- Methods for predicting Henry's law constants Hij are important as experimental data are scarce. We introduce a new machine learning approach for such predictions: matrix completion methods (MCMs) and demonstrate its applicability using a data base that contains experimental Hij values for 101 solutes i and 247 solvents j at 298 K. Data on Hij are only available for 2661 systems i + j. These Hij are stored in a 101 × 247 matrix; the task of the MCM is to predict the missing entries. First, an entirely data-driven MCM is presented. Its predictive performance, evaluated using leave-one-out analysis, is similar to that of the Predictive Soave-Redlich-Kwong equation-of-state (PSRK-EoS), which, however, cannot be applied to all studied systems. Furthermore, a hybrid of MCM and PSRK-EoS is developed in a Bayesian framework, which yields an unprecedented performance for the prediction of Hij of the studied data set.
Author: | Nicolas HayerORCiD, Fabian JirasekORCiD, Hans Hasse |
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URN: | urn:nbn:de:hbz:386-kluedo-80943 |
DOI: | https://doi.org/10.1002/aic.17753 |
ISSN: | 1547-5905 |
Parent Title (English): | AIChE Journal |
Publisher: | Wiley |
Document Type: | Article |
Language of publication: | English |
Date of Publication (online): | 2024/04/22 |
Year of first Publication: | 2022 |
Publishing Institution: | Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau |
Date of the Publication (Server): | 2024/04/22 |
Issue: | 68/9 |
Page Number: | 11 |
Source: | https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.17753 |
Faculties / Organisational entities: | Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik |
DDC-Cassification: | 6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau |
Collections: | Open-Access-Publikationsfonds |
Licence (German): | Zweitveröffentlichung |