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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
Imputation of missing gas permeability data for polymer membranes using machine learning
Journal of membrane science, Vol.627, p.119207
01/06/2021
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Source: InCites
Abstract
Polymer-based membranes have the potential for use in energy efficient gas separations. The successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. Open-source databases of gas permeabilities are of significant potential benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were imputed (filled) using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential ?missed? candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.
Details
- Title
- Imputation of missing gas permeability data for polymer membranes using machine learning
- Creators
- Qi Yuan - Imperial College LondonMariagiulia Longo - Institute on Membrane TechnologyAaron W. Thornton - Commonwealth Scientific and Industrial Research OrganisationNeil B. McKeown - University of EdinburghBibiana Comesana-Gandara - University of EdinburghJohannes C. Jansen - Institute on Membrane TechnologyKim E. Jelfs - Imperial College London
- Publication Details
- Journal of membrane science, Vol.627, p.119207
- Publisher
- Elsevier
- Number of pages
- 10
- Grant note
- Royal Society; Royal Society of London; European Commission CNR Program Short Term Mobility 2019 758370 / European Research Council under FP7 (CoMMaD)
- Identifiers
- 991013035779402368
- Academic Unit
- Faculty of Science and Engineering; Science
- Language
- English
- Resource Type
- Journal article