The aim of the dissertation to study small organic solute rejection in organic solvent nanofiltration using cheminformatics and machine learning. Chemically diverse datasets were curated using a novel medium high-throughput methodology and literature data extraction. These datasets contained thousands of datapoints of small organic solute rejections and process parameters. Different chemical fingerprinting has been used for feature generation, such as molecular descriptors, Morgan fingerprints, and latent-vector representation. These features were used to train different machine learning models, such as graph neural networks, partial least squares regression, and boosting tree algorithms. The obtained models were used to predict small organic solute rejection for nanofiltration related applications. Correlation between rejection and the molecular descriptors have been studied to deepen the understanding of transport and rejection through polymeric membranes. Explainable artificial intelligence concept was used to study the effect of solute, solvent and membrane structure on solute rejection. The conclusion of the dissertation highlights the importance of the chemical structure effect in nanofiltration and provides a future perspective on data-driven approached for nanofiltration and organic solvent nanofiltration.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/691383 |
| Date | 05 April 2023 |
| Creators | Ignacz, Gergo |
| Contributors | Szekely, Gyorgy, Physical Science and Engineering (PSE) Division, Nunes, Suzana Pereira, Grande, Carlos A., Kumar, Sanat K. |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | 2024-05-01, At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation will become available to the public after the expiration of the embargo on 2024-05-01. |
| Relation | www.osndatabase.com |
Page generated in 0.0032 seconds