- author = "Rebecca Goebel",
- title = "Towards reliable characterization and model-based evaluation of organic solvent nanofiltration",
- school = "TU Dortmund University",
- year = "2021",
- address = "Dortmund, Germany",
- month = "14 " # jul,
- keywords = "genetic algorithms, genetic programming, Organophile Nanofiltration, Modellentwicklung, Fluss, Rueckhalt, Model development, flow, support, matlab, GAMS",
-
URL = "
https://fvt.bci.tu-dortmund.de/details/doctoral-examination-of-rebecca-goebel-9923/",
-
URL = "https://eldorado.tu-dortmund.de/handle/2003/40733",
-
URL = "http://hdl.handle.net/2003/40733",
-
URL = "
https://eldorado.tu-dortmund.de/bitstream/2003/40733/1/211122_Dissertation%20Goebel_Finale%20Version.pdf",
-
DOI = "
doi:10.17877/DE290R-22591",
- size = "278 pages",
-
abstract = "The interest in organic solvent nanofiltration (OSN)
increased substantially in both academia and industry
during the last decades, since it provides a great
potential for energy savings. However, despite the
advantages, there are still limitations, that lead to
the fact that OSN is rarely considered as a competitive
separation operation in process design. For a reliable
evaluation of process design, the uncertainties in
labscale measurements and the quantification of model
parameter precision are major factors and the
prediction of flux and rejection is additionally
essential in order to reduce experimental effort for
feasibility studies during process development. These
challenges are addressed in this thesis.
The evaluation of fluxes through multiple laboratory-scale membrane samples provides an accurate approximation of flux through an industrial-scale module. The results prove to be transferable to different membrane types. Furthermore, a collaborative study at different facilities demonstrates the comparability of experimental results obtained with a standardized procedure. Moreover, the consideration of experimental uncertainties in process design and membrane selection is proven to be as relevant as for the selection of an appropriate mass transfer model. In the second part of this work, a newly developed method for automatic development of predictive models for OSN shows promising results for prediction of solvent flux and solute rejection in pure and mixed solvents. The method derives the membrane specific model structure and discriminates automatically between potential, easily retrievable descriptors based on available data. For the prediction of solvent flux, a comparison with existing phenomenological models from literature points out that the new models are superior and cover effects that are not included in the fixed model structure of phenomenological models. Models developed for the prediction of rejection are more complex compared to those for solvent flux but are comparable accurate.",
- notes = "p91 'The data-driven approach, which is based on genetic programming, generates predictive models that show superior results in terms of accuracy and parameter precision when compared to previously proposed models.'",
