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  4. Solvent recovery and deacidification by organic solvent nanofiltration: Experimental investigation and mass transfer modeling
 
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Solvent recovery and deacidification by organic solvent nanofiltration: Experimental investigation and mass transfer modeling

Publikationstyp
Journal Article
Date Issued
2017
Sprache
English
Author(s)
Werth, Kathrin  
Kaupenjohann, Paul  
Knierbein, Michael  
Skiborowski, Mirko  orcid-logo
TORE-URI
http://hdl.handle.net/11420/8218
Journal
Journal of membrane science  
Volume
528
Start Page
369
End Page
380
Citation
Journal of Membrane Science (528): 369-380 (2017)
Publisher DOI
10.1016/j.memsci.2017.01.021
Scopus ID
2-s2.0-85012262525
Non-edible oils, waste oils and animal fats are promising feedstocks for more economic and ecological production processes. However, the composition of these low-quality oils, especially the content of free fatty acids, strongly fluctuates requiring the development of flexible process concepts. Therefore, this study investigates the potential of organic solvent nanofiltration for important applications in the oleochemical industry. It is shown that an efficient recovery of non-polar extraction solvents is possible for a wide range of low-quality oils. Commercially available PDMS-based membranes enable high solvent fluxes and rejection of triglyceride and fatty acids. In deacidification experiments, high selective separation of free fatty acids from triglycerides for long-chain as well as medium-chain oils is achieved by using polar solvents and available polyimide-based membranes. The amount of solvent, which is added to the low-quality oils, is identified as crucial parameter for separation efficiency. Membrane performance is accurately described by a (reduced) solution-diffusion model with imperfections, for which mass transfer of solvents and fatty acids is described by the convective contribution, while mass transfer of triglycerides is described by the diffusive contribution.
Subjects
Free fatty acid
Oil
OSN
Renewable raw materials
Solution-diffusion model with imperfections
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