Abstract

During the past few decades organic solvent nanofiltration has received a great deal of attention and a growing number of studies has been reported on development and optimisation of solvent resistant membranes and their transport mechanism. However, most of these studies have used flat sheet membranes. On the other hand, many researchers studied fluid dynamics and mass transfer in spiral-wound membrane modules, almost exclusively in aqueous solutions. This paper reports the performance of four spiral-wound membrane modules tested in 0–20wt% solutions of sucrose octaacetate in ethyl acetate under various pressures and retentate flowrates. These modules were made of two different types of membranes (a commercial membrane, PuraMem® S600, and a development product, Lab-1, from Evonik Membrane Extraction Technology Limited) and covered three module sizes (1.8″×12″, 2.5″×40″ and 4.0″×40″). All modules had the same feed and permeate spacers. The classical solution diffusion model was applied to describe the transport of solute and solvent through the membrane and regress the unknown model parameters from flat sheet data. Correlations for characterising the fluid dynamics and mass transfer in the spiral-wound membrane modules, as well as the parameters describing the feed and permeate channels, were determined by performing the regression of experimental data of a 1.8″×12″ PuraMem® S600 membrane module. The classical solution–diffusion model, combined with the film theory, was then successfully applied to predict the performance of other modules of larger size (such as the 2.5″×40″ and 4.0″×40″ module sizes) and/or made of a different membrane material (such as Lab-1). The procedure proposed in this paper predicts the performance of a specific module by obtaining a limited number of experimental data for flat sheets and a 1.8″×12″ spiral-wound membrane module only (necessary to obtain the fitting parameters characteristic of the membrane and the module). Furthermore, with this procedure, it is not necessary to know a priori the spacer geometry, because the necessary information about the spacer geometry will be also obtained by regression of few experimental data.

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