Figure shows that the obtained fit is not perfectly matched to the experimentally measured data, which may stem from the contribution of several factors. Firstly, the employed model assumed a constant value for 𝑘 "# . However, 𝑘 "# may increase with large 𝐽 ! due to an increased convective drag of solute molecules across the membrane. This is reflected in Figure , where a reduction in 𝑅 %&' at large 𝐽 ! compared to the model predictions suggests an underestimate of the predicted transmembrane solute transport. Additionally, 𝑘 "# may vary across different parts of the membrane area, an effect which is exaggerated in a dead-end stirred cell compared to continuous cross-flow operation due to uneven shear rates across the membrane surface resulting in uneven concentration polarisation. Since the model equations are very sensitive to the magnitude of 𝑘 "# , especially as 𝐽 ! increases, unaccounted variations in 𝑘 "# will affect the best obtainable fit of the model to the experimental data. Secondly, given the model sensitivity to 𝑘 "# , uncertainties in the experimentally determined quantities (indicated by the error bars), especially in the effective osmotic pressure, may have affected the fitting process. Investigation of Figure reveals that the calculated effective osmotic pressure difference experienced by the investigated system is likely underestimated for small 𝐽 ! , since a plot of experimental 𝐽 ! vs net transmembrane pressure is expected to go through zero. This deviation may stem from the assumption that the solvent components permeate the membrane at equal proportions, when it could be that the membrane selectivity towards the smaller water molecules is larger, which would cause an increased relative flux of water compared to methanol. This would affect the compositions of the solutions on each side of the membrane and hence the calculated transmembrane osmotic pressure difference, which would directly impact the regression of model parameters, especially 𝐴 . Notably, the determined solvent permeability is 60-70% lower than pure water permeabilities reported in the literature for BW30 operated in continuous cross-flow (rather than dead-end batch). The reduced value may be related to an error in computed osmotic pressure difference as well as the effects of methanol and nopinone on the membrane permeability, compared to pure water solvent. However, this uncertainty does not affect the evaluation of 𝐵, directly deduced form the dependence of rejection on 𝐽 ! rather than pressure. Finally, the values of coefficients 𝐴 and 𝐵 may not remain constant as was assumed in the applied model. The solute permeability 𝐵 is a product of mobility and the partitioning coefficient, and is thus proportional to sorption of solute in polyamide. While 𝐴 and 𝐵 may be regarded as constant for dilute solutions where solute sorption is low, the solutions under investigation are concentrated to nearly 1 M. This amount can result in non-linear sorption and thus variation of 𝐵 with increasing solute concentration. The solvent permeability 𝐴 may be affected as well since large amounts of sorbed solute alter the properties of the polyamide membrane material. Nevertheless, the fit obtained by the Spiegler-Kedem model gives useful engineering insight that allow insight into the permeation characteristics of the investigated system.