- Email: [email protected]
Membrane selectivity in the organic solvent nanofiltration of trialkylamine bases
Desalination 218 (2008) 248–256
Membrane selectivity in the organic solvent nanofiltration of trialkylamine bases Darrell A. Pattersona*, Lay Yen Laub, Chayaporn Roengpithyab, Emma J. Gibbinsb, Andrew G. Livingstonb a
Department of Chemical and Materials Engineering, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand Tel. +64 93737999; Fax +64 9737463; email: [email protected] b Department of Chemical Engineering, Imperial College London, Prince Consort Road, South Kensington, London, SW7 2BY, United Kingdom
Received 23 November 2006; accepted 8 February 2007
Abstract Dead-end and cross-flow organic solvent nanofiltrations (OSNs) using StarMemTM 122 (220 g mol–1 nominal molecular weight cut-off (MWCO)) were conducted on a homologous series of trialkylamine bases, ranging from triethylamine (MW = 270 g mol–1) to tridodecylamine (TDDA; MW = 522 g mol–1). Dead-end OSN gave the expected rejections of 97–99% for bases larger than the MWCO, except TDDA, which unexpectedly had an average rejection of only 19%. MWCO therefore does not accurately represent the selectivity of StarMemTM122. To investigate the reasons for this, cross-flow OSN of the same trialkylamine bases was conducted. This did not give a low TDDA rejection however, and the rejections of all the other bases were lower than in dead-end OSN. These results are most likely due to greater concentration polarisation and/or fouling in the dead-end OSNs. Furthermore, by reconciling these results with the currently understood membrane mass transport models, it is thought that it indicates that both pore flow and solution diffusion play a role in the trialkylamine transport across StarMemTM 122 membranes. Keywords: Organic solvent nanofiltration; Molecular weight cut-off (MWCO); Solution–diffusion; Pore flow; Membrane enhanced dynamic kinetic resolution
*Corresponding author.
Presented in the Separation Sessions at Chemeca 2006, the 34th Australasian Chemical and Process Engineering Conference, Auckland, New Zealand, 17– 20 September 2006. Organised by the University of Auckland and the Society of Chemical Engineers New Zealand (SCENZ). 0011-9164/08/$– See front matter © 2008 Published by Elsevier B.V. doi:10.1016/j.desal.2007.02.020
D.A. Patterson et al. / Desalination 218 (2008) 248–256
1. Introduction Organic solvent nanofiltration (OSN) is a relatively new membrane process capable of producing molecular scale separations. Consequently, OSN has been found to be effective for many highvalue applications, such as recycling homogeneous catalysts and bases [1–6] and separating chiral diastereomers [7]. The selectivity of membranes, including OSN membranes, is commonly characterized by molecular weight cut-off (MWCO), which is the molecular weight (MW) for which 90% rejection of the solute is achieved by the membrane. Therefore, using MWCO, it is expected that a molecule with a MW higher than the MWCO will have a high rejection. However, MWCO does not accurately represent the range of other factors and mechanisms which also control the selectivity of a membrane separation [8]. These include shape, solubility, charge, concentration polarization and fouling [8,9]. Thus mass transport through membranes is not solely dependant on MW. Two models are commonly used describe the mass transport through most membranes: 1. The solution diffusion model [9,10] is typically used to describe transport through pore-less membranes, like those used in reverse osmosis. The solution diffusion model assumes that the permeating species dissolves in the membrane material and diffuses through the membrane down a concentration gradient. A separation is achieved between different solutes due to differences in the amount that dissolves in the membrane and the rate at which it diffuses through the membrane [9,10]. 2. The pore flow model [9,10] is used to describe the permeation through porous membranes (ultrafiltration, microfiltration). It describes the separation based on pressure-driven convective flow through tiny pores. Selectivity results from exclusion, based on the incompatibility of molecule parameters such size, shape and charge, with the pores in the membrane.
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Nanofiltration membranes, including OSN membranes, are used for molecular separations. Such separations occur in the transition between ultrafiltration and reverse osmosis membrane operations [9]: separating molecules with MWs of 200–1000 g mol–1. Consequently, it is uncertain which of the above mechanisms (or indeed a combination of both) governs mass transport through nanofiltration membranes. Laboratory scale OSN separations are typically performed in one of two modes: dead-end filtration, where the entire solvent volume passes through the membrane under applied hydrostatic or gaseous pressure (Fig. 1a); or cross-flow filtration, where solvent passes parallel to the sur-
(a)
Pressure driving force P and direction of flow Q of solvent and compounds P
Q
Q Flow of solvent and permeated compounds (b) Pressure driving force, P Direction of flow, Q, of Q solvent and compounds
Flow of solvent and rejected compounds
Q2 Flow of solvent and permeated compounds
Fig. 1. Schematic of the flows in (a) Dead-end OSN and (b) Cross-flow OSN.
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face of the membrane and only part of the solvent volume passes through the membrane due to the applied pressure (Fig. 1b). Cross-flow filtration is used by a majority of membrane processes [9]. Therefore, membrane performance parameters, such as rejection and flux, need to be representative of this configuration. Researchers and process developers often opt for dead-end filtrations because of its lower cost and simplicity. However, dead-end filtration and cross-flow filtration can have very different rejections due to the differences in operation. To address the above issues, this paper highlights an example of how MWCO misrepresents the selectivity of a membrane and investigates possible reasons for the disparity, by reconciling the results with the pore flow and solution diffusion mass transport models. Furthermore, this paper demonstrates that the use of dead-end filtration as a sole means to characterize membrane separation can lead to misrepresentative rejection data.
2. Methods and materials 2.1. Materials All chemicals used as solutes were purchased from Sigma Aldrich, UK, except tridodecylamine, which was sourced from Lancaster synthesis UK. Details are in Table 1. Analytical grade Toluene (Fisher, UK and Sigma Aldrich, UK) was used as solvent. Only straight chain trialkylamines were used. StarMemTM* 122, an integrally skinned asymmetric polyimide nanofiltration membrane, was used in all experiments. It has a nominal MWCO of 220 g mol–1. StarMemTM 122 was supplied by Membrane Extraction Technologies Ltd, UK.
* StarMem™ is a trademark of W.R. Grace, Columbia, MD, USA.
Table 1 Solutes used in OSNs Chemical
Mw (g mol–1)
Triethylamine (TEA) Trihexylamine (THexA) Triheptylamine (THeptA) Trioctylamine (TOA) Tridecylamine (TDA) Tridodecylamine (TDDA) Tetraoctylammonium bromide (TOABr)
101.19 269.52 311.59 353.68 437.83 522.00 546.80
2.2. Dead-end OSN All dead-end OSN experiments were conducted using a stainless steel, SEPA ST (Osmonics, USA) cell with an effective membrane area of 13.9 cm2. The experimental setup has been used in previous work [2,3,7]. Nitrogen gas was applied as the pressure driving force. All experiments were conducted at 25ºC using a water bath. The filtrations were all conducted at the same stirrer speed. Prior to solute filtrations, the membranes were pre-conditioned by three separate filtrations with pure solvent for 30 min each, at which point the flux became steady. The importance of this preconditioning is detailed in [11]. Filtrations were performed by loading the cell with 30 mL of 50 mM feed solution of the trialkylamine and applying 30 bar pressure until half the fed solution had permeated. The flux data was calculated from permeate volume and the time taken for permeation [Eq. (2)]. The feed, permeate and retentate concentrations were measured using gas chromatography. Experiments were conducted in quick succession to prevent reversible compaction [11] affecting the results. Filtrations were repeated at least once, to ensure the validity of the results. The rejection of TOABr with StarMemTM 122 has been well documented in previous work [2,12]. Therefore it was used as the benchmark
D.A. Patterson et al. / Desalination 218 (2008) 248–256
compound to determine the integrity of each membrane disc used. A high TOABr rejection from a filtration before and after trialkylamine filtrations indicated whether or not the StarMemTM 122 membrane disc used was in good condition (i.e. there were no irregularities). 2.3. Cross-flow OSN The cross-flow OSN setup is the same as that used in previous work in this research group [13]. The driving force across the membrane is created by the hydrostatic pressure from a diaphragm pump set at ~75 L h–1 through the membrane cells and back pressure regulator valve, keeping the system pressure regulated to 30 bar. The effect of concentration polarization is minimized during filtration, since fluid enters the cross-flow cell tangentially from the cell wall and exits the cell from the top centre, providing turbulent hydrodynamic conditions [13]. Four identical membrane discs were investigated at the same time, to minimize the effect of membrane material imperfections and manufacturing inconsistencies. Data is reported as the average over these four membranes, unless otherwise stated. Prior to running an experiment, the system was flushed with toluene and then all membranes were pre-conditioned by running the system at pressure with 1 L of fresh pure toluene for approximately 30 min. In an experiment, one litre of solution was loaded into tank A and then the system started. Permeate samples from each disc were collected from the permeate valves, by isolating the circulation line. Permeation volumes and fill times were recorded for flux calculation. Feed and permeate concentrations were measured using gas chromatography while retentate concentrations were calculated by assuming a 100% mass balance on each disc. 2.4. Analytical techniques Trialkylamine base concentrations were determined on a Unicam 610 autosampler gas chro-
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matograph (GC) controlled by the Unicam 4880 data handling and GC control software (ATI Unicam, UK). Separation was achieved on a SupelcowaxTM-10 capillary column (30 m × 0.25 mm ID × 0.25 um, Sigma Aldrich, UK). TOABr concentration was determined using an Agilent 6850 Series GC fitted with a flame ionisation detector (FID) and Agilent 7683 autoinjector. Separation was achieved on a HP-1 capillary column (30 m × 0.32 mm ID × 0.25 um, AnaChem, UK). 2.5. Calculations Rejection of species i, Ri, is defined as:
Ri = 1 −
CiP CiR
(1)
CiP and Cir are solute, i, concentration in permeate and retentate respectively. Solvent flux is defined as the volume of solvent that passes through unit area of membrane per unit time:
J=
V At
(2)
where V is volume of permeate, A is the active membrane area, and t is the filtration time. 2.6. Molecular modelling Gaussian 03W (Gaussian Inc., USA) was used to determine the optimized molecular structures and molecular properties for each of the trialkylamines in toluene in an attempt to investigate any structural differences which would affect the rejection of the various trialkylamine bases in toluene. To do this, firstly the trialkylamine base molecules were constructed using the GaussView molecular building tool. The molecule was symmetrised and cleaned using the “Clean” control panel to adjust the geometry of the molecule based on a defined set of rules to more closely match chemical intuition. The molecule was then run in
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Gaussian 03W, under optimization mode using the default Hartree-Fock ground set and 3-21G basic set. After optimization was complete, Chem3D (CambidgeSoft, UK) was used as direct interface from Gaussian to view and analyse molecular properties. 3. Results and discussion
3.2. Molecular modelling results
3.1. Dead-end OSN Since the nominal MWCO of StarMemTM 122 is 220 g mol–1, a high rejection was expected for all the trialkylamines tested, except triethylamine (TEA). However, Table 2 illustrates that this is the case for all the trialkylamine bases except TDDA, which had an unexpectedly low rejection. All experiments were repeated and good repeatability was obtained. The TDDA filtration was repeated at least five times: all gave a low rejection. The mass balance in Table 2 is the difference between the solute in the feed and that measured in the permeate and retentate. Good mass balances (except for TDA) and consistent rejections of benchmark solution TOABr (Ri ≈ 99%) for every batch also suggest no loss of materials and perfect membrane condition before and after OSN. Furthermore, the flux for all rejections was low (Table 2), indicating no leaking occurred around the membrane seal.
Table 2 Summary of trialkylamine base dead-end OSN with StarMemTM 122 (nominal MWCO = 220 g mol–1)
Base TEA THexA THeptA TOA TDA TDDA
(L/m2.h)
Rejection, Ri Mass balance (%) (%)
72.5 28.3 28.9 30.7 30.5 21.1
6.5 99.6 99.7 98.4 97.5 19.3
Flux, J
The low rejection of TDDA entirely contradicts the nominal MWCO of 220 g mol–1 for StarMemTM 122. The results in Table 2 are therefore a clear example of where MWCO is not an accurate representation of the selectivity of a membrane. To investigate the reasons for this disparity, molecular modelling and cross-flow OSN of these trialkylamines was undertaken.
84.9 97.5 90.3 95.2 76.5 118
As mention in Section 1, rejection is affected by a number of factors, including molecular size and shape. In an attempt to investigate any structural differences which would affect the rejection of the various trialkylamine bases in the solvent toluene, a Gaussian 03W molecular simulation was run to obtain the optimised structures of the trialkylamine bases in toluene. Fig. 2 shows that the optimized structures in toluene are very similar, indicating that shape may not be a factor in the low rejection of TDDA. The molecular properties generated for these molecular structures (see [14] for further details) were similarly inconclusive. As mentioned in Section 1, in dead-end filtration the entire solvent volume passes through the membrane and in cross-flow filtration, the solvent passes parallel to the surface of the membrane and only part of the solvent volume passes through the membrane (Fig. 1). Therefore, concentration polarisation and fouling are expected to be more significant in dead-end filtrations, since agitation was the only mechanism available to clear solute from the membrane surface. In crossflow, the fast flow parallel to the membrane surface should cause turbulence and provide good mixing, decreasing concentration and fouling in comparison. This is a primary reason for differences between dead-end and cross-flow OSN results. Therefore to determine if concentration polarization and fouling have an impact on the results in Table 2, the rejections under equivalent conditions in cross-flow OSN were determined.
D.A. Patterson et al. / Desalination 218 (2008) 248–256
(b)
(a)
(c)
(d)
(e)
(f)
Fig. 2. Optimised structure of trialkylamine bases in toluene — obtained from Gaussian 03W simulation.
3.3. Cross-flow OSN Results for the OSNs for trialkylamine bases in the cross-flow rig are shown in Figs. 3–6.
Only THexA, TOA, TDA and TDDA were filtered (TEA was expected to permeate and THeptA was excluded due to budget constraints). Unlike the results obtained from dead-end OSN, rejections of trialkylamine bases in cross-flow OSN were consistent with their MW. THexA had the lowest average rejection while TOA, TDA and TDDA had very similar average rejections of approximately 82–90%. The rejections were fairly consistent across discs 1–4 for TOA, TDA, TDDA (Figs. 3 and 4), except for THexA (Fig. 5), where discs 1–3 gave consistent results, whilst disc 4 gave much poorer rejections, perhaps indicating a molecular sized membrane flaw. The solution flux decreased over time for all four discs (Fig. 6). However, no physical leakage was observed during the experiment. The consistent rejections of all components also indicate that there was no membrane degradation. The flux decreases over time therefore resulted from either fouling of the solutes over time, and/or membrane compaction. The latter is likely because StarMemTM 122 is a polymer membrane (phase inverted polyimide on a polyester backing layer), so would behave elastically under pressure. Flux reduction due to membrane compaction in StarMemTM membranes has been observed before [11].
50 40 30 20
THexA
10 0
TDDA
TOA TDA
0
10
20
30
40
50
60
70
80
Filtration Time (h)
Fig. 3. Average cross-flow rejection of trialkylamine bases over the four membrane discs.
Rejection (%)
Rejection (%)
100 90 80 70 60
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100 90 80 70 60 50 40 30 20 10 0
disc 1 disc 4
0
10
disc 2 Average
disc 3
20 30 40 50 60 Filtration Time (h)
70
80
Fig. 4. Rejection of TDDA in each of the four membrane discs in the cross-flow rig.
D.A. Patterson et al. / Desalination 218 (2008) 248–256 100 90 80 70 60 50 40 30 20 10 0
12 10
-2 -1
Flux (L m h )
Rejection (%)
254
disc 1 disc 4
0
10
disc 2 Average
70
6 4 2
disc 3
20 30 40 50 60 Filtration time (h)
8
80
disc 1 disc 4
0 0
20
disc 2 Average
40
disc 3
60
80
Filtration time (h)
Fig. 5. Rejection of THexA in each of the four membrane discs in the cross-flow rig.
Fig. 6. Average flux across each of the four membrane discs in the cross-flow rig.
From these results it can be concluded that the low TDDA rejection is a result of the use of deadend OSN. As mentioned in Section 1, cross-flow filtration is used by a majority of membrane processes [9]. Therefore, membrane performance parameters, such as rejection and flux, need to be representative of this configuration. Therefore in this case, dead-end OSN seriously misrepresented the selectivity that could be expected of StarMemTM 122. This is further exemplified by comparing the magnitudes of the dead-end and cross-flow rejections: Table 2 and Figs. 3, 4 and 5, show the rejections of THexA, TOA and TDA are lower in cross-flow than for dead-end OSN. The low rejections of these high MW compounds is inconsistent with the 220 gmol–1 nominal MWCO of StarMemTM 122. This MWCO indicates these compounds should be highly rejected. Overall, the results in this paper can be explained in terms of concentration polarization and/ or fouling and the interplay of pore flow and solution diffusion mechanisms.
speed. This means, in the dead-end OSNs, concentration polarisation and fouling should be greater for the larger MW trialkylamines. This is because: (1) the longer alkyl chains can intertwine and interlock, (2) the viscosity of these solutions should increase with MW. Therefore, for longer chain (higher molecular weight) trialkylamine bases, less material can be moved away from the membrane surface by the stirrer, allowing a more concentrated bed to form there. Since concentration polarisation and fouling are minimised in cross-flow OSN, this can explain: (1) the lower in rejection of THexA, TOA and TDA in crossflow compared to dead-end OSN and (2) the low rejection of TDDA in dead-end OSN. (1) If the THexA, TOA and TDA transport across StarMemTM 122 is by pore flow, greater concentration polarisation and fouling in deadend filtration would create a mass transfer resistance layer above the membrane surface, decreasing mass transport through the membrane. Therefore the rejection in dead-end filtration will be greater than in cross-flow filtration. This is consistent with both the dead-end and cross-flow rejection results (Table 2 and Figs. 3–5) and the cross-flow fluxes (Fig. 6). The low dead-end OSN flux for the highly rejected trialkylamines THexA, THeptA, TOA and TDA compared to highly re-
3.4. Concentration polarization, pore flow and solution diffusion In this work, all of the dead-end cell filtrations were conducted at approximately the same stirrer
D.A. Patterson et al. / Desalination 218 (2008) 248–256
jected TEA in Table 2 also substantiates the presence of this mass transfer resistance layer. Note that these results cannot be as easily explained in terms of solution diffusion. With higher concentration polarisation and fouling in the deadend cell, the longer alkyl chains could possibly intertwine and interlock, preventing partitioning of the higher MW trialkylamines into the membrane. This would make the rejection higher in dead-end OSN compared to cross-flow. However, this mechanism should also result in TDDA (which has the longest alkyl chains) having the highest rejection, contradicting the current findings. (2) When concentration polarization is present and solution diffusion is the transport mechanism, the true rejection (Ri) should account for increased concentration at the surface of the membrane [(Fig. 7 and Eq. (3)]:
Ri = 1 −
CiP Cim
(3)
where CiP and Cim are solutes, i, concentration in the permeate and at the surface of the membrane respectively. As the concentration polarisation and fouling increases, the solute concentration at the membrane surface increases and the true rejection
Cim
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therefore decreases. Therefore, the disparity between TDDA rejection in dead-end and cross-flow OSN could be explained as follows: Concentration polarisation and fouling occur more readily in dead-end OSN than in cross-flow OSN and for bigger, higher molecular weight trialkylamines, such as TDDA. Therefore, for TDDA, the concentration at the membrane surface may increase to where the concentration gradient across the membrane is sufficient for mass transport by solution diffusion. As there is less concentration polarisation and/or fouling for lower MW trialkylamines, they do not reach the required concentration for this. Consequently, the dead-end OSN rejection of TDDA is low in comparison to the other trialkylamines. Pore flow cannot account for the low deadend OSN rejection of TDDA, unless there is a large difference in shape and/or molecular alignment in solution for TDDA compared to TDA. Since the structures of these two molecules are very similar (Fig. 2), this seems unlikely. Therefore, these results indicate that both pore flow and solution diffusion play a role in the transport of trialkylamines through StarMemTM 122 polyimide membranes. Finally, since MWCO cannot accurately reflect the selectivity of StarMemTM 122, a different parameter should be used. For situations where pore flow describes the mass transport well, perhaps this could be hydrodynamic volume as suggested by [8].
Retentate,
CiR Permeate
Ci Boundary layer, δ Fig. 7. Schematic of the concentration polarisation of solute i at the membrane surface (based on [9]).
4. Conclusions The organic solvent nanofiltration of a homologous series of trialkylamine bases was compared in both dead-end and cross-flow mode using StarMemTM 122 (nominal MWCO = 220 g mol–1). For dead-end OSN, although rejections of 97 to 99% where obtained for bases larger than the membrane MWCO, a molecule larger than the MWCO – TDDA (MW = 522 g mol–1) — had an
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average rejection of only 19%. This demonstrates that MWCO is not an accurate representation of the selectivity of StarMemTM 122. Conversely, cross-flow OSN did not give this low TDDA rejection, with all bases with a MW higher than trihexylamine (MW = 270 g mol–1) having rejections of at least 80%. However, the rejections of all but TDDA were lower in crossflow than in dead-end OSN. It is thought that this suggests that pore flow is the main transport mechanism for this OSN; however solution diffusion can become significant if the concentration at the membrane caused by concentration polarisation and/or fouling becomes sufficient. Since MWCO cannot accurate reflect the selectivity of StarMemTM 122, a different parameter should be used in future work.
[5]
[6]
[7]
[8]
Acknowledgements This work was supported by the ESPRC (grants GR/S13637/01 and GR/S13620/01) and Imperial College London. References [1] K. De Smet, S. Aerts, E. Ceulemans, I.F.J. Vankelecom and P.A. Jacobs, Nanofiltration-coupled catalysis to combine the advantages of homogeneous and heterogeneous catalysis, Chem. Commun., 40(8) (2001) 597–598. [2] S.S. Luthra, X.J. Yang, L.M.F. Dos Santos, L.S. White and A.G. Livingston, Homogeneous phase transfer catalyst recovery and re-use using solvent resistant membranes, J. Membr. Sci., 201 (2002) 65– 75. [3] J.T. Scarpello, D. Nair, L.M.F. dos Santos, L.S. White and A.G. Livingston, The separation of homogeneous organometallic catalysts using solvent resistant nanofiltration, J. Membr. Sci., 203 (2002) 71–85. [4] E.J. Gibbins, J.L. Irwin, A.G. Livingston, J.C. Muir, D.A. Patterson, C. Roengpithya and P.C. Taylor, An improved protocol for the synthesis and nanofiltra-
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tion of Kim and Park’s aminocyclopentadienyl ruthenium chloride racemisation catalyst, Synletts., 19 (2005) 2993–2995. E.J. Gibbins, J.L. Irwin, A.G. Livingston, J.C. Muir, D.A. Patterson, C. Roengpithya and P.C. Taylor, Dynamic kinetic resolution of racemates using membrane-separated catalysts, Abstracts of Papers of the American Chemical Society, 229 (2005) U550–U550. C. Roengpithya, D.A. Patterson, P.C. Taylor and A.G. Livingston, Investigation of the compatibility of racemisation and kinetic resolution for the dynamic kinetic resolution of an allylic alcohol, Ind. Eng. Chem. Res., 45(21) (2006) 7101–7109. N.F. Ghazali, D.A. Patterson and A.G. Livingston, Elucidation of the mechanism of diastereomeric salt formation using organic solvent nanofiltration, Chem. Commum., 40(8) (2004) 962–963. M. Meireles, A. Bessieres, I. Rogissart, P. Aimar and V. Sanchez, An appropriate molecular size parameter for porous membranes calibration, J. Membr. Sci., 103 (1995) 105–115. R.W. Baker, Membrane Technology and Applications, 2nd ed., John Wiley & Sons Ltd, Chichester, 2004. J.G. Wijmans and R.W. Baker, The solution–diffusion model: a review, J. Membr. Sci., 107 (1995) 1– 21. E. Gibbins, M. D’Antonio, D. Nair, L.S. White, L.M. Freitas dos Santos, I.F.J Vankelecom and A.G. Livingston, Observations on solvent flux and solute rejection across solvent resistant nanofiltration membranes, Desalination, 147 (2002) 307–313. L.G. Peeva, E. Gibbins, S.S. Luthra, L.S. White, R.P. Stateva and A.G. Livingston, Effect of concentration polarisation and osmotic pressure on flux in organic solvent nanofiltration, J. Membr. Sci., 236 (2004) 121–136. S. Han, H.T. Wong and A.G. Livingston, Application of organic solvent nanofiltration to separation of ionic liquids and products from ionic liquid mediated reactions, Chem. Eng. Res. Design, 83 (2005) 309–316. L.-Y. Lau, Membrane enhanced dynamic kinetic resolution of aliphatic and allylic secondary alcohols. Masters of Engineering Thesis, Imperial College, London, 2004.
Membrane selectivity in the organic solvent nanofiltration of trialkylamine bases Darrell A. Pattersona*, Lay Yen Laub, Chayaporn Roengpithyab, Emma J. Gibbinsb, Andrew G. Livingstonb a
Department of Chemical and Materials Engineering, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand Tel. +64 93737999; Fax +64 9737463; email: [email protected] b Department of Chemical Engineering, Imperial College London, Prince Consort Road, South Kensington, London, SW7 2BY, United Kingdom
Received 23 November 2006; accepted 8 February 2007
Abstract Dead-end and cross-flow organic solvent nanofiltrations (OSNs) using StarMemTM 122 (220 g mol–1 nominal molecular weight cut-off (MWCO)) were conducted on a homologous series of trialkylamine bases, ranging from triethylamine (MW = 270 g mol–1) to tridodecylamine (TDDA; MW = 522 g mol–1). Dead-end OSN gave the expected rejections of 97–99% for bases larger than the MWCO, except TDDA, which unexpectedly had an average rejection of only 19%. MWCO therefore does not accurately represent the selectivity of StarMemTM122. To investigate the reasons for this, cross-flow OSN of the same trialkylamine bases was conducted. This did not give a low TDDA rejection however, and the rejections of all the other bases were lower than in dead-end OSN. These results are most likely due to greater concentration polarisation and/or fouling in the dead-end OSNs. Furthermore, by reconciling these results with the currently understood membrane mass transport models, it is thought that it indicates that both pore flow and solution diffusion play a role in the trialkylamine transport across StarMemTM 122 membranes. Keywords: Organic solvent nanofiltration; Molecular weight cut-off (MWCO); Solution–diffusion; Pore flow; Membrane enhanced dynamic kinetic resolution
*Corresponding author.
Presented in the Separation Sessions at Chemeca 2006, the 34th Australasian Chemical and Process Engineering Conference, Auckland, New Zealand, 17– 20 September 2006. Organised by the University of Auckland and the Society of Chemical Engineers New Zealand (SCENZ). 0011-9164/08/$– See front matter © 2008 Published by Elsevier B.V. doi:10.1016/j.desal.2007.02.020
D.A. Patterson et al. / Desalination 218 (2008) 248–256
1. Introduction Organic solvent nanofiltration (OSN) is a relatively new membrane process capable of producing molecular scale separations. Consequently, OSN has been found to be effective for many highvalue applications, such as recycling homogeneous catalysts and bases [1–6] and separating chiral diastereomers [7]. The selectivity of membranes, including OSN membranes, is commonly characterized by molecular weight cut-off (MWCO), which is the molecular weight (MW) for which 90% rejection of the solute is achieved by the membrane. Therefore, using MWCO, it is expected that a molecule with a MW higher than the MWCO will have a high rejection. However, MWCO does not accurately represent the range of other factors and mechanisms which also control the selectivity of a membrane separation [8]. These include shape, solubility, charge, concentration polarization and fouling [8,9]. Thus mass transport through membranes is not solely dependant on MW. Two models are commonly used describe the mass transport through most membranes: 1. The solution diffusion model [9,10] is typically used to describe transport through pore-less membranes, like those used in reverse osmosis. The solution diffusion model assumes that the permeating species dissolves in the membrane material and diffuses through the membrane down a concentration gradient. A separation is achieved between different solutes due to differences in the amount that dissolves in the membrane and the rate at which it diffuses through the membrane [9,10]. 2. The pore flow model [9,10] is used to describe the permeation through porous membranes (ultrafiltration, microfiltration). It describes the separation based on pressure-driven convective flow through tiny pores. Selectivity results from exclusion, based on the incompatibility of molecule parameters such size, shape and charge, with the pores in the membrane.
249
Nanofiltration membranes, including OSN membranes, are used for molecular separations. Such separations occur in the transition between ultrafiltration and reverse osmosis membrane operations [9]: separating molecules with MWs of 200–1000 g mol–1. Consequently, it is uncertain which of the above mechanisms (or indeed a combination of both) governs mass transport through nanofiltration membranes. Laboratory scale OSN separations are typically performed in one of two modes: dead-end filtration, where the entire solvent volume passes through the membrane under applied hydrostatic or gaseous pressure (Fig. 1a); or cross-flow filtration, where solvent passes parallel to the sur-
(a)
Pressure driving force P and direction of flow Q of solvent and compounds P
Q
Q Flow of solvent and permeated compounds (b) Pressure driving force, P Direction of flow, Q, of Q solvent and compounds
Flow of solvent and rejected compounds
Q2 Flow of solvent and permeated compounds
Fig. 1. Schematic of the flows in (a) Dead-end OSN and (b) Cross-flow OSN.
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face of the membrane and only part of the solvent volume passes through the membrane due to the applied pressure (Fig. 1b). Cross-flow filtration is used by a majority of membrane processes [9]. Therefore, membrane performance parameters, such as rejection and flux, need to be representative of this configuration. Researchers and process developers often opt for dead-end filtrations because of its lower cost and simplicity. However, dead-end filtration and cross-flow filtration can have very different rejections due to the differences in operation. To address the above issues, this paper highlights an example of how MWCO misrepresents the selectivity of a membrane and investigates possible reasons for the disparity, by reconciling the results with the pore flow and solution diffusion mass transport models. Furthermore, this paper demonstrates that the use of dead-end filtration as a sole means to characterize membrane separation can lead to misrepresentative rejection data.
2. Methods and materials 2.1. Materials All chemicals used as solutes were purchased from Sigma Aldrich, UK, except tridodecylamine, which was sourced from Lancaster synthesis UK. Details are in Table 1. Analytical grade Toluene (Fisher, UK and Sigma Aldrich, UK) was used as solvent. Only straight chain trialkylamines were used. StarMemTM* 122, an integrally skinned asymmetric polyimide nanofiltration membrane, was used in all experiments. It has a nominal MWCO of 220 g mol–1. StarMemTM 122 was supplied by Membrane Extraction Technologies Ltd, UK.
* StarMem™ is a trademark of W.R. Grace, Columbia, MD, USA.
Table 1 Solutes used in OSNs Chemical
Mw (g mol–1)
Triethylamine (TEA) Trihexylamine (THexA) Triheptylamine (THeptA) Trioctylamine (TOA) Tridecylamine (TDA) Tridodecylamine (TDDA) Tetraoctylammonium bromide (TOABr)
101.19 269.52 311.59 353.68 437.83 522.00 546.80
2.2. Dead-end OSN All dead-end OSN experiments were conducted using a stainless steel, SEPA ST (Osmonics, USA) cell with an effective membrane area of 13.9 cm2. The experimental setup has been used in previous work [2,3,7]. Nitrogen gas was applied as the pressure driving force. All experiments were conducted at 25ºC using a water bath. The filtrations were all conducted at the same stirrer speed. Prior to solute filtrations, the membranes were pre-conditioned by three separate filtrations with pure solvent for 30 min each, at which point the flux became steady. The importance of this preconditioning is detailed in [11]. Filtrations were performed by loading the cell with 30 mL of 50 mM feed solution of the trialkylamine and applying 30 bar pressure until half the fed solution had permeated. The flux data was calculated from permeate volume and the time taken for permeation [Eq. (2)]. The feed, permeate and retentate concentrations were measured using gas chromatography. Experiments were conducted in quick succession to prevent reversible compaction [11] affecting the results. Filtrations were repeated at least once, to ensure the validity of the results. The rejection of TOABr with StarMemTM 122 has been well documented in previous work [2,12]. Therefore it was used as the benchmark
D.A. Patterson et al. / Desalination 218 (2008) 248–256
compound to determine the integrity of each membrane disc used. A high TOABr rejection from a filtration before and after trialkylamine filtrations indicated whether or not the StarMemTM 122 membrane disc used was in good condition (i.e. there were no irregularities). 2.3. Cross-flow OSN The cross-flow OSN setup is the same as that used in previous work in this research group [13]. The driving force across the membrane is created by the hydrostatic pressure from a diaphragm pump set at ~75 L h–1 through the membrane cells and back pressure regulator valve, keeping the system pressure regulated to 30 bar. The effect of concentration polarization is minimized during filtration, since fluid enters the cross-flow cell tangentially from the cell wall and exits the cell from the top centre, providing turbulent hydrodynamic conditions [13]. Four identical membrane discs were investigated at the same time, to minimize the effect of membrane material imperfections and manufacturing inconsistencies. Data is reported as the average over these four membranes, unless otherwise stated. Prior to running an experiment, the system was flushed with toluene and then all membranes were pre-conditioned by running the system at pressure with 1 L of fresh pure toluene for approximately 30 min. In an experiment, one litre of solution was loaded into tank A and then the system started. Permeate samples from each disc were collected from the permeate valves, by isolating the circulation line. Permeation volumes and fill times were recorded for flux calculation. Feed and permeate concentrations were measured using gas chromatography while retentate concentrations were calculated by assuming a 100% mass balance on each disc. 2.4. Analytical techniques Trialkylamine base concentrations were determined on a Unicam 610 autosampler gas chro-
251
matograph (GC) controlled by the Unicam 4880 data handling and GC control software (ATI Unicam, UK). Separation was achieved on a SupelcowaxTM-10 capillary column (30 m × 0.25 mm ID × 0.25 um, Sigma Aldrich, UK). TOABr concentration was determined using an Agilent 6850 Series GC fitted with a flame ionisation detector (FID) and Agilent 7683 autoinjector. Separation was achieved on a HP-1 capillary column (30 m × 0.32 mm ID × 0.25 um, AnaChem, UK). 2.5. Calculations Rejection of species i, Ri, is defined as:
Ri = 1 −
CiP CiR
(1)
CiP and Cir are solute, i, concentration in permeate and retentate respectively. Solvent flux is defined as the volume of solvent that passes through unit area of membrane per unit time:
J=
V At
(2)
where V is volume of permeate, A is the active membrane area, and t is the filtration time. 2.6. Molecular modelling Gaussian 03W (Gaussian Inc., USA) was used to determine the optimized molecular structures and molecular properties for each of the trialkylamines in toluene in an attempt to investigate any structural differences which would affect the rejection of the various trialkylamine bases in toluene. To do this, firstly the trialkylamine base molecules were constructed using the GaussView molecular building tool. The molecule was symmetrised and cleaned using the “Clean” control panel to adjust the geometry of the molecule based on a defined set of rules to more closely match chemical intuition. The molecule was then run in
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Gaussian 03W, under optimization mode using the default Hartree-Fock ground set and 3-21G basic set. After optimization was complete, Chem3D (CambidgeSoft, UK) was used as direct interface from Gaussian to view and analyse molecular properties. 3. Results and discussion
3.2. Molecular modelling results
3.1. Dead-end OSN Since the nominal MWCO of StarMemTM 122 is 220 g mol–1, a high rejection was expected for all the trialkylamines tested, except triethylamine (TEA). However, Table 2 illustrates that this is the case for all the trialkylamine bases except TDDA, which had an unexpectedly low rejection. All experiments were repeated and good repeatability was obtained. The TDDA filtration was repeated at least five times: all gave a low rejection. The mass balance in Table 2 is the difference between the solute in the feed and that measured in the permeate and retentate. Good mass balances (except for TDA) and consistent rejections of benchmark solution TOABr (Ri ≈ 99%) for every batch also suggest no loss of materials and perfect membrane condition before and after OSN. Furthermore, the flux for all rejections was low (Table 2), indicating no leaking occurred around the membrane seal.
Table 2 Summary of trialkylamine base dead-end OSN with StarMemTM 122 (nominal MWCO = 220 g mol–1)
Base TEA THexA THeptA TOA TDA TDDA
(L/m2.h)
Rejection, Ri Mass balance (%) (%)
72.5 28.3 28.9 30.7 30.5 21.1
6.5 99.6 99.7 98.4 97.5 19.3
Flux, J
The low rejection of TDDA entirely contradicts the nominal MWCO of 220 g mol–1 for StarMemTM 122. The results in Table 2 are therefore a clear example of where MWCO is not an accurate representation of the selectivity of a membrane. To investigate the reasons for this disparity, molecular modelling and cross-flow OSN of these trialkylamines was undertaken.
84.9 97.5 90.3 95.2 76.5 118
As mention in Section 1, rejection is affected by a number of factors, including molecular size and shape. In an attempt to investigate any structural differences which would affect the rejection of the various trialkylamine bases in the solvent toluene, a Gaussian 03W molecular simulation was run to obtain the optimised structures of the trialkylamine bases in toluene. Fig. 2 shows that the optimized structures in toluene are very similar, indicating that shape may not be a factor in the low rejection of TDDA. The molecular properties generated for these molecular structures (see [14] for further details) were similarly inconclusive. As mentioned in Section 1, in dead-end filtration the entire solvent volume passes through the membrane and in cross-flow filtration, the solvent passes parallel to the surface of the membrane and only part of the solvent volume passes through the membrane (Fig. 1). Therefore, concentration polarisation and fouling are expected to be more significant in dead-end filtrations, since agitation was the only mechanism available to clear solute from the membrane surface. In crossflow, the fast flow parallel to the membrane surface should cause turbulence and provide good mixing, decreasing concentration and fouling in comparison. This is a primary reason for differences between dead-end and cross-flow OSN results. Therefore to determine if concentration polarization and fouling have an impact on the results in Table 2, the rejections under equivalent conditions in cross-flow OSN were determined.
D.A. Patterson et al. / Desalination 218 (2008) 248–256
(b)
(a)
(c)
(d)
(e)
(f)
Fig. 2. Optimised structure of trialkylamine bases in toluene — obtained from Gaussian 03W simulation.
3.3. Cross-flow OSN Results for the OSNs for trialkylamine bases in the cross-flow rig are shown in Figs. 3–6.
Only THexA, TOA, TDA and TDDA were filtered (TEA was expected to permeate and THeptA was excluded due to budget constraints). Unlike the results obtained from dead-end OSN, rejections of trialkylamine bases in cross-flow OSN were consistent with their MW. THexA had the lowest average rejection while TOA, TDA and TDDA had very similar average rejections of approximately 82–90%. The rejections were fairly consistent across discs 1–4 for TOA, TDA, TDDA (Figs. 3 and 4), except for THexA (Fig. 5), where discs 1–3 gave consistent results, whilst disc 4 gave much poorer rejections, perhaps indicating a molecular sized membrane flaw. The solution flux decreased over time for all four discs (Fig. 6). However, no physical leakage was observed during the experiment. The consistent rejections of all components also indicate that there was no membrane degradation. The flux decreases over time therefore resulted from either fouling of the solutes over time, and/or membrane compaction. The latter is likely because StarMemTM 122 is a polymer membrane (phase inverted polyimide on a polyester backing layer), so would behave elastically under pressure. Flux reduction due to membrane compaction in StarMemTM membranes has been observed before [11].
50 40 30 20
THexA
10 0
TDDA
TOA TDA
0
10
20
30
40
50
60
70
80
Filtration Time (h)
Fig. 3. Average cross-flow rejection of trialkylamine bases over the four membrane discs.
Rejection (%)
Rejection (%)
100 90 80 70 60
253
100 90 80 70 60 50 40 30 20 10 0
disc 1 disc 4
0
10
disc 2 Average
disc 3
20 30 40 50 60 Filtration Time (h)
70
80
Fig. 4. Rejection of TDDA in each of the four membrane discs in the cross-flow rig.
D.A. Patterson et al. / Desalination 218 (2008) 248–256 100 90 80 70 60 50 40 30 20 10 0
12 10
-2 -1
Flux (L m h )
Rejection (%)
254
disc 1 disc 4
0
10
disc 2 Average
70
6 4 2
disc 3
20 30 40 50 60 Filtration time (h)
8
80
disc 1 disc 4
0 0
20
disc 2 Average
40
disc 3
60
80
Filtration time (h)
Fig. 5. Rejection of THexA in each of the four membrane discs in the cross-flow rig.
Fig. 6. Average flux across each of the four membrane discs in the cross-flow rig.
From these results it can be concluded that the low TDDA rejection is a result of the use of deadend OSN. As mentioned in Section 1, cross-flow filtration is used by a majority of membrane processes [9]. Therefore, membrane performance parameters, such as rejection and flux, need to be representative of this configuration. Therefore in this case, dead-end OSN seriously misrepresented the selectivity that could be expected of StarMemTM 122. This is further exemplified by comparing the magnitudes of the dead-end and cross-flow rejections: Table 2 and Figs. 3, 4 and 5, show the rejections of THexA, TOA and TDA are lower in cross-flow than for dead-end OSN. The low rejections of these high MW compounds is inconsistent with the 220 gmol–1 nominal MWCO of StarMemTM 122. This MWCO indicates these compounds should be highly rejected. Overall, the results in this paper can be explained in terms of concentration polarization and/ or fouling and the interplay of pore flow and solution diffusion mechanisms.
speed. This means, in the dead-end OSNs, concentration polarisation and fouling should be greater for the larger MW trialkylamines. This is because: (1) the longer alkyl chains can intertwine and interlock, (2) the viscosity of these solutions should increase with MW. Therefore, for longer chain (higher molecular weight) trialkylamine bases, less material can be moved away from the membrane surface by the stirrer, allowing a more concentrated bed to form there. Since concentration polarisation and fouling are minimised in cross-flow OSN, this can explain: (1) the lower in rejection of THexA, TOA and TDA in crossflow compared to dead-end OSN and (2) the low rejection of TDDA in dead-end OSN. (1) If the THexA, TOA and TDA transport across StarMemTM 122 is by pore flow, greater concentration polarisation and fouling in deadend filtration would create a mass transfer resistance layer above the membrane surface, decreasing mass transport through the membrane. Therefore the rejection in dead-end filtration will be greater than in cross-flow filtration. This is consistent with both the dead-end and cross-flow rejection results (Table 2 and Figs. 3–5) and the cross-flow fluxes (Fig. 6). The low dead-end OSN flux for the highly rejected trialkylamines THexA, THeptA, TOA and TDA compared to highly re-
3.4. Concentration polarization, pore flow and solution diffusion In this work, all of the dead-end cell filtrations were conducted at approximately the same stirrer
D.A. Patterson et al. / Desalination 218 (2008) 248–256
jected TEA in Table 2 also substantiates the presence of this mass transfer resistance layer. Note that these results cannot be as easily explained in terms of solution diffusion. With higher concentration polarisation and fouling in the deadend cell, the longer alkyl chains could possibly intertwine and interlock, preventing partitioning of the higher MW trialkylamines into the membrane. This would make the rejection higher in dead-end OSN compared to cross-flow. However, this mechanism should also result in TDDA (which has the longest alkyl chains) having the highest rejection, contradicting the current findings. (2) When concentration polarization is present and solution diffusion is the transport mechanism, the true rejection (Ri) should account for increased concentration at the surface of the membrane [(Fig. 7 and Eq. (3)]:
Ri = 1 −
CiP Cim
(3)
where CiP and Cim are solutes, i, concentration in the permeate and at the surface of the membrane respectively. As the concentration polarisation and fouling increases, the solute concentration at the membrane surface increases and the true rejection
Cim
255
therefore decreases. Therefore, the disparity between TDDA rejection in dead-end and cross-flow OSN could be explained as follows: Concentration polarisation and fouling occur more readily in dead-end OSN than in cross-flow OSN and for bigger, higher molecular weight trialkylamines, such as TDDA. Therefore, for TDDA, the concentration at the membrane surface may increase to where the concentration gradient across the membrane is sufficient for mass transport by solution diffusion. As there is less concentration polarisation and/or fouling for lower MW trialkylamines, they do not reach the required concentration for this. Consequently, the dead-end OSN rejection of TDDA is low in comparison to the other trialkylamines. Pore flow cannot account for the low deadend OSN rejection of TDDA, unless there is a large difference in shape and/or molecular alignment in solution for TDDA compared to TDA. Since the structures of these two molecules are very similar (Fig. 2), this seems unlikely. Therefore, these results indicate that both pore flow and solution diffusion play a role in the transport of trialkylamines through StarMemTM 122 polyimide membranes. Finally, since MWCO cannot accurately reflect the selectivity of StarMemTM 122, a different parameter should be used. For situations where pore flow describes the mass transport well, perhaps this could be hydrodynamic volume as suggested by [8].
Retentate,
CiR Permeate
Ci Boundary layer, δ Fig. 7. Schematic of the concentration polarisation of solute i at the membrane surface (based on [9]).
4. Conclusions The organic solvent nanofiltration of a homologous series of trialkylamine bases was compared in both dead-end and cross-flow mode using StarMemTM 122 (nominal MWCO = 220 g mol–1). For dead-end OSN, although rejections of 97 to 99% where obtained for bases larger than the membrane MWCO, a molecule larger than the MWCO – TDDA (MW = 522 g mol–1) — had an
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average rejection of only 19%. This demonstrates that MWCO is not an accurate representation of the selectivity of StarMemTM 122. Conversely, cross-flow OSN did not give this low TDDA rejection, with all bases with a MW higher than trihexylamine (MW = 270 g mol–1) having rejections of at least 80%. However, the rejections of all but TDDA were lower in crossflow than in dead-end OSN. It is thought that this suggests that pore flow is the main transport mechanism for this OSN; however solution diffusion can become significant if the concentration at the membrane caused by concentration polarisation and/or fouling becomes sufficient. Since MWCO cannot accurate reflect the selectivity of StarMemTM 122, a different parameter should be used in future work.
[5]
[6]
[7]
[8]
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