Influences of physicochemical parameters on the separation of colloidal organics

researcharticle The impact of physico-chemical parameters on solid-liquid separation of a micronized lactose-in-ethanol suspension is investigated by means of Fraunhofer diffraction, focused beam reflectance measurement (FBRM), electrokinetic sonic amplitude (ESA), sedimentation and filtration experiments. Lactose-in-ethanol dispersions are quite unstable, with a strong particle agglomeration and sedimentation tendency. Only diluted suspensions show a significant impact of physico-chemical parameters such as pH. ESA titrations show the surface properties of lactose to change strongly when acids and bases are added. High base concentrations were found to accelerate cake filtration, while high acid concentrations slow it down.

Influences of physicochemical parameters on the separation of colloidal organics Thorben Benesch1,Ulrich Meier2*, Edgar John2, Fritz Blatter3 & Wilfried Schütz1 1Hochschule Bremerhaven, An der Karlstadt 8, 27568 Bremerhaven, Germany 2Novartis Pharma AG, K-316.1.17 Postfach, 4002 Basel, Switzerland 3Solvias AG, WKL-127.5.02, Klybeckstrasse 191, Postfach, 4002 Basel, Switzerland *Corresponding author: [email protected]

C

ake filtration is mainly influenced by porosity and permeability of the cake. Particle size and shape of the dispersed phase control these parameters [1, 2, 3], and sedimentation, filtration or flotation of particles in the colloidal range require the formation of agglomerates. Thus, flocculation became an essential stage in many solid-liquid separation processes [4]. Nevertheless, little systematic research has been carried out to determine the importance of the physicochemical parameters of theses processes. Understanding the effects of liquid-particle interactions on filtration, particularly in organic systems, requires further research [5]. Conventional processes like sweep coagulation or flocculation by means of polymer bridging [2] are limited to applications like microfiltration for water treatment [6], where the separated product purity plays no role. Colloids show significant Brownian diffusion and small sedimentation rates [4], and their presence in a suspension was reported to have a dramatic impact on the rheologic characteristics and sedimentation of larger particles [7]. Attractive Van der Waals force and electrostatic repulsion [8, 9] are found to be relevant even for particles up to 10 µm [4] in cake [5, 10-14], as well as crossflow filtration [15, 16]. One may employ the Derjaguin-Landau-VerweyOverbeek (DLVO) theory [17, 18] to make a first estimation for the colloidal interaction, but exact values cannot be obtained with this model because it fails in high concentrated systems [19, 20]. Controversial results were reported about the optimal operation conditions. Experiments with TiO2 [10] showed that the filtration apparatus is optimally operated, when the ζ-potential is zero or the electrostatic double layer is squeezed. These conditions occur at the iso-electric point (IEP) or in dispersions of high ionic strength, respectively. Thus, it was assumed that the agglomerate diameter determines the effective cake porosity, and a dimensionless number, incorporating drag force to adhesion force, was introduced to describe these effects [11, 12]. Ohmori et al [13] obtained a five-fold improvement of C. glutamicum microfiltration, when cell slurry was adjusted at pH 2.0, where the surface charge of the cells is much lower than in the neutral range. Conversely, in a study on the dead-

Filtration+Separation

end ultrafiltration of proteinaceous solutions [14] the average cake resistance was maximal around the IEP, and the filtrate flux decreased when NaCl was added, which is explained by the compact gel-cake. Whether the filtration rate is enhanced or lowered depends on the nature of the colloids. A non-coagulating colloidal suspension, for example, forms a compact cake when the double layer is compressed by an increase in the ionic strength [15]. Also the structure of the flocs is crucial in practical separation processes. While the fluid drag is reduced, when high-dense flocs are formed, sedimentation slows down when the flocs have an open fractal structure. Furthermore, the cake resistance of the latter is higher because such flocs are highly compressible [21]. This article describes the influence of physicochemical parameters, such as pH, ionic strength and particle concentration, on ‘lactose in ethanol’, which serves as a model for organic systems, with emphasis on filtration and sedimentation.

Theory (i) Filtration The classical filtration model, assuming negligible settling velocity, homogeneity of the input flux and incompressible filter medium [1, 2, 3], is employed in this work. Since the pressure is fixed, the characteristic filtration equation may be used [1]. R η ∆pt η l c c, f α = Mf + m l 2 2 Mf Aρ l 2 A ρl

(1)

Here, Rm is the resistance of the filter medium, cc,ƒ is the mass of the cake with respect to the filtrate volume, and α is the cake resistance. ∆p is the total pressure drop in the cake, ηl is the dynamic viscosity of the liquid, and A the area of the filter. Mƒ is the mass of the filtrate, ρl the density of the liquid. Note that the shape of a ∆pt/Mƒ versus Mƒ – plot is expected to be a straight line. In our experiments, however, the sedimentation velocity is not negligible, so the plot has a sigmoid shape. Recently, a model was developed

October 2004 35

researcharticle (iii) Fundamentals of colloidal science

5 4

Filtration models that incorporate interparticle forces are to date not available [10]. The interaction of colloidal particles is described by the DLVO theory, including attractive Van-der-Waals and repulsive electrostatic forces. The former is characterized by particles and solvent properties; the latter by the ionic strength, and the ζ-potential that depends on the pH [8,17,18]. When two plates interact, the total energy per surface area wT is for example given by:

3

[w] µJ/m²

2 1 0 -1 -2 -3

wT= w + A

-4

w=R −

-5 0

1

2

3

4

5

6

Distance/Screening Length

Figure 1: Interaction energy between two colloids, as a function of the ζ-potential and the particle separation. The system consists of polystyrene particles (rp = 1.4 µm) in water, with T = 298 K, A = 1.44 x 10-20 J, I = 1 mM, ζhigh = 9 mV, ζlow = 5 mV (solid = DLVO: low ζ-potential, dash = DLVO: high ζpotential, dot = Van-der-Waals energy, dash-dot = electrostatic energy: low ζ-potential & dashdot-dot = electrostatic energy: high ζ-potential). [22] that takes sedimentation effect into account. For the purpose of this research, it is sufficient to split the curve into two to three parts whose slopes can be compared. By use of the following power law that relates the average cake resistance to the pressure drop in the cake ∆pc, the model for incompressible cake filtration (1) can be easily expanded to compressible cake filtration.  ∆p  α = α 0 (1 − κ ) c   ∆p 0 

κ

(2)

The compressibility κ is an empirical parameter that needs to be determined by experiments. α0 is a constant determined at the pressure difference ∆p0.

(3)

Re < 0.5

 zeζ   Z = tanh   4k B T 

κ=

2000 I N A e 2 | ε r ε 0 k BT

[I] = mol l-1

(6)

Here, z is the sign of the charge, e is the elementary charge, εr is the dielectric constant, ε0 the vacuum permittivity and NA the Avogadro number. In a suspension, adjusted to the IEP, the colloids agglomerate because the ζ-potential is zero. One can see the influence of the ζ-potential in Figure 1. The electrostatic energy is small for a low ζ-potential and the total energy is always negative. If the ζ-potential has a certain magnitude, an energy barrier hinders the interaction of two colloids where the electrostatic energy is dominant. Furthermore, one can distinguish primary and secondary minimum agglomeration at separation distances left and right from the energy barrier.

2.5

0.1

and

[W] µJ/m

2

1 0.5 0 -0.5 -1 -1.5

v sed= v sed ,s (1 − c r )n

36 October 2004

(5)

1.5

Here, νsed,s is the sedimentation velocity of an isolated sphere, ρs is the density of the solid fraction, g is the acceleration due to gravity, ds is the diameter of the particle, νl the kinematical viscosity and Re, the Reynolds number. In high concentration ranges, the diminished bulk sedimentation velocity νsed is related to νsed,s by Equation 4, where cr is the volumetric concentration given by the volume of the solid fraction Vs and the volume of the liquid fraction Vl, respectively [23].

 2000 + Re  with n = 2.2   1 + Re 

64 c 0 k B TZ 2 exp(− κD ) κ

2

The sedimentation velocity depends on the structure and size of the agglomerates [21]. If the velocity is in the Stokes’ range [3], it may be computed by Equation 3. 1 ρ s − ρl g 2 ds 18 ρ l ν l

12πD

+

2

Here, wA and wR are the attractive and repulsive specific energies, respectively, CH is the Hamaker constant, which is a characteristic value for solid and liquid medium, D is the distance between the surfaces, kB is the Boltzmann constant and T is the absolute temperature. The parameter Z increases with the ζ-potential and κ increases with the ionic strength I. c0 is the number of electron pairs per volume element, i.e. c0 = I·NA.

(ii) Sedimentation

v sed ,s =

CH

cr =

Vs Vs + Vl

(4)

-2 -2.5 0

1

2

3

4

5

6

7

8

Distance/Debye Length

Figure 2: Interaction energy between two colloids, as a function of the ionic strength and the particle separation. The system consists of polystyrene particles (rp = 1.4 µm) in water, with T = 298 K, A = 1.44 x 10-20 J, ζ = 10 mV (solid: I = 3.7 mmol/l, slash: I = 1.0 mmol/l & dot: I = 0.15 µmol/l). www.filtsep.com

researcharticle The influence of pH, ionic strength and solid fraction on filtration and sedimentation is studied by experiments using a laboratory pressure filter of own design. This filter consists of a filter medium from steel and double coat glass column, with an inner diameter of 14 mm, allowing temperature control and observation of cake formation and sedimentation. The pressure is varied between 1 bar and 5 bar using Carbagas 1066 nitrogen gas technical quality. pH and ionic strength are varied by titration with Fluka hydrochloric acid standard solution 0.1 M 71577, Ciba N17 1 M caustic acid, Novartis 2216 sodium chloride technical quality and Fluka 62480 lithium chloride anhydrous.

1.0

Maximum at pH = 2.8 (U = 246 mV)

[ESA] mPaM/V

0.5

I.E.P. at pH = 4.2 (U = 115 mV) 0.0

Minimum at pH = 10.7 (U = -215 mV) -0.5

-1.5 4

6

8

10

12

14

Results

pH

(i) ζ-potential

Figure 3: ESA signal as a function of the pH for an ethanol suspension containing 10%m lactose (circle = ESA signal). Figure 2 shows that the energy barrier decreases with increasing ionic strength because the Debye length becomes smaller, so that the particles approach distances where the Van der Waals force is more dominant. Note that the energy is plotted against the ratio between plate separation and Debye length.

Materials & methods

ζ =

ESAη ε 0 ε r uΦ∆ρGα

(7)

Here ESA is the signal, Φ is the volume fraction, u the sound velocity ∆ρ the density difference (ρs−ρl), and Gα is the inertia factor of the particles. A Metrohm LiCl electrode, especially designed for organic systems, is used in this work to measure the pH. It allows the comparison between conditions with different acid and base concentrations, respectively. Even though the pH, i.e. the activity of hydrogen protons, cannot be defined for ethanol, the terminology is used in this work, which enables the reader to judge easily the condition of the dispersion. The electrode is calibrated with Novartis P26 (pH 4.00), Novartis P27 (pH 7.00) and Novartis P13 (pH 10.00).

Filtration+Separation

(ii) Influence of pH on sedimentation To study the influence of the solid fraction and pH on the agglomeration, sedimentation tests were conducted with four samples, containing lactose with mass fractions of 10%, 2.5%, 1% and 0.5%. A qualitative result is shown in Figure 5. To have the same surface conditions of lactose in each sample, the suspensions are all conditioned with acetic acid at 215 mV LiCl electrode potential (pH 3.2). One significant feature of the suspension, with higher solid fractions, is the very definite mud

160

0.9

140

Maximum at 3 mmol

0.8

120

0.7

100 0.6 80 0.5 60

o

All dispersions are prepared with technical ethanol (89.5%m ethanol, 5.7%m water and 4.8%m isopropanol) and lactose monohydrate (5% water) with a median diameter, x50, of 2.8 µm, determined with Sympatec Helos H075, employing Fraunhofer Diffraction. This technique is also applied to study the particle interaction in very dilute systems of different pH. A Lasentec probe, employing focused beam reflectance measurement (FBRM) [24], is used to observe the agglomeration. Microscopic images are obtained with a Zeiss Axioskop and plotted with the CP 710 from Mitsubishi. The agglomerate size is determined from the settling velocity of the muddy line in high concentrated dispersions, employing Equations 3 and 4. The dependence of the ζ-potential on pH and ionic strength is recorded with the Matec Applied Sciences ESA-8000, employing the electrokinetic sonic amplitude (ESA) [25, 26], which is proportional to the ζ-potential, as given by Equation 7. The latter quantity may be measured even in high concentrated dispersions, which makes ESA an adequate alternative to the betterknown electrophoresis [27].

First the ESA signal is recorded as a function of pH by titration of 10%m lactose-ethanol dispersion with NaOH and HCl solutions, shown in Figure 3. One can observe an intense change of the ESA signal with the pH, which proves the significant influence of the pH on the surface charge of the lactose particles. Furthermore, the influence of the ionic strength on the ESA signal is recorded for the titration of lactose-ethanol dispersion with a 4 M NaCl solution. Figure 4 shows that the ESA signal cannot be totally suppressed when a salt is added. However, the very intense response upon the addition of small amounts of sodium chloride is impressive.

Phase/

2

[ESA] mPa M/V

0

0.4 40 0.3

20

0.2

0

0.1

-20 0

20

40

60

80

100

120

140

[I] mmol/l

Figure 4: ESA signal as a function of the ionic strength for an ethanol suspension containing 10%m lactose (circle = ESA signal & cross = phase). October 2004 37

researcharticle (iii) Influence of pH on filtration A

B

Figure 5: Sedimentation at 215 mV: A = start & B = 40 minutes.

A

B

Figure 6: A = 0.4%m lactose dispersion and B = 10%m lactose dispersion. line that can be observed in the filtration experiment, as well as separate sedimentation tests in measuring cylinders at normal pressure. While the remaining solution above the mud line is very clear, if the solid fraction is greater than 1% the line is less marked for a solution containing 0.5% lactose. Microscopy is used to show the extent of agglomeration in the dispersions containing solid fractions between 0.4% and 10%. None of these concentrations is sufficiently low to stabilize the particles in the suspension, but the relative fraction of primary particles, i.e. those with x50 = 2.8 µm, to all particles, i.e. agglomerates, increases with decreasing solid content (see Figure 6). Figure 7 shows the diameters of the agglomerates estimated from Equations 3 and 4 for pressures between 1-5 bar and pH values of 10.6, 5.0, 2.7 and 4.6 corresponding to a pure lactoseethanol dispersion, and dispersions fixed at the IEP and pH where the magnitude of the ESA signal is maximal. A stable dispersion is expected at the pH with high ESA, while particles agglomerate at the IEP. However, as one can read from the graph, the average diameter does not vary significantly between different pH values, but increases with increasing pressure. The average diameter differs maximal 1.9 µm between two different pH conditions, which is maximal seven particles. This is low regarding the fact that the diameter varies with the pressure between 11 µm and 16 µm, which is equal to 15-33 particles. The values of the diameter match relatively well with the values obtained by experiments with the Lasentec, when the stirring speed is zero. Latter experiments also allow insights into the flocculation mechanism: the particles agglomerate fast, but once the flocks reached the maximum size, they do not change significantly.

38 October 2004

The pH influence on ethanol dispersions containing 10% lactose was examined. The filtration of a dispersion fixed at the IEP is expected to be the fastest because of the agglomeration. Analogous to the sedimentation experiments, no influence of the pH is observed in these high concentration ranges. Also, dispersion with lower solid fractions, i.e. 0.5% lactose by mass, could not be significantly manipulated in ranges between pH 2-11. When the sodium hydroxide concentration is increased up to 0.006 mol/l, the filtration can be remarkably accelerated. This is shown for filtration of 250 g dispersion containing 0.5% lactose. The filtration of the dispersion without any additives requires 55 minutes, while the dispersion containing 0.0064 M NaOH is filtered in ten minutes. The addition of NaOH causes the formation of flocs, so that the porosity of the cake increases, which could be observed qualitatively by the increasing cake height. The parameters cc,f and α are not computed because in these concentration ranges the cake height is relatively low, so that the measurement is very inaccurate. Thus, the filtrations are quantitatively compared in Table 1 by their slopes, which is a good measure for the cake resistance. The steeper the slope the slower the filtration is. Note that in some cases the filtration curve is not linear, as mentioned above. Thus, the curve is split into two parts, marked with a1 and a2 The reason for this significant change might be either the high pH or the high ionic strength. To exclude the possibility of reactions with NaOH, the pH, adjusted to 10.9, was controlled in a closed flask, containing respectively 10% lactose-ethanol dispersion and pure ethanol. The pH was measured over a period of four hours. It sank in the former only to 10.7, and in the latter to 10.8. These changes can be explained by CO2 absorption from the air and shows that the NaOH does not react with lactose. To verify both the influence of the pH and the ionic strength, ethanol dispersions, containing 0.5% lactose and 0.0064 M of HCl and NaCl, respectively, were filtered. The comparison between these measurements and the pure lactose dispersion is shown in Figure 8. One can see that the pH is the crucial factor for the filtration. The addition of HCl worsens the filtration, the NaOH improves it and the addition of NaCl has no influence. As before 250 g of 0.5% lactose-ethanol dispersions are filtered. The filtration of the basic dispersion requires ten minutes, the acid dispersions 90 minutes and the one containing NaCl 60 minutes. A comparison of the slopes as a measure for the specific cake resistance is given in Table 2. Filtrations with bigger particles (x50 = 17 µm) show that the agglomerate size, but not the primary particle size, determine the cake resistance and porosity. The latter quantity, estimated from cake height, inner diameter of the filter and dry cake mass, is 0.35 for the bigger and between 0.47-0.5 for the smaller particles. Using the Carman-Kozeny equation, the resistance of the smaller particles would be 35 times greater if the porosity was equal, and ten times greater if the different porosities are considered. However, the resistance of the small particles has only a 2.5-fold to 5-fold value of the big particles.

(iv) Influence of pH on lactose agglomeration in highly diluted solutions The results obtained by filtration were confirmed with Fraunhofer diffraction in very diluted suspensions of 2.8 µm particles. Ethanol

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researcharticle 1.4e+10

17 16

1.2e+10

15

1.0e+10

[p*t/M] Pas/kg

d/µm

14 13 12 11

8.0e+9

6.0e+9

4.0e+9

10 2.0e+9

9 8

0.0

1

2

3

4

5

0.00

0.05

0.10

Dp/bar

0.15

0.20

0.25

M/kg

Figure 7: Average diameter of the agglomerates as a function of pressure difference and pH, x = 10%m lactose (green: pH =10.6 (-215 mV), orange: pH = 5.0 (115 mV), yellow: pH = 2.7 (246 mV) & red: pH = 4.6 (137 mV).

Figure 8: Comparison between filtration of 0.5%m lactose dispersion at high basic, high acidic and high ionic milieu at a pressure difference of 5 bar (solid = 0.0064 M NaOH, dash = 0.0064 M HCl, dot = 0.0064 M NaCl & dash-dot-dot = normal).

solutions were adjusted at pH values of 2.8, 10.7, 4.8 and 6.3, which correspond to the maximal, minimal ζ- potential, the IEP and non-treated ethanol solution, respectively. Then, one drop of the 3% lactose stock dispersion was added to the measure suspension yielding an initial optical concentration between 10% and 15%. After treatment with an ultrasonic probe for 30 seconds, the size distribution was measured at the start and after five, ten, 20 and 30 minutes. Figure 9 shows the cumulative particle size distribution of lactose after five minutes. The size distributions, as well as the strong drop of the optical concentration, show a significant agglomeration of the lactose with a primary particle size of 2.8 µm at a pH above 10. One can see from Table 3 that the x50 does not change in the pure ethanol solution. Slight changes might be observed when the HCl solution is added. The most significant agglomeration, however, is observed when the ethanol solution is conditioned with NaOH solution.

Agglomerate size also determines the cake properties, which as been shown by filtering particles of different sizes. Even though ESA measurements show significant changes of the surface charge through variation of pH and ionic strength, neither filtration nor sedimentation can be accelerated in a pH range of 2 to 11, or by addition of salt, which might be explained by the high solid concentration. However, filtration is accelerated or slowed down by addition of high amounts of NaOH and HCl, respectively. At a pH greater than 12 the lactose forms big flocs, which result in a very lose filtration cake. The addition of acids and salts, on the other hand, squeezes the double layer of the agglomerates, so that these form a compact cake. Further work is required to explain the enhanced filtration at high pH and to answer the question: Is the acceleration of the separation process possible for those compounds for which the change through physicochemical parameters is more pronounced, i.e. for zwitter-ions?

Conclusions The influence of physicochemical parameters on the separation of lactose from ethanol has been studied in this work. Fraunhofer diffraction, FBRM and sedimentation experiments show that a dispersion with lactose (x50 = 2.8 µm) is not stable. The primary particle size can only be observed when a highly diluted suspension is treated with ultrasound for 60 seconds. FBRM, as well as sedimentation, show a very fast agglomeration that depends on the particle concentration. Agglomeration can be observed qualitatively and quantitatively by sedimentation experiments. A very distinct line between turbid and clear dispersion has been found, which is an important feature of hindered settling of flocculated and coagulated systems [1].

Acknowledgement The authors would like to thank G A Taylor for editing the above manuscript.

References 1. 2. 3.

Rushton A, Ward A S & Holdich RG. 1996. Solid-Liquid Filtration and Separation Technology, VCH Weinheim, Germany. Matteson J M & Orr C (Eds). 1987. Filtration - Principles and Practices, 2nd Edition, Marcel Dekker, New York, USA. Zogg M. 1993. Einführung in die Mechanische Verfahrenstechnik 3.Aufl. Teubner Verlag.

Table 1: Slope of filtration curve as a function of the NaOH concentration. c(NaOH)/mol*l-1

0.0000

0.0015

0.0030

0.0048

0.0064

a1/1010 kg-1m-1s-1 a2/1010 kg-1m-1s-1

1.5 3.5

1.3 2.4

0.8 2.0

0.9

0.5

Filtration+Separation

October 2004 39

researcharticle 13. Ohmori K & Glatz C E. 1999. Effects of pH and ionic strength on microfiltration of C. glutamicum. J. Membrane Science, 153, p.23-32. 80 14. Iritani E, Nakatska S, Aoki H & Murase, T. 1991. Effect of solution environment on unstirred dead-end ultrafiltration characteristics of 60 proteinaceous solutions. J. Chemical Engineering Japan, 24, p.177-183. 40 15. Faibish R S, Elimelech M & Cohen Y. 1998. Effect of Interparticle Electrostatic Double Layer Interatcions on Permeate Flux Decline in 20 Crossflow Membrane Filtration of Colloidal Suspensions: an Experimental Investigation. J. Colloid and Interface Science, 204, 0 p.77-86. 0 5 10 15 20 25 30 35 40 45 16. Bowen W R, Yousef H N S & Calvo J I. 2001. Dynamic crossflow d/µm ultrafiltration of colloids - a deposition probability cake filtration Figure 9: Cumulative particle size distribution approach, Separation & Purification Technology, 24 (1-2), of lactose particles at various pH conditions p.297-308. and five minutes after ultrasonic treatment 17. Deryagin B V & Landau L D. 1941. Theory of the stability of strongly (solid: pH = 2.78, dot: pH = 4.96, charged lyophobic sols and of the adhesion of strongly charged dash: pH = 6.27 & dash-dot-dot: pH = 10.62). particles in solutions of electrolytes, Acta Physicochim. URSS, 14, p.633. 4. Gregory J. 1986. Flocculation, in Wakeman, R J (Ed): Progress in 18. Verwey E J W & Overbeek J Th G. 1948. Theory of the Stability of Filtration, Elsevier, New York, USA . Lyophobic Colloids, Elsevier, Amsterdam, The Netherlands. 5. Wakeman R J & Akay G. 1997. Membrane-Solute and Liquid-Particle 19. Sogami I. 1983. Effective potential between charged spherical particles Interaction Effects in Filtration, Filtration+Separation, Vol. 34, No.5, in dilute suspension, Phys. Letters, 96A, p.199-203. p. 511-519. 20. Ruckenstein E. 1998. Attraction between identical colloidal particles 6. Judd S J & Hills P. 2001. Optimisation of combined coagulation & caused by collective electrostatic repulsion, Advances in Colloid and microfiltration for water treatment. Water Research, 35(12), p.2895-2904. Interface Science, 75, p.169-180. 7. Bruinsma P J, Wang Y, Shari Li X, Liu J, Smith P A & Bunker B C. 21. Gregory J. 1998. The Role of Floc Density in Solid-Liquid Separation. 1997. Rheological and Solid-Liquid Separation Properties of Bimodal Filtration+Separation, Vol. 35, No. 4, p.367-371. Suspensions of Colloidal Gibbsite and Boehmite. J. Colloid and 22. Benesch T, Meier U & Schütz W. 2004. Modelling Filtration with Interface Science, 192, p.16-25. Sedimentation Corrections, Separation & Purification Technology, 8. Hunter R J. 1993. Foundations of Colloid Science, Vol. 1, Oxford 35, p.37-46. University Press, Oxford, UK. 23. Richardson J F & Zaki W N. Sedimentation and Fluidisation. 1954. 9. Israelachvili J. 1991. Intermolecular & Surface Forces, 2nd Edition, Transactions of the Institution of Chemical Engineers, 32, Academic Press, London, UK. p.35-53. 10. Wakeman R J, Thuraisingham S T & Tarleton E S. 1988. Colloid 24. Ruf A, Worlitschek J & Mazzotti M. 2000. Modeling and Science in Solid-Liquid Separation Technology - Is it important? Experimental Analysis of PSD measurements through FBRM, Part. Filtration+Separation, Vol. 26, No. 4, p.277-283. Syst. Charact. 17(4), p.167-179. 11. Dück J, Zvetanov E & Neese Th. 1999. Porositätsmodell für 25. Hinze F, Ripperger S & Stintz M. 1998. Praxisrelevante feinkörnige Filterkuchen, Chemie Ingenieur Technik, 71, p.692-696. Zetapotentialmessung mit unterschiedlichen Messtechniken. 12. Dück J, Zvetanov E & Neese Th. 2000. Characteristic number for Plenarvortrag anläßlich der GVC-Fachaus-schußsitzungen porosity and flow resistance of fine-grained filter cakes, Proceedings "Grenzflächen und Filtration", Freiburg, Germany. of 8th World Filtration Congress, Brighton, UK, Vol.1, p.33-36. 26. Hinze F, Ripperger S & Stintz M. 2000. Charakterisierung von Suspensionen nano-skaliger Partikel mittels Table 2: Slope of filtration curve after the addition of Ultraschallspektroskopie und 2 ml of 1 M NaOH, NaCl & HCl. elektroakustischer Methoden, Chemie Ingenieur Technik, 72, p.322-332. Additive/ 2 ml 1M NaOH NaCl HCl 27. Hunter R J. 1981. The Zeta-Potential in a1/1010 kg-1m-1s-1 0.5 1.1 2.0 Colloid Science, Academic Press London, UK. a2/1010 kg-1m-1s-1 2.7 7.8 %

100

Table 3: The x50 value of lactose in ethanol at various pH values & time periods after ultrasound. pH/time

0 minutes

5 minutes

10 minutes

20 minutes

2.8 4.8 6.3 10.7

3.61 µm 3.53 µm 3.50 µm 6.54 µm

4.22 µm 3.98 µm 3.46 µm 10.67 µm

4.40 µm 4.18 µm 3.49 µm

4.66 µm 4.31 µm 3.59 µm

40 October 2004

30 minutes

4.30 µm 3.62 µm

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