Patent Application
20060131236
The present invention relates to a model for microfiltration of poly-disperse suspensions and solutions.
There are presently many solutions containing macromolecular and suspended particles that are being filtered with synthetic membranes. These include waste water, surface water, automotive paint streams, and a host of streams from the biomedical and bioprocessing industries containing proteins, cells, DNA, fat globules, colloids, milk; suspended particles etc,. Microfiltration of cell culture, fermentation broths, blood, and milk are representative examples of streams from the latter industries. (Belfort et al., “The Behavior of Suspensions and Macromolecular Solutions in Crossflow Microfiltration,” J. Membr. Sci. 96:1-58 (1994); Nagata et al., “Crossflow Membrane Microfiltration of a Bacterial Fermentation Broth,” Biotecitnol. Bioeng. 34:447-466 (1988)). Because of the complex nature of these fluids, and due to the difficulty in specifying the behavior of their suspended and dissolved components, modeling microfiltration has been difficult. Belfort and coworkers have summarized the behavior of suspended particles during microfiltration. (Green et al., “Fouling of Ultrafiltration Membranes: Lateral Migration and the Particle Trajectory Model,” Desalination, 35:129-147 (1980); Altena et al., “Lateral Migration of Spherical Particles in Porous Channels: Application to Membrane Filtration,” Chem. Eng. Sci. 39:343-355 (1984); Altena et al., “Lateral Migration of Spherical Particles in Porous Tube Flows: Channels: Application to Membrane Filtration,” Physico-Chem. Hydrodyn 6:393413 (1985)). Other researchers have studied the behavior of macromolecules during microfiltration (Zydney et al., “A Concentration Polarization Model for the Filtrate Permeation Flux in Crossflow Microfiltration of Particulate Suspensions,” Chzem. Eng. Commun. 47:1-21 (1986); Rautenbach et al., “Ultrafiltration of Macromolecular Solutions and Crossflow Microfiltration of Colloidal Suspensions. A Contribution to Permeate Permeation Flux Calculations,” J. Membr. Sci. 36:231-242 (1988); Samuelsson et al., “redicting Limiting Permeation Flux of Skim Milk in Crossflow Microfiltration,” J. Membr. Sci. 129:277-281 (1997); Romero et al., “Global Model of Crossflow Microfiltration Based on Hydrodynamic Particle Diffusion,” J. Membr. Sci. 39:157-185 (1988)). Only a few studies have been reported in the literature on modeling the behavior of poly-disperse feeds containing both macromolecules and suspended particles for microfiltration (Dharmappa et al., “A Comprehensive Model for Crossflow Filtration Incorporating Polydispersity of the Influent,” J. Membr. Sci. 65:173-185 (1992); Ould-Dris et al., “Effect of Cake Thickness and Particle Polydispersity on Prediction of Permeate Permeation Flux in Microfiltration of Particulate Suspensions by a Hydrodynamic Difflusion Model,” J. Membr. Sci. 164:211-227 (2000)).
Crossflow (also known as tangential) microfiltration is a very complex phenomenon. Some of the variables which influence permeation flux and retention are membrane type and chemistry, module geometry, particle size distribution, nature of particles, interaction between individual particles and between particles and the membrane, fluid dynamics, operating mode, temperature, and pH and ionic strength of the media. This is a formidable set of variables and, to date, no unified theory exists to give a rigorous expression of permeation flux and retention for crossflow microfiltration. Existing theories render the problem tractable by concentrating on, at best, only a few aspects of the problem. Therefore, for any given case, different theories may yield widely divergent results. In applying a theoretical model, extreme care must be exercised to check the specifics of the case and critically evaluate the dominant parameters and compare these with the model assumptions. It is quite possible, particularly in dealing with complex suspensions, that no one phenomenon or class of phenomena is dominant. In such a case, an existing model may not give the true picture and a new model may need to be evolved to capture the dominant phenomena Usually, with the onset of microffitration at constant transmembrane pressure, there is a rapid decline of permeation flux due to concentration polarization and pore constriction, followed by a quasi steady state where there is a gradual decline in permeation flux due to particle deposition and increase in particle concentration (and hence viscosity) of the bulk solution (Belfort et al., “The Behavior of Suspensions and Macromolecular Solutions in Crossflow Microfiltration,” J. Membr. Sci. 96:1-58 (1994); Nagata et al., “Crossflow Membrane Microfiltration of a Bacterial Fermentation Broth,” Biotechnol. Bioeng. 34:447466 (1988)). Several models exist which attempt to evaluate the quasi steady state permeation flux. These models are based on equilibrium between transport to and back-transport of particles from the membrane wall. It is important to note that these models are valid in the laminar flow regime for fully retentive membranes, deal with the pressure-independent permeation flux regime, and ignore particle interaction with the membrane. Prediction of permeation flux in the pressure-dependent and transient regimes has been addressed by Romero and Davis (Romero et al., “Global Model of Crossflow Microfiltration Based on Hydrodynamic Particle Diffusion,” J. Membr. Sci. 39:157-185 (1988); Romero et al., “Transient Model of Crossflow Microfiltration,” Chem. Eng. Sci. 45:13-25 (1990)). A simplified version ofthe gel-polarizationmodel for describing concentration polarization in ultrafiltration for fully retentive membranes, originally presented by Blatt et al., “Solute Polarization and Cake Formation in Membrane Ultrafiltration: Causes, Consequences and Control Techniques,” in Membrane Science and Technology, J. E. Flinn, ed., New York:Plenum, pp. 47-97 (1970), is given by the permeation flux at an axial position x along the flow path,
J(x)=k(x)In(φw/φ0) (1)
where the membrane and cake resistances do not appear in this formulation and k(x), the mass transfer coefficient is given by the ratio of the molecular diffusion coefficient, D, to the x—dependent mass boundary layer thickness, δ(x).
Using the Leveque solution for laminar flow in a closed tube and the Stokes-Einstein relationship for Brownian diffusion of mono-dispersed spheres of radius a, Eq. (1) changes to Eq. (2), shown in Table 1. The Brownian (molecular) diffusion model is valid for particle diameters below ˜0.1 μM and at low axial shear rates. For microfiltration situations where α>1 μm, Eq. (2) under-predicts the permeation flux by one to two orders of magnitude. Green and Belfort (Green et al., “Fouling of Ultrafiltration Membranes: Lateral Migration and the Particle Trajectory Model,” Desalination, 35:129-147 (1980)), referring to this as the flux paradox, searched for mechanisms that induce back-migration from the membrane surface in addition to molecular diffusion. Belfort and coworkers invoked inertial lift as an additional back (or lateral) migration mechanism and used it to explain the discrepancy in permeation flux mentioned above (Green et al., “Fouling of Ultrafiltration Membranes: Lateral Migration and the Particle TrajectoryModel,” Desalination, 35:129-147 (1980); Altena et al., “Lateral Migration of Spherical Particles in Porous Channels: Application to Membrane Filtration,” Chem. Eng. Sci. 39:343-355 (1984); Altena et al., “Lateral Migration of Spherical Particles in Porous Tube Flows: Channels: Application to Membrane Filtration,” Physico-chem. Hydrodyn. 6:393-413 (1985)). Their expression for permeation flux is given by Eq. (3) in Table 1. This model is applicable for dilute solutions containing large particles (>20 μm) and high axial shear stress or fast laminar flow situations. Zydney and Colton (Zydney et al., “A Concentration Polarization Model for the Filtrate Permeation Flux in Crossflow Microfiltration of Particulate Suspensions,” Chem. Eng. Commun. 47:1-21 (1986)) resolved the permeation flux paradox by proposing that the gel-concentration polarization model should be used, provided the molecular diffusion term in the Leveque solution is replaced by the shear-induced hydrodynamic diffusivity (and the permeation flux is averaged over the axial flow path), first measured by Eckstein et al, “Self-Diffusion of Particles in Shear Flow of a Suspension,” J. Fluid Mech. 79:191-208 (1977)). This model, given by Eq. (4) in Table 1, has been experimentally validated for latex and blood suspensions for a broad range of conditions. It is suitable for particles in the diameter size range 0.1 to 20 μm. Li et al., “An Assessment of Depolarization Models of Crossflow Microfiltration by Direct Observation through the Membrane,” J. Membr. Sci. 172:135-147 (2000)), based on direct observation of particle dynamics during membrane filtration, suggested that the coefficient in Eq. (4) should be replaced with 0.0595. Using a similarity solution, Davis and Sherwood (Davis et al., “A Similarity Solution for Steady-State Crossflow Microfiltration,” Chem Eng. Sci. 45:3203-3209 (1990); Ho et al., Membrane Handbook, New York:Van Nostrand Reinhold; pp 480-505 (1992)) have accounted for a concentration-dependent fluid viscosity and diffsivity and obtained Eq. (5), shown in Table 1. The results from this expression agree with those from Eq. (4) to within 15% for φb=0.01 and φw=0.6, which is close to the maximum for monodisperse rigid spherical particles.
The models discussed above deal with crossflow microfiltration of idealized mono-disperse streams, whereas most real microfiltration applications deal with poly-disperse feed streams with particle sizes ranging from the macromolecular to the colloidal. This has generated the need to develop a generally applicable model useful for predicting length-averaged permeation flux and yield of target molecules for poly-disperse feeds under a variety of filtration conditions.
The present invention is directed to overcoming these and other deficiencies in the art.
The present invention relates to a method for predicting pressure independent permeation flux and target molecule yield in a permeate resulting from crossflow membrane filtration of particles in a poly-disperse suspension. This method involves determining the particle size distribution of the poly-disperse suspension, the equivalent spherical radii of the particles, the viscosity of the suspension, and the maximum back-transport velocity (ui) for all particles. It also involves estimating the maximum aggregate packing volume fraction (μM) for all particles at a wall of the filtration membrane from geometric considerations, and selecting the particle that gives a minimum permeation flux at a given filtration membrane shear rate, where the selected particle has a radius (αi), and determining a predicted permeation flux. The method also involves determining packing density at φwi a membrane wall for each particle size (αj for j≠i) at the predicted permeation flux. Also determined are interstitial packing density (Owiinterstice) of particles in the suspension which are smallest, and minimum pore diameter (2rminimum) based on the packing density of each particle. The yield of a target species in the filtration permeate is then estimated by calculating observed sieving coefficient (So) for the target species. As a result, permeation flux and target molecule yield of the poly-disperse suspension during crossflow filtration are predicted.
The present invention also relates to a method for determining the packing density of particles of a poly-disperse suspension at a membrane wall. This method involves providing a predicted permeation flux (J), determining the packing density for all particle sizes at the predicted permeation flux, and determining interstitial packing density (φwiinterstice) of particles in the suspension which are smallest, thereby determining the packing density at the membrane wall of particles of the poly-disperse suspension.
Another aspect of the present invention is a method for predicting pressure independent permeation flux for crossflow membrane filtration of a poly-disperse suspension. This method involves determining the viscosity of the suspension, determining the maximum back-transport velocity (ui) for all particles, and estimating the maximum aggregate packing volume fraction (φM) for all particles at a wall of the filtration membrane wall from geometric considerations. The particle that gives a minimum permeation flux at a given filtration membrane shear rate is selected, where the selected particle has a radius (αi). A predicted permeation flux (J) is determined, and the packing density (ha) at the membrane wall for each particle size (αj for j≠i) is determined at the predicted permeation flux.
Another aspect of the present invention is a method for calculating yield of a target molecule in a permeate for a poly-disperse suspension during crossflow membrane filtration. This method involves determning the minimum pore diameter (2rminimum) based on the packing density of each particle, and estimating the yield of a target species in the filtration permeate by calculating the observed sieving coefficient (So) for the target species.
The present invention also relates to a method for designing a crossflow membrane filtration system for a poly-disperse suspension. This method involves selecting a poly-disperse suspension and predicting pressure independent permeation flux and target molecule yield in a permeate resulting from crossflow membrane filtration of particles in a poly-disperse suspension as described above. Conditions for filtration based on the prediction of permeation flux and target molecule yield are then optimized to design a filtration system for the selected poly-disperse suspension.
Yet another aspect of the present invention is a method of selecting operating conditions of a crossflow filtration system for poly-disperse suspensions. This method involves predicting the pressure independent permeation flux and target molecule yield in a permeate resulting from crossflow membrane filtration of particles in a poly-disperse suspension as described above. Operating conditions of the system are selected using the limiting pressure independent permeation flux determined for a given shear rate to obtain an optimal balance between permeation flux and yield of a target species.
The present invention also relates to a method of modeling a process for filtration of a poly-disperse suspension. This method involves applying the method for predicting pressure independent permeation flux and target molecule yield in a permeate resulting from crossflow membrane filtration of particles in a poly-disperse suspension, as described above, and using a computer-generated program to model a process for filtration of a poly-disperse suspension.
Most microfiltration applications deal with poly-disperse feed streams with particle sizes ranging from the macromolecular to the colloidal. This has generated the need to develop a model which can predict the pressure independent permeation flux, a priori, and can provide an estimate of the yield of target molecules (i.e., the product concentration in the permeate) in the permeate for poly-disperse suspensions. Thus, the present invention relates to a method for calculating yield of a target molecule in a permeate for a poly-disperse suspension during crossflow membrane filtration. The iterative methodology is capable of predicting crossflow microfiltration performance of poly-disperse suspensions in a variety of settings. For example, it is suitable for filtration of a poly-disperse suspension of waste water, surface water, paints, metallurgical wastes, environmental pollutants, industrial waste streams and streams from biochemical and bio-processing industries containing proteins, cells, DNA, colloids, milk, suspended particles etc.
Several researchers have reported that there exists a critical ratio between permeation flux and wall shear rate beyond which protein transmission drops drastically and permeation flux does not increase with increasing transmembrane pressure (Berre et al., “Skim Milk Crossflow Microfiltration Performance Versus Permeation Flux to Wall Shear Stress Ratio,” J. Membr. Sci. 117:261-270 (1996); Gesan-Guiziou et al., “Critical Stability Conditions in Crossflow Microfiltration of Skimmed Milk: Transition to Irreversible Deposition,” J. Membr. Sci. 158:211-222 (1999); Gesan-Guiziou et al., “Performance of Whey Crossflow Microfiltration During Transient and Stationary Operating Conditions,” J. Membr. Sci. 104:271-281 (1995); Gesan-Guiziou et al., “Critical Stability Conditions in Skimmed Milk Crossflow Microfiltration: Impact on Operating Modes,” Lait 80:129-140 (2000)). The method presented here provides a quantitative framework to explain this phenomenon and also provides the rationale for operating at uniform axial transmembrane pressure along the flow path.
For simplicity, the adhesion of particles to the surface of the membrane, charge interactions between particles and the membrane, and between particles, are ignored. Several papers have demonstrated that electrostatic effects actually can increase permeation flux during filtration (Zydney et al., “Intermolecular Electrostatic Interactions and Their Effect on Permeation Flux and Protein Deposition During Protein Filtration,” Biotechnol. Prog. 10:207-213 (1994); Aimar et al., “Fouling and Concentration Polarization in Ultrafiltration and Microfiltration,” Membrane Processes in Separation and Purification, Crespo et al., eds., Dordrecht, Boston, London:Kluwer Academic Publishers (1994)). Under these conditions, this renders the current approach conservative and compensates to some extent for neglecting particle aggregation and particle adhesion to the membrane. All particles are assumed to be spherical, with volumes equal to the actual particle volumes.
The following scenario is proposed. There are three regimes of operation, as shown schematically in
A theory of cake filtration relating to the composition and sieving characteristics of the deposited cake has been developed based on an experimental determination of a rheological parameter related to the specific cake resistance. (Landman et al., “Pressure Filtration of Flocculated Suspensions,” AIChE J. 41:1687-1700 (1995)). Other researchers have experimentally evaluated the sieving characteristics of filter cakes in microfiltration formed by protein deposits (Palecek et al., “Hydraulic Permeability of Protein Deposits Formed During Microfiltration: Effect of Solution Ph and Ionic Strength,” J. Membr. Sci. 95:71-81 (1994); Meireles et al., “Effects of Protein Fouling on the Apparent Pore Size Distribution of Sieving Membranes,” J. Membr. Sci. 56:13-28 (1991) (NOTE: This paper has a definitional error—for correction see Zydney et al., “Use of tde Log-Normal Probability Density Function to Analyze Membrane Pore Size Distributions: Functional Forms and Discrepancies,” J. Membr. Sci. 91:293-298 (1994)); Mochizuki et al., “Sieving Characteristics of Albumin Deposits Formed During Microfiltration,” J. Colloid Interface Sci. 158:136-145 (1993)). In this invention, the predominant nature of the cake is considered to be determined by the propensity of particles of different sizes to diffuse back to the bulk for a given shear rate. The cake is thought to be primarily composed of particles with the least back-transport velocity. This does not preclude the possibility of smaller particles from lodging themselves in the interstices of a compact layer of larger particles, as shown in
FIGS. 1A-C are schematic representations of various parameters that influence the microfiltration of poly-disperse suspensions.
FIGS. 2A-B are graphs of predicted values for a hypothetical suspension comprising particles of three sizes.
FIGS. 6A-C are a schematic of variation in pressure inside the bore of a hollow fiber (P1 to P2) with varied length.
FIGS. 9A-B are graphs of predicted pressure-independent permneation flux values.
The present invention relates to a method for predicting pressure independent permeation flux and target molecule yield in a permeate resulting from crossflow membrane filtration of particles in a poly-disperse suspension. This method involves determining the particle size distribution of the poly-disperse suspension, the equivalent spherical radii of the particles, the viscosity of the suspension, and the maximum back-transport velocity (ui) for all particles. It also involves estimating the maximum aggregate packing volume fraction (φM) for all particles at a wall of the filtration membrane from geometric considerations, and selecting the particle that gives a minimum permeation flux at a given filtration membrane shear rate, where the selected particle has a radius (αi), and determining a predicted permeation flux. The method also involves determining pacling density (φwi) at the membrane wall for each particle size (αj for j≠i) at the predicted permeation flux. Also determined are the interstitial packing density (φwiinterstice) of particles in the suspension which are smallest, and minimum pore diameter (2rminimum) based on the pacling density of each particle. The yield of a target species in the filtration permeate is then estimated by calculating observed sieving coefficient (So) for the target species. As a result, permeation flux and target molecule yield of the poly-disperse suspension during crossflow filtration are predicted.
The viscosity of the poly-disperse suspension is determined by experimental data or by using a modified Einstein-Smoluchowski equation: η/η0=1+2.5φb+k1φb2, where η is bulk fluid viscosity (kg/m.s) of the suspension, η0 is bulk fluid viscosity of the suspension without solute (kg/m.s), k1 is particle shape factor (−), and φb is particle volume fraction in the bulk suspension (−). Alternatively, viscosity can be determined experimentally.
Determination of the maximum back-transport velocity (ui) is carried out by calculating Brownian diffusion (JB) for all particles, where JB=0.114(γκ2T2/η2α2L)1/3ln(φw/φo); calculating inertial lift (J1) for all particles, where J1=0.036ρα3γ2/η; and calculating shear induced diffusion (JS) for all particles where JS=0.078(α4/L)1/3γln(φw/φb) (see Table 1). γ is wall shear rate (s−1), T is temperature (K), η is bulk fluid viscosity kg/m.s), αi is radius of species i(m), L is tube length (m), φw is particle volume fraction at the filtration membrane (−), φb is the particle volume fraction in the bulk suspension (−); and ρ is particle density (kg/m3). The maximum value of J (Jmax) is selected for each particle. This maximum value of J is=ui, the maximum back-transport for each particle in the suspension.
Estimating maxiinum aggregate packing volume fraction for the poly-disperse suspension (φM) at the membrane wall is carried out by determining the particle sizes (αi) in the suspension, where as is radius of species i(m), determining the size ratios of the particles, and using φM=φm+0.74 (1−φm), for a suspension or solution having two particle sizes such that α1>10α2, where φm is the maximum packing volume fraction for monodisperse spheres. Although a poly-disperse suspension is more complex than a suspension having monodisperse spheres, the value for a monodisperse sphere, i.e., φm=0.64, can be used in the first iteration of the present invention in order to estimate the maximum packing volume fraction. Therefore, in one aspect of the present invention, φm=0.64 for all particles for the first iteration. In another aspect of the present invention, maximum aggregate packing volume fraction at the membrane wall involves determining the particle sizes (αi) of species (i) in the suspension, and determining if the size ratio of the particles is >10, such that αi+1>10αi for all α1. If αi+1>10αi, the maximum aggregate packing volume fraction (φMn) is calculated by
φMn=φm+φm(1−φMn−1),
where φM1=φm is the maximum packing volume fraction for monodisperse spheres set to 0.64.
In another aspect of the present invention, the maximum aggregate packing volume fraction (φm) is estimated by determinkig the particle sizes in the suspension (αi) and calculating a maximum radius ratio of all the particles. If the maximum radius ratio is <10, φM=0.68 is set as the value of the maximum packing volume fraction.
Yet another aspect of the present invention provides that if the radius ratio of a suspension having two particle types is ≧10, then φM=φm+0.74 (1−φm), where φm may be set to 0.64 to denote the highest packing volume fraction for a single particle. The packing volume fraction (φM) at the membrane wall for a suspension having three particle sizes such that α1>10α2>100 α3, is carried out by
φm=φm+φm(1−φm)+0.74[1−{φmφm(1−φm)}]
where φm is the maximum packing volume fraction for monodisperse spheres set to 0.64. An extension of the above equation can be used for more than three particles. For a suspension where there are greater than three particle types, each having a size ratio greater than 10 such that, α1>10α2>100α3>1000α4, etc., the maximum aggregate packing volume fraction (φMn) at the membrane wall can be calculated by the recursive formula
φMn=φm+φm(1−φMn−1)
where φM1=φm is the maximum packing volume fraction for monodisperse spheres set to 0.64.
Determining a predicted minimum permeation flux for a poly-disperse suspension is carried out by comparing the values of (Jmax) determined for each particle type in the suspension by calculating JB, JI, and JS as described above. The predicted minimum permeation flux (JMIN) is determined by selecting from among the (Jmax) values for all particles the (Jmax) for a given particle (αi) having the lowest numerical value.
The packing density (φwj) for all other particles at the predicted permeation flux (αj for j≠i) is also determined. This determination is carried out by calculating the value of φwj such that φwj gives the predicted permeation flux (J) of selected particle (αi), using the equation for back-transport that establishes maximum back transport for each particle (αj for j=i). As described above, J is calculated by either JB=0.114(γκ2T2/η2α2L)1/3ln(φw/φb), J1=0.036ρα3γ2/η, or JS=0.078(α4/L)1/3γln(φw/φb). γ is wall shear rate (s−1), Tis temperature (K), η is bulk fluid viscosity (kg/m.s), αi is radius of species i(m), L is tube length (m), φw is particle volume fraction at the membrane wall (−), φb is the particle volume fraction in the bulk suspension (−), and ρ is particle density (kg/m3). When J for (αj) has been calculated using either JB or JS, the value of φwj can be “backed-out” of the equation mathematically. However, when J1 is the governing back-transport mechanism, the value of φwj cannot be directly back-calculated. In that embodiment of the present invention, the value of φwj is instead calculated by estimating mass balance and the propensity of a particle to lift off from the membrane wall. In one aspect of the present invention, this can be carried out by determining if ujI≧10J for the particle, where if ujI≧10J, the determination of the packing density (φwj) for that particle is carried out using φwjI=0, as the particle back-transport velocity is much higher than the polydisperse permeation flux and will be readily lifted off from the membrane wall. Otherwise, i.e., if ujI<10J, (φwj) for that particle is carried out using
φwjI=φM−Σφwj
where j≠jI. If there is more than one particle type whose back-transport is governed by inertial lift, jI1 and jI2, their contributions to the cake at the wall can be approximately apportioned in the direct ratio of their volume fractions in the bulk suspension and inverse ratio of their back-transport by
φwjI1+φwjI2=φM−Σφwj
φwjI1:φwjI2=φbjI1uji2:φbjI2 ujI1
where j≠jI1 or jI2 and UjI1, UjI2<10J. This logic can be extended for more than two particle types whose back transport is governed by inertial lift.
In order to determine the permeation flux and target yield of a poly-disperse suspension during crossflow microfiltration, it is also necessary to determine the interstitial packing density (φwiinterstice) of the smallest particle in the suspension. This is carried out by φwiinterstice=φwicorrected/(1−Σ φwjcorrected), where φwicorrected=φM[(φwi)/Σ φwi], and where φwi is the particle volume fraction at the membrane wall (−) for particle i.
Another factor to be determined in this aspect of the present invention is the minimum pore diameter (2rminimum) available at the membrane wall, based on the corrected packing density of each particle. The minimum pore diameter (2rminimum) is estimated from geometric considerations, based on a face centered cubic packing for the cake where there are four spherical particles per cube. Here it is assunmed that the gap available for transmission in a face centered cubic cake is (2rminimum)=α√2−2αi, where a is the side of the cube and αi is the radius of the particles located at the vertices and face centers of the cube. Therefore, in the present invention, 2rminimum=αi{√2[4(4/3)π/φwiinterstice]1/3−2}, where α is radius of species i (m) and rminimum is a minimum equivalent cake void radius for all cake types (m).
The method of the present invention also involves estimating yield of a target molecule. This involves calculating the observed sieving coefficient (So), where So=Sα/((1−Sα)exp(−J/k)+Sα). The actual sieving coefficient Sα is obtained from Sα=(Soexp(Pem))/(S∝+exp(Pem)−1), the wall Peclet number, Pem is obtained from Pem=(Jδm/D)(S∝/εφKd), δm is taken as the side of the face centered cube of the particles of radius αi that forms the controlling cake for transmission, δm=α=αi[(4(4/3)π)/φinterstice]1/3, intrinsic sieving coefficient S∝is obtained from S4(1−λ)2[2−(1−λ)2exp(−0.7146λ2), λ=rs/rmin, λ is a ratio of solute to pore radii (rs/rmin)(−), rmin a minimum equivalent cake void radius for all cake types (m), rs is solute radius (m); and φKd=(1−λ)9/2, where φ is the equilibrium partition coefficient between membrane pore and suspension (−), and Kd is a hindrance factor for diffusive transport (−).
In one aspect of the present invention, the crossflow filtration process includes diafiltration. Diafiltration is a process whereby a filtration membrane is used to remove, replace, or lower the concentration of salts or solvents from a suspension containing biological material (Schwartz L., “Diafiltration: A Fast, Efficient Method for Desalting or Buffer Exchange of Biological Samples,” Scientific and Technical Report, Pall Life Sciences (2003), which is hereby incorporated by reference in its entirety). According to the present invention, yield of the target species in a diafiltration experiment is estimated after Nd diavolumes as follows: yield=1−exp(−NdSoaverage). Soaverage is average observed sieving coefficient during diafiltration (−). So=Sd/((1−Sα)exp(−J/k)+Sα ). Actual sieving coefficient Sα is obtained from Sα=(S∝exp(Pem))/(S∝+exp(Pem)−1), wall Peclet number, Pem, is obtained from Pem=(Jδm/D)(S∝/εφKd), Sm is taken as the side of the face centered cube of the particles of radius ai that forms the controlling cake for transmission, where δm=α=αi [(4(4/3)π)/φinterstive]1/3; intrinsic sieving coefficient S∝ is obtained from S∝=(1−λ)2[2−(1−λ)2]exp(−0.7146λ2), where λ=rs/rmin, φKd=(1−λ)9/2, rmin is minimum equivalent cake void radius for all cake types (m), rs is solute radius (m), and λ is the ratio of solute to pore radii (rs/rmin)(−).
By carrying out the steps described above the pressure independent permeation flux and the yield of the target species are thus determined for a permeate resulting from crossflow membrane filtration of particles of any poly-disperse suspension.
Once the predicted permeation flux and target yield are determined according to the present invention as described above, the performance of the filtration system as to permeation flux and yield can be refined using the information acquired by carrying out the above determinations. Thus, the present invention further involves re-calculating the packing density at the membrane wall determinations for all particles in the suspension and determining if the packing constraints are met for all particles. If packing constraints are not met, the estimations made earlier require some correction. Packing density of a particle is corrected by using φwicorrected=φM[(φwi)/Σφwi]. The predicted permeation flux, J, is then reevaluated for the particle selected as having the minimum permeation flux based on φwicorrected=φm[(φwi)/Σ φwi]. The maximum back-transport velocity (ui), determined as described above, is also reevaluated, and the maximum aggregate packing volume fraction for all particles (φM) at the membrane wall is re-estimated.
The present invention also involves refining the determination of the yield of the target species. Refining the yield involves determining whether the suspension has a low, intermediate, or high operating shear rate (So). A suspension is considered to have a low operating shear rate when So≧0.75, corresponding to a yield ≧0.95, an intermediate operating shear rate when 0<So≧0.75, corresponding to yield range of from 0 to 95%, and a high operating shear rate when So≅0. The calculation for So is carried out using So=Sα/((1−Sα)exp(−J/k)+Sa) The actual sieving coefficient Sα is obtained from Sα=(S∝exp(Pem))/(S∝+exp(Pem)−1), the maximum back-transport velocity is (ui)(obtained as described above), the wall Peclet number, Pem is obtained from Pem=(Jδm/D)(S∝/εφKd), δm is taken as the side of the face centered cube of the particles of radius αi that forms the controlling cake for transmission, δm=α=αi [(4(4/3)π)/φiinterstice]1/3, the intrinsic sieving coefficient S∝ is obtained from S∝=(1−λ)2[2−(1−λ)2]exp(−0.7146λ2), λ=rs/rmin, where λ is a ratio of solute to pore radii (rs/rmin)(−), rmin is a mirnimum equivalent cake void radius for all cake types (m), rs is solute radius (m), and φ Kd=(1−λ)9/2, where φ is the equilibrium partition coefficient between membrane pore and suspension (−), and Kd is the hindrance factor for diffusive transport (−).
When an intermediate operating shear rate is determined, the value of (J) is refined by calculating the stagnant film flux (J) equation for non-retentive membranes, where J=k ln [(φwi−φpermeatei)/(φbi−φpermeatei)]≅ln [φwi/φbi(1−So)], wherein (φwi>>φpermeatei). So is then corrected by replacing J=solvent permeation flux (m/s) with the stagnant film flux (J) equation for non-retentive membranes in the equation for observing sieving coefficient, So, where So=Sα/((1−Sα)exp(−J/k)+Sα).
The present invention also involves constructing a plot of the predicted permeation flux and yield versus wall shear rate, such as shown in
Filtration can involve microfiltration and ultrafiltration and may be carried out with a flat sheet filter, a hollow-fiber filter, or a helical filter. Suitable suspensions in all aspects of the present invention include, without limitation, waste water, surface water, environmental pollutants, industrial waste streams, industrial feed streams, and streams from biomedical and bio-processing industries. Such streams may contain, without limitation, proteins, cells, nucleic acids, colloids, milk, and suspended particles, in any combination.
The present invention also provides a method for determining the packing density of particles of a poly-disperse suspension at a membrane wall. This method involves providing a predicted permeation flux (J), determining the packing density at a membrane wall for all particle sizes at the predicted permeation flux, and determining the interstitial packing density (φwiinterstice) of particles in the suspension which are smallest, thereby determining the packing density at membrane wall of the particles of the poly-disperse suspension.
Another aspect of the present invention is a method for predicting pressure independent permeation flux for crossflow membrane filtration of a poly-disperse suspension. This method involves determining viscosity of the suspension, determining the maximum back-transport velocity (ui) for all particles, and estimating the maximum aggregate packing volume fraction (φM) for all particles at a wall of the filtration membrane wall from geometric considerations. The particle is selected that gives a minimum permeation flux at a given filtration membrane shear rate, where the selected particle has a radius (αi). A predicted permeation flux (j) is determined, and the packing density (φwj) at the membrane wall for all particle sizes at the permeation flux (αj for j≠i) at the predicted permeation flux is determined. In this aspect of the present invention the determinations of viscosity of the suspension, maximum back-transport velocity, and maximum aggregate packing volume fraction at the membrane wall (φM) for all particles are carried out using the equations given above for these factors. The minimum permeation flux value is selected by determining a Jmax value, as described above, for each particle type in the suspension, then selecting from all the Jmax values that Jmax having the lowest value. This method may be refined by also carrying out a recalculation of the packing density at the membrane wall determination for all particle sizes, determining if packing constraints are met, and correcting for packing density if packing constraints are not met. The determinations of whether packing constraints are met or not met, and the calculations for correction of packing density are as described herein above.
Another aspect of the present invention is a method for calculating yield of a target molecule in a permeate for a poly-disperse suspension during crossflow membrane filtration. This method involves determining minimum pore diameter (2rminimum) based on the packing density of each particle, and estimating yield of a target species in the filtration permeate by calculating observed sieving coefficient (So) for the target species. Determination of the minimum pore diameter (2rminimum) is carried out as described above. The determination of minimum pore diameter (2rminimum) is then used to estimate the yield of a desired target molecule in the poly-disperse suspension by carrying out the calculation for the observed sieving coefficient (So), as described above. This aspect of the present invention may further involve diafiltration. The yield of the target species on a diafiltration experiment can be estimated after Nd diavolumes, as described above.
The present invention also relates to a method for designing a crossflow membrane filtration system for a poly-disperse suspension. The performance parameters of a crossflow membrane filtration system can be designed for any selected poly-disperse suspension by applying the methods described herein for predicting pressure independent permeation flux and determining target molecule yield. This involves determining the particle size distribution of the poly-disperse suspension and the equivalent spherical radii of the particles, and determining the viscosity of the suspension and the maximum back-transport velocity (ui) for all particles. It also involves estimating the maximum aggregate packing volume fraction (φm) for all particles at the filtration membrane from geometric considerations; selecting the particle that gives a minimum permeation flux at a given filtration membrane shear rate, where the selected particle has a radius (αi), and determining a predicted permeation flux. The method also involves determining packing density at the membrane wall for all particle sizes at the predicted permeation flux, interstitial packing density (φwiinterstice) of particles in the suspension which are smallest, and minimum pore diameter (2rminimum) based on the packing density at the membrane wall of each particle. The yield of a target species in the permeate is then estimated by calculating observed sieving coefficient (So) for the target species. All of these determinations are carried out as described above for other aspects of the present invention. Conditions for filtration based on the prediction of permeation flux and target molecule yield are then optimized to design a filtration system for the selected poly-disperse suspension.
Yet another aspect of the present invention is a method of selecting operating conditions of a crossflow filtration system for poly-disperse suspensions. This method involves predicting the pressure independent permeation flux and target molecule yield in a permeate resulting from crossflow membrane filtration of particles in a poly-disperse suspension as described in detail above. This predicts permeation flux (process time) and target molecule yield of the poly-disperse suspension during crossflow membrane filtration. Operating conditions of the system are selected using the determination of limiting pressure independent permeation flux for a given shear rate to obtain an optimal balance between permeation flux and yield of a target species.
The present invention also relates to a method of modeling a process for filtration of a poly-disperse suspension. This method involves applying the method for predicting pressure independent permeation flux and target molecule yield in a permeate resulting from crossflow membrane filtration of particles in a poly-disperse suspension, using the calculations described above, and using a computer-generated program to model the process for filtration of a poly-disperse suspension.
With the framework described above, the aggregate transport model of the present invention is summarized below. Refer to Table 2 for a definition of symbols used herein.
Step 1. Determine the particle size distribution of the feed suspension and evaluate the equivalent spherical radii. This can be obtained from literature, by size exclusion chromatography or by membrane fractionation.
Step 2. Evaluate the viscosity of the suspension by experiment or estimate it using the modified Einstein-Smoluchowski equation (1)
η/η0=1+2.5φb+k1φb2 (6)
Step 3. Evaluate the maximum back-transport velocity, ui, for a particle based on Brownian diffusion, shear induced diffusion, and inertial lift at the proposed operating wall shear rate assuming full retention for all solutes, using
ui=Max[B(αi, γ, φb, φw, L, η), S(αi, γ, φb, φw, L, η), I(αi, γ, φb, φw, L, η)] (7)
where B, S, and I denote the finctionalities for Brownian diffusion, shear induced diffusion and inertial lift models, respectively, shown in Table 1, above. It is suitable to set φw=0.64 for each species for the first iteration (Dodds, “The Porosity and Contact Points in Multicomponent Random Sphere Packings Calculated by a Simple Statistical Geometric Model,” J. Colloid Interface 77:317-327 (1980), which is hereby incorporated by reference in its entirety).
Step 4. Estimate the maximum aggregate packing volume fraction for all particles, φM at the wall, from geometric considerations. For the poly disperse case, this could be much larger than the widely used value 0.64 depending on the size ratios of the particles. If the size ratio is more than 10, the small particles behave as a continuous fluid with respect to the large particles and can migrate into the interstices easily (Farris, “Prediction of the Viscosity of Multimodal Suspensions from Unimodal Viscosity Data,” Trans. Soc. Rheol. 12:281-301 (1968); Probstein et al., “Bimodal Model of Concentrated Suspension Viscosity for Distributed Particle Sizes,” J. Rheol. 38:811-829 (1994); Gondret et al., “Dynamic Viscosity of Macroscopic Suspensions of Bimodal Sized Solid Spheres,” J. Rheol. 41:1261-1274 (1997), which are hereby incorporated by reference in their entirety). For example, for a poly-disperse mixture comprising particles of three sizes such that α1>10α2>100 α3 the relation (8) may be used
φM=φm+φm(1−φm)+0.74[1−{φm+φm(1−φm)}] (8)
where φm is the maximum packing volume fraction for monodisperse spheres=0.64 (36). In this special case, φM=0.96.
Step 5. Repeat step 1 for all particle sizes and select the particle that gives the minimum permeation flux at the given wall shear rate. The corresponding permeation flux is the predicted one,
J=Min [u, u2,. . .un]. The selected particle has a radius αi (9)
Step 6. Evaluate packing density for other particle sizes (αj for j≠i) at this permeation flux. Calculate φwj from the equation
J×Max[B(αj, γ, φb, φwj, L, η), S(αj, γ, φb, φwj, L, η), I(αj, γ, φb, φwj, L, η)] (10)
for all j≠i, using the equations for J shown in Table 1. For particles whose back transport is governed by inertial lift [Table 1, equation (3)], the wall concentration cannot be calculated explicitly by equation (10), but can be estimated by using mass balance and the propensity to lift off. For example, if there is only one such particle type, jI, check if ujI≧10J. If yes, set φwjI=0 as the particle back-transport velocity is much higher than the polydisperse permeation flux and will be readily lifted off from the membrane wall. If ujI is <10J where one particle type is governed by inertial lift, then φwjI is calculated by
φwjI=φM−Σφwj (11)
where j≠jI. If there is more than one particle type whose back-transport is governed by inertial lift, jI1 and jI2, their contributions to the cake at the wall can be approximately apportioned in the direct ratio of their volume fractions in the bulk solution and inverse ratio of their back-transport by
φwjI1+φwjI2=φM−Σφwj (12)
φwjI1:φwjI2=φujI1uji2:100bjI2ujI1 (13)
where j≠jI1 or jI2 and ujI1, ujI2<10J.
This logic can be extended for more than two particle types whose back transport is governed by inertial lif
Step 7. Check Σφwi≦φM and other packing constraints. These depend on the particle sizes in the cake and have to be developed specifically for each case. In Example 1, below, a typical case of a tridisperse suspension with two large and one small particle type is illustrated. The packing constraints for this case are depicted in
φwicorrected=φM[(φwi)/Σφwi] (14)
For the particle selected in Step 5, re-evaluate J based on φwicorrected instead of 0.64 by repeating Steps 3 and 5.
Step 8. Evaluate interstitial packing density, φwiinterstice of the smallest particle by
φiinterstice=φwicorrected/(1−Σφwjcorrected) (15)
Step 9. Based on the corrected packing density of each particle, estimate the rnunimum pore diameter 2rminimum from geometric considerations
2rminimum=αi{√2[4(4/3)π/φwiinterstice]1/3−2} (16)
This is based on a face centered cubic packing for the cake where there are four spherical particles per cube. Here it is assumed that the gap available for transmission in a face centered cubic cake is 2rminimum=α√2−2αi where α is the side of the cube and αi is the radius of the particles located at the vertices and face centers of the cube. a is evaluated based on the value of ai and φwiinterstice. If this gap is greater than 2rs, a particle of radius rs can be transmitted through the cube by displacing two particles located at face centers on the way. Please refer to Example 2 and
Step 10. Estimate the yield of target species (with equivalent radius=rs) in the permeate by calculating the observed sieving coefficient, So (30).
So=Sα/((1−Sα)exp(−J/k)+Sα) (17)
where actual sieving coefficient Sα is obtained from (30)
Sα=(S∝ exp(Pem))/(S∝+exp(Pem) -1). (18)
The wall Peclet number, Pem is obtained from
Pem=(Jδm/D)(S∝/εφKd) (19)
where δm is taken as the side of the face centered cube of the particles of radius ai that forms the controlling cake for transmission. In general, the governing case for flux and product transmission (corresponding to rminimum) may be different. Hence,
δm=α=αi[(4(4/3)π)/φiinterstice]1/3 (20)
The intrinsic sieving coefficient S∝ is obtained from (40)
S∝=(1−λ)[2−(1−λ)2]exp(−0.7146λ2) (21)
where λ=rs/rminimum, φKd is estimated from (41)
φKd=(1−λ)9/2 (22)
The yield of the target species in a diafiltration experiment can be evaluated after Nd diavolumes by using the following relation (30)
Yield=1−exp(−NdSoaverage) (23)
Step 11. There are three possible scenarios corresponding to low, intermediate and high operating shear rates. For low operating shear rates, the cake will be dominated by the larger particles leading to high observed sieving coefficients where So≧0.75, corresponding to Yield≧0.95 according to equation (20) for 4 diavolumes. For such cases no finther refinement is needed. For intermediate operating shear rates, 0<So<0.75, leading to a yield range from 0 to 95%. For this case, So is further corrected by using the stagnant film flux equation for non-retentive membranes (for φwi>>φpermeatei).
J=k ln[(φwi−φpermeatei)/(φbi−φpermeatei)]≅k ln [φwi/φbi(1−So)] (24)
for the transmitted species in Step 3. Steps 3 to 10 are repeated until the values of So obtained by equations (14) and (20) are within 10% of each other. For high operating shear rates, the cake is dominated by the smallest particles and So≅0 implying no transmission of the smallest particles. Thus, in this case, the original assumption of fall retention is valid and no fiiter iterations are required.
Step 12. Construct plot of predicted permeation flux and yield versus wall shear rate for the pressure-independent regime.
The method of the present invention is first illustrated by applying it to a hypothetical suspension comprising three different sized particles: 10, 180, and 300 nm. The relevant data for the tridisperse suspension and microfiltration system are shown below in Table 3 and the calculations are described step-wise below.
Calculations were carried out as follows.
Step 1: Evaluate particle size and equivalent radii. Particle sizes are given above in Table 3.
Steps 2-7: Evaluate the pressure independent permeation flux at the given conditions. This is evaluated iteratively by a spreadsheet program (incorporating Steps 2-7 of the method of the present invention) at the shear rate of 32,400 sec−1. J=30 lmh and the selected particle has a radius=180 nm and φw180nm=0.435. In this example, there are three different particle radii, 10 nm, 180 nm, and 300 nm. The maximum packing fraction considering the 180 um and 300 nm will be 0.68 (Gondret et al., “Dynamic Viscosity of Macroscopic Suspensions of Bimodal Sized Solid Spheres,” J. Rheol. 41:1261-1274 (1997), which is hereby incorporated by reference in its entirety). The 10 nm particles are less than 1/10th of the next higher size particles and hence, can freely move around the interstices of the bigger particles (Probstein et al., “Bimodal Model of Concentrated Suspension Viscosity for Distributed Particle Sizes,” J. Rheol. 38:811-829 (1994), which is hereby incorporated by reference in its entirety). Assuming that the 10 nm particles can reach a maxiinum packing of 0.74 (corresponding to face centered cubic packing) in the interstices of the bigger particles, the maximum aggregate packing=0.68+0.74(1−0.68)=0.92. This value is used for φM in steps 4 and 7.
Step 6: Evaluate the packing densities of the other particles. The spreadsheet program based on Step 6 gives φw10nm=0.27 and φW300 nm=0.179.
Step 8: Evaluate the interstitial packing density of the smallest particle. φw10 nm interstice=0.27/[1−(0.435+0.179)]=0.7 using equation (12).
Step 9: Evaluate the minimum pore radius for transmission. Using equation (13), rmin=10.38 nm.
Step 10: Estimate the yield of the target species. Using λ=rs/rminimum, λ=0.963. Using equations (18) and (19), S∝=0.001382 and φKd=3.446E-7, where Pem=(Jδm/D)(S∝/εφKd)(16). Cake thickness, δm=28.8 nm using equation (20), ε=1−φw10 nm interstice=0.3. Next, D is estimated. Using Einstein's equation, D=(κT)/(6πηαi)=1.67×10−11 m2/sec. Using the value of J calculated earlier, Pem=384. Using equation (16) and (17), Sα=0.00138 and So=0.088. Finally, using equation (20) for Soaverage=0.088 and for a diafiltration run after 4 diavolumes, yield is determined to be 30%. Therefore, for this sample problem, J=30 lmh and Yield=30% for the first iteration.
Step 11: Refine the yield of the target species. Adopting
J=k ln [(φwi=φpermeatei)/(φbi=φpermeatei)]≅k ln [φwi/φbi(1−So)] (24)
for the 10 nm particles as 0<So<0.75 Step 9 is executed to obtain, after a few iterations, J=31.5 lmh, φw10 nm=0.205, φw180 nm=0.49 and φw300 nm=0.19. Following the same procedure as for the first iteration, So=0.37 and after 4 diavolumes for 10 nm particles, assuming Soaverage=0.37 the Yield=77%. Therefore, for this sample problem, J=31.5 lmh and Yield=77%.
On the other hand, if the shear rate was increased to 44,600 sec−1, the pressure-independent permeation flux would have increased to 36 lmh but the yield of the 10 mn radius target species is predicted to be 0! In actual practice, there will be a small finite yield because close to the exit of the module the permeation flux will be less than the pressure-independent permeation flux due to low transmembrane pressure in that region. The model is expected to be more accurate for the uniform transiembrane pressure case or in the case of a stirred cell where the transmembrane pressure is constant.
The sample calculations for a hypothetical suspension comprising particles of three sizes indicate three different curves for pressure-independent permeation flux versus shear rate. This is shown in
The predicted poly-disperse permeation flux and packing densities in the cake for each particle size at different shear rates are derived and plotted as shown in
The sensitivity of permeation flux with respect to module length and total solids volume fraction in the bulk suspension is shown in
In addition, even at low transmembrane pressures, a compact cake may form near the entrance of the filter due to higher transmembrane pressure and resultant permeation flux in this region. Therefore, good sieving of the target molecule will only be achieved after some distance along the membrane. To alleviate this difficulty, short modules to reduce the axial pressure drop are recommended, as shown in
Another beneficial mode of operation can be to maintain the wall volume fraction K of the species corresponding to the least void radius, constant as evaluated in step 6 of the method. This will result in a fairly uniform sieving coefficient during diafiltration. To achieve this, the constant φw, the method recommended by van Reis et al (Reis et al., “Constant Cwall Ultrafiltration Process Control,” J. Membr. Sci., 130:123-140 (1997), which is hereby incorporated by reference in its entirety) could be adopted.
Packing constraints of the cake formed at the membrane wall depend on the size distribution of the particles in the bulk suspension. A few aspects have been covered in Steps 4 of the aggregate transport model described above herein, and as shown in
Step B1. Estimate the maximum aggregate packing volume fraction for all particles. Variants of equation (8) of Step 4 may be used. If the maximum radius ratio of the particles is <10, φM can be set to 0.68 based on reference (Gondret et al., “[Dynamic Viscosity of Macroscopic Suspensions of Bimodal Sized Solid Spheres, J. Rheol. 41:1261-1274 (1997), which is hereby incorporated by reference in its entirety). If there are two distinct groups of particles separated by a factor of ≧10 in radii, a truncated version of equation (8) may be used
φM=φm+0.74(1−φm) (B1)
where φm may be set to 0.64 to denote the highest packing volume fraction for a single species. In a manner similar to equations (B) and (B1), φM for the case for more than three distinct particle size groups can be estimated. The particle composition of the cake and the bulk suspension will be different because of the different back-transport mechanisms applicable for different particle types. It is possible that certain particles get swept away from the wall at very high back-transport rates. These particles can be eliminated from the cake if their back-transport rates are more than 10 times higher than the poly-disperse flux evaluated in Step 5. This will simplify the problem greatly.
Step B2. Evaluate the interstitial packing of the smallest particles. It is assumed that the smallest particles can pack tightly in an FCC structure within the interstices of particles which have radii more than 10 times that of the smallest particles. If there is a range of sizes within the smallest size group, the mean radius may be considered as an approximation. Thus:
φsmallest≦0.74(1−Σφj) (B2)
φsmallest≦0.64 (B3)
φsmallest interstice=(φsmallest)/(1−Σφj) (B4)
where j≠smallest particle.
In a face centered cubic arrangement each face center particle is shared by two cubes and each comer particle is shared by eight cubes. Hence, total number of particles per cube=6/2+8/8=4. Let α=side of the cube, αi=radius of the particle and φwiinterstice=interstitial packing fraction of the particle, then
φwiinterstice=(4(4/3)παi3 1α(C1)
After rearrangement,
α=αi[(4(4/3)π)/φwiinterstice]1/3 (C2)
The gap for particle transmission
α√2−2αi=αi{√2[4(4/3)π/φwiinterstice]1/3−2} (C3)
The method of the present invention was used to design a predictive aggregate transport model to meet the technical challenge of recovering human IgG fusion protein from transgenic whole goat milk by microfiltration at reasonable cost with high purity and yield. To test the model's predictability of permeate flux and mass transport, a comprehensive series of experiments with varying wall shear rate, feed temperature, feed concentration, and module design are presented herein.
A very good fit was obtained between the model predictions and measurements for a wide variety of experimental conditions. For microfiltration module design comparison, a linear hollow fiber module (representing current commercial technologies) gave lower permeation flux and higher yield than a helical hollow fiber module (representing the latest self-cleaning methodology). These results are easily explained with the model which is now being used to define operating conditions for maximizing performance.
The procedure described by the model is generally applicable and can be used to obtain optimal filtration performance for applications other than milk.
Here, the aggregate transport model of the present invention is tested using whole transgenic goat milk, an enormously complex and challenging fluid, for recovery of a desirable molecule such as a heterologous immunoglobulin (IgG). The transgenic process has evolved recently as an economically attractive way of producing large amounts of human therapeutic proteins (Kreeger, “Transgenic Mammals Likely to Transform Drug Maling,” The Scientist 11(15) 1997); Pollock et al., “Transgenic Milk as a Method For The Production of Recombinant Antibodies,” J. Immunol Methods 231:147-157 (1999); John et al., “Expression of an Engineered Form of Recombinant Procollagen in Mouse Milk,” Nature Biotech. 17: 385-389 (1999); which are hereby incorporated by reference in their entirety). In contrast to the traditional method of using large scale batch cell cultures or blood plasma fractionation, transgenic production involves the creation of genetically altered animals which express the desired protein in their milk. A DNA construct, comprising the sequence that will encode the target human protein and an adjacent promoter sequence which facilitates expression only in the mammary glands, is inserted into a goat cell line by transfection. The nucleus is removed from an oocyte which is extracted from an animal. A transfected, selected transgenic cell is then fused with the enucleated oocyte by electrofusion. After 24-48 hours in culture, the embryo is transferred to a surrogate mother. The putative transgenic animals are identified by screening the offspring for the transgene by PCR and Southern blotting. After the selected females mature, they are bred and the milk produced after gestation is tested for protein expression. The process therefore involves two gestation periods and one maturing period. For goats and cows this period is 16-18 months and 3 years, respectively (Pollock et al., “Transgenic Milk as a Method For The Production of Recombinant Antibodies,” J. Immunol. Methods 231:147-157 (1999), which is. hereby incorporated by reference in its entirety).
Several companies and organizations now use this technology. A plethora of therapeutic proteins such as human monoclonal antibodies, tissue plasminogen activator, antithrombin III, and human lactoferrin are proposed for manufacture by the transgenic process and are in various stages of FDA approval (Kreeger, “Transgenic Mammals Likely to Transform Drug Making,” Tize Scientist 11(15) 1997); Pollock et al., “Transgenic Milk as a Method For The Production of Recombinant Antibodies,” J. Immunol. Methods—231:147-157 (1999); Prunkard et al., “High-Level Expression of Recombinant Human Fibrinogen in the Milk of Transgenic Mice,” Nature Biotech. 14: 867-871 (1996); Mckee et al., “Production of Biologically Active Salmon Calcitonin in the Milk of Transgenic Rabbits,” Nature Biotech. 16:647-651 (1998); John et al., “Expression of an Engineered Form of Recombinant Procollagen in Mouse Milk,” Nature Biotech. 17: 385-389 (1999), which are hereby incorporated by reference in their entirety). Remaining technical challenges include proper post translational modification of the secreted proteins and adequate product recovery from milk.
Although milk from transgenic farm animals can become a large source of therapeutic proteins, the complexity of milk combined with the low concentration of target protein complicates the recovery process. Whole milk consists of more than 100,000 different molecules dispersed in three phases namely, lipid, casein, and whey (Dairy Processing Handbook, Tetra Pak Processing Systems, AB, S-221 86, Lund Sweden, (1995), which is hereby incorporated by reference in its entirety). The composition and properties of the main constituents in goat milk are given below in Table 4. Essentially, goat milk consists of 4 wt. % each of protein, fat, and low molecular weight moieties like carbohydrates, sugars, and salts. About 80% of the proteins exist in the form of casein micelles. Heterologous recombinant proteins can be overproduced in the range of 0.2 to 1 wt. %. The first step in isolating heterologous proteins from transgenic milk involves the removal of casein micelles and fat globules from the milk leaving behind low molecular weight salts and sugars. The traditional methods used by the dairy industry to isolate proteins from milk include pasteurization followed by enzymatic coagulation or acid precipitation at pH 4.6 (pI of casein). These steps are often unsuitable for the recovery of heterologous proteins, because they can be temperature and pH sensitive. Additionally, the coagulation process traps most of the target protein within casein pellets resulting in poor yields (Morcol et al., “Model Process for Removal of Caseins from Milk of Transgenic Animals,” Biotechnol. Prog. 17:577-582 (2001), which is hereby incorporated by reference in its entirety). The removal of fat is another important issue. There are inherent difficulties with centrifugation regarding scale-up and contamination. Milk is skimmed industrially at a centrifugal force of 500 g. Fat globules smaller than 5 μm escape this process. Sub-micron size fat globules can be removed by ultracentrifugation (600,00 g), but this is only practical for small volumes (Gardner et al., “Delipidation Treatments for Large Scale Protein Purification Processing,” Master's thesis at Virginia Polytechnic Institute and State University (1998), which is hereby incorporated by reference in its entirety). Instead, microfiltration can be used for the removal of casein and fat (retained in the retentate) with the target protein passing with the permeate (Pollock et al., “Transgenic Milk as a Method For The Production of Recombinant Antibodies,” J Immunol. Methods 231:147-157 (1999); Meade et al., Gene Expression Systems: Using Nature for the Art of Expression, Academic Press. pp. 399-427 (1999); which are hereby incorporated by reference in their entirety). Thus, microfiltration followed by ultrafiltration with various chromatographic steps becomes an attractive method for transgenic milk processing. Many of these processes have been patented for similar applications (U.S. Pat. No. 5,756,687 to Denman et al.; U.S. Pat. No. 6,183,803 to Morcol et al., which are hereby incorporated by reference in their entirety). Transgenic milk is neither pasteurized nor homogenized in order to prevent damage and loss of the target heterologous proteins. Fat globules and casein micelles are the putative foulants for whole milk microfiltration, because the other moieties are much smaller than the average pore size of the 0.1 μm microfiltration membrane, they easily pass through the membrane with the permeate.
Most of the lipids (>95%) in milk exist in the form of globules ranging from 0.1 to 20 μm in diameter (Goffet al., “Dairy Chemistry and Physics,” in Dairy Science and Technology Handbook, Vol. 1, Principles and Properties, Hui, Y. H. ed., New York:VCH Publishers, Chap. 1, pp. 1-81 (1993), which is hereby incorporated by reference in its entirety). In non-homogenized milk, the liquid fat droplets are encased by a 8 to 10 nm thick membrane called the native fat globule membrane (FGM). This is comprised of apical plasma membrane of the secretory cell which continually envelopes the fat droplets as they pass into the lumen of the mammary gland. The FGM is therefore, composed of phospholipids and proteins and is characterized by a very low interfacial surface tension, 1 to 2.5 mN/m, between the fat globules and the serum phase. This prevents the globules from flocculating and from enzymatic degradation. Homogenization decreases the diameter of the fat globules, thereby significantly increasing the surface area of the fat globules resulting in insufficient native FGM to cover all the fat globules. On disruption of the native FGM, the interfacial surface tension increases to a value of ˜15 mN/m allowing serum proteins and casein micelles freely to adsorb onto the exposed fat globules. Thus, homogenization leads to a reduction in the size of fat globules as well as loss of proteins through adsorption (Meade et al., “Gene Expression Systems: Using Nature for the Art of Expression,” Academic Press. pp. 399-427 (1999), which is hereby incorporated by reference in its entirety). The latter effect is expected to reduce the yield of target protein and the former effect increases membrane fouling because of a lower value of back-transport due to shear or inertial lift. Thus, the large fat globules in non-homogenized whole milk may not be the chief foulant in whole milk microfiltration. This hypothesis is tested herein.
There is considerable dispute as to the exact nature of the casein micelle. The present consensus is that the casein micelle is a roughly spherical, fairly swollen particle of 0.1 to 0.3 μm diameter with a hairy outer layer (Walstra, “Casein Sub-Micelle: Do They Exist? Int. Dairy J. 9:189-192 (1999), which is hereby incorporated by reference in its entirety). This is supported by electron microscopy studies (McMahon et al., “Rethiking Casein Micelle Structure Using Electron Microscopy,” J. Dairy Sci. 81:2985-2993 (1998), which is hereby incorporated by reference in its entirety). The hairy layer is comprised of C-terminal ends of ic-casein. This prevents further aggregation of micelles and flocculation by steric and electrostatic repulsion at pH values higher than 4.6, the pI of casein. Thus, at the physiological pH of milk, i.e., 6.4-6.6, the casein micelles predominantly exist as distinct particles of a size range comparable to the mean pore size (0.1 μm) of the poly(ether sulfone) microfiltration membrane. This is expected to result in a low shear-induced diffusion coefficient as well as fouling by pore blockage, cake formation, and pore constriction for larger pores. It is thus expected that for this case, the casein micelles are the main candidates for pore plugging and cake formation (fouling). This is corroborated by polyacrylamide gel electrophoresis studies of permeate samples of milk clarified by microfiltration with a 0.2 μm average pore size ceramic membrane which indicate negligible casein transmission through the membrane.
Here, crossflow microfiltration of raw goat milk is carried out, the first step in the protein recovery process from transgenic whole goat milk. A working predictive model for describing the rather complex process of transgenic whole milk microfiltration has been developed applying the method of the present invention. The next steρ is to develop an optimizing strategy for diafiltration using this model and then to conduct diafiltration experiments as a validation. In this study experiments were undertaken for both linear and helical hollow fiber modules (Luque et al., “A New Coiled Hollow-Fiber Module Design for Enhanced Microfiltration Performance in Biotechnology,” Biotechnol. Bioeng. 65:247-257 (1999), which is hereby incorporated by reference in its entirety). It is well known that the main problems in milk microfiltration are low flux and poor protein transmission due to concentration polarization and fouling. High crossflow velocity, back pulsing, pulsatile flow, and Taylor and Dean vortices represent some of the techniques used by researchers to mitigate this problem (13elfort et al., “The Behavior of Suspensions and Macromolecular Solutions in Crossflow Microfiltration,” J. Membr. Sci. 96:1-58 (1994), which is hereby incorporated by reference in its entirety). The performance of a traditional linear hollow fiber module is compared with the Dean vortex helical hollow fiber module (U.S. Pat. No. RE 37,759 to Belfort, which is hereby incorporated by reference in its entirety).
As described above, the goal of the model provided by the present invention is to predict the performance of microfiltration of poly-disperse suspensions in terms of permeation flux and yield of a target species. The simplifying assumptions in this model are laminar flow, absence of inter-particle and particle-to-membrane interactions. The first steρ is to establish the particle size distribution of the suspension. Existing back-diffusion and inertial lift laws are then employed to calculate the hypothetical mono-disperse permeation fluxes for each particle size and concentration (]3elfort et al., “The Behavior of Suspensions and Macromolecular Solutions in Crossflow Microfiltration,” J. Membr. Sci. 96:1-58 (1994), which is hereby incorporated by reference in its entirety). The lowest of these permeation fluxes is then considered the determining flux of the poly-disperse suspension. This permeation flux is then used in the back-transport laws to calculate the concentration of each species in the filter cake. Essentially, this is the equilibrium concentration at the membrane wall that can ensure a balance between forward and back-transport of each species from the membrane. The evaluated packing densities of various particles are then tested with respect to packing constraints that limit the cake depending on the particle sizes. If the packing constraints are not satisfied, the highest packing density is lowered and the steps executed once again. This is repeated until all the packing constraints are satisfied. Thus, the nature of the filter cake is evaluated. The interstitial gap between the particles is estimated and steric arguments are used to estimate the yield of the target species (Zeman et al., “Microfiltration and Ultrafiltration Principles and Applications,” New York: Marcel Dekker, Inc. (1996), which is hereby incorporated by reference in its entirety). If the yield of the target particle is between 0 and 0.94 for four diavolumes, the non-retentive stagnant film model is employed for the transmitted species and all the steps are repeated to evaluate the corrected flux and yield.
Transgenic goat milk to be used as feed suspension was supplied by GTC Biotherapeutics (Charlton, MA). The average composition of the transgenic goat milk is shown in Table 4, below (Dairy Processing Handbook. Tetra Pak Processing Systems, AB, S-221 86, Lund Sweden, (1995), which is hereby incorporated by reference in its entirety). The human IgG concentration in the transgenic goat milk (˜8 gll) was diluted with non-transgenic milk to between 1.75 to 3 g/l.
Tubular hollow fiber membrane modules were provided by Mllipore Corporation (Bedford, Mass.). Each module had six 0.1 μm mean pore-size poly(ether sulfone) hollow fibers. For the helical module, the fibers were wound in a single-wrap helix around an acrylic rod as shown in
Notes:
aAm(cm2) is the membrane surface area
bL(cm) is the active fiber length
cLp(lmh/kPa) is the hydraulic permeability
dNominal pore diameter = 0.1 μm
eNumber of fibers per module, m = 6
fFiber inside diameter, dI = 1.27 mm
gFiber outside diameter, do = 1.92 mm
hRod diameter, drod for the helical modules = 6.35 mm
iPitch of helical module, p = (m/2π)do = 1.83 mm
jRadius of curvature of the tube in one plane, r = (drod + do)/2 = 4.14 mm
kRadius of curvature, rc = (r2 + p2)/r = 4.95 mm
Twelve hollow fiber modules (6 linear and 6 helical; six pairs, each pair differing in hollow fiber membrane length) were characterized using deionized water with respect to flow parameters. For each module, axial pressure drops were varied and the corresponding axial flow rates recorded. This was first done with the permeate ports closed. Later, this was repeated with permeate ports opened to give a flow rate of 40 ml/min. Hydraulic permeability experiments were then conducted by varying the TMP from 5 kPa to 70 kPa For all the hydraulic permeability tests, the axial flow was maintained at 0.5 I/min corresponding to an exit Reynolds number of 1075. Two similar pairs (based on the length of the hollow fiber membranes) comprising of a linear and a helical module were selected for further experimentation with milk.
This phase of experiments were conducted to understand and compare the permeation flux and protein transmission behavior of whole transgenic goat milk of the linear and helical modules at different Reynold's numbers (shear rates) and protein concentrations of milk. To study the effect of protem concentration, experiments were run at a fixed Reynolds number with milk samples corresponding to 3, 2, 1, ½, and ⅓ times the normal milk concentration. Dilutions were achieved with addition of DI water and concentrations were obtained by filtering at a very low TNT (15 kPa). To maintain a fixed protein concentration in the bulk, both retentate and permeate were completely recycled to the feed reservoir. In these experiments, the TMP was gradually raised from a low value to the pressure independent region of permeation flux. Thus, flux variation with respect to TMP could also be obtained. Each experiment was conducted separately for a helical and a linear module. The milk samples were preheated to 25° C. in a water bath. Samples (5 ml) were taken from both the retentate and the permeate and analyzed for analyzed for protein concentration by the Bradford (protein) assay.
Milk at concentrations corresponding to 1, 1.5, 2, 2.5, and 3 times the normal milk concentration were used for constant volume diafiltration experiments up to 5 diavolumes. For 1× concentration, diafiltration was started after flushing the system with milk. For 2× concentration, one system volume was collected prior to diafiltration, while maintaining a constant reservoir level with milk addition. The permeate collection volumes for 1.5×, 2.5×, and 3× were ½, 1.5, and 2 times the system volume, respectively. For the first protocol, collected permeate was recycled to the feed along with DI water in the ratio 1:1. These experiments were run at a fixed Reynolds number and at 90% of the pressure corresponding to the pressure independent flux for the linear and helical modules. To study the effect of Reynolds number (shear rate), additional diafiltration experiments were conducted at different Reynolds numers with the same milk concentration of 2×. For these experiments, the permeate was not recycled. Therefore, for these experiments concentration of the feed occurred during the first filtered diavolume. Samples (5 ml) were taken from the retentate and permeate streams at regular intervals and analyzed for protein and IgG concentrations.
The following cleaning protocol was used. After each experiment, the entire system was rinsed with deionized water at an axial flow velocity of 2 m/s for 5 minutes with the permeate ports fully opened. This was followed by recycling cleaning agents Ultrasil 10—detergent at 0.5 wt. % and Ultrasil 02—surfactant at 0.1 wt. % at an axial velocity of 2 m/s at 45° C. for 30 minutes. The cleaning agents (altrasil 02, 10, Ecolab, St. Paul, Minn.) were then flushed from of the system for 10 minutes with deionized water. This was followed by sterilization with 0.1 wt. % NaOCl at 40° C. for 10 minutes at 0.33 m/sec. This low velocity was chosen to give sufficient residence time for the bleach to act on the membrane modules. The membranes were stored in this dilute bleach solution till the next experiment. Prior to a new experiment the dilute bleach solution was flushed out by rinsing with deionized water for 10 minutes at 2 m/s velocity.
The Bradford assay (Bio-Rad, Hercules, Calif.) was used to determine protein concentration. Bovine lyophilized casein powder (Sigma, St. Louis, Mo.) was used as a standard and readings were taken in disposable 5 ml polystyrene cuvettes (Bio-Rad, Hercules, Calif.). The absorbance readings with the spectrophotometer (Hitachi, Japan) were taken in the visible range at 595 nm wavelength.
IgG assay was based on the protocol provided by GTC Biotherapeutics (Framingham, Mass.). Briefly, a protein A affinity chromatography (PA ImmunoDetection™ sensor cartridge (2.1×30 mm) (PerSeptive Biosystems, Framingham, Mass.) was used to obtain IgG concentrations in the various goat milk streams. 1.5 ml of milk samples were pipetted into 2 ml Eppendorf centrifuge tubes and centrifuged at 21000 g for 30 min. The milk separated into a top fat layer, a clear whey solution, and a casein pellet. 0.75 ml of the clear whey phase was carefullly extracted with a pipette after puncturing the fat layer. This was pipetted into centrifuge tubes (Spin-X tubes, Corning, N.Y.) with 0.45 μm pore size cellulose acetate membranes and was centrifuged at 21000 g for 15 min. The clarified permeate was then injected into the HPLC column. For the permeate samples, sample preparation was unnecessary. A HPLC (Waters 510 with Millennium 2010 operating system) with a 486 UV detector and U6K sample injector were used (Waters Corp., Milford, Mass.). The loading buffer was 10 mM phosphate buffer and 150 mM NaCl at pH 7.20+0.05, while the elution buffer was 12 mM HCl with 150 mM NaCl. The pump flow rate was set at 2 ml/min., and the detector wave length at 280 nm. The injection volume was 10 μL for milk and 20 to 40 μL for permeate samples. A calibration graph was constructed by injecting different dilutions of IgG fusion protein (GTC Biotherapeutics, Framingham, Mass.). Loading buffer was passed through the column for 10 minutes followed by sample injection and loading buffer again for 5 minutes. After this, elution buffer was run for 10 minutes. A clean peak corresponding to IgG fusion protein was detected at around 6.5 minutes into the elution phase. Area obtained by peak integration was compared with the calibration graph to obtain the IgG concentration of the sample after dividing by the sample volume. Care was taken to ensure that all readings were within the range of the calibration graph. This was done by adjusting the sample injection quantities.
Fat content was measured by the Gerber method which is approved for use by dairies in USA. Eleven ml of preheated milk sample (37° C.) was added to 10 ml of sulfuric acid in a butyrometer. 1 ml of amyl alcohol was added, and the butyrometer was capped with a special stopper. Shaking the butyrometer ensured digestion of the proteins by sulfuric acid. The butyrometer was then inverted and centrifuged for 6 minutes at 350 g. After this, the butyrometer was immersed in water bath at 65° C. for 5 min. The fat appeared as a clear liquid, and the quantity was read out as a volume percentage in the graduated section of the butyrometer.
Model predicted pressure-independent permeation fluxes during microfiltration of whole transgenic goat milk at 298 K, with a 6-fiber hollow fiber module of length 300 mm, internal diameter 1.27 mm, and pore diameter of 100 nm, were plotted against mean axial shear rate for mono-disperse suspensions of IgG, casein micelles, and fat globules, shown in
Experiments were performed with DI water for 12 modules as per Table 5 to determine the hydraulic permeability and friction factor. It is seen in
The data in
A series of diafiltration experiments were conducted to study the IgG transmission and permeation flux behavior with the passage of diafiltration. Experiments at a fixed Reynolds number of 1400 and different starting milk concentrations indicated similar flux trends for both modules, shown in
In the second phase of diafiltration, shown in
A very good fit (r2=0.92) between the model and experiments for a wide range of operating conditions (near the pressure-independent flux regime) with variations in milk concentration, temperature, and Reynolds numers was obtained, as seen in
This concept has been addressed by others (Field et al., “Critical Flux Concept for Microfiltration Fouling,” J. Membr. Sci. 100:259-272 (1994); Ould-Dris et al., “Effect of Cake Thickness and Particle Polydispersity on Prediction of Permeate Flux in Microfiltration of Particulate Suspensions by a Hydrodynamic Diffusion Model,” J. Membr. Sci. 164:211-227 (2000); Gesan-Guiziou et al., “Critical Stability Conditions in Skimmed Milk Crossflow Microfiltration: Inpact on Operating Modes,” Lait 80:129-140 (2000), which are hereby incorporated by reference in their entirety). The model allows the establishment, a priori, of a safe operating permeation flux. This can be generalized to mnicrofiltration of other well characterized poly-disperse suspensions to arrive at the safe permeation flux. This also underscores the concept of constant permeation flux operation as preferable to constant pressure microfiltration. This is because with a constant pressure approach, it is likely that the safe value of permeation flux, above which a cake layer forms completely, will be exceeded during the initial stages of the run and during the course of diafiltration due to the reduction of protein volume fraction and bulk viscosity due to lower volume fraction of solids in the bulk feed.
A series of experimental results with whole transgenic goat milk under various operating conditions of temperature, wall shear rate, and milk concentrations were presented herein. These results validate the predictive aggregate transport model for microfiltration of combined macromolecular solutions and poly-isperse suspensions of the present invention. Milk, being an extremely complicated poly-disperse suspension, provided a suitable test suspension for the method disclosed herein. Further to the recommendations herein, salient features and benefits that can be derived from this model. Crossflow microfiltration performance of different poly-disperse suspensions can be predicted apriori. Currently, in the absence of a general theory, numerous experiments need to be performed in the laboratory and bench scales for each feed type. Therefore, using the model, there is a tremendous potential for saving time and labor. The model can be used with different geometries. The model can determine the nature of the filter cake. The model, being theoretical, can be used for scale-up and scale-down for industrial or laboratory applications. This is a major pitfall of empirical methods which are valid only for the operating region and scale. The computerized version of the model can be interfaced with other software packages for optimzg diafiltration for the optimum plant operation.
Although preferred embodiments have been depicted and described in detail herein, it will be apparent to those skilled in the relevant art that various modifications, additions, substitutions, and the like can be made without departing from the spirit of the invention and these are therefore considered to be within the scope of the invention as defined in the claims which follow.
This application claims the benefit of U.S. Provisional Patent Application Ser. No. 60/403,575, filed Aug. 14, 2002, and U.S. Provisional Patent Application Ser. No. 60/471,603, filed May 19,2003.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US03/25230 | 8/13/2003 | WO | 12/22/2005 |
| Number | Date | Country | |
|---|---|---|---|
| 60403575 | Aug 2002 | US | |
| 60471603 | May 2003 | US |
