Environ. Sci. Technol. 2002, 36, 5426-5433
Conductivity Reduction Due to Emulsification during Surfactant Enhanced-Aquifer Remediation. 1. Emulsion Transport V I V E K J A I N † A N D A V E R Y H . D E M O N D * ,‡ Department of Petroleum and Geosystems Engineering, The University of Texas, Austin, Texas 78712, and Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, Michigan 48109
Surfactant-enhanced aquifer remediation (SEAR) is a promising technology for the remediation of subsurface zones contaminated with organic liquids. To ensure the success of SEAR, the potential reduction in hydraulic conductivity must be evaluated. The objective of this study was to examine the process of conductivity reduction due to the transport of an emulsion, generated by mixing tetrachloroethylene with 4% solutions of two nonionic surfactants, in packed beds of sand-sized silica particles. The injection of the emulsion resulted in a 75-85% reduction in conductivity, depending on the properties of the surfactant and the porous medium. The greater viscosity of the emulsion relative to that of water accounted for about 25% of the reduction. The remainder was attributed to the clogging of the porous medium by the emulsion. The relative sizes of the emulsion droplets and the packed bed’s pores, coupled with measurements of zeta potential of the emulsion droplets and silica particles, suggested that multilayer deposition was the principal mechanism of clogging. This hypothesis was corroborated by direct observation of the emulsion transport process in a micromodel. To simulate the reduction in hydraulic conductivity in these systems accurately, it was necessary to modify the emulsion transport model by Soo and Radke to include the phenomena of viscosity variation and multilayering.
Introduction Surfactant-enhanced aquifer remediation (SEAR) is a promising technology to improve the effectiveness of water flooding of subsurface zones contaminated with organic liquids. Surfactants have the ability both to increase the solubility of organic liquids in water and to decrease the interfacial tension, thus enhancing the ability of a pump and treat system to extract greater quantities of the contaminant. Nonionic surfactants have been widely studied in laboratory experiments for their solubilization potential of denser-thanwater nonaqueous phase liquids (DNAPLs) where increased mobility may lead to the migration of the contaminant liquid deeper into the subsurface (1-3). Despite the reports of successful remediation of laboratory columns containing * Corresponding author fax: (734)763-2275; e-mail: averyd@ engin.umich.edu. Corresponding author address: Room 105, EWRE Bldg., 1351 Beal Ave., Ann Arbor, MI 48109-2125. † The University of Texas. ‡ The University of Michigan. 5426
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DNAPLs using solubilization-based SEAR, a number of issues need to be resolved before such a remediation process can be implemented with confidence in the field. Nash and Traver (4) observed that clogging of soil pores led to a diversion of surfactant solution from the contaminated zone and, in fact, Pope and Wade (5) cite conductivity reduction due to emulsion formation as a major reason surfactant-enhanced oil recovery may fail. The impact of SEAR on conductivity is often not quantified in laboratory studies, despite reports of emulsion formation (6, 7) and the proposed use of emulsion injection to recover DNAPLs (8). To ensure the success of SEAR, the potential reduction in conductivity must be quantified and its dependence on system properties must be determined. Most of the quantitative studies examining the reduction of conductivity due to emulsion transport involve the formation of an emulsion ex situ and its subsequent injection into a porous medium (9, 10). Yet in SEAR, the emulsion may be formed ex situ and injected (8) or it may be formed in situ and subsequently transported. To make use of existing theory, the problem needs to be analyzed in two parts: first, the transport of the emulsion and second, the process of in situ formation. Thus, the objective of this paper is to examine the process of conductivity reduction when an ex situ-generated emulsion, formed from a DNAPL and a solution of a surfactant selected for its solubilization potential, is injected into a porous medium. The subsequent paper focuses on the process of in situ emulsion formation due to the injection of a surfactant solution into a porous medium containing a DNAPL and its ensuing impact on the conductivity of the system.
Background The conductivity of the medium can be altered by changing the liquid properties and /or the pore structure of the porous medium. According to Nutting (9), the conductivity of a porous medium to a particular permeant, K, can be calculated as
K)
kFg µ
(1)
where k ) permeability, a function of the pore structure of the porous medium, µ and F ) dynamic viscosity and density, respectively, of the permeant, and g ) gravitational acceleration constant. Most studies investigating the degree of conductivity reduction resulting from surfactants do not systematically examine the contributions of the various processes that might occur during SEAR. For instance, Allred and Brown (12) observed that flushing with a surfactant solution resulted in conductivity reductions of up to 47% in sand and up to 2 orders of magnitude in loam, changes that could not be attributed solely to changes in viscosity. Yet, the experiments were conducted in the absence of a NAPL, which is necessary for the formation of an emulsion which may be more viscous. On the other hand, Jain and Demond (13) examined the changes in viscosity and density in systems composed of tetrachloroethylene (PCE) and three surfactant solutions. Their results showed that the decrease in K was due to the increase in viscosity as the surfactant concentration in water increased, decreasing the conductivity by 23% at a surfactant concentration of 4 wt %. The solubilization of PCE and the subsequent formation of a dilute emulsion increased the viscosity somewhat, but their impact was considerably less. Still, the conductivity reductions reported by Allred and 10.1021/es0113955 CCC: $22.00
2002 American Chemical Society Published on Web 11/07/2002
Brown (12) are larger than those observed by Jain and Demond (13) for emulsions, suggesting that changes in pore structure and their impact on hydraulic conductivity need to be analyzed also. According to previous studies analyzing the impact of emulsion transport on pore structure, the primary mechanism of permeability reduction is the reduction of the diameter of pore throats by the deposition and/or interception of emulsion droplets. If the diameter of the droplets exceeds that of the pores, the pores may be blocked directly by the interception of the droplets. If the emulsion is stable, the droplets do not coalesce. Thus, they remain lodged in the pore throats, restricting the flow and reducing the overall permeability of the medium (14). If the emulsion is unstable, the droplets may initially be smaller than the pore diameters; however, as the emulsion coalesces, it will create larger droplets. These droplets will then block pores, constricting the flow through the porous medium (15). Alternatively, the diameter of the emulsion droplets may always be smaller than the pores. In this instance, the droplets may be retained on the porous medium’s surface or in crevices or pockets formed by the particles making up the porous medium (9), with the result that the pore volume available for flow decreases. The permeability reduction then depends on two factors: the volume of drops retained and how effective these drops are in restricting the flow. Whether the permeability reduction is due predominantly to interception or deposition may be determined by the value of the ratio of the droplet diameter, dd, to the pore diameter, dp. For interception to dominate, dd/dp needs to be close to unity; on the other hand, deposition dominates if dd/dp e 0.2 (9). To predict quantitatively the reduction in permeability due to the flow of emulsion in a porous medium, a theoretical model is required that can describe the restriction in flow that arises from both interception and deposition within a pore. An analysis of the various models available (16) suggests that the model developed by Soo and Radke (17) incorporates the necessary mechanisms for an accurate description of permeability reduction. In this model, transient flow behavior is described by three parameters: a filter coefficient, λii, which describes the distance the emulsion droplet travels before being captured, a flow-redistribution parameter, R, which defines the redistribution of the flow due to the deposition and interception of the emulsion, and a flow-restriction parameter parameter, β, which describes the effectiveness of the retained droplets in reducing flow. Using the equations given in Soo et al. (18), these parameters can be estimated from the following measurable parameters: average droplet diameter, average pore diameter, pore-size distribution, average grain diameter, porosity, and inlet droplet volume concentration of emulsion. Then, assuming the maximum coverage of the porous medium’s surface is a monolayer, the permeability in a column as a fraction of the initial permeability can then be calculated as
k0 k(τ)
)
∫ [1 - βσ/ ] 1
0
-1
0
and T ) shifted time ) τ - xj . Soo et al. (18) successfully applied this model to describe the permeability reduction resulting from the injection of an oil-in-water emulsion (prepared from a refined mineral oil [Chevron 410H] with carbon tetrachloride added to render it neutrally buoyant, and stabilized by sodium oleate and oleic acid at pH 10) into porous media composed of quartz sandpacks with permeabilities ranging from 0.6 to 2.0 µm2. Based on the values of dd/dp, these systems were dominated by interception. Furthermore, strong repulsive forces existed between the droplets and between the droplets and the quartz particles’ surface, resulting in a maximum coverage of the pore surfaces by emulsion droplets of about 10-15% of a monolayer. Yet, in a SEAR system, the amount of coverage may be significantly different. Work done on the dynamics of colloidal deposition in porous media (19-21) shows that the number of particles deposited depends on the relative values of the zeta potential of the porous medium grains and the particles being deposited. For example, using glass beads, polystyrene latex particles and varying electrolyte concentrations, Elimelech and O’Melia (19) observed that if sufficiently strong interparticle repulsive forces are present, the deposition rate decreases as particles accumulate due to the blocking of available sites. In this case, the maximum coverage is a monolayer and the fraction of the surface covered, θ, may be obtained according to the relationship (20)
θ)
πdd2qdgci
∫ (1 - c(t)/c ) dt t
0
i
(4)
24L(1 - 0)
where dg ) diameter of grains making up the porous medium, and ci and c(t) ) particle number concentration at the inlet and outlet, respectively. However, in the absence of strong repulsive forces, the deposition rate increases as the particles accumulate because the retained particles act as additional collectors. In this instance, multilayer coverage is possible (21), the extent to which will be strongly affected by the relative degree of particle-particle and particle-surface interactions and the volume of previously captured particles (22). Although the formation of emulsions during solubilization-based SEAR has been reported, its impact on permeability has largely gone uninvestigated. Furthermore, the studies that quantitatively examine permeability reduction in a porous medium due to the transport of an emulsion have not focused on systems similar to those in SEAR. Although the model developed by Soo and Radke (18) proved to be satisfactory in a system where interception and strong repulsive forces existed, its applicability to SEAR systems has yet to be evaluated. In particular, its extension to nonionic surfactant systems where multilayer coverage may occur needs to be examined.
Materials and Methods dxj
(2)
where k0 ) initial permeability of the column, k(τ) ) permeability of column at reduced time τ ) qt/L0 and q ) superficial flow velocity, t ) time, L ) length of column, and 0 ) initial porosity; β ) flow restriction parameter, σ ) droplet volume retention, and xj ) reduced distance ) x/L. The ratio, σ/0, can be determined using
1 - exp(RΛiiCiT) σ(T, xj ) ) 0 R[1 - exp(Λiixj ) - exp(RΛiiCiT)]
(3)
where R ) flow redistribution parameter, Λii ) reduced filter coefficient ) λiiL, Ci ) inlet droplet volume concentration,
To investigate the permeability reduction that may occur during solubilization-based SEAR in unconsolidated clean sands, a series of experiments were conducted using tetrachloroethylene (PCE), a DNAPL, as the target contaminant (23, 24). The organic liquid was of purified grade (>99%), purchased from Fisher Scientific (Chicago, IL). Ultrapure water was used to prepare all aqueous solutions and was obtained by passing deionized, distilled water through Milli-Q cartridge filters (Model CDOFO1-1205, Millipore, Bedford, MA). The properties of both liquids are given in Table 1. The ionic strength of water was adjusted to 0.01 N using NaCl and the pH was adjusted to 6 using analytical reagent grade HCl or NaOH (Mallinckrodt, Paris, KY). Two different commercial nonionic surfactants were used in this study: Witconol 2722 (Witco, Greenwich, CT), a polyVOL. 36, NO. 24, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Properties of Pure Liquids Used in Experimentsa property (g/cm3)
density solubility in distilled water (mg/L) viscosity (cP) surface tension (dyn/cm) interfacial tension (dyn/cm)
water
tetrachloroethylene
0.9970 NAb 0.8904 71.99 NAb
1.6143 150 0.844 31.30 47.48 (20 °C)
a Data compiled from refs 25-28. All values are at 25 °C unless otherwise indicated. b NA, not applicable.
TABLE 2. Properties of Surfactants Used in Experimentsa property average molecular formula
Witconol 2722
C18H34O2C6H10O4(CH2CH2O)20 molecular weight (g/mol) 1310 hydrophile-lipophile balance 15 critical micelle concn (mg/L) 13 aggregation number 110 a
Witconol SN 120 C10-12H21-25O(CH2CH2O)9H 569 14 54 105
Data from ref 29.
oxyethylene(20) sorbitan monooleate, and Witconol SN 120 (Witco, Greenwich, CT), an ethoxylated dodecyl alcohol (Table 2). They were selected for use based on the evaluation by Pennell et al. (1, 2, 29) for their potential suitability in solubilization-based SEAR. The surfactants were used as received. All sample preparations and measurements were conducted at room temperature, 23 ( 2 °C. The emulsion was made by mixing 50 mL of PCE per liter of 4% (by weight) surfactant solution (0.01 N NaCl; pH ) 6) to give a solution containing 81 g/L of PCE using a stir plate (Model PC-420, Corning, New York, NY). The emulsion was continuously stirred at the same rate, as the emulsion tended to settle over time, but the droplet characteristics did not depend on the stirring time. To characterize the emulsion, its droplet-size distribution, kinetic stability and zeta potential were measured. The droplet-size distribution of the emulsion was measured using a CAPA-700 particle-size distribution analyzer (Horiba Instruments, Irvine, CA), based on the changes in light transmission during sedimentation under centrifugal acceleration. The emulsion stability was assessed by two methods. First, the emulsion was visually observed to see if there was any phase separation of the PCE and surfactant solution over time. Second, the droplet-size distribution of the emulsion was measured over time to observe if it changed. The electrokinetic properties (zeta potential) of the emulsions were measured using an electrokinetic sonic amplitude (ESA) apparatus (ESA-8000, Matec Applied Sciences, Hopkinton, MA), employing the technique described in detail by Desai et al. (30). To observe the mechanism of permeability reduction due to emulsion transport, visualization experiments were conducted in a 2.4 cm × 1.2 cm etched glass micromodel. This was constructed by fusing together two glass plates, one of which was etched with a uniform micropore structure consisting of rounded triangular shapes, with five to six of them forming a pore body. The diameters of the pore bodies were 300 µm, while those of the pore throats varied from 30 to 70 µm. The micromodels were first saturated with de-aired Milli-Q water. Then PCE, dyed red using a preferentially oil-soluble dye, Oil Red-O, (Fisher Scientific, Chicago, IL) (which Pennell et al. (29) have shown to have no major impact on the organic liquid properties, such as viscosity, density and interfacial tension), at a concentration of 0.5 g/L, was introduced in the model, displacing the water. Then water was again flushed through 5428
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TABLE 3. Properties of Silica Particles Used in Column Experimentsb property
F-65
F-95
specific gravity particle size range (µm) d50 (µm) uniformity coefficienta angularity
2.65 106-425 188 1.77 rounded
2.66 53-300 123 1.61 rounded
a
Uniformity coefficient ) d60/d10.
b
Data from U.S. Silica (Ottawa,
IL).
the model to create a residual saturation of PCE in the model. A 4% surfactant solution containing PCE at concentrations ranging from 0 to 40 g/L was introduced into the model at a flow rate of 0.01 mL/min using a syringe infusion pump (Model #22, Harvard Apparatus, South Natick, MA) equipped with 20 mL disposable syringes (Becton Dickinson, Franklin Lakes, NJ). To enhance the visualization process, a bright light (Series 1800, Dolan-Jenner, Lawrence, MA) was directed onto the top of the micromodel. The image was magnified using a zoom video microscope (Infini Var, Infinity PhotoOptical, Boulder, CO) and photographed in real time with a high-resolution color CCD camera (Sony, Tokyo, Japan). Although useful for qualitative observation, the micromodels were too small for measuring the changes in permeability quantitatively. The quantitative measurements were accomplished using two columns, packed with either F-65 or F-95 silica particles (U.S. Silica, Berkeley Springs, WV), whose properties are given in Table 3. Prior to packing, the silica particles were first cleaned with 0.1 M HCl and 15% hydrogen peroxide in order to remove surface impurities (31). They were then thoroughly rinsed with Milli-Q water to remove the residual acid and peroxide and air-dried. The porous media were characterized by measuring the zeta potential of the silica and the pore-size distribution. The zeta potential was measured using the same methodology as for the emulsion droplets. Since the F-65 and F-95 particles used in the column experiments were too big to oscillate in the electric field generated by the ESA apparatus, #40 quartz (U.S. Silica, Berkeley Springs, WV), with a median particle diameter of 5.1 µm, was utilized for these measurements, similar to the protocol outlined by Demond et al. (32). To obtain the pore-size distribution, the drainage air-water capillary pressure-saturation relationships were measured using the pressure cell and procedure developed by Salehzadeh and Demond (33). These data were fitted with the equation given by van Genuchten (34) using the statistical package SYSTAT (SPSS, Chicago, IL). Then using the YoungLaplace equation (35), the capillary pressure was converted to a pore diameter (36, 37), assuming that the contact angle was approximately equal to 0° and that the surface tension of water was about 72 dyn/cm (32). The smaller column (Column 1) consisted of a custommade glass cylinder, with a length of 20 cm and a diameter of 5 cm, equipped with two stainless steel end plates and Viton O-rings. The end plates were held in place by three threaded rods that were secured at both ends. Each endplate was fitted with a 40-mesh stainless steel screen and a stainless steel porous plate whose purpose was to retain the sand and to distribute the liquids evenly across the face of the column. Later experiments employed a longer column (Column 2) to increase the resolution of the pressure readings. This column was a modified chromatography column (Kontes, Vineland, NJ), with a length of 60 cm and a diameter of 4.8 cm, outfitted with PTFE-shielded O-rings, bed supports and fittings at both ends of the column. To obtain pressure measurements as a function of time, the columns were equipped with tensiometers constructed
TABLE 4. Conditions of Column Experiments Witconol 2722
Witconol SN 120
property
F-65
F-95
F-65
F-95
column length (cm) column diameter (cm) porosity (-) pore volume (cm3) volumetric injection rate of emulsion (mL/min) number of pore volumes of emulsion injected influent total PCE concentration (g/L) PCE solubility limit in 4% surfactant solution (g/L)
20 5.07 0.336 136 0.55 9.0 81 35
20 5.07 0.340 137 0.55 9.0 81 35
60 4.8 0.338 367 0.50 7.5 81 27
60 4.8 0.342 371 0.50 7.5 81 27
from stainless steel tubes to which were epoxied hydrophilic membranes (0.45 µm, Nylaflo, Gelman Sciences, Ann Arbor, MI) supported by 140-mesh stainless steel screens. The tensiometers were connected to stainless steel pressure transducers (Validyne, Northridge, CA) whose signals were conditioned using a carrier-demodulator card (UPC-607, Validyne, Northridge, CA) and then stored on a computer. The columns (oriented vertically) were packed saturated with Milli-Q water (as packing wet has proven to give a more homogeneous porous medium than packing dry [38]), with the help of an electrical vibration rod (Burgess Vibrocrafters, Grayslake, IL). The porosity was determined either by the weight of the column or by gamma-ray attenuation (38). Then, the column was saturated with water and the initial hydraulic conductivity of the column was determined. Following that, the emulsion was injected into the column from the top at a constant flow rate of 0.5-0.55 mL/min (depending upon the column), using a single piston rapid refill pump (Rainin SD-200, Varian Associates, Woburn, MA) equipped with a back pressure regulator to dampen the pulse. The injection continued until it appeared that the pressure signals had stabilized. Table 4 gives the conditions for the column experiments. To obtain the hydraulic conductivity, K, of the experimental columns, Darcy’s law, written here for one-dimensional flow in the z-direction
q)K
d P +z dz Fg
(
)
(5)
where P ) pressure and z ) vertical height above a datum, was assumed to be valid, as the emulsions created here were dilute and behaved as Newtonian fluids (13). According to eq 5, the gradient in density of the permeant must be accounted for. Theoretically, this parameter would vary as the emulsion droplets were removed from the flow stream within the porous medium. The maximum density of the surfactant solution-PCE mixture used here is 1.05 g/cm3 (13), a 5% increase over the density of pure water. Because this difference proved to have a small impact in this situation, the gradient in density was neglected. To determine the change in hydraulic conductivity, measurements of the pressure head as a function of z were made under the following conditions: (1) under no flow conditions (this gave the gravitational head at each tensiometer, which was assumed to remain constant over time); (2) at different flow rates of water (these measurements gave the initial hydraulic conductivity); and (3) during the steady injection of the ex situ-generated emulsion. In addition, samples of the effluent were analyzed for PCE content throughout the emulsion injection into the column. Both the solubilized and emulsified PCE were extracted from the effluent using methanol. Then, the total PCE concentration was determined in duplicate using a HP 5890A Gas Chromatograph (Hewlett-Packard, Novi, MI) outfitted with a DB-FFAP fatty acid column (Model #125-
TABLE 5. Properties of the Emulsions and Porous Media emulsions
4% Witconol 2722-PCE
4% Witconol SN 120-PCE
mean droplet diameter (µm) zeta potential (mV) densitya (g/cm3) viscosityb (cP) porous media Rc nc residual saturation of water mean pore diameter (µm) zeta potential of #40 silica (mV)
1.1 0.3-0.5 1.05 1.25 F-65 0.0162 11.76 0.143 47 -25
0.98 1.2-1.3 1.05 1.23 F-95 0.0127 9.77 0.045 37 -25
a Estimated using a weighted-volume average, as recommended by Jain and Demond (13). b Estimated using the equation by Taylor (39), as recommended by Jain and Demond (13). c Determined by fitting the equation developed by van Genuchten (34):
Se )
[
1 1 + (RPc)n
]
(1-1/n)
Sw - Swr , Sw ) saturation of water, Swr ) residual saturation 1 - Swr of water, and Pc ) capillary pressure (given in cm H2O), to the drainage air-water capillary pressure-saturation relationship. where Se )
3232, J&W Scientific, Folsom, CA) and a flame ionization detector. To calculate the amount of PCE associated with the emulsion, it was assumed that the PCE present in dissolved form was equal to the solubility limit of 35 g/L for the 4% Witconol 2722 solution and 27 g/L for the 4% Witconol SN 120 solution, determined during the preparation of the injected mixtures by the onset of emulsification. Subtracting this amount from the total amount of PCE present gave the amount of PCE present as dispersed droplets. Based on this information and data such as the density of PCE and the average droplet diameter, the concentration parameters called for in eqs 3 and 4 could be calculated. More details on how the filter coefficient, λii, the flow-redistribution parameter, R, and the flow-restriction parameter, β, are determined from the measured characteristics of average droplet diameter, average pore diameter, pore-size distribution, average grain diameter, and porosity are available in refs 16, 18, and 37.
Results and Discussion Experimental. To determine the mechanism of permeability reduction, the emulsion must be characterized; in particular, its droplet-size distribution, stability and zeta potential must be determined. The droplet-size distributions of the two emulsions examined in this study are given in Figure 1, showing that the two systems had similar mean droplet diameters of 1.1 and 0.98 µm for the 4% Witconol 2722-PCE and 4% Witconol SN 120-PCE systems, respectively (Table 5). The figure also shows the droplet-size distributions of the emulsions as a function of time, with the first measurement made right after the formation of the emulsion and the second VOL. 36, NO. 24, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Pore-size distributions of packed beds of F-65 and F-95 silica calculated from the drainage air-water capillary pressuresaturation relationships.
FIGURE 1. Droplet-size distributions of the ex situ-generated emulsions of (a) a 4% Witconol 2722 solution and PCE and (b) a 4% Witconol SN 120 solution and PCE. measurement made after 48 h. The fact that the droplet-size distributions did not dramatically shift to favor larger droplets suggests that little coalescence occurred in the system. The emulsion droplets did settle to the bottom of the sample tubes due to the density difference between the dispersed phase (PCE) and continuous phase (4% surfactant solution); however, the breakdown of the emulsion into its component phases did not occur even after several days. The pore-size distributions obtained from the drainage air-water capillary pressure-saturation drainage relationships are shown in Figure 2. Based on these curves, the average pore diameters were determined to be 48 µm for the packed bed of F-65 particles and 38 µm for the bed of F-95 (Table 5). Thus, the dd/dp ratios for the four systems range from 0.020 to 0.029. Since these ratios are about an order of magnitude smaller than 0.2, the criterion given by Soo and Radke (9) for the domination of the pore clogging process by deposition, deposition should be the main mechanism of permeability reduction in these systems. The zeta potential of the #40 silica was -25 mV, whereas the zeta potentials measured for the emulsion droplets were 0.3-0.5 mV and 1.2-1.3 mV for the 4% Witconol 2722-PCE and the 4% Witconol SN 120-PCE emulsions, respectively (Table 5). Since the emulsions and the porous media have opposite charges, the charge interaction between them can promote the deposition of the emulsion droplets on the surface of the porous media. Furthermore, the zeta potential of the droplets are sufficiently close to zero that rapid ripening or multilayering may occur (21, 22) due to the minimization of interparticle repulsion. These speculations were confirmed by the results of the micromodel experiments. The video footage of these experiments showed the emulsion droplets piling up rapidly in 5430
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FIGURE 3. Hydraulic conductivity reduction in a column packed with F-65 silica and flushed with an ex situ-generated emulsion formed from a 4% Witconol 2722 solution and PCE. uneven layers on the surface of the porous medium and in the crevices between particles. Although a single emulsion droplet was not large enough to block a pore throat directly, flow paths did become completely blocked if enough layers were deposited. Once formed, it was very difficult to dislodge these layers of droplets. If they were dislodged, however, the droplets moved as an aggregate, plugging another pore down gradient. Thus, it appeared that deposition in multilayers was the primary mechanism of flow network reduction, with interception playing an increasing role as the emulsion droplets aggregated into larger masses and the pore throat dimensions were reduced due to already deposited layers of emulsion. The ratio between the hydraulic conductivity measured during the injection of the ex situ-generated emulsion of 4% Witconol 2722 and PCE into a packed bed of F-65, and the initial hydraulic conductivity is shown in Figure 3. The results in this and subsequent figures are plotted as a function of pore volumes injected. The spacing between the tic marks is uneven because the plotting program used, EXCEL 5.0, gauged the spacing based on the number of data points rather than on the basis of time. The results indicate about an 80% reduction in hydraulic conductivity occurs in the column over the injection of 9 pore volumes of emulsion. Since the viscosity of this emulsion is about 1.25 cP (Table 5), about 25% of the reduction may be attributed to the increase in viscosity, with the rest apparently caused by the clogging of the pore networks by the emulsion droplets. Initially, the hydraulic conductivity decreases slowly, then its rate of decrease is accelerated. This trend may be attributed to the time that it takes to build up the layers of emulsion, followed
FIGURE 4. Comparison of hydraulic conductivity reduction in columns packed with F-65 and F-95 silica and flushed with ex situ-generated emulsions formed from a 4% Witconol 2722 solution and PCE.
FIGURE 5. Comparison of hydraulic conductivity reduction in columns packed with F-65 silica and flushed with ex situ-generated emulsions formed from 4% Witconol 2722 and Witconol SN 120 solutions and PCE. by the onset of interception and the greater efficiency of interception as a mechanism for reducing hydraulic conductivity (10). The rate of decrease is greater in the packed beds of F-95 than in those of F-65 (Figure 4) due to the smaller size of the pores, which means fewer layers or smaller aggregates can effectively clog a pore throat. A faster reduction in hydraulic conductivity in the same porous medium occurs with the 4% Witconol SN 120-PCE emulsion than with the 4% Witconol 2722-PCE emulsion (Figure 5). This observation reflects the behavior observed in the micromodel experiments: that multilayers form faster and interception begins earlier in the Witconol SN 120 system, perhaps due to the stronger attractive forces between the emulsion droplets and the porous medium’s surface. Modeling. To determine whether the trends in hydraulic conductivity could be described quantitatively, the emulsion transport model developed by Soo and Radke (17) was applied to the systems under study here utilizing the parameters reported in Tables 3-5. Figure 6 shows a comparison between the prediction of Soo and Radke model’s and the experimental data obtained here for the 4% Witconol 2722-PCE emulsion injected into a packed bed of F-65. The lack of agreement can be attributed to the conditions present in the experimental system under study here, which were absent in Soo and Radke’s. In Soo and Radke’s system (an oil-in-water emulsion of mineral oil and carbon tetrachloride stabilized by sodium oleate and oleic acid at pH ) 10), the zeta potential measured -80 and -60 mV for the emulsion droplets and porous medium, respectively (37). Thus, there existed sufficiently strong repulsive forces between the particles and the solid surface, and among the particles themselves, that the surface coverage was limited to about 10-15% of a
FIGURE 6. Modeling of hydraulic conductivity reduction in a column packed with F-65 silica and flushed with an ex situ-generated emulsion formed from a 4% Witconol 2722 solution and PCE using the unmodified Soo and Radke (17) model. monolayer (18). Also, the ratio of dd to dp was on the order of 0.2, suggesting that interception played a greater role in their systems than in the ones considered here. Finally, the model was formulated to predict the reduction in permeability [eq 1], not hydraulic conductivity, so the reduction due to the greater viscosity of the emulsion was not included. If minimal surface coverage and no change in viscosity are assumed, the reduction in hydraulic conductivity that is predicted for this experimental system is only 3% (Figure 6). Since, in reality, a reduction of about 80% was observed, the necessity of including multilayering and viscosity changes in a model simulating the hydraulic conductivity of SEAR systems becomes apparent. Because of the observed need for the inclusion of additional phenomena, Soo and Radke’s model was modified. The change in viscosity was accounted for by assuming that the increase occurred linearly over time. If there were piston flow displacement in the column, then the injected liquid would replace the water initially present in the column over the course of one pore volume. In reality, this does not occur due to dispersion and channeling. Hence, the change in viscosity of the permeant was assumed to occur over the injection of 1.5 pore volumes
µ ) µw + (µe - µw)
t T1.5
(6)
where µw and µe ) viscosity of water and emulsion (Tables 1 and 5), respectively, and T1.5 ) time necessary for injection of 1.5 pore volumes. To incorporate the phenomena of multilayer coverage, a multilayer coverage factor, θm, was introduced into the calculations of the flow redistribution parameter. Equation 4 accounts for nonuniform surface coverage, but it still assumes that the maximum quantity that can be deposited is a monolayer. Since it was clear from the micromodel images that multiple layers were formed, the relationship was modified by the introduction of an empirical factor, χ, similar to the “ripening factor” included by Darby et al. (40) to express the fraction of retained droplets that act as additional collectors, to give
θm )
πDd2qDgci
∫ (1 - c(t)/c ) dt t
0
24L(1 - 0)
i
(7)
χ
where θm ) multilayer coverage factor. The functional form of χ is shown schematically in Figure 7. The change in slope from δd to δdi reflects the change in the mechanism of clogging from deposition dominated to both deposition and interVOL. 36, NO. 24, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 7. Functional form of χ, an empirical parameter describing the multilayering process.
TABLE 6. Results of Column Experiments and Modeling Witconol 2722 property initial hydraulic conductivity (cm/min) final hydraulic conductivity (cm/min) filter coefficient, λii (1/cm) δda δdia χ1a χmaxa a
F-65
F-95
Witconol SN 120 F-65
F-95
2.35
0.50
2.25
0.45
0.42
0.075
0.320
0.1
0.0414 0.25 0.98 153 690
0.0802 0.30 1.16 157 566
0.0092 0.27 2.20 101 750
0.0218 0.32 3.00 117 615
FIGURE 8. Comparison of model calculations and experimental results of hydraulic conductivity reduction in columns packed with (a) F-65 and (b) F-95 silica and flushed with an ex situ-generated emulsion formed from a 4% Witconol 2722 solution and PCE.
Parameters defined by Figure 7.
ception, as the pore throats narrow and the emulsion droplet aggregates grow. Since interception is more efficient than deposition in reducing the hydraulic conductivity, the slope for the latter is greater. Once the system attains steady state, the maximum amount of emulsion entrapment has been reached, at which point χ does not change with time and is equal to χmax. Although the other parameters in this model could be obtained by independent measurements of system characteristics, the parameters defining χ are empirical and were obtained by fitting the model to the experimental data using the optimization code LM OPT (41), based on the Levenberg-Marquardt algorithm. The values of δd, δdi, χ1, and χmax determined in this manner are given in Table 6 for the various systems. Figures 8 and 9 show the improvement in the fit between the model simulations and the experimental data for the 4% Witconol 2722-PCE and 4% Witconol SN 120-PCE systems, respectively, with eqs 6 and 7 included as part of the model. The values for the empirical parameters defining χ (Table 6) are consistent with the differences observed in the behavior of hydraulic conductivity reduction among the experimental systems examined. χmax is smaller for the systems where F-95 is the porous medium than the systems utilizing F-65. The smaller values of χmax suggest that fewer multilayers are ultimately formed, presumably because of the smaller pores in the packed beds of F-95. Furthermore, the values of δd and δdi for the F-95 systems are larger, indicating a faster rate of clogging, again due to the smaller pores. The trends in χ are also consistent with the differences between the two sur5432
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FIGURE 9. Comparison of model calculations and experimental results of hydraulic conductivity reduction in columns packed with (a) F-65 and (b) F-95 silica and flushed with an ex situ-generated emulsion formed from a 4% Witconol SN 120 solution and PCE.
factant systems. A faster reduction in conductivity occurred with the 4% Witconol SN 120-PCE systems than with the 4% Witconol 2722-PCE systems (Figure 5). Analogously, δdi is larger for the former systems, suggesting the greater formation of aggregates and the enhanced importance of interception as the process of clogging continued. The results presented here suggest that the generation of even a dilute emulsion with a small droplet diameter may significantly reduce the hydraulic conductivity of permeable sands. In the systems examined here, a reduction of about 25% could be attributed to the increase in permeant viscosity and about 50% could be attributed to the clogging of the porous medium through the formation of droplet layers and aggregates. To evaluate the possibility of such an event occurring during SEAR, the formation potential of an emulsion must be considered and the emulsion’s dropletsize distribution, stability, viscosity, and zeta potential must be measured. In the field, however, the emulsion may be generated either ex situ or in situ. If it is formed in situ, it will be formed under different circumstances than those examined here. Such an emulsion might have different characteristics, with, consequently, a different impact on the hydraulic conductivity. In view of this, additional experiments were performed to simulate the phenomenon where the emulsion is formed in the porous medium due to contact between the surfactant solution and the organic contaminant. The results for the in situ column experiments are presented in Part 2 of this series.
Acknowledgments Funding for this work was provided by The Great Lakes and Mid-Atlantic Hazardous Substance Research Center (HSRC) under grant R-815750 from the Office of Research and Development, U.S. Environmental Protection Agency. Partial funding for HSRC research activities was also provided by the State of Michigan Department of Environmental Quality and the U.S. Department of Energy. This paper has not been subject to agency review and therefore does not necessarily reflect the views of the sponsoring agencies, and no official endorsement should be inferred.
Literature Cited (1) Pennell, K. D.; Abriola, L. M.; Weber, W. J., Jr. Environ. Sci. Technol. 1993, 27(12), 2332-2340. (2) Pennell, K. D.; Jin, M.; Abriola, L. M.; Pope, G. A. J. Contam. Hydrol. 1994, 16(1), 35-53. (3) Okuda, I.; McBride, J. F.; Gleyzer, S. N.; Miller, C. T. Environ. Sci. Technol. 1996, 30(6), 1852-1860. (4) Nash, J.; Traver, R. P. Field evaluation of in situ washings of contaminated soils with water surfactants. Proceedings of the Twelfth Annual Research Symposium EPA/600/9-86/022, Cincinnati, OH, 1986. (5) Pope, G. A.; Wade, W. H. In Surfactant-Enhanced Subsurface Remediation; Sabatini, D. A., Knox, R. C., Harwell, J. H., Eds.; American Chemical Society: Washington, DC, 1995; pp 142160. (6) Fountain, J. C.; Klimek, A.; Beikirch, M.; Middleton, M. T. J. Hazard. Mater. 1991, 28, 295-311. (7) Ouyang, Y.; Mansell, R. S. Rhue, R. D. Ground Water 1995, 33, 399-406. (8) Ramsburg, C. A.; Pennell, K. D.; Kibbey, T. C. G.; Hayes, K. F. Trichloroethene emulsification and recovery in saturated porous media. Abst. 288, 76th American Chemical Society Colloid and Surface Science Symposium, June 23-26, 2002, Ann Arbor, MI. (9) Soo, H.; Radke, C. J. Ind. Eng. Chem. Fundam. 1984, 23, 342347.
(10) Hofman, J. A. M. H.; Stein, H. N. Colloids Surf. 1991, 61, 317329. (11) Nutting, P. G. Bull. Am. Assoc. Pet. Geol. 1930, 14, 1337-1349. (12) Allred, B.; Brown, G. O. Ground Water Monit. Rev. 1994, 14(2), 174-184. (13) Jain, V.; Demond, A. H. J. Contam. Hydrol. 1999, 40, 25-35. (14) McAuliffe, C. D. J. Pet. Technol. 1973, 25, 721-726. (15) Spielman, L. A.; Goren, S. L. Ind. Eng. Chem. 1970, 62(10), 1024. (16) Jain, V. Hydraulic conductivity reduction in surfactant-enhanced aquifer remediation due to emulsification, Ph.D. Dissertation, Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, MI, 2000. (17) Soo, H.; Radke, C. J. Chem. Eng. Sci. 1986, 41(2), 263-272. (18) Soo, H.; Williams, M. C.; Radke, C. J. Chem. Eng. Sci. 1986, 41(2), 273-281. (19) Elimelech, M.; O’Melia, C. R. Environ. Sci. Technol. 1990, 24(10), 1528-1536. (20) Song, L.; Elimelech, M. Colloids Surf. A: Physicochem. Eng. Aspects 1983, 73, 49-63. (21) Liu, D.; Johnson, P. R.; Elimelech, M. Environ. Sci. Technol. 1995, 29(12), 2963-2973. (22) Darby, J. L.; Lawler, D. F. Environ. Sci. Technol. 1990, 24, 10691079. (23) Canter, L. W.; Sabatini, D. A. Int. J. Environ. Stud.: Sect. A Environ. Stud. 1994, 46(1), 35-57. (24) Knox, R. C.; Canter, L. W. Water, Air, Soil Pollut. 1996, 88, 205226. (25) Demond, A. H.; Lindner, A. S. Environ. Sci. Technol. 1993, 27(12), 2332-2340. (26) Lide, D. R. CRC Handbook of Physics and Chemistry, 74th ed.; CRC Press: Boca Raton, FL, 1993. (27) Vargaftik, N. B.; Volkov, B. N.; Voljak, L. D. J. Phys. Chem. Ref. Data 1983, 12(3), 817-820. (28) Riddick, J. A.; Bunger, W. B.; Sakano, T. K. Organic Solvents: Physical Properties and Methods of Purification, 4th ed.; Wiley: New York, 1986. (29) Pennell, K. D.; Adinolfi, A. M.; Abriola, L. M.; Diallo, M. S. Environ. Sci. Technol. 1997, 31(5), 1382-1389. (30) Desai, F. N.; Hammad, H. R.; Hayes, K. F. Langmuir 1993, 9(11), 2888-2894. (31) Desai, F. N.; Demond, A. H.; Hayes, K. F. In Transport and Remediation of Subsurface Contaminants: Colloidal, Interfacial, and Surfactant Phenomena; Sabatini, D. A., Knox, R. C., Eds.; American Chemical Society: Washington, DC, 1992; pp 133148. (32) Demond, A. H.; Desai, F. N.; Hayes, K. F. Water Resour. Res. 1994, 30(2), 333-342. (33) Salehzadeh, A.; Demond, A. H. J. Environ. Eng. 1999, 125(4), 385-388. (34) van Genuchten, M. Th. Soil Sci. Soc. Am. 1980, 44, 892-898. (35) Adamson, A. W. Physical Chemistry of Surfaces, 5th ed.; Wiley: New York, 1990. (36) Bentsen, R. G.; Anli, J. Soc. Pet. Eng. J. 1997, 17(1), 57-64. (37) Soo, H. Ph.D. Dissertation, University of California, Department of Chemical Engineering, Berkeley, CA, 1983. (38) Oliveira, I. B.; Demond, A. H.; Salehzadeh, A. Soil Sci. Soc. Am. J. 1996, 60(1), 49-53. (39) Taylor, G. I. Proc. R. Soc. London, Ser. A: Math. Phys. Sci. 1932, 138(A834), 41-48. (40) Darby, J. L.; Attanasio, R. E.; Lawler, D. F. Water Res. 1992, 26(6), 711-726. (41) Clausnitzer, V.; Hopmans, J. W. General-purpose optimization code based on the Levenberg-Marquardt algorithm; Land, Air, Water Resources paper, #100032, University of California at Davis, Davis, CA, 1995.
Received for review October 31, 2001. Revised manuscript received August 23, 2002. Accepted September 11, 2002. ES0113955
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