Environ. Sci. Techno/. 1995, 29, 2208-2216
ln Situ Mixed ReBian Vapor Stripping in Low-VenneaiiIity Media. -3. Modeling of Field Tests -
J O H N S. GIERKE,*m+ CONGLI WANG,' OLIVIA R. WEST,* AND R O B E R T L . SIEGRIST'as Department of Geological Engineering and Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, Michigan 49931 -1295, and Enuironmental Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831 -6036
A model was developed to simulate in situ mixed region vapor stripping process for removal of volatile organic compounds from fine-textured soils. The model consists of a thermal response submodel for simulating temperature, moisture content, and exhaust gas flow rate changes in the zone being treated and a mass transfer submodel for predicting the removal of a linearly sorbing, dissolved contaminant. The treatment zone was assumed to be completely mixed. Model parameters were obtained independently from measurements and literature correlations where possible. Calibration of first-order mass transfer rates and energy input (via soil mixing and high-pressure gas injection) was completed using the results of a full-scale field test. The calibrated thermal submodel predicted temperature changes observed in three other field tests. The model could simulate contaminant mass removal rates for the three independent tests. The mass transfer rates, ranging between 0.0009 and 0.0015 s-l, were independent of temperature up to at least 30 "C but appeared to be dependent on the frequency of contact between the mixing blades and various soil depths.
Introduction This is the third paper in a three-part series that describes the results of a comprehensive study of mixed region vapor stripping (MRVS), a coupled process (insitu mixing and vapor stripping enhanced extraction) for removing volatile organic compounds (VOCs) from fine-grained soils. The comprehensive study consisted of laboratory experiments, field testing, and mathematical modeling. The first paper * Corresponding author e-mail address:
[email protected]; Fax: (906) 487-3371. +
Michigan Technological University.
* Oak Ridge National Laboratory.
5 Present address: Environmental Science and Engineering Division, Colorado School of Mines, Golden, CO, 80401-1887.
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(1)describes the treatment process and laboratory results. The results of full-scale field experiments are given in the second paper (2). This paper focuses on the development of a theoretical model for analyzing the field results and process operation. The goal of the modelingwas to develop a design tool that could be used to evaluate treatment performance and enhance system operation. Existing thermal transport models have focused primarily on steam injection processes (cf.ref 31, which began in the petroleum industry for enhancing oil recovery. The codes developed for simulating steam injection and oil withdrawal are either proprietary or do not consider a particular organic liquid species when its amount is below residual saturation. Most do not account for mass transfer between the soil and sweeping-gas phase. The focus of remediation research on thermally enhanced fluid injectionprocesses has been toward mobilizing VOCs at residual saturation. For example, Hunt et al. (4) developed a model to account for one-dimensional movement of a thermal front in a saturated, homogeneous soil system. Dissolved- and sorbed-phase contamination were not considered, and the model assumes that only saturated steam is injected. A model called STMVOC was developed to simulate three-dimensional movement of three fluid phases (air, water, and organic) in a porous system undergoing rapid temperature changes (3). Chemical and thermal equilibrium between the fluid phases and the soil were assumed. Sorption was assumed to be independent of temperature. Both of these models were developed for simulating VOC removal from granular materials where it is likely that chemical equilibrium may be a valid approximation. A first-generation MRVS model was developed by Gierke etal. (5)based on the assumption of instantaneous chemical equilibrium. The chemical equilibrium model predicted that injecting hot air enhanced the removal only if the compound was strongly sorbed andlor its Henry's partition coefficient was low. Otherwise, the contaminant was removed with the exhaust gas prior to any increase in soil temperature. Heating enhancement is realized primarily when a liquid organic phase is present; the focus of their work was on removing dissolved levels of contamination so the organic phase was neglected. Although their model could be calibrated to laboratory data, the calibrated model could not predict results at different flow rates (6). It is common that the removal of VOCs from clay soils is rate limited (1, 2, 7, 8 ) . This paper describes the development of a heat and mass transfer model for simulating the MRVS process. Model predictions were compared to results from four field tests.
Model Development The model was developed to simulate the results of isolated soil column field tests at theX-231B site (2),the conceptual basis of which is depicted in Figure 1. During the isolated soil column tests, the sides and bottom of the mixed regions were surrounded by undisturbed soil, which was assumed to act as a barrier to heat and mass transfer. Therefore, energy and mass were removed only with the exhaust gas. In the field, very little heat was lost to the surrounding soil (2).
0013-936X/95/0929-2208$09.00/0
0 1995 American Chemical Society
third term is additional heat input from mixing and other sources. The right side of eq 1 represents the total change in internal energy of the treatment zone. The notations are defined in the Glossary. The internal energies werc assumed to varylinearlywith temperature, i.e., dui/dT= q,where j = a (dry air), s (soil solids), v (water vapor). and w (liquid water). The linear approximations for u, and u, were checked against correlations developed by the 1967 International Formulation Committee (IFC) that account for actual changes with temperature (in ref 12). The maximum deviation for water vapor was only 2% for temperatures up to 100 "C. and the deviations were negligible for liquid water. The internal energy of air is negligible relative to water and soil. Water content changes in the treatment zone were determined according to FIGURE 1. Conceptual picture of the MRVS process that was used to develop the mathematical model.
In general, the model is applicable for the removal of contamination in the vapor, dissolved, and sorbed phases with concentrations up to 500-2000 mg kg-I. It does not account for the presence of a liquid organic phase. The soil underlying the X-231B site was predominantly contaminatedwithtrichloroetheneCTCE),1,l.l-trichloroethane (l,l,l-TCA), and 1.1-dichloroethene (1,l-DCE) at concentrations below 100mg kg-l(S). Nonaqueous phase liquids (NAF'Ls) were not suspected to be present at these contamination levels. Heat transfer due to VOC volatilization was negligible due to the low concentrations of VOCs. Therefore a separate thermal submodel was developed independent of the VOC transport submodel. Thermal submodel predictions were used in the VOC submodel calculations through the temperature dependence of some of the parameters. ThermalSubmodeL Thermalequilibriumwasassumed toexist inside the treatmentzone. Because the contaminant concentrationswere low, the internal energy,enthalpy, and partial pressureoftheorganiccontaminantswere neglected. A previous field demonstration of MRVS observed that moisture movement was minimal during treatment operation (10).soonlythegasphasewasconsidered tobemobile. Porosity during mixing was assumed to be constant. In addition to hot air and steam, there are several other potential external sources of heat. For example, heat is generated bythe frictional forcesbetween the mixingblades and soil and from the dissipation of the high-pressure/ bigh-velocity gas injection. Other potential heating methods include radio frequency and resistance heating (cf. ref 11). The model allows for a constant rate of energy input fromthesumofallsourcesothertbanthehotgasinjection. An energy balance on the treatment zone depicted in Figure 1 yields
The left side of eq 2 represents the net difference between the rates ofwater vapor entering and leaving the treatment zone, which is equal to the total water mass accumulation rate in the treatment zone. Inside the treatment zone and in the exhaust gas, the water vapor pressure was assumed to be equal to the saturation pressure, i.e., a constant state of 100% relative humidity, which is reasonable for finegrained soils in proximity to the water table. The IFC formulation was used to calculate the saturation pressure of water vapor. The following is a material balance on the gas phase:
dt
1-1
The left side of eq 3 represents the net difference between the rates of air entering and leaving the treatment zone, which is equal to the rate of air mass accumulating in the treatment zone. For this derivation, it was assumed that the solubilities of the gases comprising air were negligible and that the VOC vapor pressure was negligible compared to the air and water vapor pressures. Given that the maximum totalgas concentrationofVOCsduringtreatment was below 1000ppm by volume (213,the maximum error introduced by ignoring the VOC partial pressure was less than 0.1%. Air and water vapor were assumed to behave as ideal gases. Dalton's law for partial pressures resulted in the following expression for calculating ea as a function of T and ev (4)
The first term on the left side of eq 1is the enthalpy input rate with the gas injected into the treatment zone, the second term is the rate exiting with the exhaust. and the
The total gas pressure (9) during the field tests was essentially constant (13). Forthe thermalsubmodel, theprimarytime-dependent variables are Q, T, and 8,. The system of equations was linear in terms of three unknowns (Q, dT/dt, and dO,/dr). so it was rearranged to solve for each unknown explicitly. The operating conditions (Q, ID,eao,eve, and 0 ) ) were input as constants. The other input parameters (C, C,, C, C, Pb V, e$,e,, 8, and Om) were assumed to remain constant. The initial conditions are given by VOL. 29. NO. 9.19951 ENVIRONMENTAL SCIENCE &TECHNOLOGY.
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T(t = 0) = T
and
8Jt = 0) = 8',
(5)
A fourth-order,Runge-Kutta integratorwas used to solve eqs 1-4. The thermal submodel calculates the temporal change in the exhaust gas flow rate and treatment zone temperature and water content. Neither temperature nor water content change rapidly under complete mixing conditions. The calculated values are then used in the VOC removal submodel calculations. The thermal modeling approach described above is a simplification of that used by Falta et al. (3). The primary reason for simplifying their approach was the current lack of understanding of flow and heat transport in fine-textured soils undergoing mixing, which precluded the use of higher levels of sophistication. For example, moisture characteristic curve and relative permeability parameters are important input for multiphase models, but these parameters cannot be estimated for a MRVS system with a reasonable level of confidence. VOC Removal Submodel. The VOC removal submodel accounts for the thermal transport of a single, nondegradable VOC. Biological degradation was ignored because treatment times were always less than 2 h (2). The model is applicable only when the VOC vapor concentrations are below the pure vapor pressure, which corresponds to total soil concentrations less than 500-2000 ppm. Removal rates were assumed to be mass transfer limited. In contrast, most models for thermally enhanced transport assume chemical equilibrium exists between the sorbed, gaseous, aqueous, and liquid phases (3). Because of the tendency of fine-grained soils to aggregate, especiallywhile being physically mixed, it is reasonable to expect the VOC removal rate to be limited by nonequilibrium processes (1, 7, 8). A first-order mass transfer mechanism was used to account for nonequilibriumvapor transport. It is a lumped method, which can be used to account for the combined effects of gas diffusion between and inside aggregates,liquid diffusion through the aqueous phase, and sorption kinetics. The first-order mass transfer approach was satisfactory for describing the laboratory treatability results ( I ) . The VOC submodel equations were derived by dividing the completely mixed treatment zone into two regions as depicted in Figure 1. The interaggregate region is space created from the mixing and contains mobile gas (air,water vapor, and vapor-phase VOC), 'The intraaggregate region is the intact soil aggregates, which are composed of immobile gas, water, soil solids, and VOC partitioned between all three phases. The interaggregate mass balance is
dC -QC - aV(C - pS) = V&dt
The first term on the left side of eq 6 represents the rate of VOC removed from the treatment zone, and the second term represents the mass transfer rate between the interand intraaggregate regions. The right side of eq 6 is equal to the VOC mass accumulation rate in the interaggregate gas. It was assumed for this derivation that VOC exited the treatment zone only with the stripping gas and that no additional VOC entered the treatment zone from the surroundings. In addition, air-water-soil phase parti2210
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tioning inside the aggregates was assumed to be always in equilibrium. Sorption was assumed to occur from the aqueous phase. The MRVS process is typically used in finegrained soils where it is likely that liquid water covers all of the solid surfaces, so vapor sorption should be unimportant (14). Linear sorption from the gas phase could be included by modifying the definition of fi. Accordingly, the intraaggregate mass balance is
dS
a(C - pS) = e,(l - 8)dt
(7)
The right side of eq 7 represents the rate of change in intraaggregate VOC mass. The initial condition for the VOC removal submodel is
S ( t = 0) = S'
and
C(t = 0) = PS'
(8)
The same fourth-order, Runge-Kutta routine that was used to solve the thermal submodel was also applied to eqs 6 and 7. A n analytical solution to eqs 6 and 7 exists for constant flow rate, temperature, and moisture content (6). Agreement between the numerical solution for isothermal conditions (timestep = O.OOlV/Qo) and the analytic solution was within 2%. Many parameters for chemicalfate and transport models are functions of temperature. Therefore, correlations that are used for parameter estimation become an inherent part of a thermal model. The results and conclusions of this work are based, in part, on the literature correlations reported below. These correlations were chosen based on theoretical justifications and the extent of independent validation and accepted use. If different correlations were used, modifications of the numerical code would be necessary. Ashworth et al. (15) measured Henry's constants over a temperature range of 10-30 "C and fit the results to a van7 Hoff-type expression, resulting in the following correlation
H=- lo' 325 exp(A RT Kerfoot (16) demonstrated the use of conventional thermodynamic equations based on Gibbs free energy change and the Gibbs-Helmholtz equation for estimating the effects of temperature on partitioning in soil-watergas systems. The integration of these thermodynamic equations result in van't Hoff type expressions. The expression for sorption is (10)
Temperature correlations for estimating mass transfer coefficients for soil systems do not exist. If nonequilibrium is due to Fickian diffusion inside aggregates, there is an equivalencyrelationship between spherical, intraaggregate effective diffusion and first-order mass transfer (cf. ref 1). Diffusion rates can be adjusted for temperature using literature correlations. West etal. (1,s)were able to describe the removal of TCE from laboratory MRVS experiments at different injection flow rates with a single value of a. Carpenter (8)observed no effects of temperature on mass transfer in laboratory column experiments of MRVS at 25 and 50 "C. The field test results presented herein could
TABLE 1
Physical and Thermodynamic Properties of Air, Water, Trichloroethene (TCE), and the Minford Soil at the X9231B Site Dry Air 0.71 kJ k g - l K-’ 29 k g k m o l - l 60 kJ k g - l
ca
Ma ua at 293 K Water
1.4 kJ kg-l K-’ 4.2 kJ kg-l K-’ 18 k g k m o l - l 2394 kJ kg-l 83 kJ k g - l 999 k g m-3
cv
c, M W uv at 293 K uwat 293 K ew
Soil Solids
cs
0.92b kJ kg-’ K-l 285OCk g m-3 0.40d
es
e
Trichloroethene (TCE) A B Kd Tb
7.845e 37Q2e K 0.41 cm3 g-l 360g K
a Unless otherwise noted, the parameter values can be found in a standard thermodynamic text. Ref 17. Ref 8. Ref 13. e Ref 15. ‘Ref 1. g Ref 18.
also be simulated with a single value of a, so temperature effects on mass transfer were not considered.
Parameter Estimation The model parameters are based primarily on basic soil and chemical properties. For many of the parameters, there is a lack of measurements at different temperatures, and this is especiallythe case for sorption equilibrium and mass transfer parameters. The parameter estimation focuses on estimating properties of the vadose zone soils at the X-231B site (1,2). The vadose zone at the X-231B site comprises a fluvio-lacustrine silty clay (Minfordclay),and the organic carbon content is in the range of 0.05-0.10 wt % (1). Table 1 lists the pertinent physical and thermodynamic properties of air, water, and soil used in the model input. The properties for air and water were available from the literature. Measured saturated water contents (13)and the measured solid density (8) were used to estimate the soil porosity prior to mixing. The porosity of the mixed region (see Table 2) was based on volume estimates of the berm created during mixing (2). A typical value for the specific heat of clay (0.92 kJ kg-l K-l) was used for all the model simulations. Ninety-five percent of the contaminant mass at the X-231B site was predominantly l,l,l-TCA, TCE, and 1,lDCE. These compounds are highly volatile and sorb to soils moderately ( K < 1 cm3 g-l). The relative fractions of these compounds in the off-gas varied erratically with no clear trend for any of the field tests. This was probably due to the heterogeneous distribution of contaminants throughout the X-231B site. Therefore, it was deemed impractical to attempt to model each target compound separately.West et al. (1)used TCE in their experiments, and Siegrist et al. (13) reported that more than 50% of the VOCs removed during the field tests was TCE. For these reasons, the parameters for TCE were used to represent total VOCs.
Table 1also lists the physicochemical properties of TCE that are pertinent to the model calculations. The parameters A and E for eq 6 are tabulated in Ashworth et al. (15) for 45 organic chemicals. West et al. (1)measured a value of & for the Minford clay in an isotherm experiment. Heats of adsorption are usually not available for wet soils. Under oven-dry conditions, Ong and Lion (14) measured a AH of 54 kJ mol-’ for TCE onto kaolinite and 46 kJ mol-’ for montmorillonite. In the absence of experimental measurements, Kerfoot (16) proposed that the enthalpy change for vaporization can be used as a lower limit for vapor sorption and an upper limit for aqueous sorption. The enthalpy change for vaporization can be estimated from Trouton’s rule:
Equation 11 predicts that AH for TCE is 32 kJ mol-’. Four of the seven treatment columns reported by Siegrist et al. (2, 13) were used to test the model: two ambient air injection (IE1 and IE2) and two hot air injection (TE1 and Dl). A summary of the operating conditions for the four tests is listed in Table 2. Three of the columns (IE1, IE2, and TE1) were treated to a depth of 4.6 m, and D1 was treated to a depth of 6.7 m. Each of the four columns was isolated from the others during treatment. Each column was 3.0 m in diameter and treated in two stages. For the first hour, the upper 2.1 m of each column was treated (stage 11, after which was followed immediately by almost three more hours of treatment of the entire depth (stage 2). Injection and extraction gas flow rates, pressures, and temperatures were measured at least five times during a test. The injection flow rate measurements were more accurate, and so they were used in the model calculations. The average shroud pressure was 100.6 kPa (1k0.6&a). The initial soil temperature for stage 1 was taken as the average of several thermocouple measurements employed around the columns. The initial water contents were determined from gravimetric analysis of soil probe samples. The average posttreatment water contents were less than 10% higher than the pretreatment measurements, so an average of all values was used. Total VOC concentration in the off-gas was measured every 10 s with a flame ionization detector (FID)calibrated to methane. Periodically, at least five times per test, offgas samples were analyzed by gas chromatography (GC). Since the GC measurements were fewer and more variable (131,the FID response was used for comparing to the model simulations. Because there is no method for independently estimating the mass transfer coefficient for this system, the results of the IEl soil column test were used for calibration.
Results and Discussion Since the thermal response was assumed to be independent of the contaminant removal, the temperature response of each column was considered first. Once the temperature response was satisfactorily described by the thermal submodel, VOC removal for each column was simulated. Simulations of stage 1 and stage 2 operations were performed sequentially. First, stage 1 operation was simulated according to the conditions given in Table 2. The conditions predicted at the end of stage 1were assumed to pertain to the upper 2.1 m depth. The initial conditions for stage 2 were obtained by “mixing”the stage 1zone with VOL. 29, NO. 9,1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY I 2 2 1 1
TABLE 2
MRVS Process Conditions for Field Tests at the X=231B Site ambient core 1 (IEl) parameter
ambient core 2 (IE2)
thermal core 1 (TE1)
deep thermal core (01)
stage 1
stage 2
stage 1
stage 2
stage 1
stage 2
stage 1
stage 2
29 1 0.32 0.15 0.40 302 15.6 0.67 63 1.14 0.01 1 60 2407 80 0.0015 100
295 0.32 0.15 1.1 302 33.4 0.67 165 1.14 0.01 1 60 2407 80 0.0009 100
29 1 0.32 0.15 3.6 302 15.6 0.73 74 1.14 0.01 1 60 2407 80 0.0015 100
295 0.32 0.15 4.4 302 33.4 0.73 165 1.14 0.01 1
290 0.32 0.15 0.20 394 15.6 0.89 61 0.89 0.0087 56 2535 80 0.0015 100
299 0.32 0.15
292 0.32 0.15 1.6 394 15.6 1.o 77 0.89 0.0087 56 2535 80 0.001 5 100
290 0.32 0.15 1.8 394 49.0 1.o 165 0.89 0.0087 56 2535 80 0.0009 100
60 2407 80 0.0009 100
0.90 394 33.4 0.89 165 0.89 0.0087 56 2535 80 0.0009 100
a Measured by Siegrist et a/. (2,73). Taken from extrapolation of mass removal curves reported by Siegrist et a/. ( 2 ) . Volumetric injection flow rate reported by Siegrist et a/. ( 2 )adjusted to P. E q 4 for ev = evn(other parameters listed in Table 1). e Corresponding to an assumed ambient air relative humidity of 60% at 20 “C.‘Based on linear change in internal energy for T = P (other parameters listed in Table 1). 9 Value calibrated to I E l stage 1 temperature data from Siegrist et a/. (73). *Values calibrated to both stages of I E l mass fraction removed data obtained from Siegrist et a/. (2).
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the lower zone. The conditions of the lower zone were assumed to be the same as pretreatment. Thermal Response. The soil temperature in each soil column was assumed to be equal to the off-gastemperature (Figure 2). The injection air temperature was measured before entering the kelly bar, and it increased approximately linearly from the beginning of treatment to the end for 2212
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both of the ambient air columns (2, 13). Except for an initial 30-min lag, the temperature of the off-gas also increased almost linearly during each stage (IE1 and IE2, Figures 2a and 2b). The initial lag was probably due to thermal equilibration of the shroud and off-gas piping, where the off-gas temperature was monitored with the offgas. Even though the injection temperature varied, prob-
ably also due to thermal equilibration of the injection piping with the injection air, the injection temperature had no effect on the model-predicted thermal response because the heat content of the injection air was so low. When w = 0, the model predicted a temperature increase of less than 2 "C for the duration of ambient treatment. To account for the observed temperature increase, the value of the mixinglinjection power (w)was adjusted. The apparent linear temperature increase indicates that a constant value of w is appropriate for the completelymixed modeling approach. Calibration of the thermal submodel to the IEllstage 1 data resulted in a value of w = 80 kW, and this value was used in all subsequent model predictions. The drop in temperature after 60 min of treatment was due to cooling of the air as the mixing blades first penetrated the second stage and came in contact with cooler soil. The initial temperature of stage 2 was based on a mixing calculation of the soil temperature in the upper 2.1 m depth at the end of stage 1 and the pretreatment temperature of the soil between 2.1 m and the ultimate treatment depth (either 4.6 or 6.7 m deep). The thermal submodel prediction shown in Figure 2b was based on measured conditions for the IE2 column and the IE1 /stage 1 calibration of w. The model overestimated the temperature by at most 6 "C. The slopes of the two curves are about the same, which implies that the calibrated value of w is appropriate. Hot air injection consisted of ambient air heated to approximately 121 "C. The model prediction of the TEl thermal response is shown in Figure 2c. The initial drop in air temperature was due to cooling of the shroud and off-gas piping, which were heated to about 35 "C prior to the commencement of soil mixing (13). The hot air injection caused larger increases in soil and off-gas temperatures, approximately twice as much as the ambient air injection. For the D1 core, hot air was not injected into the soil until after the mixing blades penetrated the ground surface. The model prediction of the D1 thermal response is shown in Figure 2d. It is likely that the calibrated w = 80 kW is due to some combination of friction and pressure dissipation of the injection gas. Neglecting energy losses in the gas delivery system, the change in pressure and kinetic energy of the injection gas as it passed through the mixing blade nozzles would correspond to about 5 kW for the ambient air conditions and 100kWfor hot air. Iffrictionwas responsible for the 80 kW of power input, then the friction factor between the soil and mixing blades would have to be approximately 0.4, which corresponds to an average depth of 2.1 m and an average rotational speed of 7.5 rpm. VOC Removal. The FID-determined mass removal curves for tests IE1, IE2, TE1, and D1, which are shown in Siegrist et al. (2),were normalized by the initial total mass ofVOCs in each column. The initial total VOC mass values (see VOC mass listed under columns labeled stage 2 in Table 2) were determined from extrapolating the FID responses to an assumed asymptotic value based on the trends in the mass removal data. For example, the initial total VOC mass for column IE1 was approximately 1.1 kg based on extrapolation of the total mass removal curve. The initial masses in the stage 1 treatment volume for each test (Le., the upper 2.1 m depth) were estimated in the same manner. For example, the initial mass in stage 1/IE1
was approximately 0.4 kg based on the mass removal trend during stage 1. The initial total VOC mass for the stage 2 operation was calculated by subtracting the model estimate of the mass removed at the end of stage 1 from the total pretreatment mass. From a model validation/design guidance standpoint, it would have been preferable to estimate initial masses or concentrations based on the soil sample measurements. The measured soil sample concentrations from Siegrist etal. (13)resulted in mass estimates that were more than a factor of 2 lower than what was removed based on the off-gas FID monitoring. A value of a for TCE was calibrated to the observed removal during the first stage of IE1 (al= 0.0015 s-l). All other parameters were determined independently as described above and listed in Tables l and 2. The criteria used in the calibration of the first stage was to simulate the mass removed at the end of stage 1. The value of alwas then used to predict the second stage removal, which is shown by the dashed line in Figure 3a (al= a2= 0.0015 s-9. Even though the rotational speed of the mixing blades was doubled, the observed mass removal rate for stage 2 was lower than the rate for stage 1. A different value of the mass transfer coefficient was calibrated to the observed removal from stage 2 (a2 = 0.00090 s-l), and this value was used to predict the stage 1 removal (dotted curve). The stage 2 calibration was performed by adjusting a2 until the model simulations agreed with the data according to the graphical appearance. The separate calibrations of stage 1 and stage 2 are shown by the solid curve. The values of a1 and a2calibrated to IE1 were used to predict the IE2 mass removal as shown in Figure 3b. All other parameters were obtained independently as before. The maximum difference between the model prediction and the observed removal was about 12%. The model prediction of TCE removal during the TE1 test is shown as a solid line in Figure 3c. The mass transfer coefficients used in the model for each stage were those calibrated in the IE1 test. In this case, the model predicted a faster removal for stage 2 than what was observed. The model prediction of the stage 1 removal agrees about as well as for IEl andtIE2. This is because during stage 1 of TE1, the treatment zone temperature had not increased enough to affect the removal rate. For the second stage, however, the model predicted that the observed temperature increase would increase the removal rate, primarily due to increases in H (the effect of decreasing K was less than 1%). To assess the effects of temperature, a model prediction for isothermal conditions is shown as a dashed curve. For the isothermal calculation, the injection temperature was reduced to the initial soil temperature so that the predicted temperature did not increase during treatment. The isothermal prediction resulted in a better agreement with the data, which implies that although the injection of hot air increased soil temperatures significantly, the removal rate was not affected by the temperature increase as much as might be expected according to theoretical changes in H. If a NAPL phase was present, then a greater temperature effect might have occurred. Figure 3d depicts the results from the deepest treatment (core Dl). Almost 80%oftheVOCmassremovedfromD1, however, was from the upper 2.1 m. The model prediction shown by the solid curve includes temperature effects on Hand K,while the dashed line is a prediction for isothermal operation. VOL. 29, NO. 9,1995 /ENVIRONMENTAL SCIENCE &TECHNOLOGY 2213
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Time (minutes)
FIGURE 3. Model simulations of the VOC removal during four field tests reported by Siegrist et a/. (2):(a) calibration of first ambient air injection test (IEl); (b) calibrated model prediction of second ambient air injection test (IE2); (c) calibrated model prediction of first heated air injection test for both non-isothermal end isothermal conditions (TE1); and (d) calibrated model prediction of deep heated air injection test (Dl) for both non-isothermal and isothermal conditions. The soil properties are listed in Table 1, the chemical properties are listed in Table 2, and the operating conditions for each test are listed in Table 2. The modeled treatment zone temperatures for the non-isothermal conditions are depicted in Figure 2. The field data are measured off-gas total VOC concentrations. The mass transfer coefficients (a,, a*) were calibrated to the observed removal rates during both stages of the IEl test.
Due to a lack of field tests at other operating conditions, it is difficult to attach physical significance, such as intraaggregate molecular diffusion, to the mass transfer rates calibrated to the field results. The calibrated mass transfer coefficients however were consistent with previously reported values. The mass transfer coefficient calibrated to the laboratory data was 0.0038 ( I ) . In controlled column experiments of TCE removal from aggregated Minford soil, Carpenter (8) observed a mass transfer rate of 0.0008 s-l, which was attributable to intraaggregate liquid diffusion. Sawhney and Gent (7) measured mass transfer rates for nonlinear desorption of TCE from dry (0% relative humidity) illite and montmorillonite in the range of 0.000083-0.00022 s-l. Brusseau et al. (19) correlated first-order desorption rates to sorption partitioning in aqueous-soil systems. Based on their data, the mass transfer coefficient for TCE would be approximately 0.003 s-l for the Minford soil. Since the apparent mass transfer rate was different for stages 1 and 2 but consistent among the soil columns, it is logical to assume that the removal rate may be a function of the operating conditions or treatment volume. A simple explanation might be the fact that during stage 1a particular depth encountered the mixing blades-where the stripping gases were injected-on an average of every 7 min, while during stage 2 the frequency of contact was every 15 min (2). The calibrated mass transfer coefficients for stages 1 2214
ENVIRONMENTAL SCIENCE &TECHNOLOGY / VOL. 29, NO. 9. 1995
and 2 correspond to characteristic times (a-9 of 11 and 18 min, respectively. The fact that there was only nominal enhancement of VOC removal from hot air injection also suggests that the frequency of bladelsoil contact was governing the removal rates.
Conclusions A completely mixed model was able to simulate temperature changes and the removal of a mixture of dissolved-phase volatile organic chemicals from an unsaturated, finetextured soil treated by mixed region vapor stripping. Thermal equilibrium between the stripping gas and the mixed soil was avalid assumption for describingthe general trend of the off-gas temperature evolved from the treatment zone. The observed temperature increases in the field tests were not solely due to the injection air temperature but were also due to energy input from mixing and highpressure gas injection. A first-order mass transfer rate and linear vapor-water-soil partitioning were appropriate approaches for modeling the contaminant mixture removal. On the basis of simulations of four field tests, it was determined that the mass transfer coefficient was dependent on the treatment volume and independent of temperature up to at least 30 "C. The mass transfer rate was probably a function of the process operating conditions
such as the rate of motion of the mixing blades.
Acknowledgments Support for this research was provided by Martin Marietta Energy Systems, Inc. (Contract 19X-SJ598V) and by U.S. Department of Energy Environmental Restoration Distinguished Junior Faculty Award Program. The results and conclusionspresented herein are the opinions of the authors and do not necessarily reflect the views of the U.S. Department of Energy nor Martin Marietta Energy Systems, Inc. The report has been authored by a contractor of the U. S. Government under Contract DE-AC05840R21400.
V
Greek Letters a
B Qa @ao QS
QV
QVO QW
Glossary dimensionless parameter used for estimating H parameter used for estimating H (K) VOC concentration in the (mobile) gas phase (mg L-9 constant volume specific heat of air (kJkg-' K-1) specific heat of soil (kJ kg-l K-l) constant volume specific heat of water vapor (kJ kg-' K-I) specific heat of liquid water (kJ kg-I K-I) dimensionless Henry's law partition coefficient molar heat of adsorption (kJ kmol-I) temperature varying sorption partition coefficient based on equilibrium with the aqueous phase (m3kg-') ambient temperature (20 "C) sorption partition coefficient based on equilibrium with the aqueous phase (m3 kg-I) air molecular weight (mol g-l) water molecular weight (mol g-l) total gas pressure in the treatment zone (Pa) volumetric flow rate of the exhaust gas (m3 s-1) volumetric flow rate of the injection gas (m3s-1) ideal gas constant (kJ kmol-' K-l) treatment zone VOC concentration in the soil (mg g-9 pretreatment VOC concentration in the soil (mg
g-9 time (s) treatment zone and exhaust gas temperature (K) VOC boiling point (K) pretreatment temperature of the treatment zone (K)
temperature of the injection gas (K) specificinternal energy of the air in the treatment zone and exhaust gas (kJ kg-') specific internal energy of the air in the injection gas (kJ kg-9 specific internal energy of the soil solids in the treatment zone (kJ kg-l) specific internal energy of the water vapor in the treatment zone and exhaust gas (kJ kg-l) specific internal energy of the water vapor in the injection gas (kJ kg-l) specific internal energy of liquid water in the treatment zone (kJ kg-l)
pretreatment volume of the soilbeing mixed (m3)
e 6,
OW
ewi w
overall mass transfer coefficient (s-l) ~ ~- e( ) 1w e - eWw+eW+ Q,(i- e m (g ~ - 1 ) air density in the treatment zone and exhaust gas (kg m-3) air density in the injection gas (kg m-3) soil solid density (kg m-3) water vapor density in the treatment zone and exhaust gas (kg m-3) water vapor density in the injection gas (kg m-3) liquid water density (kg mT3) soil porosity, void volume per volume of soil being treated (m3 m-3) mixed porosity, void volume created by mixing per volume of soil being treated (m3 m-3) water content, water volume per volume of soil being treated (m3m-3) pretreatment water content of the treatment zone (m3m-3) heat supplied to the treatment zone in addition to the heat from the injection gas (N m s-l)
Subsci-2ts air, considered to be only the gases comprising dry air S soil solids water vapor and liquid, respectively v, w stages 1 and 2, respectively 1, 2 Superscripts initial and injection conditions, respectively i, o a
literature Cited (1) West, 0. R; Siegrist, R. L.; Gierke, J. S.; Schmunk, S. W.; Lucero, A. J.; Jennings, H. L. Environ. Sci. Technol. 1995,29,2191-2197. (2) Siegrist, R. L.; West, 0. R.; Morris, M. I.; Pickering, D.A.; Greene,
D. W.; Muhr, C. A.; Davenport, D. D.; Gierke, J. S. Environ. Sci. Technol. 1995, 29, 2198-2207. (3) Falta, R. W.; Pruess, K.; Javandel, I.; Witherspoon, P. A. Water Resour. Res. 1992, 28, 433-449. (4) Hunt, J. R.; Sitar, N.; Udell, K. S. Water Resour. Res. 1988, 24, 1247- 1258. (5) Gierke, J. S.; Reyes, 0. M.; Siegrist, R. L. Proceedings of the 1992 Solving Ground Water Problems with Models Conference, Dallas, TX, Feb 11-13,1992; Ground Water Management: Dublin, OH, 1992; pp 397-411. (6) West, 0.R.; Siegrist, R. L.; Jennings, H. L.; Lucero,A. J.; Schmunk, S. W.; Green, D. W. Laboratow Evaluation of In Situ Vauor Snipping; ORNLITM-12260;Oakkidge National iaboratory: Oak Ridge, TN, 1993. (7) Saihney, B. L.; Gent, M. P. N. Clays Clay Miner. 1990,38,14-20. (8) Carpenter, M. D. M.S. Thesis, MichiganTechnologicalUniversity, Houghton, MI, 1994. (9) Siegrist, R. L.; Morris, M. I.; Donaldson, T. L.; Palumbo, A. V.; Herbes, S. E.; Jenkins, R.A.; Morrissey, C. M.; Harris, M. T.X-231B
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Sciences Division Publication No. 3820: Oak Ridge National Laboratory, Oak Ridge: TN, 1993. d e Perch, P. R. 1.Air Waste Manage. Assoc. 1991, 41, 873-877. Smith, L. A.; Hinchee, R. E. In Situ Thermal Technologiesfor Site Remediation; Lewis Publishers: Boca Raton, FL, 1993. Schmidt, E. Properties of Water and Steam in SI-Units; Springer-Verlag: New York, 1982. Siegrist, R. L.; Morris, M. I.; West, 0. R.; Gates, D. D.; Pickering, D. A.; et al. Field Evaluation of Mixed Region Vapor Stripping, Chemical Oxidation, and SolidiJcation Processes; ORNL/TM12261; Oak Ridge National Laboratory: Oak Ridge, TN, 1994.
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(14) Ong, S. K.; Lion, L. W. J. Environ. Qual. 1991, 20, 180-188. (15) Ashworth, R. A.; Howe, G. B.; Mullins, M. E.; Rogers, T. N. J. Hazard. Mater. 1988, 18, 25-36. (16) Kerfoot, H. B. Ground Water 1991, 29, 678-684. 117) Domenico, P. A.; Schwartz, F. W. Physical and Chemical Hydrogeology, 1st ed.; J. Wiley and Sons, Inc.: New York, 1990; p 324. (18) Verschueren, K. Handbook ofEnvironrnentul Data on Organic Chemicals, 2nd ed.; Van Nostrand Reinhold: New York, 1983; p 1132.
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(19) Brusseau, M. L.; Jessup, R. E.; Rao, P.S. C. Environ. Sci. Technol. 1991, 25, 134-142.
Received for review August 24, 1994. Revised manuscript received February 22, 1995. Accepted May 1 d, 1995.@
ES9405437 @
Abstract published in Advance ACS Abstracts, July 1, 1995.
