Modeling for Volatilization and Bioremediation of Toluene-contaminated Soil by Bioventing

BIOTECHNOLOGY AND BIOENGINEERING Chinese Journal of Chemical Engineering, 19(2) 340ü348 (2011)

Modeling for Volatilization and Bioremediation of Toluene-contaminated Soil by Bioventing* SUI Hong (䳁㓘)1,2 and LI Xingang (ᶄ䪡䫘)1,2,** 1 2

National Engineering Research Center for Distillation Technology, Tianjin University, Tianjin 300072, China School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China

Abstract A two-dimensional numerical model is developed to simulate the flow, transport and biodegradation of toluene during bioventing (BV) processes in the unsaturated zones. The simulation for a single well BV system is used to illustrate the effect of air injection rate on remediation efficiency. The air is injected into the vadose zone to create a positive pressure. Simulation results show that air injection rate is a primary parameter governing the dispersal, redistribution and surface loss of contaminant. At injection rates of 81.504 m3·d1 (Run 1) and 407.52 m3·d1 (Run 2), the total removed mass of toluene is 169.14 kg and 170.59 kg respectively. Ratios of volatilization to biodegradation in Run 1 and Run 2 are 0.57Ή1 and 0.89Ή1, respectively, indicating that lower air injection rate enhances the biodegradation efficiency greatly. Air injection rate should be optimized to meet oxygen demand and to minimize the operational cost. Keywords bioventing, biodegradation, unsaturated zone, toluene, remediation

1

INTRODUCTION

The widespread release of petroleum hydrocarbons has led to soil contamination by toxic components such as benzene, toluene, and xylene [1]. Bioventing (BV) technique is a potentially less expensive in-situ bioremediation method for unsaturated soil [2]. A number of mathematical models have been presented in the literature to describe the transport and biotransformation process of contaminants in subsurface. Models considering only gas flow behavior are used for the assessment and design of BV systems [3]. Several two-phase models have been developed, without considering the presence of NAPLs (non-aqueous phase liquids) [4, 5]. Since the BV remediation schemes involve the sites where NAPL residual is present in the vadose zone, the absence of a pure organic phase limits the application of these models. Some models [69] are limited by local equilibrium assumption (LEA). Other models assume the first-order degradation kinetics to describe biotransformation in BV process [1012], which is applicable only if substrate concentration is much less than the half-saturated coefficient. At most sites, the first-order degradation kinetics cannot accurately describe microbial degradation processes [13]. Rathfelder et al. [14] have presented a field scale soil vapor extraction (SVE)/BV simulator including non-equilibrium interphase mass transfer and Monod kinetics for aerobic biodegradation. The model is referred to as the Michigan soil vapor extraction remediation model (MISER), which is the most sophisticated model. In the present paper a two-dimensional model is presented, in which the multicomponent transport, potential mass transfer limitation, and microbial deg-

radation are considered. The model integrates physical, chemical, and biological processes in one framework. Upstream weighted multiple cell balance method is used to solve the two-dimensional contaminant transport in unsaturated zone. The method effectively eliminates the oscillation of numerical solutions with only a nominal increase of formulation effort over that of the general Galerkin finite-element method. 2

MODEL DESCRIPTION

Two parts are included in the model, flow equations and transport equations. Three fluid phases are modeled: organic phase (NAPL), gas phase, and aqueous phase. Non-steady state fluid field will be obtained by solving the flow equations, and pressure distribution will be obtained. Porosity and disperse coefficient distribution solved by Darcy law are used as the initial condition of transport equations. The transport equations include convection, diffusion, interphase mass transfer and biotransformation of multicomponent in a BV system. 3 3.1

MODEL DEVELOPMENT Fluid phase

A number of assumptions are employed to simulate BV systems: the soil is heterogeneous with variable porosity and bulk density, diffusive and convective transport occurs only in the vapor phase, aqueous phase and NAPLs are immobile, no sorption occurs between the gas and solid phases, and the biodegradation occurs only in the aqueous phase. The movement of gas phase is described by a

Received 2010-08-04, accepted 2010-12-09. * Supported by the National High Technology Research and Development Program (“863” Program) of China (2009AA063102, 2007AA061202). ** To whom correspondence should be addressed. E-mail: [email protected]

341

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011

macroscopically averaged flow equation [14]. w IUg Sg  ’ ˜ ª¬ Ug Og  ’Pg  Ug g º¼ Eg  Ug Qg (1) wt where I is the porosity, Ug is the mass density of gas phase, Sg is gas phase saturation, Og kkrg / Pg is the phase mobility in soil, k is the intrinsic permeability tensor, krg is relative permeability in gas phase, Pg is the viscosity of gas phase, Pg is gas phase pressure, Qg is the source/sink of gas phase, Eg ¦ i ¦ E EDi E , where EDi E is the rate of interphase mass transfer to gas phase from all contiguous phase, and g is the gravitational acceleration. Equation (1) can be simplified as [15]

The dispersive flux is J gi

where is the effective dispersion coefficient. The rate of interphase mass transfer is modeled with a linear driving force [5], EDi E

With the assumption of u tion), Eq. (2) becomes

M air Pg

(3)

RT Pg2 (for numerical solu-

wu 2 Pg  ’ ˜  Og ’u 0 wt T g

(4)

where Tg is volumetric gaseous content, Tg

I Sg ,

Pw  Patm , Pw is the pressure in injection well, 2 and Patm is air pressure. The pressure field is derived from the following equation, Pg

(5)

P(r , z , t ) sqrt[u (r , z , t )]

Then the velocity distribution of infiltration flow field is obtained from the Darcy law. 3.2

Phase composition

A general macroscopic equation governing the transport of component i in phase D in unsaturated soil may be written as [16] w I SD cDi  ’ ˜  cDi qD  ’ ˜ I SD J Di wt

EDi E  BDi (6)

where cDi is the concentration of component i in D phase and SD is phase saturation. The Darcy velocity is given by qg

I Sg v 

kkrg

Pg

 ’Pg  Ug g’z

where v is the pore velocity and z is the elevation.

(7)

ODi E  cDieE  cDi

(9)

where ODi E is effective mass transfer rate coefficient. Interphase partitioning of species can be described by kinetic mass transfer relationships since non-ideal phase partitioning condition is assumed due to limited interphase contact areas. Biodegradation is incorporated into the model using dual Monod formulation.

wUg

Ug | Uair

(8)

i Dg,eff

 ’ ˜  Og Ug ’Pg 0 (2) wt Under the assumption of idea gas behavior, the mass density of gas phase is represented as

I Sg

i  Dg,eff ’cgi

·§ cwO2 ¸¸¨¨ O2 O2 ¹© KS  cw

Bwl

§ cl  P max X ¨¨ l w l © KS  cw

BwO2

§ cl  Pmax FX ¨¨ l w l © KS  cw

· ¸¸ ¹

·§ cwO2 ¸¸¨¨ O2 O2 ¹© KS  cw

(10) · ¸¸ ¹

(11)

§ cl · (12) Y Pmax X ¨¨ l w l ¸¸  kd X © KS  cw ¹ where superscript l represents substrates, X is microbial population, F is oxygen utilization ratio, Y is the yield coefficient, KS is the half-saturation coefficient, ȝmax is the maximum specific substrate utilization rate, and kd is the decay coefficient. dX dt

4

NUMERICAL SOLUTION

Numerical solutions are obtained with an upstream weighted multiple cell balance method, in which the flow, transport, and biodegradation equations are solved separately using operator splitting method (OS). The OS approach is often used for numerical solution of the advection-dispersion-reaction equations (ADREs) [17, 18]. OS splits ADREs into a system of linear partial differential equations involving the advection-dispersionequations (ADEs) and a system of nonlinear ordinary differential equations involving the reaction equations (REs), especially the microbial transformation. The ADREs are solved in two stages. In the first stage, the ADEs are solved using finite element approach, and in the second stage, the REs are solved by Runge-Kutta iteration using the solution of the first stage as the initial condition. Time steps are varied in response to flow and transport conditions and are self-adaptively adjusted based on the convergence rate. Time steps are constrained between 1.0u106 h and 0.1 h. 5

PARAMETERS

Physicochemical parameters used in the simulation are shown in Table 1 and biodegradation parameters are listed in Table 2.

342

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011 Table 1

Soil and physicochemical parameters used in numerical model

Parameters

Value

prosity I

0.33

[19]

permeability k/m2

8.40u1012

[19]

effective dispersive coefficient Dg,eff/m2·s1

1.957u106

[20]

NAPL-gas interphase mass transfer coefficient Ȝog/s1

5.78u104

[14]

5.79u10

6

[21]

5.79u10

5

1

aqueous-NAPL interphase mass transfer coefficient Ȝao/s aqueous-gas interphase mass transfer coefficient Ȝag/s

1

aqueous-solid interphase mass transfer coefficient Ȝas/s1

Table 2

[5]

5.79u105

[22]

Microbial parameters used in numerical model

Parameters

Value

3

Source 3

initial uniform biomass/kg·m

1.62×10

maximum specific substrate utilization rate Pmax,g/s1

1.16×105

[24, 25]

substrate half-saturation coefficient KSl /kg·m3

0.5×103

[24, 25]

3

1.0×104

[24]

0.50

[24, 25]

1.16×106

[24]

oxygen half-saturation coefficient K

O2 S

/kg·m

yield coefficient Y (cell/substrate)/kg·kg1 decay coefficient kd/s1

6

Source

NUMERICAL SIMULATIONS

Figure 1 shows the axisymmetric numerical domain used for BV simulation of single well. The domain includes both saturated and unsaturated zones, with the water table placed 6 m below the surface. An injection well is screened between 2 m and 5 m below the surface. An impermeable cap is placed along the soil surface to a radius of 10 m from the injection well. The finite element discretization containing 1465 nodes and 2784 elements is shown in Fig. 2. Finer discretizations are used near the well.

Figure 1 Numerical domain used in simulation

[23]

The boundary conditions used in BV simulations are as follows. For the flow field, the initial condition is t

0,

and the boundary conditions are as (0,0)ĺ(R,0)Ĥ(0,Z)ĺ(RS,Z),

wu wz

0

as (0,0)ĺ(0,Z1)Ĥ(0,Z2)ĺ(0,Z),

wu wr

0

Pw2 (constant pressure)

as (0,Z1)ĺ(0,Z2), u

2 (conas (RS,Z)ĺ(R,Z) Ĥ(R,Z) ĺ(R,0), u Patm stant pressure) where R is the radius of polluted zone, RS is the radius of impermeable cap on soil surface, Z is the depth of polluted zone, Z1 is the depth of bottom of single well screen, Z2 is the depth of top of single well screen. For the mass transfer, the boundary conditions are as (0,0)ĺ(R,0)Ĥ(0,Z)ĺ(RS,Z)Ĥ(0,0)ĺ(0,Z1)Ĥ (0,Z2)ĺ(0,Z), c 0 (constant concentration)

as (0,Z1)ĺ(0,Z2),  D as (RS,Z)ĺ(R,Z), D as (R,Z)ĺ(R,0), D

Figure 2 Triangle elements in the studied domain

u 1

wc  vc wr

wc wz

wc wr

vcin

0 0

The initial distribution of total soil toluene concentration and that of biomass concentration used in the simulation are shown in Fig. 3.

343

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011

(a) Initial toluene concentration

(b) Initial biomass concentration

Figure 3 Initial conditions used in BV simulations

(a) t 1 min

(c) t 60 min

(b) t 10 min

(d) t 100 min

Figure 4 The process of coming into being steady fluid field during BV simulation (Run 1)

7

RESULTS AND DISCUSSION

We use air injection well in simulations. When the air is injected into the vadose zone, a positive pressure is created, resulting in depression of the water table. The water table depression has important implications. At many sites, the capillary fringe is highly contaminated, and lowering the water table allows for more effective treatment of the capillary fringe. In addition, this dewatering effect often results in an increased radius of influence and greater soil gas permeability. In the simulation, the air injection rate of 81.504 m3·d1 (Run 1) and 407.52 m3·d1 (Run 2) were used, and the changes of fluid fields with time are shown in Figs. 4 and 5, respectively. Though the injection rate in Run 2 is 5 times that in Run 1, the simulation shows

that the required time to develop steady fluid fields in both cases is about 1 h. With the fluid field, Darcy velocities were calculated from Darcy’s Law. Then, the toluene removal was simulated for BV remediation. With the air injection rate of 81.504 m3·d1 (Run 1), the evolution of predicted toluene mass distribution in Fig. 6 shows that toluene removal is the greatest in the early stages of remediation. After 100 days of remediation, most of the toluene is removed, so the highest rate of NAPL removal occurs along the path line between the well screen and the edge of the subsurface cap. Persistence of the NAPL is observed in areas that are relatively less accessible to the gas phase (e.g., at the top of the domain below the impermeable cap). Another BV simulation (Run 2) was performed

344

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011

(a) t 1 min

(c) t 60 min

(b) t 10 min

(d) t 100 min

Figure 5 The process of coming into being steady fluid field during BV simulation (Run 2)

(a) 3 day

(b) 5 day

(c) 10 day

(d) 15 day

Figure 6 Predicted toluene concentrations at specified times for BV simulation with air injection rate of 81.504 m3·d1 (Run 1)

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011

(e) 60 day

(g) 200 day

345

(f) 100 day

(h) 300 day

Figure 6 Predicted toluene concentrations at specified times for BV simulation with air injection rate of 81.504 m3·d1 (Run 2)

(a) 3 day

(b) 5 day

(c) 10 day

(d) 60 day

Figure 7 Predicted toluene concentrations at specified times for BV simulation with air injection rate of 407.52 m3·d1 (Run 2)

346

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011

(e)100 day

(f) 300 day

(g) 365 day Figure 7 Predicted toluene concentrations at specified times for BV simulation with air injection rate of 407.52 m3·d1 (Run 2)

to compare the effect of injection rate on contaminant remediation. Except for injection rate, the condition was identical to that of Run 1. The predicted toluene mass distribution is shown in Fig. 7. By comparing Run 1 with Run 2, we obtain following results. When the injection rate is increased from 81.504 m3·d1 to 407.52 m3·d1, the removal rate of toluene increases greatly in the early stages of BV process since the volatilization rate of toluene increases with the injection rate. However, after 100 days, as same as in Run 1, the removal rate is much smaller. Higher flow rates do not improve the removal but increase off-gas treatment costs, because the organic compounds are mainly removed by biodegradation, and increasing air flow rate can not increase biodegradation rate or make the biotransformation more effective. In BV sites, higher injection rate is used to accelerate the volatilization of contaminants in early stages of remediation. However, in the later stages of BV, the objective is to provide sufficient air for the oxygen demand without the emission to the atmosphere, so venting flow rates should be appropriate in order to reduce off-gas volumes. Under the two air injection conditions, the accumulated toluene removal by volatilization and biodegradation is shown in Figs. 8 and 9. There is no significant difference in the total removal of toluene, which is 169.14 kg and 170.59 kg, respectively, at the air injection rate of 81.504 m3·d1 (Run 1) and 407.52 m3·d1 (Run 2), which is five times the former. In the early stage of simulation of Run 1 and Run 2 (before 90 days), biodegradation exhibits a minor influence on mass recovery, accounting for less than 42% of the total toluene removal in Run 1 and 31% in Run 2. After

Figure 8 Cumulative toluene removed by volatilization and biodegradation with air injection rate of 81.504 m3·d1 (Run 1) ƽ total accumulated toluene removal; × toluene removal by biodegradation; toluene removal by volatilization

Figure 9 Cumulative toluene removed by volatilization and biodegradation with air injection rate of 407.52 m3·d1 (Run 2) ƽ total accumulated toluene removal; × toluene removal by biodegradation; toluene removal by volatilization

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011

200 days of simulation, toluene removal by volatilization decreases greatly, and toluene mass loses are attributable almost solely to biodegradation. In Run 1, toluene removal by volatilization and biodegradation is 61.355 kg and 107.785 kg, respectively, and the ratio of volatilization to biodegradation is 0.57Ή1. In Run 2, toluene removal by volatilization and biodegradation is 80.35 kg and 90.24 kg, respectively, and the ratio is 0.89Ή1. This result indicates that the lower air injection ratio enhances the biodegradation significantly, and the injection rate should be adjusted to optimize oxygen retention time and minimize the cost. 8

VALIDATION

The simulation results are compared with those of Lang [23], as shown in Fig. 10. The result from the present model is in agreement with the published data.

I

gravitational acceleration, m2·s1 half-saturation coefficient, kg·m3 intrinsic permeability tensor, m2 decay coefficient, s1 relative permeability in gas phase air pressure, Pa gas phase pressure, Pa pressure in injection well, Pa source/sink of gas phase gas phase saturation phase saturation pore velocity, m·s1 microbial population yield coefficient, kg·kg1 elevation, m volumetric gaseous content effective mass transfer rate coefficient, s1 viscosity of gas phase, kg·m1·s1 maximum specific substrate utilization rate, s1 gas phase mass density, kg·m3 porosity

l

substrates

g KS k kd krg Patm Pg Pw Qg Sg SĮ v X Y z șg

ODi E ȝg ȝmax ȡg

347

Superscript Subscripts a g

aqueous gas

REFERENCES 1

Figure 10 Comparison of toluene removal from this model and from Lang [23] ƽtotal remove (Lang);ƻbiodegradation (Lang);Ʒtotal remove (this model);Ƹbiodegradation (this model)

2

3

9

CONCLUSIONS 4

This paper presents a model that integrates the flow field, non-equilibrium interphase mass transfer, and aerobic biodegradation. The simulation result for bioventing of a single well shows that the air injection rate is a primary parameter for contaminant dispersal, redistribution, and surface loss. However, when the air is sufficient in the subsurface for oxygen demand in biodegradation, higher injection rates will not increase the biodegradation rate and the total removal efficiency. A lower flow rate is more appropriate in the later stages of bioventing process to minimize contaminant emissions to the atmosphere. NOMENCLATURE cDi i Dg,eff i EDE F

concentration of component i in D phase, kg·m3 effective dispersion coefficient, m2·s1 rate of interphase mass transfer to gas phase from all contiguous phase oxygen utilization ratio

5

6 7 8 9 10 11 12

Shim, H., Ma, W., Lin, A.J., Chan, K.C., “Bio-removal of mixture of benzene, toluene, ethylbenzene, and xylenes/total petroleum hydrocarbons/trichloroethylene from contaminated water”, J. Environ. Sci., 21 (6), 758763 (2009). Magalhaes, S.M.C., Ferreira Jorge, R.M., Castro, P.M.L., “Investigations into the application of a combination of bioventing and biotrickling filter technologies for soil decontamination processesü A transition regime between bioventing and soil vapour extraction”, J. Hazard. Mater., 170, 711715 (2009). Mohr, D.H., Merz, P.H., “Application of a 2D air flow model to soil vapor extraction and bioventing case study”, Ground Water, 33 (3), 433444 (1995). Shikaze, S.G., Sudicky, E.A., Mendoza, C.A., “Simulation of dense vapor migration in discretely fractured geologic media”, Water Resour. Res., 30 (7), 19932009 (1994). Armstrong, J.E., Frind, E.O., McClellan, R.D., “Nonequilibrium mass transfer between the vapor, aqueous, and solid phases in unsaturated soils during vapor extraction”, Water Resour. Res., 30 (2), 355368 (1994). Johnson, P.C., Kemblowski, M.W., Colthart, J., “Quantitative analysis for the cleanup of hydrocarbon-contaminated soils by in-situ soil venting”, Ground Water, 28 (3), 413429 (1990). Rathfelder, K.M., Yeh, W.W.G., MacKay, D., “Mathematical simulation of soil vapor extraction system: model development and numerical examples”, J. Contam. Hydrol., 8, 263297 (1991). Ho, C.K., Udell, K.S., “An experimental investigation of air venting of volatile liquid hydrocarbon mixtures from homogeneous and heterogeneous porous media”, J. Contam. Hydrol., 11, 291416 (1992). McClure, P.D., Sleep, B.E., “Simulation of bioventing for soil and groud-water remediation”, J. Environ. Eng., 122 (11), 10031012 (1996). Salanitro, J.P., “The role of bioattenuation in the management of aromatic hydrocarbon plumes in a quifers”, Ground Water Monitor. Remed., 13 (4), 150161(1993). Angelakis, A.N., Kadir, T.N., Rolson, D.E., “Analytical solutions for equations describing coupled transport of two solutions and a gaseous product in soil”, Water Resour. Res., 29 (4), 945956 (1993). Nair, S., Longwell, D., Seigneur, C., “Simulation of chemical transport

348

13 14

15 16 17 18 19

Chin. J. Chem. Eng., Vol. 19, No. 2, April 2011 in unsaturated soil”, J. Environ. Eng., 116 (2), 214235 (1990). Bekins, B.A., Warren, E., Godsy, E.M., “A comparison of zero-order, first-order and Monod biotransformation moders”, Ground Water, 36 (2), 261268 (1998). Rathfelder, K.M., Lang, J.R., Abriola, L.M., “A numerical model (MISER) for the simulation of coupled physical, chemical and biological processes in the vapor extraction and bioventing systems”, J. Contam. Hydrol., 43 (3/4), 239270 (2000). Huang, G.Q., “Transport of organic contaminants during soil vapor extraction and mathematic simulations”, Ph.D. Thesis, Tianjin University, Tianjin (2002). (in Chinese) Abriola, L.M., “Modeling multiphase migration of organic chemicals in groundwater systemsüA review and assessment”, Environ. Health Perspect, 83, 117143 (1989). Rifai, H.S., Bedient, P.B., “Comparison of biodegradation kinetics with an instantaneous reaction model for groundwater”, Water Resour. Res., 26 (4), 637645 (1990). Valocchi, D.J., Micheal, M., “Accuracy of operator splitting for advection-dispersion-reaction problems”, Water Resour. Res., 28 (5), 14711476 (1992). Demond, A.H., Roberts, P.V., “Effect of interfacial forces on two-phase

20 21 22

23 24 25

capillary pressure relationships”, Water Resour. Res., 27 (3), 423437 (1991). Sui, H., Li, X.G., Jiang, B., Huang, G.Q., “Flow, transport and biodegradation of toluene during bioventing”, Chin. J. Chem. Eng., 12 (3), 439443 (2004). Fisher, U., Hinz, C., Schulin, R., Stauffer, F., “Assessment of nonequilibrium in gas water mass transfer during advective gas-phase transport in soil”, J. Contam. Hydrol., 33 (1/2), 133148 (1998). Armstrong, J.E., Croise, J., Kaleris, V., “Simulation of rate-limiting processes controlling the vapor extraction of trichlorethylene in sand soils”, In: Proceeding of the Conference on the Environment and Geotechnics, Paris, April 68 (1993). Lang, J.R., “Self adaptive hierachic finite element solution of multiphase/multicomponent transport with microbial growth and degradation”, Ph.D. Thesis, University of Michigan, USA (2000). Chen, Y.M., “Mathematical modeling of in-situ bioremediation of volatile organics in variably saturated aquifers”, Ph.D. Thesis, The University of Michigan, Ann Arbor, MI (1996). Essaid, H.I., Bekins, B.A., “Simulation of aerobic and anaerobic biodegradation processes at a crude oil spill site”, Water Resour. Res., 31 (12), 33093327 (1995).