Modeling the Concentration of Volatile and Semivolatile Contaminants

Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX-XXX

pubs.acs.org/est

Modeling the Concentration of Volatile and Semivolatile Contaminants in Direct Contact Membrane Distillation (DCMD) Product Water Judith M. Winglee, Nathan Bossa, David Rosen, Jonathan T. Vardner, and Mark R. Wiesner* Duke University, 121 Hudson Hall, Durham North Carolina 27708, United States S Supporting Information *

ABSTRACT: Direct contact membrane distillation (DCMD) is an emerging water treatment technology that has high salt rejection; however, its commercialization potential for applications such as seawater desalination or industrial wastewater reuse may be limited by low rejection of volatile and semivolatile contaminants. In this manuscript, a contaminant concentration (CC) model describing the transport of volatile and semivolatile contaminants for DCMD systems was developed and validated using data from the bench-scale DCMD treatment of synthetic wastewaters. The DCMD tests showed that the more volatile contaminants (methyl-tert-butyl ether, acetone, pentanone, butanol, and hexanol) accumulated in the permeate collection stream at greater concentrations than in the feed stream. The validated CC model (average normalized root mean squared error ≤11.3%) was then used to evaluate the product water quality from the large-scale DCMD treatment of oil and gas produced waters. The modeled product water contaminant concentrations exceeded the Environmental Protection Agency limits for discharging to publicly owned treatment works. This indicated that DCMD treatment of produced waters may require additional processing to meet discharge requirements.

1. INTRODUCTION Direct contact membrane distillation (DCMD) is a promising water treatment technology that can treat seawater and saline wastewaters with low capital and operating costs when energy from waste heat is utilized. In the DCMD process, the feedwater is distilled at low temperatures (40−80 °C) within a low pressure membrane module. This arrangement creates a large amount of surface area for distillation while maintaining a relatively small plant footprint. In the DCMD process, water is distilled from the heated feed stream (FS) across a porous membrane and collected in the permeate collection stream (PCS) while the nonvolatile contaminants remain in the brine.1 By using waste heat to create the heat gradient necessary for distillation, DCMD systems can produce high purity water at low cost. Despite the benefits of DCMD, adoption of this technology has been slow, in part due to limited information on the conditions that favor or restrict its application. DCMD research on feed streams such as seawater and oil and gas produced waters has focused on the efficacy of salt removal. However, these streams also contain significant concentrations of volatile and semivolatile contaminants that can cause human safety and environmental concerns if these contaminants accumulate in the PCS during the treatment process. Since the DCMD product water is withdrawn from the PCS, characterizing the PCS contaminant concentration is important for determining whether DCMD can meet the treatment requirements for target applications. In addition to contaminating the DCMD © XXXX American Chemical Society

product water, these compounds can also cause membrane wetting and fouling, which reduces the quality and quantity of DCMD product water.2−4 While contaminant accumulation in the DCMD product water is an important issue, few groups have reported on the rejection of volatile and semivolatile compounds by DCMD. The most comprehensive experimental analysis of organic compound removal was conducted by Wijekoon et al. (2014) in their study on the DCMD rejection of 29 trace organic compounds (TrOCs) in a synthetic wastewater. The results showed that the rejection of the TrOCs was primarily determined by their Henry’s constant (H) and, to some extent, their hydrophobicity.5 The results from their 24 h flux tests showed that the nonvolatile compounds (15.19 > −log10(H) > 9, Henry’s constant in units of m3atm/mol) had >80% rejection, and the semivolatile compounds (5.06 < −log10(H) < 9) had >54% rejection.5 Notably, a mass balance of the TrOCs at the end of the test revealed significant mass losses for many of the compounds, indicating that evaporation and adsorption of the TrOCs was a significant concern.5 A collection of studies by Gryta et al. also analyzed the flux of volatile and semivolatile solutes in DCMD. Gryta et al. conducted DCMD studies on the concentration of acids as Received: November 9, 2016 Revised: May 25, 2017 Accepted: May 26, 2017

A

DOI: 10.1021/acs.est.6b05663 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

made of a 0.5 M sodium chloride solution with seven different contaminants (methyl-tert-butyl ether (MTBE), acetone, pentanone, butanol, hexanol, dimethyl phenol (DMP), and phenol). The target initial feed concentration of these contaminants was 60 mg/L each. These contaminants represent a range of Henry’s constants and molecular weights (contaminant characteristics listed in Supporting Information (SI) Table S1). The chemicals were of analytical grade and obtained from Sigma-Aldrich. 2.2. DCMD System. The bench-scale, batch-operated DCMD system was built to test the contaminant transport in DCMD, and a schematic of this system is depicted in Figure 1.

well as on the extraction of ethanol and other volatile compounds from bioreactor broth.6−9 To describe the transport of these compounds, Gryta et al. developed a flux model that described the transport of the solute and aqueous solvent based on the Stefan-Maxwell equations for multicomponent diffusion. This model was used to calculate the solute flux, but did not model the interplay between the changing FS, PCS, and flux concentrations as the system approached equilibrium. Since DCMD systems are typically designed with a recycled PCS,1,5,6,10 accounting for the accumulation of volatile solutes in the PCS is important for predicting the contaminant flux during DCMD operation and determining the steady state PCS concentration. To accurately model the accumulation of volatile contaminants in the PCS during the course of DCMD operation, the contaminant concentration (CC) model described in this manuscript uses a mass balance approach rather than contaminant rejection evaluations, which have been reported in previous works.2,5,11 The contaminant rejection (R) describes the ratio of the contaminant concentration in the permeate, (Cc,p) to the feed concentration (Cc,f) (R = 1 − Cc,p/ Cc,f). The permeate concentration is commonly calculated by measuring both the increase in the amount of contaminant and the volume gained by the PCS over a time interval. The ratio of these measurements represents the average permeate concentration for this time interval.9 However, for systems in which the PCS is recycled, the permeate concentration of volatile contaminants during the startup phase of DCMD operation can be substantially higher than the permeate concentration at steady state operation. As a result, calculating the contaminant rejection during short flux tests, in which the startup phase comprises a large portion of the measured time interval, underestimates the contaminant rejection. Underestimating the contaminant rejection may lead to overpredicting the DCMD product water contaminant concentration for long-term studies where steady state is reached, which may erroneously disqualify DCMD systems from being utilized in certain applications. Alternatively, the mass balance approach accurately predicts the DCMD product water contaminant concentration throughout the course of DCMD operation and can be used to translate the results from bench-scale testing to large-scale systems. This manuscript presents data showing that the mass balance based CC model accurately predicted the FS and PCS contaminant concentrations during bench-scale testing. The validated CC model was also used to predict the contaminant concentrations for a large-scale DCMD system. Trends in the contaminant accumulation from the large-scale system were identified based on the contaminant physiochemical properties, such as Henry’s constant and molecular weight. The modeled product water contaminant concentrations were used to assess the viability of DCMD for treating oil and gas produced waters to be discharged into publicly owned treatment works (POTWs). The ability to conduct these assessments regarding the concentration of volatile and semivolatile contaminants in the DCMD product water are an important advance in the characterization of DCMD operating capabilities.

Figure 1. Schematic of the bench-scale DCMD system.

The membrane (Millipore GVHP) was held in a flat sheet membrane module (5 cm × 5 cm × 2 mm per module side) with diagonal mesh spacers (0.3 mm fiber diameter and 2.5 mm between fibers), to support the membrane and increase turbulent mixing. According to the manufacturer specifications, the membrane pore size was 220 nm and the porosity was 75%. Membrane tortuosity was not available from the manufacturer but was reported to be 2.1 by Khayet et al.12 The FS and PCS were circulated using peristaltic KNF Liquiport Lab pumps at a flow rate of 31 mL/sec in both streams. The pressure in the FS and PCS was less than 20 psi, and the pH of the streams was 5.5. The temperatures of the FS and PCS were measured at the module inlets and outlets using Vernier Stainless Steel Temperature Probes, and the conductivity of the PCS was measured using a Vernier Conductivity Probe. The starting volume of the FS and PCS was about two liters, and the two streams were held in glass containers covered with Scentroid polytetrafluoroethylene gas sampling bags to reduce evaporative losses. A pair of Ohaus Adventurer Pro AV 3103 balances measured the change in mass of the FS and PCS streams. The reservoirs were sampled at regular time intervals and the contaminant concentration was measured using a Shimadzu GC-2014 gas chromatograph with a flame ionization detector and Agilent HP-Innowax column (30 m length, 0.25 mm inner diameter, film thickness 0.25 μm). The GC analysis was conducted with a split injection and an initial oven temperature of 40 °C for 1.5 min. The oven temperature was then increased to 230 °C at a ramp of 20 °C/min and maintained at 230 °C for 1.5 min. 2.3. Flux and Mass Balance Tests. Flux tests were conducted by first equilibrating the FS and PCS to their respective temperatures for each run. Flux tests were conducted at inlet temperatures of 40 °C for the FS and 20 °C for the PCS, and the outlet temperatures were within 0.5 °C of the inlet temperatures. After equilibration, the saline FS was spiked

2. MATERIALS AND METHODS 2.1. Synthetic Wastewater Composition. To calibrate and validate the CC model, contaminant transport was measured during the treatment of a synthetic wastewater with a bench-scale DCMD system. The synthetic wastewater was B


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the membrane interfaces, and these conditions were determined using the mass and heat transfer equations described in the following sections. 3.1. Mass Transfer in DCMD. Modeling the contaminant flux for the DCMD treatment of complex feed waters, such as seawater or industrial wastewaters, describes the transport of multiple contaminants at relatively low concentrations. Several different approaches can be used to model the transport of these compounds, such as the Fickian model for binary diffusion and the Stefan-Maxwell model for multicomponent diffusion. The Stefan−Maxwell equations account for the diffusive interactions between contaminants, but at low concentrations, the impact of these interactions is relatively minor.13 Prior works in air-gap membrane distillation systems have shown that at low contaminant concentrations binary Fickian models predicted compound flux with good accuracy.14,15 As a result, the Fickian approach was selected to describe the contaminant flux in the CC model due to its performance and relative simplicity. 3.1.1. Overall Mass Transport Coefficient and Contaminant Flux. To evaluate the contaminant flux, the contaminant transport from the FS to the PCS was modeled across three stages. The three stages included (1) diffusion of the contaminant as a liquid from the bulk FS (f) to the feedmembrane (fm) interface, (2) diffusion of the contaminant as a gas through the membrane pores from the feed-membrane interface to the permeate collection stream-membrane (pm) interface, and (3) diffusion of the contaminant as a liquid from the permeate collection stream-membrane interface to the bulk PCS. Due to the different compound diffusion rates, the contaminant concentrations at the membrane interfaces can differ from the bulk concentrations. This phenomenon is called concentration polarization and accounting for concentration polarization is important for accurately predicting the contaminant flux. The flux of the contaminants across the three stages was described using the following equations:

with the contaminants, mixed, and sampled over time to monitor the contaminant concentration. To ensure adequate mixing during the sample addition, the contaminant spike was well stirred and circulated through the system for 5 min before the initial concentrations in the FS and PCS were evaluated. Due to volatilization of the contaminant, there was some variation in the starting concentration of the flux tests. To account for this variation, measurements of the reservoir concentrations after the contaminant spike was added were used as the basis for the subsequent modeling work. Flux tests were conducted over 6−30 h, and the salt rejection of all the tests was above 99.99%, which indicated that the transport of contaminants into the PCS was due to volatilization, not pore wetting. Mass balance tests were conducted to evaluate the rate of contaminant losses due to volatilization and adsorption during system operation. The mass loss coefficients for a first order loss expression were calculated from data obtained using similar procedures to those used in the flux tests. The flux test procedure was modified by replacing the membrane with an impermeable plastic sheet to isolate the FS and PCS. Additionally, both the FS and PCS were spiked with the contaminants to track the change in contaminant concentration in both loops. 2.4. Sorption Affinity with Tubing. Sorption tests to determine the affinity of the contaminants with the DCMD system tubing were conducted. Glass vessels were prepared with a solution of 60 mg/L synthetic wastewater solution. In addition to the synthetic wastewater, the glass vessels either contained the plastic tubing that was used in the DCMD system for the sorption test or did not contain tubing for a control. The containers were gently agitated for 5 days. At the end of the sorption equilibration period, the contaminant concentration in the control vessel had not changed significantly, while the contaminant concentrations in the container with tubing had decreased due to sorption to the tubing.

Nc = kL,c,f (Cc,f − Cc,fm)

3. DCMD CONTAMINANT CONCENTRATION MODEL DEVELOPMENT In order to predict the contaminant concentration in the PCS during a batch DCMD process, a mass balance was developed to describe the transport of contaminants from the FS to the PCS reservoir. To write the mass balance of the FS and PCS reservoirs, both the contaminant flux from the FS to the PCS and the mass losses from the system were described in Equations (1) and (2): d(Vf Cc,f ) dt

d(VpCc,p) dt

= −A m Nc − kl,c,f Cc,f Vf

(1)

= A m Nc − kl,c,pCc,pVp

(2)

Nc =

k m,c RTm

(pc,fm − pc,pm )

Nc = kL,c,p(Cc,pm − Cc,p)

(3)

(4) (5)

where kL is the liquid mass transfer coefficient of the contaminant through the DCMD module, p is the partial pressure of the contaminant, R is the gas constant, and Tm is the temperature of the membrane. Contaminant concentrations at the air−liquid interfaces were assumed to be at equilibrium and Henry’s law was applied to determine the corresponding partial pressures and liquid concentrations. The contaminant Henry’s constant was calculated using the equation below: * Vw̅ Hc = γcpc,T

where V is the average volume of the reservoir during the flux test, Cc is the concentration of the contaminant, t is the time since the sample was added, Am is the area of the membrane, Nc is the contaminant flux, and kl,c is the contaminant mass loss coefficient. The subscripts f and p indicate a parameter that is either evaluated in the bulk FS or the bulk PCS respectively. In DCMD, the contaminant flux is driven by the partial pressure gradient across the membrane. The partial pressures depend on the contaminant concentrations and temperatures at

(6)

where γc is the activity coefficient of the contaminant at infinite dilution in the aqueous solution, p*c,T is the pure component vapor pressure of the contaminant at temperature (T), and V̅ w is the molar volume of water.16 The pure component vapor pressures of the contaminants at the specified temperatures were calculated using the Antoine equation and Antoine coefficients from Yaws et al. (2009) and Stephenson et al. (1987).17,18 C


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molecular weight in units of grams/mol.23 The Knudsen diffusion coefficient was calculated using eq 10 where dp represents the average pore diameter:24

The salt composition of the FS and PCS influenced the contaminant activity coefficients. Since the salt concentration in the PCS was negligible, the PCS contaminant activity coefficient was modeled using the aqueous activity coefficient at infinite dilution (γ∞,c). The values of the aqueous activity coefficients at infinite dilution were obtained from Yaws (2001) and are included in SI Table S1 for the contaminants evaluated in this manuscript.19 In comparison, the FS salt concentration strongly influenced the contaminant activity coefficients, and the FS contaminant activity coefficient (γs,∞,c) was modified to incorporate the effects of sodium chloride. The salt effects were incorporated by using the Setschenow equation and the sodium chloride salting constant determined by Xie et al. (1997).20 The Setchenow equation has been shown to have good validity over a wide range of salt concentrations for a variety of different organic compounds.16 The equations describing the contaminant mass transfer from the bulk FS to the bulk PCS and the contaminant Henry’s constant (eqs 3−6) were used to determine the overall contaminant mass transfer coefficient (kcp,c). To solve this system of equations, the liquid contaminant concentrations in eqs 3 and 5 were converted to partial pressures by using the contaminant Henry’s constant. The Henry’s constant values were corrected for the salt and temperature effects in the FS and PCS of the membrane module. The salt effects on the contaminant Henry’s constant were modeled at the bulk salt concentrations in the FS and PCS, whereas the temperature effects were addressed by calculating the Henry’s constants using the average of the bulk and interfacial temperatures. Using these conditions for calculating the contaminant Henry’s constants, the overall mass transfer coefficient was described using eq 7 and the contaminant flux was described by eq 8. kcp,c

−1 ⎡ Hc,f,avg Hc,p,avg ⎤ RTm ⎥ =⎢ + + k m,c kL,c,p ⎥⎦ ⎢⎣ kL,c,f

DcKn =

k m,c

(

Sh = 0.664SRe Pr

kL,c = Sh

1 Ma

⎟ ⎠

(12)

dh,s

(13)

qf = hf (Tf − Tfm)

(14) n

qm = qd + qv = NwH v,w +

∑ NiHv,i + λm i=1

Tfm − Tpm δm (15)

−1 ⎞1/2

)

⎛ 2dh,s ⎞0.5 ⎜ ⎟ ⎝ ls ⎠

0.33

L Dc,w

qp = hp(Tpm − Tp) +

(11)

where Sh is the Sherwood number, S is the spacer correction factor, Re is the Reynolds number, Pr is the Prandlt number, dh,s is the spacer-filled hydraulic diameter of the module channel, ls is the spacer mesh length, and DLc,w is the liquid diffusion coefficient of the contaminant in water.26 The calculation of the spacer correction factor is fully described by Da Costa et al.,26 and the liquid diffusion coefficient was empirically determined using the method developed by Siddiqi and Lucas.27 3.2. Heat Transfer in DCMD. To accurately predict the compound flux, the temperatures at the membrane interfaces were calculated. The temperatures of the membrane interfaces were calculated by modeling the heat transfer from the FS to the PCS across the following three stages: (1) convective heat transfer from the bulk FS to the FM interface (qf), (2) heat transfer across the membrane (qm) via both conduction through the membrane polymer (qd) and transfer of latent heat from evaporation (qv), and (3) convective heat transfer from the PM interface to the bulk PCS (qp). The equations for these processes are described in eqs14−16:

(8)

1 Mc

−1 ε ⎛ 1 1 ⎞ ⎜ ⎟ = ⎜ m + Kn ⎟ τδm ⎝ Dc,a Dc ⎠

0.5

0.00143Tm1.75 ⎛ 2 PT(∑1/3 + Σ1/3 v,a ) ⎜2 v,c ⎝

(10)

where ε is the membrane porosity, τ is the membrane tortuosity, and δm is the membrane thickness.25,13 3.1.3. Liquid Mass Transfer Coefficient. The liquid mass transfer coefficient describes the boundary layer resistance to contaminant transport in the FS and PCS. To describe the flow through the spacer-filled channels, a semiempirical model for the mass transport coefficient was developed by Da Costa et al. and is summarized in the following equations:

3.1.2. Membrane Mass Transfer Coefficient. The membrane mass transfer coefficient describes the gaseous diffusion mechanisms of molecules through the membrane pore. While there are a variety of models for describing diffusion across the membrane pores, this manuscript implemented a model based on the kinetic theory of gases describing the effect of both Knudsen and molecular diffusion.21 Other methods include the Dusty-Gas Model and the model by Kim (2012), which describes an alternate method for calculating the membrane mass transfer coefficient.1,22 Based on the evaluation of the Knudsen number for the diffusing compounds, both molecular and Knudsen diffusion were expected to take place in the membrane pores.21 The molecular diffusion coefficient of the contaminant through the stagnant air (a) in the pore (Dm c,a) was estimated by using the empirical correlation reported by Fuller et al. in units of meters2/sec23 m Dc,a =

3

8RTm πMc

The mass transport coefficient was calculated by accounting for the molecular and Knudsen diffusion coefficients and the membrane structure in the following equation:

(7)

Nc = kcp,c(Cc,f Hc,f − Cc,pHc,p)

dp

(16)

where h is the heat transfer coefficient, λm is the membrane thermal conductivity, Hv is the enthalpy of vaporization, and n is the number of contaminants. Since the FS contaminant concentration was small compared to the amount of water present, the heat transferred by the total contaminant flux was expected to be negligible compared to the water flux and was

(9)

where ∑v is the molecular diffusion volume in units of centimeters3/mol and was calculated by summing the atomic and structural diffusion volume increments and M is the D


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Environmental Science & Technology eliminated from the calculations. The membrane thermal conductivity was calculated by accounting for the thermal conductivity of the membrane polymer (λPVDF) and the air in the membrane pores (λair) using the equation below:21 λm = ελair + (1 − ε)λPVDF

(17)

The heat transfer across each stage was assumed to be conserved (qf = qm = qp), and the energy balance around these equations was used to find the temperatures at the FS and PCS membrane interfaces described in the eqs 18 and 19:21

Tfm =

Tpm =

λm ⎛ ⎜T δm ⎝ p

λm ⎛ ⎜T δm ⎝ f

+

⎛ hf ⎞ ⎞ ⎜ ⎟T ⎟ + h T − N H f f w v,w ⎝ hp ⎠ f ⎠ ⎛ λm λ ⎞ + hf ⎜1 + h δm ⎟ δm ⎝ p m⎠

+

( )T ⎞⎠ + h T + N H + h (1 + ) hp hf

λm δm

p

⎟

p

p p

λm hf δm

w

Figure 2. Dependence of the overall mass transfer coefficients on the contaminant Henry’s constant. Contaminant Henry’s constants were calculated at 25 °C in aqueous, nonsaline solutions. Circles, squares, and triangles represent low, moderate, and high Henry’s constant compounds, respectively.

(18)

the membrane mass transfer coefficient, whereas the overall mass transfer coefficient of high Henry’s constant compounds (H > 10 000 PaL/mol) decreased proportionally with values of Henry’s constant. The trends in the overall mass transport coefficients were used to identify patterns in the accumulation of contaminants in the DCMD product water. 4.1.2. Evaluation of Mass Loss Rate Constants for DCMD Mass Balance. The results of the mass loss tests indicated that the first order loss model described the measured mass losses well. The results of the mass loss tests for acetone, pentanone and phenol are depicted in Figure 3, and the reservoir

v,w

(19)

To solve for the temperature at the membrane interfaces in the equations above, the heat transfer coefficients for the FS and PCS were evaluated. Using the Chilton-Colburn analogy, the method of determining the mass transfer coefficients by Da Costa et al. was used to determine equivalent equations for the heat transfer coefficients.13,26,28,29 The heat transfer coefficients were determined by the following equations: ⎛ 2dh,s ⎞0.5 Nu = 0.664SRe 0.5Pr 0.33⎜ ⎟ ⎝ ls ⎠ h = Nu

λw dh,s

(20)

(21)

where Nu is the Nusselt number and λw is the thermal conductivity of water. 3.3. Mass Loss Coefficient Evaluation. While the experimental DCMD system was designed to minimize contaminant volatilization from the system, losses due to volatilization and adsorption were still expected to be significant in the overall mass balance. The volatilization and adsorption from the FS and PCS were modeled as first order losses, and these losses were described by combined loss rate constants (kl,c,) for both the FS and PCS in the following equation: d(VCc) = −kl,cVCc dt

Figure 3. (a), (b), and (c) depict the decrease in acetone, pentanone, and phenol concentration, respectively, due to mass losses in the FS (red) and PCS (blue) during a mass loss test. The data points represent the averaged FS and PCS concentrations from samples withdrawn at each time point and the error bars indicate the standard deviation associated with triplicate measurements of each sample. The solid lines depict the modeled mass losses. Panel (d) depicts the average FS and PCS mass loss coefficients for each compound.

(22)

4. RESULTS AND DISCUSSION 4.1. Experimental Validation of DCMD Contaminant Concentration Model. 4.1.1. Evaluation of Contaminant Overall Mass Transfer Coefficients. Depending on the volatility of the compound, the overall mass transfer coefficient was primarily influenced by either the Henry’s constant or the membrane mass transfer coefficient of the contaminant. To analyze trends in the overall mass transfer coefficient, the overall mass transfer coefficients of 60 volatile and semivolatile contaminants (listed in SI Table S1) were calculated for the bench-scale DCMD system (Figure 2). The overall mass transfer coefficient of low Henry’s constant compounds (H < 100 PaL/mol) was primarily determined by

concentrations of the other compounds (MTBE, butanol, hexanol, and DMP) showed a similar exponential decrease during the tests. The loss model fit the shape of the decreasing FS and PCS concentrations for all compounds, and the accuracy of the model was evaluated using the average normalized root-mean-squared error (RMSE). The average normalized RMSE was calculated by evaluating the RMSE, normalizing the RMSE by the starting contaminant concentration, and averaging the normalized RMSE over the mass loss test replicates. The average normalized RMSE of the model E


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Figure 4. Measured and modeled contaminant concentration in the FS (red) and PCS (blue). The points represent averaged FS and PCS concentrations from samples withdrawn at each time point, and the error bars indicate the standard deviation associated with triplicate measurements of each sample. Solid lines depict the modeled FS and PCS concentrations. The contaminant reservoir concentration profiles are arranged in order of decreasing value of the contaminant Henry’s constant.

concentrations. This demonstrated that the DCMD system magnified the concentration of volatile contaminants, which could result in product water that is unusable for certain applications. 4.1.4. Validation of the Contaminant Concentration Model. The CC model was used to describe the contaminant transport during the DCMD treatment of the synthetic wastewater. The modeled results for each contaminant are overlaid on the measured results in Figure 4. The CC model predictions described the measured contaminant concentrations during the flux test with good accuracy. The average normalized RMSE for the contaminants tested ranged between 6.3 and 11.3%, which indicated that the CC model was a good descriptor of the DCMD system. While the CC model generally described the transport in DCMD systems well, the CC model tended to under-predict the PCS concentration of compounds with high mass loss coefficients such as MTBE (11.3% average normalized RMSE) and pentanone (8.4% average normalized RMSE). This trend may be due to the differences in introducing the contaminants to the PCS reservoir between the mass loss tests and the flux tests. During the mass loss tests, a spiking solution of the contaminants was prepared, added to the reservoir and mixed vigorously to dissolve the contaminants. However, during the flux tests, the contaminants were slowly introduced to the PCS through the membrane interface. This difference in introducing the compounds may have resulted in an overprediction of the mass loss coefficient for the more volatile compounds. 4.2. Estimates of DCMD Performance for Treatment of Oil and Gas Produced Waters. 4.2.1. Modeling the Product Water Contaminant Concentration in Large-Scale DCMD Systems. One promising application for large-scale DCMD systems is treatment of oil and gas produced waters. Produced waters can have high total dissolved solids (TDS) concentrations, ranging up to 500g/L TDS, which makes this water difficult to treat by reverse osmosis systems.30−32 Treatment of produced waters would allow the water to be reused or be discharged to POTWs and returned to the environment. This manuscript assesses the viability of incorporating DCMD systems into Centralized Waste Treat-

ranged between 3.8 and 9.5% for the different compounds, indicating that the first order loss model predicted the measured losses with good accuracy. While volatilization was expected to play a large role in the rate of contaminant loss, contaminant sorption to the DCMD system greatly influenced the magnitude of the mass loss coefficients as well. For the bench-scale DCMD system, the mass loss coefficients showed higher correlation to the measurements of the contaminant tubing affinity than to the values of the contaminant Henry’s constant. The calculated mass loss coefficients were plotted in Figure 3d with respect to the contaminant affinity to the tubing. 4.1.3. Accumulation of Contaminants in the PCS during DCMD Flux Tests. DCMD flux tests using the synthetic wastewater were conducted to monitor the transport of the contaminants from the FS to the PCS. The changes in the FS and PCS concentrations of MTBE, acetone, pentanone, butanol, DMP, and phenol were illustrated in Figure 4, and the hexanol results illustrated the same trends as the results of the other compounds. During the course of the flux test, the FS contaminant concentration decreased over time due to both the contaminant flux into the PCS and the volatilization and adsorption losses. Concurrently, the measured PCS contaminant concentration initially increased due to the high incoming flux of contaminant, which was greater than the PCS losses due to volatilization and adsorption. As the PCS contaminant concentration increased, the vapor pressure gradient between the FS and PCS decreased causing the PCS concentration to plateau and reach a maximum as the contaminant flux decreased. The subsequent decrease in the PCS concentration of MTBE, acetone, pentanone, butanol, hexanol, and DMP was due to the relatively large volatilization and adsorptive mass losses compared to the contaminant flux. The phenol PCS concentration, however, did not reach a critical point maximum within the time scale of the flux test (Figure 4f) illustrating that the low volatility of the compound affected the flux of the contaminant. Notably, the concentration profiles of MTBE, acetone, pentanone, and butanol (Figure 4a−d) showed that the PCS concentrations reached concentrations higher than the FS F


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Environmental Science & Technology ment (CWT) facilities for treating produced waters and discharging to POTWs. The product water quality from the large-scale DCMD treatment of a representative oil and gas produced water was evaluated by using the CC model. Trends in the contaminant accumulation based on the contaminant physiochemical properties were established to identify contaminants with high magnification potential. The calculated PCS contaminant concentrations from large-scale DCMD treatment were compared with the EPA CWT Effluent Limitations Guidelines and Standards to determine if the product water contaminant concentrations were within the limits for discharging to a POTW. The large-scale DCMD system was designed to treat a 4.5 m3/h produced water feed flow in a once-through configuration. The PCS had a volume of 2 m3 and was recycled to reduce the amount of water needed to provide cooling and collect the permeate. The system was operated with a FS temperature of 50 °C, a PCS temperature of 20 °C, and FS and PCS crossflow velocities of 31 cm/sec. The system was assumed to have negligible mass losses due to volatilization or adsorption. This assumption was made to evaluate the situation that presents the greatest potential for contaminant accumulation in the PCS. The system was designed with two membrane elements of 90 m2 membrane area in series, and the membranes were designed with the same specifications as the Millipore GVHP membranes. It was assumed that adequate energy was available to adjust the FS and PCS temperature between each element to maintain the desired temperature gradient, and a recovery of 25% was achieved. The treated product water was continuously withdrawn from the PCS at a rate equal to the flow of the permeate across the membrane (Am Nw) in order to maintain a volume balance in the PCS. Mass and heat transfer coefficients were calculated using the method described in the CC model. With these assumptions, the CC model mass balance describing the contaminant concentration in the PCS was written as Vp

dCc,p dt

Figure 5. Illustration of the magnification and rejection of several contaminants in the PCS when treated by the large-scale DCMD system. Equilibrium PCS concentrations below the FS concentration indicate that the contaminant was rejected, whereas equilibrium PCS concentrations greater than the FS concentration indicate that the contaminant concentration was magnified in the product water.

ratio values greater than one indicate that the contaminant concentration is magnified in the product water, and equilibrium ratio values less than one indicate that the contaminant is rejected. The contaminant magnification or rejection at steady state was predicted by calculating the equilibrium ratio values for the large-scale system using the equation below: Ec =

Hc,f kcp,c Hc,pkcp,c + Nw

(24)

4.2.2. Identification of Contaminants of Concern in DCMD Treatment. To predict which compounds were magnified or rejected by the large-scale DCMD system, the equilibrium ratio values of the 60 low, moderate, and high Henry’s constant compounds, as characterized by the trends in the overall mass transport coefficient, were analyzed. The contaminant equilibrium ratio values were evaluated when modeling DCMD treatment using operating conditions describing a range of membrane pore sizes (50−450 nm), operating temperatures (FS range of 40−80 °C and PCS range of 5−25 °C) and crossflow velocities (3.6−72 cm/sec). Trends in the contaminant equilibrium ratio values were identified based on the physiochemical properties of the contaminants. These trends enable product water concentration estimates for contaminants beyond those included in this study. For low Henry’s constant compounds, the contaminant equilibrium ratio values were primarily influenced by the Henry’s constant of the compound (Figure 6a). Compounds with low values of Henry’s constant had lower equilibrium ratio values, and compounds with H < 30 PaL/mol were rejected. For high Henry’s constant compounds, the equilibrium ratio values were greater for compounds with higher molecular weights (Figure 6b). The equilibrium ratio of high Henry’s constant contaminants was primarily influenced by difference in the activity and volatility of the compounds in the FS relative to the PCS, and both of these parameters relate to the contaminant molecular weight. At the range of conditions tested, the concentrations of high Henry’s constant contaminants were all magnified in the PCS. The equilibrium ratio values of contaminants with moderate values of Henry’s constant (100 < H < 10 000 PaL/mol) reflected the transition between the trends of low and high Henry’s constant contaminants. The Henry’s constants of the moderate Henry’s constant compounds were greater than the

= A mkcp,c(Cc,f Hc,fm − Cc,pHc,pm) − A mNwCc,p − k l,c,pCc,pVp

(23)

To evaluate the magnification or rejection of the contaminants in the DCMD product water, the treatment of a feedwater comprising of 2 mg/L of each contaminant and 50 g/L TDS was modeled for the large-scale system. The PCS concentration results of four contaminants, butanone, acetone, phenol, and o-cresol, were selected to illustrate the potential for contaminant magnification or rejection of depending on the characteristics of the compound. As the DCMD system started up, the contaminant concentration in the PCS increased from zero (Figure 5) to a maximum equilibrium PCS concentration. The equilibrium PCS concentrations of acetone, butanone, and phenol were greater than the FS concentration indicating that the concentrations of these compounds were magnified in the PCS. Conversely, the equilibrium PCS concentration of ocresol was lower than the FS concentration indicating that this compound was rejected. The magnification or rejection of the contaminants in the DCMD product water was described by the equilibrium ratio (E) which describes the ratio of the PCS concentration to the FS concentration at equilibrium (E = Cc,p/Cc,f). Equilibrium G


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The mass balance approach in the CC model was also used to calculate the contaminant equilibrium ratio, which describes the contaminant accumulation in the PCS at steady state. Trends in the equilibrium ratio values across 60 different contaminants showed that the accumulation of low Henry’s constant compounds was greater for compounds with higher Henry’s constants, whereas the accumulation of high Henry’s constant compounds was greater for compounds with greater molecular weights. By using the CC model to determine the contaminant equilibrium ratios, the quality of the DCMD product water from the treatment of a representative oil and gas produced water was assessed, and it was found that additional treatment was necessary before the product water could be discharged to a POTW. While this article describes an important advance in the predicting the operating capabilities of DCMD systems, further research can be done to optimize DCMD systems to reduce the contaminant accumulation in the DCMD product water. DCMD operating parameters influence the contaminant equilibrium ratio values and can be adjusted to reduce the contaminant flux. pH adjustment can also be used to reduce the product water concentration of acidic or basic contaminants. Additionally, in practice, volatile compounds are likely to evaporate from the FS and PCS during DCMD treatment, which would reduce the contaminant concentration in the product water, but potentially require treatment of these fugitive emissions. To address these issues, continued research characterizing the accumulation of volatile and semivolatile compounds during large-scale DCMD treatment is critical for optimizing the performance of DCMD systems.

Figure 6. (a) The contaminant equilibrium ratio was proportional to values of Henry’s constant for low Henry’s constant compounds (H < 100 PaL/mol). b) For high Henry’s constant compounds, (H > 10 000 PaL/mol), compounds with higher molecular weight had higher equilibrium ratios. Contaminant Henry’s constants were calculated at 25 °C in aqueous, nonsaline solutions. Dashed line in both figures represents an equilibrium ratio of one.

threshold for contaminant rejection for low Henry’s constant compounds, and the concentrations of the moderate Henry’s constant compounds all magnified in the PCS stream. 4.2.3. Comparison of DCMD Treated Produced Water with Discharge Standards. The high equilibrium ratio values calculated for many of the evaluated contaminants showed that additional treatment may be needed to reduce the product water contaminant concentration. To determine if additional treatment was necessary, the product water from the DCMD treatment of a representative oil and gas produced water was compared with the CWT Effluent Limitations Guidelines and Pretreatment Standards for discharging to POTWs.33 The produced water was classified as an Organic (Subcategory C) wastewater according to guidelines issued by the EPA on wastewater from oil and gas operations in the Marcellus Shale.34 The predicted DCMD product water concentrations of six representative contaminants were compared with the monthly average maximum effluent concentrations of these compounds in the New Source Performance Standards (NSPS)33 (Table 1). The results showed that due to the magnification of the contaminant concentrations, the product water concentration of compounds with high feed concentrations or high equilibrium ratios exceeded the CWT standards. This analysis shows that accounting for contaminant magnification is critical when designing DCMD systems. 4.3. Implications for DCMD Applications. The transport of contaminants through DCMD systems is a critical constraint for DCMD treatment, and the CC model is an important advance in predicting the quality of the DCMD product water. The findings from this work show that utilizing a mass balance approach, as adopted in the CC model, is important for predicting the DCMD product water contaminant concentration during the course of DCMD operation.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b05663. List of compounds used to model overall mass transfer coefficients and equilibrium ratio values for treatment by the large-scale DCMD system (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Phone: (919) 660-5200; fax: (919) 660-5219; e-mail: [email protected]. ORCID

Judith M. Winglee: 0000-0002-4584-8967 Mark R. Wiesner: 0000-0001-7152-7852 Notes

The authors declare no competing financial interest.

Table 1. Comparison of the Predicted Product Water Concentration of a Representative Oil and Gas Produced Water to the CWT Effluent Guidelines and Standards Requirements for NSPS compound

low/high Henry’s constant compound (log10(H) [PaL/mol])

eq ratio

produced water conc. [mg/L]

predicted product water conc. [mg/L]

CWT monthly average maximum [mg/L]

additional treatment needed

o-cresol phenol acetone butanone n-decane

low (0.49) low (1.61) high (3.74) high (3.80) high (9.18)

0.23 1.2 3.0 3.5 7.1

0.152a 0.83a 16.0a 0.240a 0.116b

0.03 1.00 48.0 0.91 0.82

0.561 1.08 7.97 1.85 0.437

no no yes no yes

Contaminant concentration of a hydraulic fracturing flowback from Colorado35 bContaminant concentration of a coproduced water from conventional oil and gas36

a

H


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ACKNOWLEDGMENTS This material is based upon work supported by the Department of Defense (DOD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program, the Advanced Manufacturing Office (AMO) under the Office of Energy Efficiency & Renewable Energy (EERE) of the U.S. Department of Energy (DOE) under Cooperative Agreement No. DE-EE0005758 and the National Science Foundation under grant number 1243433. Thank you to Zachary Hendren and Lora Toy for their guidance during this project.

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