Determination and correlation of the partition

Chemical Engineering Journal 310 (2017) 72–78

Contents lists available at ScienceDirect

Chemical Engineering Journal journal homepage: www.elsevier.com/locate/cej

Determination and correlation of the partition coefficients of 48 volatile organic and environmentally relevant compounds between air and silicone oil Megha J. Patel a, Sudeep C. Popat a,b, Marc A. Deshusses a,⇑ a b

Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708 United States Department of Environmental Engineering and Earth Sciences, Clemson University, Clemson, SC 29625 United States

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


The air-silicon oil partition coefficient

P of 48 relevant chemicals was determined.
An experimental mass balance procedure was reliable, the EPICS method was not.
The effect of the viscosity of silicon oil on P values was quantified.
Within functional groups (alcohols, ketones, etc.), P correlated with the Wiener index.

a r t i c l e

i n f o

Article history: Received 1 September 2016 Received in revised form 15 October 2016 Accepted 18 October 2016 Available online 19 October 2016 Keywords: Air-silicon oil partition coefficient Henry’s constant Partitioning bioreactor QSAR

a b s t r a c t Many emerging bioprocesses include a second, non-aqueous phase liquid in which the key substrate or product partitions for either enhanced interphase mass transfer, concentration dampening, or in-situ product extraction. Knowledge of the partition coefficient between air and the non-aqueous is relevant for process optimization and modeling. In here, experiments were conducted to determine the air-silicon oil partition coefficients (P) of 48 environmentally relevant compounds including many volatile organic compounds, chlorinated solvents, and aromatics compounds. A mass balance method gave reliable results, whereas the EPICS method did not. The value of the ratio of Henry’s constant (H) divided by P which is the partition coefficient between silicone oil and water served as a parameter to distinguish which compound would most benefit from including silicon oil as a non-aqueous phase. P values decreased slightly with decreasing viscosity of silicon oil between 50 and 5 cSt, and a simple relationship was proposed to calculate P at 5 or 50 cSt based on values at 20 cSt. Lastly, a quantitative structure activity relationship (QSAR) model using the Wiener index as a single parameter could successfully fit P values within specific chemical groups. Overall, the mini-database of P values and models developed in this study provide new tools for the optimization and modeling of processes that include silicon oil as a second liquid phase. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction

⇑ Corresponding author. E-mail address: [email protected] (M.A. Deshusses). http://dx.doi.org/10.1016/j.cej.2016.10.086 1385-8947/Ó 2016 Elsevier B.V. All rights reserved.

Environmental biotechnology deals with the use of microorganisms conducting various important biological transformations relevant to energy and the environment [1]. Many microbial

M.J. Patel et al. / Chemical Engineering Journal 310 (2017) 72–78

technologies developed over the last few decades include the use of multiple phases. For example, biofilters and biotrickling filters for air pollution control, or gas-phase bioreactors, are fed pollutants or a substrate via the gas phase. The pollutant or substrate must normally be transported through a film of liquid and be absorbed into a liquid phase before being converted by microorganisms generally growing as biofilms attached on a carrier material or growing as flocs [2–4]. The liquid phase used in such bioreactors is generally an aqueous phase, which provides suitable conditions for bacterial growth by allowing to provide nutrients. In many cases, but more often when dealing with hydrophobic pollutants or substrates, the volumetric performance of these gasphase bioreactors can be limited by the rate of mass transfer of the gaseous chemical species between the phases [3–5]. For instance, the rate of treatment of hydrophobic contaminants such as methane, hexane, nitrous oxide (NO) and several others, in waste gases using biofilters and biotrickling filters, has been limited because the unfavorable partition of these compounds in water results in slow mass transfer [5–7]. Similarly, the use of syngas as a substrate for fermentation to produce ethanol or other liquid fuels has so far not made it to the mainstream in part because of limited rates of providing carbon monoxide and hydrogen, both hydrophobic gases, to the microorganisms at sufficiently fast rates [4,8]. As an attempt to remedy the poor transfer rates of hydrophobic chemicals from the gaseous to the aqueous phase, several investigators have explored adding to the process a second, non-water miscible, liquid phase to enhance gas-liquid mass transfer rate [9]. These efforts have led to the development of bioreactors called two-phase partitioning bioreactors (TPPB) or to the incorporation of a second liquid phase in biotrickling filters, biofilters and similar bioreactors [9–12]. In both cases, a second liquid phase nonmiscible with water is added, in which the chemical species of interest transfer and partition favorably, resulting in enhanced bioreactor performance [10,13]. Examples of such non-aqueous phases include silicon oil, vegetable oil, fatty acids such as oleic acid and even ionic liquids [6,11,13–17]. While there are many reports of enhanced performance by incorporating a second liquid phase, the exact mechanisms leading to enhanced performance are complex and remain poorly understood. In particular, a universally accepted expression for the gas-biphasic liquid mass transfer rate of volatiles compounds is still lacking [5,18–21]. In any case, expression of interphase mass transfer requires knowledge of the air-non aqueous phase partition coefficient of the compounds of interest, a knowledge which is currently mostly lacking. A related benefit and application of using a second liquid phase is that it can act as a reservoir or sink for the key compounds, thereby dampening concentration fluctuations. This can prevent toxicity to the microorganisms during shock loading by spreading the load over time as well as allowing to size bioreactors using the average loading, rather than the peak conditions. This effect has been demonstrated with successful treatment of high concentrations of compounds such as benzene, toluene, styrene etc., in TPPB, which otherwise would be inhibitory (see e.g. [22,23]). The second liquid phase can also release key compound during periods of low concentrations. A simple example is best to illustrate these dampening effects. As determined in this study, the partition coefficient of toluene between silicon oil and water is close to 300 at 23 °C. This means that a bioreactor in which the liquid phase includes just 10% vol. of silicon oil will have (assuming thermodynamic equilibrium between the phases) 30 times more toluene mass in the silicon oil phase than in the aqueous phase (reaction in the aqueous phase can make this factor even greater), thereby constituting a large reservoir of toluene. Another potential application of partitioning bioreactors is in biorefineries, where they can be used to efficiently recover fermentation products, while also reducing product toxicity on

73

microorganisms [24]. This is especially relevant to the bioethanol industry, where the rapid removal of ethanol from the fermentation broth is important to maintain high activity. Additional benefits would also include lowering downstream processing costs. Key to the application of a second liquid phase in bioprocesses is the selection of the non-aqueous phase. The non-aqueous phase should have properties such as biocompatibility and nonbiodegradability, apart from excellent partition for the chemical species of interest [11]. With respect to air pollution control and gas treatment, silicone oil has many of the desired properties, and thus has been the most frequent choice in laboratory studies. However, despite its widespread use in research studies, the partitioning properties of chemicals of interest in silicone oil have not been systematically investigated. This limits our ability to model the performance of TPPB, or to extend applications to other relevant pollutants or chemicals. Thus, in this study, the air-silicon oil partition coefficients (P) of nearly 50 environmentally relevant compounds were determined. Two different methods were used and the results were compared. A quantitative structure-activity relationship (QSAR) model that allows one to predict the partition coefficients of unknown compounds was developed and the effect of silicone oil viscosity, a relatively unexplored parameter, on partition coefficients was also determined. 2. Materials and methods 2.1. Chemicals Silicone oils (Dow Corning Corporation 200Ò fluid) of viscosities 5, 20 and 50 cSt were obtained from Sigma-Aldrich (St. Louis, MO). All gaseous VOCs, as well as H2S and CO2, were obtained in the highest purity available from Airgas National Welders (Morrisville, NC). Liquid VOCs were obtained in the highest purity available from Sigma-Aldrich (St. Louis, MO), EMD Chemicals (Gibbstown, NJ) and Mallinckrodt Baker (Phillipsburg, NJ). 2.2. Air-liquid partitioning experiments All partitioning experiments were performed in 40 mL I-CHEM vials for EPA method 5035 with leak-proof closure and low-bleed septum, which were obtained from VWR (Durham, NC). For each compound, three separate vials were used in which different volumes of silicone oil (3, 5 and 7 mL) and gas headspace (37, 35 and 33 mL) were taken, but the same amount of compound was injected. These were allowed to equilibrate on a rotary shaker at 150 RPM for 2–4 h, and then analyzed for the compound in the gas headspace. All experiments were performed at 23 ± 1 °C and atmospheric pressure. 2.3. Partition coefficients by the mass balance method The equilibrium liquid concentration (C⁄L, in g m3) was determined by performing a mass balance from the initial amount of compound injected in each vial and the equilibrium gas concentration (C⁄G, in g m3) determined through gas analysis (Eq. (1)). The partition coefficient P was determined as the ratio of C⁄G to C⁄L, and value were averaged for the three flasks used for each compound. Uncertainties in the reported values were calculated as standard deviations of the three values.

C L ¼

M T  ðC G  V G Þ VL

ð1Þ

where MT = total mass of VOC injected in the vial initially (g); VG = gas headspace volume (m3); and VL = volume of silicone oil (m3). P is then calculated as C⁄G/C⁄L.

74

M.J. Patel et al. / Chemical Engineering Journal 310 (2017) 72–78

2.4. Partition coefficients using EPICS method

3. Results and discussion

A modification to the EPICS (Equilibrium Partitioning in Closed Systems) method was proposed by Gossett [25] as an alternative to the mass balance method for determining partition coefficients of compounds between gas and liquid phases. This method requires knowing only the gas chromatograph peak area of a particular compound in the gas headspace, thus circumventing the need for calibrating the gas chromatograph. Determination of the partition coefficient requires the use of two vials containing different gas and liquid volumes that are injected with the same amount of the compound. Eq. (2) then is used to determine the partition coefficient of the compound.

3.1. Database of partition coefficients

P¼

V L2  ðR  V L1 Þ ðR  V G1 Þ  V G2

ð2Þ

where P (uniteless) is the partition coefficient = C⁄G/C⁄L; VL1 and VL2 are the liquid volumes (in m3) in vials 1 and 2, respectively; VG1 and VG2 are the gas volumes (in m3) in vials 1 and 2, respectively; and R is the ratio of the gas chromatograph peaks’ areas from gas headspace of vial 1 to vial 2. In the present study, three vials, each with the same amount of injected compound but different gas and liquid volumes (as described before), were used, and thus partition coefficients were calculated using gas chromatograph peak area of the compound in headspace from a combination of any two of the three vials. Three values for partition coefficient of each compound were thus determined, and the reported values are averages of the three. Uncertainties in the reported values were calculated as standard deviations of the three values. 2.5. Quantitative structure activity relationships (QSARs) To develop some understanding of the effect of functional groups of chemicals on the partition coefficients, and perhaps predict partition coefficients for untested compounds in silicon oils, Quantitative structure activity relationships (QSARs) were developed. QSARs for the partition coefficient of each of the different classes of compounds (alcohols, ketones, etc.) were correlated using the Wiener index (W). The Wiener index is a topological index, which translates the structure of a molecule to a number that depends on the chemical bonds and on its branching [26]. The index has been used in correlating a number of physical and chemical properties, such as density, viscosity, critical point and others [26,27]. The Wiener index of many compounds is available in the literature; those that are not readily available can be easily calculated using the methodology described by Wiener [26]. 2.6. Analytical methods The concentrations of all VOCs in the gas headspace of the vials were measured on Shimadzu GC-2014 gas chromatograph (Columbia, MD) fitted with a DB-624 capillary column 30 m in length, 0.32 mm in ID and 1.8 lm in film thickness (Agilent Technologies, Santa Clara, CA), and a flame ionization detector maintained at 225 °C. Different oven temperature programs were used for different VOCs, to ensure that accurate quantification could be carried out (details not listed here). A minimum 3-point calibration was performed for each VOC using standards prepared by injecting known amounts in 5 L Tedlar bags. Gas-phase H2S concentrations were determined using a Jerome 631X series meter (Arizona Instruments, Tempe, AZ), while gas-phase CO2 concentrations were determined using a Vernier NDIR probe with data logger (Vernier Software & Technology, Beaverton, OR).

The air/water Henry’s constants (H) and the partition coefficients (P) between air and silicone oil (20 cSt, P) of the 48 compounds that were investigated, organized by functional group, are listed in Table 1. The H values are from Sander [28] while P values reported in Table 1 are those determined from the mass balance method, as the EPICS method did not provide reliable values, as will be discussed later. The different classes of compounds in this database categorized by their functional group include alkanes, alcohols, aromatic hydrocarbons, chlorinated compounds, esters, ketones, ethers and other miscellaneous compounds relevant to gas phase bioreactors and environmental biotechnology applications. Table 1 reports standard deviations for individual P values that are in the range of 2–15% (average 8%) which was deemed acceptable for this type of measurement. Methane was the highest at 20% for no obvious reasons, while P values for the rest of alkanes had low standard deviations. Examination of the standard deviations vs. P values or within classes of compounds did not reveal any particular trend. There are not many references for validation of these P values. Heymes et al. [29] lists P values for toluene in 20 cSt silicon oil ranging from 6.9  104 to 9.7  104, at 20 °C which is comparable to the value determined here of 8.9 (±0.2)  104. This provides some degree of validation of our data. Table 1 also lists values for H/P, which is the ratio of the Henry’s constant of a particular compound to its partition coefficient in silicone oil. This ratio is also the partition coefficient between silicone oil and water. If H/P is greater than 1, it means that the compound preferably partitions in silicone oil rather than water, while if H/P is smaller than 1, the compound will preferentially partition in water. Thus the H/P parameter can help to determine if there could be any enhancement in mass transfer by inclusion of a nonaqueous silicone oil phase for any particular compound. The H/P parameter together with the P value and the volumetric fraction of the non-aqueous phase also allow to quantify how much of a ‘‘sink” the silicon oil will be for the particular compound. Simple calculations similar to the one presented for toluene in the introduction will enable rapid assessment of the buffering or sink effect brought by the non-aqueous phase. This is particularly relevant for compounds that are toxic and whose concentrations are fluctuating. The non-aqueous phase will act as a buffer for these compounds, limiting the aqueous concentrations and thereby resulting in a decrease in the toxicity to the microorganisms [22,29,30]. For example, for all n-alkanes used in this study, the H/P ratio ranges from 34 for methane, and increases with increasing numbers of carbon atoms to over 300,000 for decane. Thus the use of silicone oil as a non-aqueous phase in gas phase bioreactors such as TPPBs dealing with vapors of alkanes would be useful since significant enhancements in gas-liquid mass transfer are probable. Indeed, several authors have demonstrated significant improvements of hexane vapor treatment in biotrickling filters when incorporating silicon oil in the trickling liquid (see e.g. [6]). Similarly, H/P ratios for volatile aromatic hydrocarbons and chlorinated compounds were found to be significantly larger than 1 suggesting possible beneficial use of silicon oil when dealing with, or treating these compounds. Examples of such application include toxicity reduction [29,30]. In contrast, the H/P ratios for all alcohols studied were less than 1, indicating unfavorable partition of alcohols in silicon oil. This can be explained by the highly hydrophilic nature of the alcohols that were tested, and the hydrophobic nature of silicon oil. With

75

M.J. Patel et al. / Chemical Engineering Journal 310 (2017) 72–78

Table 1 Dimensionless Henry’s constants H for air/water partition at 23 °C (reported as Cg/Cwater in mass concentration from Sander [28] and corrected for temperature), air-silicone oil partition coefficients (P) at 23 ± 1 °C determined experimentally in this study with 20 cSt silicone oil (±standard deviation of three measurements, values from the mass balance method), ratios H/P = Csilicone/Cwater, and Wiener index, for the compounds used in this study, organized by functional group. Compound

Henry’s Constant, H ()

Air-Silicone Oil Partition Coefficient, P ()

Alkanes Methane Ethane Propane Isopentane n-Pentane n-Hexane n-Heptane n-Decane

H/P ()

Wiener Index, W ()

28 20 26 56* 47 61 84 292*

8.2 3.2 8.7 1.6 1.1 4.4 2.5 9.3

(±1.6)  101 (±0.1)  101 (±0.2)  102 (±0.1)  102 (±0.1)  102 (±0.2)  103 (±0.2)  103 (±0.1)  104

34 63 297 3500 4283 13,791 33,422 313,978

0 1 4 18 20 35 56 165

Alcohols Methanol Ethanol 2-Propanol 1-Propanol tert-Butanol 2-Butanol n-Butanol

1.6  104 1.8  104 2.6  104 2.5  104 4.8  104 3.1  104 2.8  104

5.0 1.9 9.2 6.0 9.0 4.2 2.4

(±0.5)  102 (±0.1)  102 (±0.9)  103 (±0.6)  103 (±0.9)  103 (±0.4)  103 (±0.2)  103

3.3  103 9.7  103 2.8  102 4.1  102 5.4  102 7.4  102 1.2  101

1 4 9 10 16 18 20

Aromatic hydrocarbons Benzene Toluene o-Xylene m-Xylene Styrene Ethylbenzene

0.22 0.24 0.18 0.26 0.10 0.28

2.4 8.9 3.0 3.5 4.1 4.2

(±0.1)  103 (±0.2)  104 (±0.3)  104 (±0.5)  104 (±0.6)  104 (±0.1)  104

90 274 610 749 236 658

27 42 60 61 64 64

Chlorinated Compounds Dichloromethane Chloroform cis-1,2-dichloroethene Carbon tetrachloride 1,1,1-Trichloroethane 1,1,2-Trichloroethene 1,1,2,2-Tetrachloroethene Chlorobenzene

0.10 0.15 0.16 1.1 0.62 3.3  102 1.5  102 0.14

8.7 3.4 3.6 2.2 2.8 1.4 5.4 4.6

(±0.7)  103 (±0.1)  103 (±0.2)  102 (±0.1)  103 (±0.1)  103 (±0.1)  103 (±0.7)  104 (±0.1)  104

12 43 4 490 221 24 28 298

4 9 16 16 18 29 47 58

Ketones Acetone Methyl ethyl ketone 3-Pentanone Methyl isobutyl ketone 2-Heptanone

1.3  103 2.0  103 2.2  103 8.5  103 5.2  103

1.4 4.6 1.6 1.1 1.9

(±0.1)  102 (±0.3)  103 (±0.1)  103 (±0.1)  103 (±0.2)  104

0.1 0.4 1.4 8 27

9 18 32 44 NA

Esters Ethyl formate Ethyl acetate Isobutyl acetate n-Butyl acetate Ethyl butyrate

1.1  102 6.0  103 1.3  102 1.0  102 1.2  102

1.0 3.9 7.7 6.3 6.6

(±0.1)  102 (±0.4)  103 (±0.5)  104 (±0.7)  104 (±0.5)  104

1.1 2 17 16 18

20 32 71 75 79

Ethers Diethyl ether Methyl tert-butyl ether Tert-amyl methyl ether Di-n-butyl ether

3.2  102 1.9  102 4.0  102 0.17

1.2 3.7 2.1 3.1

(±0.2)  102 (±0.2)  103 (±0.3)  103 (±0.5)  104

3 5 19 534

NA

Miscellaneous Cyclohexane Cyclohexanone Tetrahydrofuran Hydrogen sulfide Carbon dioxide

7.0 9.2  104 2.5  103 0.38 1.2

2.1 4.8 3.3 1.0 1.5

(±0.1)  103 (±0.8)  104 (±0.4)  103 (±0.1)  101 (±0.2)  101

3336 2 0.8 4 8

NA

NA = not available. * Value at 25 °C.

the exception of butanol, all alcohols tested were fully water soluble. Thus using silicone oil when alcohols are involved, for example during fermentation to collect the produced alcohols, will probably not be effective. Instead a different non-aqueous phase with a higher H/P ratio for the alcohol of interest, will be necessary. Only moderately favorable partition towards silicon oil was found for ketones (except acetone and methyl ethyl ketone that have H/P ratios 0.99). The partition coefficient in 5 cSt and 50 cSt silicon oils could be adequately determined from the partition coefficient in 20 cSt silicone oil using the following relationships.

3.2. Comparison of mass balance and EPICS methods A comparison of the P values between air and silicone oil (20 cSt) obtained by mass balance and the modified EPICS methods is shown in Fig. 1. It is obvious from that figure that for most compounds, the two methods yielded very different values, in some cases apart by over an order of magnitude, and that P values obtained using the EPICS method were generally higher. Further, while standard deviations for P determined by the mass balance method were reasonable, this was not the case with the modified EPICS method, for which standard deviations sometimes extended into negative values. We attribute the failure to obtain consistent P values to some underlying assumptions of the method, one being that GC calibration passes through the origin, and the high sensi-

Fig. 2. Relationship for P in 5 and 50 cSt silicone oil vs. P in 20 cSt silicone oil at 23 ± 1 °C. Six compounds (tetrachloroethene, hexane, n-pentane, acetone, ethanol, and methanol from left to right) representing the different functional groups were used to establish the relationship.

Fig. 1. Comparison of air-silicone oil partition coefficients (23 ± 1 °C in 20 cSt silicone oil) as determined using the EPICS and mass balance methods (methane and ethane are not shown, P = 0.82 and 0.32, respectively). The dashed line represents perfect match between both methods. Each data point is a single compound; error bars are standard deviations from three measurements (not shown for mass balance method because the error bars were too small to display). Inset shows a zoomed in version of the main graph.

77

M.J. Patel et al. / Chemical Engineering Journal 310 (2017) 72–78

Table 2 List of QSAR equations to determine P at 23 ± 1 °C for different functional groups of chemicals. Two relationships (power and exponential) are shown, with the R2 values for each fit. Class

Power

Exponential R2

QSAR Alkanes Alcohols Aromatic hydrocarbons Chlorinated hydrocarbons Ketones Esters

1.193

P = 0.3774 W P = 0.0556 W0.877 P = 3.4549 W2.213 P = 0.0483 W1.098 P = 0.4405 W1.592 P = 4.4481 W2.034

0.99 0.88 0.95 0.93 0.99 1.00

P5cSt ¼ 0:9193  P20cSt

ð3Þ

P50cSt ¼ 1:0748  P20cSt

ð4Þ

The above relationships suggest that the partition coefficients decrease with decreasing the oil viscosity, but only marginally. This contrasts with the recent results of Guillerm et al. [34] who found that the partition coefficient of toluene did not vary with the viscosity of silicon oil between 5 and 100 cSt. It is not clear why different outcomes were found, though it is possible that Guillerm et al. experiments with only toluene, four different silicon oils and standard deviations of about 10% did not allow to discern small (7–9%) differences in P. The correlations reported in Eqs. (3) and (4) indicate that silicone oils with lower viscosities would have more favorable properties for application in gas-phase bioreactors. Because only three different silicone oils were used, it is not possible to derive an accurate relationship between partition coefficients and the viscosity of other silicone oils. Nonetheless, it appears that this relationship would probably not be linear. We note that a lower viscosity of the non-aqueous phase should also ease mixing of the two liquid phases and facilitate liquid-liquid or liquid-biofilm mass transfer because of reduced boundary layer thickness with lower viscosities. 3.4. QSAR relationships For the prediction of P for compounds other than those used in this study, quantitative structure activity relationships (QSARs) would be useful. As mentioned earlier, attempts were made to use the Wiener index to correlate P. No single correlation was suited for all compounds. This was expected given the inability of a single number to capture the large range of physical and chemical

R2

QSAR 0.028W

P = 0.0414e P = 0.0371e0.127W P = 0.0083e0.05W P = 0.0069e0.051W P = 0.0195e0.07W P = 0.0209e0.046W

0.62 0.83 0.92 0.93 0.93 0.98

properties of the 48 compounds that were tested. However, within each functional group (with the exception of alkanes and the exponential fit), good to excellent correlation of P was obtained both with a power law or with an exponential relationship (Table 2). Most studies that have used the Wiener index for QSARs have used exponential relationships [27]. Here, Fig. 3 shows a comparison of experimental and fitted P values using both power and exponential relationships. The Figure illustrates the excellent fit obtained with both relationships. One obvious outlier was observed: cis-1,2dichloroethene for which the misfit is about one order of magnitude. The reason for the discrepancy is unknown. 4. Conclusions The air-silicon oil partition coefficients (P) of 48 environmentally relevant compounds including many volatile organic compounds, chlorinated solvents, and aromatics compounds were determined experimentally. The mass balance method provided reliable results, whereas the EPICS method did not. The ratio of Henry’s constant H divided by P, which correspond to the partition coefficient between silicone oil and water, was proposed as a parameter allowing screening non-aqueous phases and key compound combinations for process improvements when including a non-aqueous phase. Typically, all alcohols tested had H/P well below 1, indicating that silicon oil was not a favorable second liquid phase for alcohols. Most other compounds tested had H/P values well above 1, sometimes up to several hundreds or thousands indicating that the compounds had a high affinity for silicon oil and that significant process enhancements could be expected when including silicon oil as a second liquid phase. In the range of 5– 50 cSt, decreasing the viscosity of the silicon oil decreased P and simple prorating equations were developed that allowed to determine P values for 5 cSt and 50 cSt silicon oils from the P value in 20 cSt silicon oil. Finally, a QSAR model was developed which enabled calculation of P values for specific chemical groups (alkanes, alcohols, aromatic hydrocarbons, chlorinated hydrocarbons, ketones, esters) using the Wiener index. Both power and exponential relationships resulted in excellent fit of the P values. Overall, the results of this study provide useful data for the optimization and modeling of partitioning bioreactors. References

Fig. 3. Comparison of power and exponential fits for P values at 23 ± 1 °C using QSAR relationships based on the Wiener index for alkanes, alcohols, aromatic hydrocarbons, chlorinated hydrocarbons, ketones, and esters (see Table 2 for equations).

[1] B.E. Rittmann, P.L. McCarty, Environmental Biotechnology: Principles and Applications, McGraw-Hill, 2001. [2] M.A. Deshusses, Biological waste air treatment in biotrickling filters, Curr. Option Biotechnol. 8 (1997) 335–339. [3] C. Kennes, E.R. Rene, M.C. Veiga, Bioprocesses for air pollution control, J. Chem. Technol. Biotechnol. 84 (2009) 1419–1436, http://dx.doi.org/10.1002/ jctb.2216. [4] P.C. Munasinghe, S.K. Khanal, Biomass-derived syngas fermentation into biofuels, Biofuels 101 (2011) 79–98, http://dx.doi.org/10.1016/B978-0-12385099-7.00004-8. [5] N.J.R. Kraakman, J. Rocha-Rios, M.C.M. Van Loosdrecht, Review of mass transfer aspects for biological gas treatment, Appl. Microbiol. Biotechnol. 91 (2011) 873–886, http://dx.doi.org/10.1007/s00253-011-3365-5.

78

M.J. Patel et al. / Chemical Engineering Journal 310 (2017) 72–78

[6] R. Munoz, S. Arriaga, S. Hernandez, B. Guieysse, S. Revah, Enhanced hexane biodegradation in a two phase partitioning bioreactor: overcoming pollutant transport limitations, Process Biochem. 41 (2006) 1614–1619, http://dx.doi. org/10.1016/j.procbio.2006.03.007. [7] W.F. Yang, H.J. Hsing, Y.C. Yang, J.Y. Shyng, The effects of selected parameters on the nitric oxide removal by biofilter, J. Hazard. Mater. 148 (2007) 653–659, http://dx.doi.org/10.1016/j.jhazmat.2007.03.023. [8] M. Köpke, C. Mihalcea, F. Liew, J.H. Tizard, M.S. Ali, J.J. Conolly, B. Al-Sinawi, S.D. Simpson, 2,3-Butanediol production by acetogenic bacteria, an alternative route to chemical synthesis, using industrial waste gas, Appl. Environ. Microbiol. 77 (2011) 5467–5475, http://dx.doi.org/10.1128/AEM.00355-11. [9] R. Munoz, S. Villaverde, B. Guieysse, S. Revah, Two-phase partitioning bioreactors for treatment of volatile organic compounds, Biotechnol. Adv. 25 (2007) 410–422, http://dx.doi.org/10.1016/j.biotechadv.2007.03.005. [10] G. Quijano, M. Hernandez, F. Thalasso, R. Munoz, S. Villaverde, Two-phase partitioning bioreactors in environmental biotechnology, Appl. Microbiol. Biotechnol. 84 (2009) 829–846. [11] A.J. Daugulis, Two-phase partitioning bioreactors: a new technology platform for destroying xenobiotics, Trends Biotechnol. 19 (2001) 457–462, http://dx. doi.org/10.1016/S0167-7799(01)01789-9. [12] S.M. Zamir, S. Babatabar, S.A. Shojaosadati, Styrene vapor biodegradation in single- and two-liquid phase biotrickling filters using Ralstonia eutropha, Chem. Eng. J. 268 (2015) 21–27, http://dx.doi.org/10.1016/j.cej.2015.01.040. [13] L.D. Collins, A.J. Daugulis, Benzene/toluene/p-xylene degradation. Part I. Solvent selection and toluene degradation in a two-phase partitioning bioreactor, Appl. Microbiol. Biotechnol. 52 (1999) 354–359, http://dx.doi.org/ 10.1007/s002530051531. [14] R. Melgarejo-Torres, C.O. Castillo-Araiza, P. López-Ordaz, N.V. CallejaCastañeda, J.L. Cano-Velasco, R.M. Camacho-Ruíz, G.J. Lye, S. Huerta-Ochoa, Evaluation of ionic liquids as dispersed phase during the production of lactones with E. coli in a three phase partitioning bioreactor, Chem. Eng. J. 279 (2015) 379–386, http://dx.doi.org/10.1016/j.cej.2015.04.127. [15] M.D. Baumann, A.J. Daugulis, P.G. Jessop, Phosphonium ionic liquids for degradation of phenol in a two-phase partitioning bioreactor, Appl. Microbiol. Biotechnol. 67 (2005) 131–137, http://dx.doi.org/10.1007/s00253-004-17682. [16] E. Kan, M.A. Deshusses, Development of foamed emulsion bioreactor for air pollution control, Biotechnol. Bioeng. 84 (2003) 240–244, http://dx.doi.org/ 10.1002/bit.10767. [17] A. Arca-Ramos, G. Eibes, M.T. Moreira, G. Feijoo, J.M. Lema, Vegetable oils as NAPLs in two phase partitioning bioreactors for the degradation of anthracene by laccase, Chem. Eng. J. 240 (2014) 281–289, http://dx.doi.org/10.1016/j. cej.2013.11.076. [18] E. Dumont, G. Darracq, A. Couvert, C. Couriol, A. Amrane, D. Thomas, Y. Andrès, P. Le Cloirec, Volumetric mass transfer coefficients characterising VOC absorption in water/silicone oil mixtures, Chem. Eng. J. 221 (2013) 308–314, http://dx.doi.org/10.1016/j.cej.2013.01.102. [19] E. Dumont, Y. Andrès, P. Le Cloirec, Mass transfer coefficients of styrene into water/silicone oil mixtures: new interpretation using the ‘‘equivalent

[20]

[21]

[22]

[23]

[24]

[25] [26] [27] [28] [29]

[30] [31]

[32]

[33]

[34]

absorption capacity” concept, Chem. Eng. J. 237 (2014) 236–241, http://dx. doi.org/10.1016/j.cej.2013.10.021. A.D. Dorado, E. Dumont, R. Muñoz, G. Quijano, A novel mathematical approach for the understanding and optimization of two-phase partitioning bioreactors devoted to air pollution control, Chem. Eng. J. 263 (2015) 239–248, http://dx. doi.org/10.1016/j.cej.2014.11.014. R. Melgarejo-Torres, C.O. Castillo-Araiza, A. Dutta, G. Bény, D. Torres-Martinez, M. Gutiérrez-Rojas, G.J. Lye, S. Huerta-Ochoa, Mathematical model of a three phase partitioning bioreactor for conversion of ketones using whole cells, Chem. Eng. J. 260 (2015) 765–775, http://dx.doi.org/10.1016/j.cej.2014.08.097. D.R. Nielsen, A.J. Daugulis, P.J. McLellan, Transient performance of a two-phase partitioning bioscrubber treating a benzene contaminated gas stream, Environ. Sci. Technol. (2005) 8971–8977, http://dx.doi.org/10.1002/bit.20876. E.R. Rene, M. Montes, M.C. Veiga, C. Kennes, Styrene removal from polluted air in one and two-liquid phase biotrickling filter: steady and transient-state performance and pressure drop control, Bioresour. Technol. 102 (2011) 6791– 6800, http://dx.doi.org/10.1016/j.biortech.2011.04.010. J.J. Malinowski, Two-phase partitioning bioreactors in fermentation technology, Biotechnol. Adv. 19 (2001) 525–538, http://dx.doi.org/10.1016/ S0734-9750(01)00080-5. J.M. Gossett, Measurement of Henry’s law constants for C1 and C2 chlorinated hydrocarbons, Environ. Sci. Technol. 21 (1987) 202–208. H. Wiener, Structural determination of paraffin boiling points, J. Am. Chem. Soc. 69 (1947) 17–20. S. Nikolic, N. Trinajstic, Z. Mihalic, The Wiener Index – Development and applications, Croat. Chem. Acta 68 (1995) 105–129. R. Sander, Compilation of Henry’s law constants (version 4.0) for water as solvent, Atmos. Chem. Phys. 15 (2015) 4399–4981. F. Heymes, P. Manno-Demoustier, F. Charbit, J.L. Fanlo, P. Moulin, A new efficient absorption liquid to treat exhaust air loaded with toluene, Chem. Eng. J. 115 (2006) 225–231, http://dx.doi.org/10.1016/j.cej.2005.10.011. Z.Z. Ismail, I.A. Abdulrazzak, Enhanced crude oil biodegradation in a two-liquid phase partitioning bioreactor, Chem. Biochem. Eng. Q. 29 (2015) 467–473. M.C. Tomei, S. Rita, D. Mosca Angelucci, M.C. Annesini, A.J. Daugulis, Treatment of substituted phenol mixtures in single phase and two-phase solid-liquid partitioning bioreactors, J. Hazard. Mater. 191 (2011) 190–195, http://dx.doi. org/10.1016/j.jhazmat.2011.04.061. N.Y. Fortin, M.A. Deshusses, Treatment of methyl tert-butyl ether vapors in biotrickling filters. 2. Analysis of the rate-limiting step and behavior under transient conditions, Environ. Sci. Technol. 33 (1999) 2987–2991, http://dx. doi.org/10.1021/es981338k. S. Kim, M.A. Deshusses, Understanding the limits of H2S degrading biotrickling filters using a differential biotrickling filter, Chem. Eng. J. 113 (2005) 119–126, http://dx.doi.org/10.1016/j.cej.2005.05.001. M. Guillerm, A. Couvert, A. Amrane, É. Dumont, E. Norrant, N. Lesage, C. Juery, Characterization and selection of PDMS solvents for the absorption and biodegradation of hydrophobic VOCs, J. Chem. Technol. Biotechnol. 91 (2016) 1923–1927, http://dx.doi.org/10.1002/jctb.4792.