Article pubs.acs.org/jced
Separation of Acetic Acid, Formic Acid, Succinic Acid, and Lactic Acid Using Adsorbent Resin Hee-Geun Nam, Geon-Woo Lim, and Sungyong Mun* Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea ABSTRACT: The single-component adsorption equilibria of acetic acid and formic acid on an Amberchrom-CG300C resin were measured using a staircase frontal analysis method over the temperature range from (30 to 50) °C at liquid-phase concentrations of up to 10 g·L−1. The resultant adsorption data were correlated with the Langmuir model, and the corresponding model parameters were determined. In addition, the effect of temperature on the Langmuir equilibrium constants was investigated, and the results were utilized to determine the thermodynamic parameters for the adsorption of interest. The results of these investigations indicated that the adsorption of each organic acid onto Amberchrom CG300C is exothermic and controlled by physical mechanisms. Furthermore, a temperature-modulated adsorption model for each organic acid was developed by incorporating its thermodynamic parameters into the Langmuir model. The resultant temperature-modulated Langmuir model equation was confirmed to be highly accurate in predicting all of the adsorption equilibrium data acquired over the investigated temperature range. The adsorption equilibrium data and the model parameters reported in this study will be of great value in designing a chromatographic process for separating the organic acid mixture resulting from fermentation based on Actinobacillus bacteria.
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INTRODUCTION The use of fermentation technology for producing highly useful organic acids has attracted worldwide attention because of its potential merits in both economical and environmental aspects.1−3 In connection with such a trend, it is particularly worth paying attention to the fermentation process based on Actinobacillus bacteria.3−5 This fermentation was reported to produce three useful organic acids, namely, acetic acid, formic acid, and succinic acid,3−5 all of which have a variety of applications in industry. First, acetic acid (or ethanoic acid) is known to be used in the production of poly(vinyl acetate) for paints and adhesives.1 It also plays an important role in the production of ethyl acetate as a green solvent.1 Its further application scope includes the production of food additives and aspirin. Second, formic acid (or methanoic acid) is known to be used in the production of food preservatives and antibacterial agents.6 Like these two acids, succinic acid (or butanedioic acid) is also regarded as highly useful because it can be used as a specialty chemical for the production of surfactants, detergents, foaming agents, ion chelators, cosmetics, pharmaceuticals, and antibiotics.1−3 Because of the above noteworthy applications, there has been increasing interest in the development of an efficient chromatographic process for separating acetic acid, formic acid, and succinic acid with high productivity. To facilitate the development of such a chromatographic process, it is essential to optimize several parameters associated with the process dimension and the process operation.7−9 The role of this optimization becomes more influential as the targeted process © 2012 American Chemical Society
belongs to higher levels like simulated moving bed and temperature-gradient processes.9−11 One of the key prerequisites for such process optimization is to select a proper adsorbent and to obtain the relevant adsorption equilibrium data on the selected adsorbent. The objective of this study was to accomplish the aforementioned prerequisites, which can serve as essential information in the stage of optimizing the chromatographic process for separation of acetic acid, formic acid, and succinic acid. First, an Amberchrom-CG300C resin was chosen as the adsorbent of this study and tested to determine whether it would be appropriate for the separation of interest. The Amberchrom-CG300C resin was chosen because its effectiveness in the separation of other organic acids such as succinic acid and lactic acid was verified in our previous study.12 It was thus expected that the Amberchrom-CG300C resin would also be effective in separating the three organic acids of interest in this work (i.e., acetic acid, formic acid, and succinic acid). Second, the adsorption equilibria of acetic acid and formic acid on the Amberchrom-CG300C resin were measured over the temperature range from (30 to 50) °C using a staircase frontal analysis method, which has been recognized as a highly accurate one for measuring adsorption equilibria.12−16 In case of succinic acid and lactic acid, the relevant equilibrium data on the Amberchrom-CG300C resin have already been reported in Received: October 3, 2011 Accepted: June 20, 2012 Published: July 3, 2012 2102
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the literature.12 These reported data were utilized for the purpose of comparison with the data for acetic acid and formic acid obtained in this study. Furthermore, the effects of temperature on the adsorption equilibria of each organic acid were investigated, after which the thermodynamic parameters were clarified for the adsorption system considered. Finally, an appropriate model for predicting the measured equilibrium data over the investigated temperature range has been suggested, and its relevant parameters were determined.
(Cfeed,i) to remain the same, which means that Ci virtually belongs to the category of controllable variables. It is thus straightforward to determine a series of adsorption equilibrium data (qi vs Ci) in which Ci = Cfeed,i and qi are obtained from the aforementioned procedures. This is how a series of adsorption equilibrium data that covers a targeted range of liquid-phase concentrations are acquired using the SFA method.
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EXPERIMENTAL SECTION Materials. Acetic acid and formic acid were purchased from Mallinckrodt Baker (Paris, KY) and Fluka (St. Louis, MO) respectively. The Amberchrom-CG300C resin with an average diameter of 120 μm that was used as the adsorbent (solid phase) was obtained from Rohm and Haas (Philadelphia, PA). This adsorbent was packed into an Omnifit chromatographic column (Bio-Chem Fluidics Co., Boonton, NJ) having a diameter of 1.5 cm and a packing length of 11.6 cm. The interparticle and intraparticle void fractions of the Amberchrom-CG300C column were reported to be 0.376 and 0.723, respectively.12 A column jacket (Bio-Chem Fluidics) was used to control the column temperature. Apparatus. The experiments were conducted with the same HPLC system that was employed in our previous work.12 As shown in Figure 1, the HPLC system consisted of two HPLC
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THEORY Adsorption Equilibrium in Continuous Chromatographic Separation Processes. In a continuous chromatographic process, various solute molecules are separated on the basis of the difference in their adsorption affinities for the solid particles that are packed in multiple columns. In connection with the adsorption equilibrium, each such chromatographic column creates two distinct phases, the solid phase (or adsorbent phase) and the liquid phase.16 Under these conditions, the solute molecules present in the liquid phase are moved toward the solid phase and then adsorbed onto the solid surface. At this time, the amount of adsorbed solute per unit volume of solid (i.e., the solid-phase concentration) is always related to the concentration of the solute in the liquid phase (i.e., the liquid-phase concentration). Such a relationship between the liquid-phase and solid-phase concentrations at equilibrium is usually called an adsorption isotherm, which is known to serve as a key piece of information in the design of a continuous chromatographic separation process.16 To clarify the adsorption isotherm for this purpose, one should acquire a series of adsorption equilibrium data, that is, a set of solid-phase concentrations in equilibrium with a given range of liquid-phase concentrations. Determination of Adsorption Isotherms by the Staircase Frontal Analysis Method. One of the wellestablished methods for obtaining adsorption equilibrium data with high accuracy is the staircase frontal analysis (SFA) method.12−16 The SFA method begins with the feeding of a solution with known concentration into a chromatographic column packed with adsorbent, which is continued until the equilibrium state between the solid and liquid phases is attained. Once the equilibrium state is maintained inside the column, the liquid-phase concentration is kept uniform over the entire chromatographic bed, including the column void space. Under such circumstances, the equilibrium liquid-phase concentration (C) becomes identical with the concentration of the feed solution (Cfeed). This state enables us to estimate the solid-phase concentration in equilibrium with the uniform liquid-phase concentration (i.e., Cfeed) through material balance over the entire bed. The above procedure is then repeated while increasing Cfeed stepwise. Such a stepwise increase in Cfeed is implemented after the confirmation of a perfect equilibrium state inside the column, which can easily be discerned by monitoring the concentration of effluent from the column outlet. These patterns of repetitive procedures are continued until the final value of Cfeed reaches the highest liquid-phase concentration investigated (i.e., the upper bound for the liquid-phase concentration). This can eventually allow us to determine a set of solid-phase concentrations (qi) in equilibrium with a given range of liquid-phase concentrations (Ci). As mentioned above, the SFA method allows the equilibrium liquid-phase concentrations (Ci) and the feed concentrations
Figure 1. Schematic diagram of the experimental system used in this study.
pumps (Young Lin SP-930D), a refractive index detector (Young Lin 750F), an HPLC mixer (Analytical Scientific Instruments Co., El Sobrante, CA), a circulator (HST-250WL, Hanbaek Co., Bucheon, South Korea), and a water bath (BW20G, Jeio Tech Co., Daejon, South Korea). The experimental data from this HPLC system were collected and analyzed with the help of automatic data acquisition software (Young Lin Autochro-3000) operating in the Windows environment. A Milli-Q system (Millipore, Bedford, MA) was used to obtain distilled deionized water (DDW), which constituted the liquid phase in the adsorption equilibrium experiments performed. Procedure. As mentioned earlier, the procedure for measuring the adsorption equilibria in this study was based on the SFA method, whose principles were explained in detail in the theory section. To realize the SFA principle experimentally, the two HPLC pumps in Figure 1 were utilized in accordance with the procedure described in our previous publication.12 As shown in Figure 1, the first pump was used to deliver DDW and the second pump to deliver the stock solution, which was prepared by dissolving either acetic acid or 2103
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Adsorption Equilibria of Acetic Acid and Formic Acid on Amberchrom-CG300C. Since the Amberchrom-CG300C adsorbent under consideration turned out to be effective in the separation of interest, it was quite worthwhile to secure the adsorption equilibrium data for all four acids on the proven adsorbent over a wide range of temperature and liquid-phase concentration. In relation to such a study, only the equilibrium data for succinic acid and lactic acid were reported in our previous publication.12 In contrast, no equilibrium data for acetic acid and formic acid on Amberchrom-CG300C resin are available in the literature. Such data were obtained in the present study. For the aforementioned task, a series of chromatographic experiments based on the SFA method were carried out to measure the single-component adsorption equilibria of acetic acid and formic acid on Amberchrom CG300C over the temperature range from (30 to 50) °C and the liquid-phase concentration range from (0 to 10) g·L−1. The resultant equilibrium data are listed in Table 1. On the basis of these
formic acid in DDW. The concentration of each organic acid in the stock solution was kept constant at 10 g·L−1 throughout the experiments. The streams from the two pumps were mixed before being fed into the column packed with AmberchromCG300C. Such a mixing process was facilitated by the HPLC mixer, which enabled the two streams to attain a state of perfect mixing before they were fed into the packed column. The total flow rate for the mixed stream fed into the column was kept constant at 2 mL/min. The concentration of the solution fed into the column (i.e., Cfeed) was controlled by adjusting the ratio of the two streams. The ratio was changed only after a concentration plateau was fully developed at the column outlet. The column effluent was monitored using the refractive index detector. To maintain a constant temperature during the experiments, water at a fixed temperature was continuously circulated through the jacket enclosing the packed column using the HST-250WL circulator (Figure 1). In addition, the reservoirs containing DDW and the stock solution were immersed in the BW-20G water bath, which was maintained at constant temperature.
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Table 1. Single-Component Adsorption Equilibrium Data for Acetic Acid and Formic Acid on Amberchrom-CG300C over the Temperature Range from T = (30 to 50) °C
RESULTS AND DISCUSSION Potential of Amberchrom-CG300C for Use as the Adsorbent for Separating Acetic Acid, Formic Acid, Succinic Acid, and Lactic Acid. Before the measurement of the adsorption equilibria for the system of interest, the potential of the Amberchrom-CG300C resin to be used as the adsorbent for separating the four organic acids considered (acetic acid, formic acid, succinic acid, and lactic acid) was first investigated. This task was performed by a series of single-component pulse tests, which were carried out with each of the four organic acids at a temperature of 30 °C. The resultant chromatogram data for the four organic acids were then compared (Figure 2). It is
acetic acid
formic acid
C
q
C
q
g·L−1
g·L−1
g·L−1
g·L−1
2 4 6 8 10
9.000 16.482 23.358 30.149 36.579
2 4 6 8 10
2.322 4.634 6.776 9.092 11.190
2 4 6 8 10
8.123 14.798 20.755 26.751 32.879
2 4 6 8 10
2.036 4.220 5.998 8.041 10.262
2 4 6 8 10
6.889 12.556 17.845 23.402 29.275
2 4 6 8 10
1.875 3.689 5.325 7.281 8.819
T = 30 °C
T = 40 °C
T = 50 °C
data, a comparison between acetic acid and formic acid was made first at 30 °C. The results of comparison (Figure 3) clearly show that acetic acid is more strongly adsorbed onto the Amberchrom-CG300C resin than formic acid. For further comparison with succinic acid and lactic acid, their relevant data from the literature12 have been included in Figure 3. We see that there is a marked difference in the equilibrium solid-phase concentration (q) among the four organic acids. This result supports the aforementioned statement that the Amberchrom CG300C resin is highly effective in separating the four organic acids of interest. To investigate the effect of temperature on the adsorption equilibrium of each organic acid, the adsorption data acquired at three different temperatures were plotted collectively in Figure 4a for acetic acid and in Figure 4b for formic acid. It is evident that the adsorption equilibrium of each acid is strongly affected by temperature. In particular, it is worth noting that the
Figure 2. Results of the preliminary pulse tests performed to confirm the suitability of Amberchrom-CG300C as the adsorbent in chromatographic processes for the separation of acetic acid, formic acid, succinic acid, and lactic acid. In this test, the effluent concentration (Ceffluent) was monitored as a function of time (t). Lines: thin solid black, formic acid; thick solid gray, acetic acid; thick solid black, succinic acid; thin dashed black, lactic acid.
clearly seen that their retention times are quite different from one another. This indicates the existence of a definite discrepancy among the adsorption affinities of the four organic acids for Amberchrom CG300C, which is therefore sufficiently applicable to a chromatographic process for the targeted separation. 2104
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temperature increases. Similar trends were reported for the adsorption equilibria of succinic acid and lactic acid.12 The adsorption equilibrium data for the four organic acids at various temperatures are compared in Figure 5. It is clearly seen that the adsorption affinities of the four organic acids for the Amberchrom-CG300C resin obey the following order at all of the investigated temperatures: succinic acid > acetic acid > lactic acid > formic acid.
Figure 3. Comparison of acetic acid, formic acid, succinic acid, and lactic acid in terms of the solid-phase concentration (q) in equilibrium with the liquid-phase concentration (C) at 30 °C. Symbols: gray circle, formic acid; black ◆, acetic acid; □, succinic acid; △, lactic acid. The data for succinic acid and lactic acid were taken from ref 12.
solid-phase concentration (i.e., the amount of each acid adsorbed onto Amberchrom CG300C) decreases as the
Figure 5. Comparison of the single-component adsorption equilibria for the four organic acids at (a) T = 30 °C, (b) T = 40 °C, and (c) T = 50 °C: gray circle, formic acid; black ◆, acetic acid; □, succinic acid; △, lactic acid. The solid and dashed lines indicate the results predicted using the Langmuir model (eq 1) and the temperature-modulated Langmuir model (eq 6), respectively. The data for succinic acid and lactic acid were taken from ref 12.
Figure 4. Single-component adsorption equilibria (plot of solid-phase concentration (q) versus liquid-phase concentration (C) at equilibrium) of (a) acetic acid and (b) formic acid over the temperature range from (30 to 50) °C. Experimental data: ○, T = 30 °C; △, T = 40 °C; □, T = 50 °C. The solid and dashed lines indicate the results predicted using the Langmuir model (eq 1) and the temperaturemodulated Langmuir model (eq 6), respectively. 2105
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Correlation of the Adsorption Equilibrium Data. One of the important requirements for making the above-reported equilibrium data (Table 1) more useful for the design of the related separation process is to establish an appropriate model that can allow accurate correlation of the acquired equilibrium data. Such a correlation task is usually accomplished by selecting a proper adsorption equilibrium model (or isotherm model) and determining its relevant parameters (i.e., saturation capacities and equilibrium constants). First, a clue to the type of isotherm model needed can be grasped by examining closely the trend of the increase in solidphase concentration (q) with increasing liquid-phase concentration (C) in Figure 4. It should be noted that the rate of increase in q decreases gradually with increasing C, which is the major feature of adsorption systems with convex upward isotherm curves. Several isotherm models have been suggested for such types of adsorption systems. Among them, the Langmuir model has been reported to describe successfully many of the systems relevant in the field of chromatographic separation processes.7,8 The Langmuir model equation is
q=
Table 2. Parameters of the Langmuir Adsorption Model and the Corresponding Average Relative Errors (ARE) and Standard Deviations (σ) for the Four Organic Acids
acetic acid
formic acid
succinic acidb
lactic acidb
100 % n
(1)
⎛ |q − qexp , i| ⎞ predict, i ⎜ ⎟ ∑⎜ ⎟ qexp , i ⎠ i=1 ⎝
KL
ARE
σa
°C
g·L−1
L·g−1
%
g·L−1
30 40 50 30 40 50 30 40 50 30 40 50
172.6554
0.0266 0.0232 0.0199 0.0069 0.0062 0.0054 0.0823 0.0647 0.0534 0.0192 0.0167 0.0147
1.96
0.41
1.45
0.11
0.99
0.33
0.80
0.12
173.0491
112.7448
167.7283
The standard deviation was calculated as σ = {[∑ni=1(qpredict,i − qexp,i)2]/(n − p)}1/2, where qexp and qpredict are the experimentally measured solid-phase concentration and the solid-phase concentration predicted using eq 1, respectively, n = 15 is the number of data points, and p = 4 is the number of model parameters fitted. bThe data for succinic acid and lactic acid were taken from ref 12.
where qs is the saturation capacity and KL is the Langmuir equilibrium constant, which is related to the affinity of the adsorbent site and the energy of adsorption.17 These two parameters (qs and KL) are called the Langmuir model parameters. In regard to the temperature dependences of the Langmuir model parameters, it is widely accepted in the area of adsorption separation process development that only KL is affected by temperature, while qs is independent of temperature.17−21 In this study, the above-mentioned Langmuir model was employed to correlate the adsorption equilibrium data for acetic acid and formic acid in Table 1. The relevant Langmuir model parameters for each organic acid were determined by minimizing the sum of squared deviations between the experimental data in Table 1 and the values calculated using eq 1. This minimization task was handled by a well-known optimization program based on a genetic algorithm11−13,22 in which a total of four parameters [qs, KL(30 °C), KL(40 °C), and KL(50 °C)] were optimized simultaneously for each organic acid. In each optimization run, a total of 15 experimental data points, including the adsorption equilibrium data for the particular organic acid at (30, 40, and 50) °C (Table 1), were used as the objects to be fitted by the model equation. The resultant parameter values from the above optimizations are summarized in Table 2. These parameter values and the model equation (eq 1) were used to predict the solid-phase concentrations in equilibrium with the liquid-phase concentrations. The predicted results were then compared with the experimental data (Figure 4). It is clearly seen that the predicted results from the Langmuir model agree well with the experimental data. This was also confirmed by checking the average relative error (ARE), which is defined as follows:. ARE =
qs
a
qsKLC 1 + KLC
T
data points. It is noteworthy that the ARE values for acetic acid and formic acid are less than 2 % (Table 2), which means that the Langmuir model is highly accurate in predicting the adsorption equilibria of interest. Thermodynamic Parameters for the Adsorption Equilibria of Interest. On the basis of the Langmuir equilibrium constants (KL) determined above, the thermodynamic parameters associated with the adsorption of each acid onto Amberchom-CG300C were estimated. For this task, we used the van’t Hoff’s equation, which relates KL to the enthalpy change (ΔH) and the temperature in the following manner:17,21,23,24 ΔH (3) RT where R = 8.314 J·K−1·mol−1 is the gas constant and T is the absolute temperature. This equation can be rearranged into the form of Arrhenius-type equation:17 ln KL = ln KL0 −
⎛ ΔH ⎞ ⎟ KL = KL0 exp⎜ − ⎝ RT ⎠
(4)
K0L can 21,23,24
in which the pre-exponential factor terms of the entropy change (ΔS) as ⎛ ΔS ⎞ ⎟ KL0 = exp⎜ ⎝ R ⎠
be expressed in
(5)
The values of ΔH and ΔS in eqs 3 and 5 were determined from the slopes and y-intercepts, respectively, of plots of ln KL versus 1/T (Figure 6), which were obtained by linear regression. The resulting values of ΔH and ΔS are listed in Table 3. It should be noted that the sign of ΔH is negative for both acetic acid and formic acid, which means that the adsorption of each acid onto Amberchrom CG300C is exothermic. The values in Table 3 also show that the absolute value of ΔH for acetic acid is larger than that for formic acid. This result implies that acetic acid has a higher adsorption affinity for Amberchrom CG300C than formic acid does.
n
(2)
where qexp and qpredict are the experimentally measured solidphase concentration and the solid-phase concentration predicted using eq 1, respectively, and n is the number of 2106
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can sometimes be a barrier to the immediate application of the Langmuir model to the design or simulation of a temperaturegradient chromatographic process, where the process temperature is usually allowed to undergo continuous variation along the axial distance.18,19 In this case, it would be more efficient to utilize the Langmuir model expressed as a continuous function of temperature. Such a temperature-modulated model that can account for the adsorption data in Table 1 is described in this section. This task was facilitated by exploiting the functional relationship between the Langmuir equilibrium constant KL and the thermodynamic parameters ΔH and ΔS (eqs 3 to 5) to modify eq 1. The resulting temperature-modulated model equation is ΔS ΔH − RT ) ( R q= ΔS ΔH 1 + C exp( R − RT )
qsC exp
where all the relevant parameter values are reported in Table 3. On the basis of the above model equation and the reported parameter values, the adsorption equilibria of each acid at all three temperatures investigated were predicted at once. The resulting predictions were found to be in close agreement with the experimental data (Figure 4), as confirmed by the corresponding ARE values (Table 3), which were only about 2.0 % for both acetic acid and formic acid. These results indicate that the above temperature-modulated model equation based on the reported parameter values is sufficiently applicable to the optimal design of a temperature-gradient chromatographic process with a continuous profile of temperature variation along the axial distance.
Figure 6. Relationship between the natural logarithm of Langmuir equilibrium constant (KL) and the reciprocal of temperature (T) for (a) acetic acid and (b) formic acid.
Table 3. Values of the Parameters in the TemperatureModulated Langmuir Adsorption Model and the Corresponding Average Relative Errors (ARE) and Standard Deviations (σ) qs −1
g·L acetic acid formic acid succinic acidb lactic acidb
172.6554 173.0491 112.7448 167.7283
ΔH
ΔS −1
kJ·mol
−11.8063 −9.9739 −17.6303 −10.8771
−1
J·mol
−69.0641 −74.1925 −78.9671 −68.7488
ARE
σa
%
g·L−1
2.07 1.57 1.10 0.80
0.42 0.12 0.38 0.12
(6)
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CONCLUSIONS A series of chromatographic experiments based on the staircase frontal analysis method were performed to obtain singlecomponent adsorption equilibrium data for acetic acid and formic acid on Amberchrom-CG300C resin over the temperature range from (30 to 50) °C. It was first found that a marked difference existed between the equilibrium solid-phase concentrations of acetic acid and formic acid, that is, between the adsorption affinities of the two organic acids for AmberchromCG300C. Such a marked difference in the adsorption equilibria was also confirmed among the three organic acids including acetic acid, formic acid, and succinic acid, which are known to be produced from the fermentation process based on Actinobacillus bacteria. This indicated that Amberchrom CG300C is sufficiently qualified for use as the adsorbent in a chromatographic process for the separation of the three organic acids resulting from the related fermentation process. To correlate the measured adsorption equilibrium data of acetic acid and formic acid, the Langmuir model was applied, and the relevant parameters (saturation capacities and equilibrium constants) were determined using a highly robust optimization program. The resulting equilibrium constants were found to decrease with increasing temperature. From the temperature dependence of the equilibrium constants, the thermodynamic parameters for the adsorptions of interest were determined. The determined thermodynamic parameters were then incorporated into the Langmuir model equation. This led to a temperature-modulated Langmuir model equation, which was confirmed to be highly accurate in predicting all of the adsorption equilibrium data acquired over the investigated
a The standard deviation was calculated as σ = {[∑ni=1(qpredict,i − qexp,i)2]/(n − p)}1/2, where qexp and qpredict are the experimentally measured solid-phase concentration and the solid-phase concentration predicted using the temperature-modulated Langmuir model equation (eq 6) respectively, n = 15 is the number of data points, and p = 3 is the number of parameters in the model equation. bThe data for succinic acid and lactic acid were taken from ref 12.
Another interesting observation in Table 3 is that the absolute values of ΔH for acetic acid and formic acid are both less than 20 kJ·mol−1. This indicates that the adsorption of each acid onto the adsorbent surface is controlled by physical mechanisms rather than chemical mechanisms. In addition, the negative values of ΔS in Table 3 suggest that randomness decreases during the adsorption. The comparison of the ΔS values of acetic and formic acid also indicates that formic acid has a larger decrease in randomness during the adsorption than acetic acid. Correlation of the Adsorption Equilibrium Data Using the Temperature-Modulated Langmuir Model. In the previous section, the Langmuir model parameters were determined at three discrete temperatures (Table 2). This 2107
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3-(4-hydroxyphenyl)-propanoic Acid in a Capcell Pak C18 Chromatography. J. Chem. Eng. Data 2008, 53, 2613−2621. (14) Xie, Y.; Phelps, D.; Lee, C. H.; Sedlak, M.; Ho, N.; Wang, N. H. L. Comparison of Two Adsorbents for Sugar Recovery from Biomass Hydrolyzate. Ind. Eng. Chem. Res. 2005, 44, 6816−6823. (15) Sancho, M. I.; Blanco, S. E.; Castro, E. A. Adsorption Isotherms of Substituted Benzophenones on a Reverse-Phase Liquid Chromatography System: Effect of the Mobile-Phase Composition. J. Chem. Eng. Data 2010, 55, 4768−774. (16) Nam, H. G.; Kim, T. H.; Mun, S. Effect of Ethanol Content on Adsorption Equilibria of Some Useful Amino Acids in Poly-4vinylpyridine Chromatography. J. Chem. Eng. Data 2010, 55, 3327− 3333. (17) Wankat, P. C. Rate-Controlled Separations; Kluwer Academic Publishers: Amsterdam, 1990. (18) Kim, J. K.; Abunasser, N.; Wankat, P. C. Thermally Assisted Simulated Moving Bed Systems. Adsorption 2005, 11, 579−584. (19) Jin, W.; Wankat, P. C. Thermal Operation of Four-Zone Simulated Moving Beds. Ind. Eng. Chem. Res. 2007, 46, 7208−7220. (20) Ruthven, D. M. Principles of Adsorption and Adsorption Processes; Wiley: New York, 1984. (21) Al-Muhtaseb, S. A.; Al-Rub, F. A. A.; Zarooni, M. A. Adsorption Equilibria of Nitrogen, Methane, and Ethane on BDH-Activated Carbon. J. Chem. Eng. Data 2007, 52, 60−65. (22) Kasat, R. B.; Gupta, S. K. Multi-Objective Optimization of an Industrial Fluidized-Bed Catalytic Cracking Unit (FCCU) Using Genetic Algorithm (GA) with the Jumping Genes Operator. Comput. Chem. Eng. 2003, 27, 1785−1800. (23) Gokmen, V.; Serpen, A. Equilibrium and Kinetic Studies on the Adsorption of Dark Colored Compounds from Apple Juice Using Adsorbent Resin. J. Food Eng. 2002, 53, 221−227. (24) Gueu, S.; Yao, B.; Adouby, K.; Ado, G. Kinetics and Thermodynamics Study of Lead Adsorption on to Activated Carbons from Coconut and Seed Hull of the Palm Tree. Int. J. Environ. Sci. Technol. 2007, 4, 11−17.
temperature range. The results of this study will pave the way for the development of either a continuous chromatographic process or a temperature-gradient process for the separation of acetic acid, formic acid, and succinic acid, all of which have valuable applications in many industries.
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AUTHOR INFORMATION
Corresponding Author
*Address: Department of Chemical Engineering, Hanyang University, Haengdang-dong, Seongdong-gu, Seoul 133-791, South Korea. E-mail:
[email protected]. Telephone: +822-2220-0483. Fax: +82-2-2298-4101. Funding
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant No. 2012R1A2A2A01019790). Also, it was partially supported by the Manpower Development Program for Energy & Resources supported by the Ministry of Knowledge and Economy (MKE), Republic of Korea. Notes
The authors declare no competing financial interest.
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REFERENCES
(1) Sauer, M.; Porro, D.; Mattanovich, D.; Branduardi, P. Microbial Production of Organic Acids: Expanding the Markets. Trends Biotechnol. 2008, 26, 100−108. (2) Zeikus, J. G.; Jain, M. K.; Elankovan, P. Biotechnology of Succinic Acid Production and Markets for Derived Industrial Products. Appl. Microbiol. Biotechnol. 1999, 51, 545−552. (3) Kang, S. H.; Chang, Y. K. Removal of Organic Acid Salts from Simulated Fermentation Broth Containing Succinate by Nanofiltration. J. Membr. Sci. 2005, 246, 49−57. (4) Datta, R.; Glassner, D. A.; Jain, M. K.; Roy J. R. V. Fermentation and Purification Process for Succinic Acid. U.S. Patent 5,168,055, 1992. (5) Guettler, M. V.; Jain, M. K.; Rumler, D. Method for Making Succinic Acid Bacterial Variants for Use in the Process, and Methods for Obtaining Variants. U.S. Patent 5,573,931, 1996. (6) Kollu, N. R.; Baireddy, V. R.; Sivadevuni, G.; Solipuram, M. R. Influence of Volatile Compounds and Food Preservatives in the Management of Fumonisins (B1) Production by Fusarium moniliforme. Afr. J. Biotechnol. 2009, 8, 4207−4210. (7) Xie, Y.; Hritzko, B.; Chin, C. Y.; Wang, N. H. L. Separation of FTC-Ester Enantiomers Using a Simulated Moving Bed. Ind. Eng. Chem. Res. 2003, 42, 4055−4067. (8) Lee, K. B.; Chin, C. Y.; Xie, Y.; Cox, G. B.; Wang, N. H. L. Standing Wave Design of a Simulated Moving Bed under a Pressure Limit for Enantioseparation of Phenylpropanolamine. Ind. Eng. Chem. Res. 2005, 44, 3249−3267. (9) Hur, J. S.; Wankat, P. C.; Kim, J. L.; Kim, J. K.; Koo, Y. M. Purification of L-Phenylalanine from a Ternary Amino Acid Mixture Using a Two-Zone SMB/Chromatography Hybrid System. Sep. Sci. Technol. 2007, 42, 911−930. (10) Xie, Y.; Wu, D. J.; Ma, Z.; Wang, N. H. L. Extended Standing Wave Design Method for Simulated Moving Bed Chromatography: Linear Systems. Ind. Eng. Chem. Res. 2000, 39, 1993−2005. (11) Lee, K. B.; Kasat, R. B.; Cox, G. B.; Wang, N. H. L. Simulated Moving Bed Multiobjective Optimization Using Standing Wave Design and Genetic Algorithm. AIChE J. 2008, 54, 2852−2871. (12) Nam, H. G.; Park, K. M.; Lim, S. S.; Mun, S. Adsorption Equilibria of Succinic Acid and Lactic Acid on Amberchrom CG300C Resin. J. Chem. Eng. Data 2011, 56, 464−471. (13) Han, Q.; Yoo, C. G.; Jo, S. H.; Yi, S. C.; Mun, S. Effect of Mobile Phase Composition on Henry’s Constants of 2-Amino-3-phenylpropanoic Acid, 2-Amino-3-(3-indolyl)-propanoic Acid, and 2-Amino2108
dx.doi.org/10.1021/je201065u | J. Chem. Eng. Data 2012, 57, 2102−2108
