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Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response surface methodology
Journal of Molecular Structure xxx (xxxx) xxx
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Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response surface methodology € _ _ Ozge Çelebican, Ismail Inci, Nilay Baylan* _ _ Department of Chemical Engineering, Istanbul University-Cerrahpas¸a, Avcılar, 34320, Istanbul, Turkey
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 February 2019 Received in revised form 3 September 2019 Accepted 28 October 2019 Available online xxx
Formic acid is extensively utilized in various chemical industries and applications. So, formic acid can be present in the wastewaters of these industries. It is widely produced in aqueous solutions by fermentation processes. And also, it forms as a by-product in the production of chemical processes. Thus, the removal of formic acid from waste streams and production medium is very significant topic. The objective of this study is to remove formic acid from its aqueous solutions by adsorption and to optimize the adsorption process. In this context, in this study, the optimal conditions for formic acid adsorption by multiwall carbon nanotube were investigated by using response surface methodology. Face-centered central composite design based on response surface methodology was applied to investigate the effects of the initial acid concentration (2e10%, w/w), amount of adsorbent (0.01e0.03 g) and temperature (25e45BC) on the adsorption capacity (qe, mg acid adsorbed/g adsorbent). The acquired experimental results were appraised by means of analysis of variance. A second-degree model equation for the adsorption capacity was obtained to explain adsorption characteristics of formic acid by multiwall carbon nanotube. The acquired model equation was well in agreement with the experimental results. The response surface plots were illustrated and they also supported the compatibility of the model equation. The design study also showed that MWCNT is an effective adsorbent for the removal of formic acid from aqueous solutions. © 2019 Elsevier B.V. All rights reserved.
Keywords: Formic acid Multiwall carbon nanotube Adsorption Response surface methodology
1. Introduction Formic acid is utilized mainly in textile, leather and dyeing industries. It is mostly used as an antibacterial and preservative agent in livestock feed. It is also used in rubber manufacture and as an intermediate in the pharmaceutical and chemical industries [1,2]. Therefore, it is found with high concentrations in the wastewater of these industries. Especially, higher concentrations of formic acid are corrosive. Skin contact with formic acid can cause pain, burns and ulcers. Therefore, it is important to remove formic acid from the effluents of chemical industries. Formic acid can be manufactured by hydrolysis of formamide or methyl formate or from its salts. It is also obtained as by-product in the manufacturing of acetic acid with liquid-phase oxidation of naphtha or butane [1]. Formic acid is commonly produced by the fermentation process. At the end of the fermentation process, it is obtained not only as a individual product but as a by-product with
* Corresponding author. E-mail address: [email protected] (N. Baylan).
different carboxylic acids and aqueous solutions. It is also produced as a by-product in the production of carboxylic acids such as levulinic acid [3] and succinic acid [4]. The formation of by-product is a major problem that decreases the acid production yield and increases the complexity and the cost of acid recovery process [5,6]. In order to continue the fermentation process, the by-products must be removed from the broths. Because of all these reasons, it is of great importance to remove and purify formic acid from the aqueous solutions resulting from the fermentation process or from other production process. A lot of separation techniques like extraction [7e11], membrane separation technologies [12e15] and adsorption [16e19] can be applied for the removal of formic acid from aqueous solutions. Among the separation techniques, adsorption has many benefits like higher removal efficiency, easy scale-up, low operating cost, quick response, absence of sludge and applicability of various adsorbents. Other separation techniques can be require a large amount of solvent which can be harmful to the environment or a huge amount of energy [20]. Recently, the use of nano-adsorbents, especially in adsorption
https://doi.org/10.1016/j.molstruc.2019.127312 0022-2860/© 2019 Elsevier B.V. All rights reserved.
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
€ Çelebican et al. / Journal of Molecular Structure xxx (xxxx) xxx O.
2
processes, has gained importance because of their better adsorption features [21,22]. Carbon nanotubes are unique and onedimensional macromolecules that have high potential as superior adsorbents. These nanomaterials have a very large surface area and porous-rich structure that provide strong affinity for separation of many various materials [23,24]. A few adsorption studies related to the separation of organic acids such as humic acid [25,26], benzoic acid [27,28], fulvic acid [29,30], tannic acid [31] and lactic acid [20] by carbon nanotubes have been reported in the literature. Several researchers have investigated formic acid adsorption using diverse adsorbents like Lewatit MP-64 [19], Amberlite IRA-96 [19], Amberlite IRA-67 [16], D-II07 resin [17], SY-01 resin [32] polymeric adsorbents [18] and TiO2 nanoparticles [33,34]. The literature research shows that the adsorption studies of formic acid are limited. Since carbon nanotubes give successful results in the adsorption of some other organic acids and it has not been used for formic acid, the adsorption of formic acid has been examined by using multiwall carbon nanotube (MWCNT) in this study. Response surface methodology (RSM) has been extensively used for experimental design and optimization in recent years. RSM is a collection of mathematical and statistical methods that enables the correlation between the independent variables and the dependent variable (response). The optimum process conditions can be determined and a mathematical model representing process can be generated by using RSM. Box-Behnken, Central Composite, and Doptimal designs are the most widely utilized the experimental design techniques in RSM [35,36]. The aim of this work is to remove formic acid from its aqueous solutions by adsorption using MWCNT and optimize and model this adsorption process. For this aim, face-centered central composite design (FCCCD) based on RSM was used to investigate the effects of adsorption parameters including the initial formic acid
Independent variables X1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Independent
X1
X2 X3
1 0 1 þ1 1 þ1 1 0 þ1 þ1 0 0 0 0 0 1 0 þ1 0 0 variables
Dependent variable
X2
X3
Y
þ1 0 þ1 0 1 þ1 1 0 þ1 1 þ1 0 0 0 1 0 0 1 0 0
þ1 0 1 0 þ1 1 1 þ1 þ1 þ1 0 1 0 0 0 0 0 1 0 0 Levels
38.34 170.05 56.67 689.31 89.34 610.85 101.41 76.57 227.05 794.26 65.18 207.61 164.52 177.42 296.05 62.47 172.00 1236.26 166.58 175.59
Initial formic acid concentration (%) MWCNT amount (g) Temperature ( C)
2. Materials and methods 2.1. Materials MWCNT was supplied by Shenzhen Nanotech. Port. Co. Ltd. (China). The diameter of MWCNT is 10e20 nm and the length of MWCNT is 1e2 mm. The purity of MWCNT is >95%. Formic acid (98e100%) was purchased from Merck. Formic acid solutions with different concentrations (2e10%, w/w) were prepared using distilled water. MWCNT was used without further treatment. 2.2. Adsorption experiments Adsorption experiments were conducted by batch method. Initially, the equilibrium time for formic acid adsorption acid by MWCNT was determined. The mixtures of known amount of MWCNT and aqueous formic acid solution (5 mL) were placed in glass flasks. Experiments were carried out by shaking these glass flasks in thermostatic shaker bath (Nüve ST 30) at 150 rpm for adsorption equilibrium time (120 min). MWCNT was separated from solution by filtration. Formic acid concentration before and after the adsorption was detected by volumetric NaOH titration method with using the automatic titrator (SI Analytics, Schott Titroline). The adsorbed amount of formic acid by MWCNT at equilibrium that is the adsorption capacity (qe, mg/g) was calculated by using Equation (1).
qe ¼
Table 1 FCCCD experimental layout and results. Run
concentration, the amount of MWCNT and temperature. Analysis of variance (ANOVA) was used for evaluating the obtained experimental results. Furthermore, the optimal conditions for formic acid adsorption by MWCNT were detected.
ðC0 Ce Þ *V M
(1)
where C0 and Ce are the initial and equilibrium formic acid concentrations of solution (mg/L), M is the mass adsorbent (g) and V is the volume of solution (L). 2.3. Design of experiments and statistical analysis FCCCD was applied to design the experiments and model the correlation between the independent variables and the dependent variable. To this end, the software Design-Expert® 11.0 Trial Version (Stat-Ease, Inc., Minneapolis, USA), was used. Independent variables were chosen as initial formic acid concentration (X1),
1
0
þ1
2
6
10
0.01
0.02
0.03
25
35
45 Fig. 1. The equilibrium adsorption time for formic acid adsorption on MWCNT.
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
€ Çelebican et al. / Journal of Molecular Structure xxx (xxxx) xxx O.
3
Table 2 ANOVA data for the adsorption capacity (Y). Source
Sum of Squares
df
Mean Square
F-value
p-value Prob>F
Model X1 X2 X3 X1 X2 X1 X3 X2 X3 X21 X22 X23 Residual Lack of Fit Pure Error Cor. Total Fit Statistics R2 Adjusted R2 Predicted R2
1.839.106 1.030.106 2.308.105 97464.28 1.504.105 79082.64 337.22 1.314.105 1494.05 636.73 7492.76 7367.05 125.71 1.847.106
9 1 1 1 1 1 1 1 1 1 10 5 5 19
2.044.105 1.030.106 2.308.105 97464.28 1.504.105 79082.64 337.22 1.314.105 1494.05 636.73 749.28 1473.41 25.14
272.77 1374.78 308.04 130.08 200.72 105.55 0.4501 175.36 1.99 0.8498
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.5175 < 0.0001 0.1883 0.3783
58.60
0.0002
0.9959 0.9923 0.9658
SD CV AP
27.37 9.82 60.4433
significant
Fig. 2. The statistical plots for the comparison of predicted and experimental values for the adsorption capacity (a) Predicted vs. actual plot (b) Residuals vs. predicted plot.
adsorbent amount (X2) and temperature (X3) and dependent variable was selected as adsorption capacity (Y). Table 1 displays the variables and levels in the actual and coded values. Adsorption experiments were planned according to these variables and their levels in Design-Expert® software. FCCCD experimental layout were given in Table 1. These experiments were carried out experimentally and the values of adsorption capacity (qe or Y) were calculated from obtained experimental results by using Equation (1) and also given in Table 1. Analysis of variance (ANOVA) was used for the statistical analysis. The model equation representing the dependent variable was determined by using the multiple regression method. An empirical model that correlated the dependent variable to the independent variables can be represented with a second-degree polynomial equation as follows [37]:
Y ¼ b0 þ
k X i¼1
bi xi þ
k1 X k X i¼1 j¼2
bij xi xj þ
k X
bii x2i þ ε
(2)
i¼1
where Y is the predicted dependent variable, Xi and Xj are the independent variables, b0 is the constant coefficient, bi, bii, bij are the linear, quadratic and interaction coefficients, k is the variable number and ε is experimental error. 3. Results and discussion 3.1. Determination of equilibrium adsorption time The equilibrium adsorption experiments were carried out for the determination of equilibrium time by using MWCNT of 0.01 g
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
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Fig. 3. 3D response surface plots for the adsorption capacity (Y) (a) the effect of initial formic acid concentration (X1) and MWCNT amount (X2) at the intermediate value (35 C) of temperature. (b) the effect of initial formic acid concentration (X1) and temperature (X3) at the intermediate value (0.02 g) of MWCNT amount (c) the effect of MWCNT amount (X2) and temperature (X3) at the intermediate value (6%, w/w) of initial formic acid concentration.
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
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Table 3 The optimum adsorption conditions. Initial acid concentration (%,w/w) MWCNT amount (g) Temperature C Predicted adsorption capacity (mg/g) Experimental adsorption capacity (mg/g) Desirability 10
0.01
25
1206.84
and formic acid solution of about 10% (w/w) at 25 C. The acquired experimental results of equilibrium time for formic acid adsorption were illustrated in Fig. 1. As shown in Fig. 1, the adsorption process reached the equilibrium in about 120 min for formic acid adsorption on MWCNT. 3.2. Response surface methodology The adsorption of formic acid by MWCNT was investigated under diverse conditions according to the experimental runs as proposed by FCCCD. The analysis of obtained experimental data was carried out by ANOVA. Table 2 shows ANOVA data for the adsorption capacity (Y). F-value in ANOVA implies that the model is significant for dependent variable. As can be seen in Table 2, F-value is 272.77 for adsorption capacity that indicates the model is significant. Prob> F or p-value less than 0.05 demonstrates that these model terms are important [38]. In present study, six model terms (X1, X2, X3, X1X2, X1X3, X21) were most important because of their p-values less than 0.05. These model terms must be shown in the model equation. Based on ANOVA results, the empirical model equation in terms of coded variables was developed for the adsorption capacity as follows:
Y ¼ 165:54 þ 320:95 X1 151:92 X2 98:72 X3 137:11 X1 X2 99:43 X1 X3 þ 218:58 X21
(3)
As can be seen in this model equation, all independent variables, initial formic acid concentration (X1), adsorbent amount (X2) and temperature (X3), had an effect on the adsorption capacity (Y). The initial formic acid concentration (X1) that having the highest coefficient in the equation was the most effective variable on the adsorption capacity (Y). Taking into account the coefficients of other model terms, adsorbent amount (X2) was more effective variable than temperature (X3). The validity of model is checked by some statistical parameters including the determination coefficient (R2), adjusted R2, predicted R2, adequate precision (AP) and coefficient of variation (CV). R2 is defined as a measure of the degree of fit. As R2 approaches to unity, the degree of fit increases. The similarity between R2 and adjusted R2 shows the compatibility of the model to predict the dependent variable. The difference between predicted R2 and adjusted R2 must be less than 0.2. It is indicated that predicted R2 is in acceptable agreement with adjusted R2. AP is defined as a measure of the ratio of the signal to noise. This ratio greater than 4 is desirable [39,40]. In our current study, all findings in Table 2 were acceptable, which confirmed the fitness of the model with the experimental results. Other checkpoints for the verification of experimental results are the analysis of the predicted vs. actual plot or residuals vs. predicted plot. Predicted vs. actual plot shows that the points must be aligned a straight line [36]. Further, residuals vs. predicted plot shows whether the points are in a normal distribution [39]. Fig. 2 a displays predicted vs. actual plot that illustrated between predicted and actual values. As can be seen in Fig. 2 a, the experimental values were followed as a straight line. It is indicated that the predicted values were well in agreement with the actual values. Fig. 2 b presents residuals vs. predicted plot that it was normally distributed and all points are in the range.
1236.26
0.975
Response surface plots of model equation (Fig. 3) were plotted to explain the effect of variables on the adsorption capacity and the interaction among variables. It can be seen from Fig. 3, the adsorption capacity increased with increase in initial formic acid concentration. Usually, the adsorption capacity (amount of molecule adsorbed per unit mass of adsorbent) increases with increasing the initial adsorbate concentration. The reason for this increasing trend is that more acid molecules present in the adsorbate solution at higher initial concentrations, and more acid molecules are adsorbed by the adsorbent [41,42]. It can be seen from Fig. 3 that there was a decrease in adsorption capacity with increasing MWCNT amount. This result was in agreement with similar adsorption studies in literature [20]. It was also observed from Fig. 3 that the adsorption capacity was decreased as increases in temperature. This clearly showed that adsorption process was € exothermic. Similarly, Ozcan et al. have investigated the effect of temperature on acetic acid adsorption by MWCNT. It was concluded that the adsorption process is exothermic for adsorption of acetic acid by MWCNT [43]. An optimization study for formic acid adsorption by MWCNT was exerted by keeping all the independent variables within in range by using RSM in Design-Expert® software. The dependent variable, the adsorption capacity, was targeted to the maximum value. The optimum adsorption conditions were summarized in Table 3. Under optimized conditions, an experiment was conducted to compare the predicted result. As compared with given results in Table 3, it demonstrated that a good agreement of the values of predicted and experimental adsorption capacity. 4. Conclusion In this work, it has been investigated the separation of formic acid from its aqueous solutions by adsorption method. For this purpose, MWCNT has been used as an adsorbent. Parameters that affect the adsorption of formic acid were examined by FCCCD based on RSM and a second-degree model equation representing adsorption capacity were obtained. The model adequately represented by all independent variables, namely initial formic acid concentration, adsorbent amount and temperature. All independent variables had an effect on the adsorption capacity. It is concluded that the second-degree model equation can be used to explain for formic acid adsorption by MWCNT. Also, the optimal adsorption conditions to obtain the higher adsorption capacity were determined. The higher adsorption capacity value was obtained in system where the initial formic acid concentration of 10% (w/w), MWCNT amount of 0.01 g and adsorption temperature of 25 C. This design study has also showed that MWCNT as an adsorbent can be successfully employed for the uptake of formic acid from aqueous solutions. Acknowledgement _ This study was funded by Istanbul University with the Project Number BYP-2018-31279. References [1] W. Reutemann, H. Kieczka, Formic Acid, Ullmann’s Encyclopedia of Industrial
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€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
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Journal of Molecular Structure journal homepage: http://www.elsevier.com/locate/molstruc
Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response surface methodology € _ _ Ozge Çelebican, Ismail Inci, Nilay Baylan* _ _ Department of Chemical Engineering, Istanbul University-Cerrahpas¸a, Avcılar, 34320, Istanbul, Turkey
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 February 2019 Received in revised form 3 September 2019 Accepted 28 October 2019 Available online xxx
Formic acid is extensively utilized in various chemical industries and applications. So, formic acid can be present in the wastewaters of these industries. It is widely produced in aqueous solutions by fermentation processes. And also, it forms as a by-product in the production of chemical processes. Thus, the removal of formic acid from waste streams and production medium is very significant topic. The objective of this study is to remove formic acid from its aqueous solutions by adsorption and to optimize the adsorption process. In this context, in this study, the optimal conditions for formic acid adsorption by multiwall carbon nanotube were investigated by using response surface methodology. Face-centered central composite design based on response surface methodology was applied to investigate the effects of the initial acid concentration (2e10%, w/w), amount of adsorbent (0.01e0.03 g) and temperature (25e45BC) on the adsorption capacity (qe, mg acid adsorbed/g adsorbent). The acquired experimental results were appraised by means of analysis of variance. A second-degree model equation for the adsorption capacity was obtained to explain adsorption characteristics of formic acid by multiwall carbon nanotube. The acquired model equation was well in agreement with the experimental results. The response surface plots were illustrated and they also supported the compatibility of the model equation. The design study also showed that MWCNT is an effective adsorbent for the removal of formic acid from aqueous solutions. © 2019 Elsevier B.V. All rights reserved.
Keywords: Formic acid Multiwall carbon nanotube Adsorption Response surface methodology
1. Introduction Formic acid is utilized mainly in textile, leather and dyeing industries. It is mostly used as an antibacterial and preservative agent in livestock feed. It is also used in rubber manufacture and as an intermediate in the pharmaceutical and chemical industries [1,2]. Therefore, it is found with high concentrations in the wastewater of these industries. Especially, higher concentrations of formic acid are corrosive. Skin contact with formic acid can cause pain, burns and ulcers. Therefore, it is important to remove formic acid from the effluents of chemical industries. Formic acid can be manufactured by hydrolysis of formamide or methyl formate or from its salts. It is also obtained as by-product in the manufacturing of acetic acid with liquid-phase oxidation of naphtha or butane [1]. Formic acid is commonly produced by the fermentation process. At the end of the fermentation process, it is obtained not only as a individual product but as a by-product with
* Corresponding author. E-mail address: [email protected] (N. Baylan).
different carboxylic acids and aqueous solutions. It is also produced as a by-product in the production of carboxylic acids such as levulinic acid [3] and succinic acid [4]. The formation of by-product is a major problem that decreases the acid production yield and increases the complexity and the cost of acid recovery process [5,6]. In order to continue the fermentation process, the by-products must be removed from the broths. Because of all these reasons, it is of great importance to remove and purify formic acid from the aqueous solutions resulting from the fermentation process or from other production process. A lot of separation techniques like extraction [7e11], membrane separation technologies [12e15] and adsorption [16e19] can be applied for the removal of formic acid from aqueous solutions. Among the separation techniques, adsorption has many benefits like higher removal efficiency, easy scale-up, low operating cost, quick response, absence of sludge and applicability of various adsorbents. Other separation techniques can be require a large amount of solvent which can be harmful to the environment or a huge amount of energy [20]. Recently, the use of nano-adsorbents, especially in adsorption
https://doi.org/10.1016/j.molstruc.2019.127312 0022-2860/© 2019 Elsevier B.V. All rights reserved.
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
€ Çelebican et al. / Journal of Molecular Structure xxx (xxxx) xxx O.
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processes, has gained importance because of their better adsorption features [21,22]. Carbon nanotubes are unique and onedimensional macromolecules that have high potential as superior adsorbents. These nanomaterials have a very large surface area and porous-rich structure that provide strong affinity for separation of many various materials [23,24]. A few adsorption studies related to the separation of organic acids such as humic acid [25,26], benzoic acid [27,28], fulvic acid [29,30], tannic acid [31] and lactic acid [20] by carbon nanotubes have been reported in the literature. Several researchers have investigated formic acid adsorption using diverse adsorbents like Lewatit MP-64 [19], Amberlite IRA-96 [19], Amberlite IRA-67 [16], D-II07 resin [17], SY-01 resin [32] polymeric adsorbents [18] and TiO2 nanoparticles [33,34]. The literature research shows that the adsorption studies of formic acid are limited. Since carbon nanotubes give successful results in the adsorption of some other organic acids and it has not been used for formic acid, the adsorption of formic acid has been examined by using multiwall carbon nanotube (MWCNT) in this study. Response surface methodology (RSM) has been extensively used for experimental design and optimization in recent years. RSM is a collection of mathematical and statistical methods that enables the correlation between the independent variables and the dependent variable (response). The optimum process conditions can be determined and a mathematical model representing process can be generated by using RSM. Box-Behnken, Central Composite, and Doptimal designs are the most widely utilized the experimental design techniques in RSM [35,36]. The aim of this work is to remove formic acid from its aqueous solutions by adsorption using MWCNT and optimize and model this adsorption process. For this aim, face-centered central composite design (FCCCD) based on RSM was used to investigate the effects of adsorption parameters including the initial formic acid
Independent variables X1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Independent
X1
X2 X3
1 0 1 þ1 1 þ1 1 0 þ1 þ1 0 0 0 0 0 1 0 þ1 0 0 variables
Dependent variable
X2
X3
Y
þ1 0 þ1 0 1 þ1 1 0 þ1 1 þ1 0 0 0 1 0 0 1 0 0
þ1 0 1 0 þ1 1 1 þ1 þ1 þ1 0 1 0 0 0 0 0 1 0 0 Levels
38.34 170.05 56.67 689.31 89.34 610.85 101.41 76.57 227.05 794.26 65.18 207.61 164.52 177.42 296.05 62.47 172.00 1236.26 166.58 175.59
Initial formic acid concentration (%) MWCNT amount (g) Temperature ( C)
2. Materials and methods 2.1. Materials MWCNT was supplied by Shenzhen Nanotech. Port. Co. Ltd. (China). The diameter of MWCNT is 10e20 nm and the length of MWCNT is 1e2 mm. The purity of MWCNT is >95%. Formic acid (98e100%) was purchased from Merck. Formic acid solutions with different concentrations (2e10%, w/w) were prepared using distilled water. MWCNT was used without further treatment. 2.2. Adsorption experiments Adsorption experiments were conducted by batch method. Initially, the equilibrium time for formic acid adsorption acid by MWCNT was determined. The mixtures of known amount of MWCNT and aqueous formic acid solution (5 mL) were placed in glass flasks. Experiments were carried out by shaking these glass flasks in thermostatic shaker bath (Nüve ST 30) at 150 rpm for adsorption equilibrium time (120 min). MWCNT was separated from solution by filtration. Formic acid concentration before and after the adsorption was detected by volumetric NaOH titration method with using the automatic titrator (SI Analytics, Schott Titroline). The adsorbed amount of formic acid by MWCNT at equilibrium that is the adsorption capacity (qe, mg/g) was calculated by using Equation (1).
qe ¼
Table 1 FCCCD experimental layout and results. Run
concentration, the amount of MWCNT and temperature. Analysis of variance (ANOVA) was used for evaluating the obtained experimental results. Furthermore, the optimal conditions for formic acid adsorption by MWCNT were detected.
ðC0 Ce Þ *V M
(1)
where C0 and Ce are the initial and equilibrium formic acid concentrations of solution (mg/L), M is the mass adsorbent (g) and V is the volume of solution (L). 2.3. Design of experiments and statistical analysis FCCCD was applied to design the experiments and model the correlation between the independent variables and the dependent variable. To this end, the software Design-Expert® 11.0 Trial Version (Stat-Ease, Inc., Minneapolis, USA), was used. Independent variables were chosen as initial formic acid concentration (X1),
1
0
þ1
2
6
10
0.01
0.02
0.03
25
35
45 Fig. 1. The equilibrium adsorption time for formic acid adsorption on MWCNT.
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
€ Çelebican et al. / Journal of Molecular Structure xxx (xxxx) xxx O.
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Table 2 ANOVA data for the adsorption capacity (Y). Source
Sum of Squares
df
Mean Square
F-value
p-value Prob>F
Model X1 X2 X3 X1 X2 X1 X3 X2 X3 X21 X22 X23 Residual Lack of Fit Pure Error Cor. Total Fit Statistics R2 Adjusted R2 Predicted R2
1.839.106 1.030.106 2.308.105 97464.28 1.504.105 79082.64 337.22 1.314.105 1494.05 636.73 7492.76 7367.05 125.71 1.847.106
9 1 1 1 1 1 1 1 1 1 10 5 5 19
2.044.105 1.030.106 2.308.105 97464.28 1.504.105 79082.64 337.22 1.314.105 1494.05 636.73 749.28 1473.41 25.14
272.77 1374.78 308.04 130.08 200.72 105.55 0.4501 175.36 1.99 0.8498
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.5175 < 0.0001 0.1883 0.3783
58.60
0.0002
0.9959 0.9923 0.9658
SD CV AP
27.37 9.82 60.4433
significant
Fig. 2. The statistical plots for the comparison of predicted and experimental values for the adsorption capacity (a) Predicted vs. actual plot (b) Residuals vs. predicted plot.
adsorbent amount (X2) and temperature (X3) and dependent variable was selected as adsorption capacity (Y). Table 1 displays the variables and levels in the actual and coded values. Adsorption experiments were planned according to these variables and their levels in Design-Expert® software. FCCCD experimental layout were given in Table 1. These experiments were carried out experimentally and the values of adsorption capacity (qe or Y) were calculated from obtained experimental results by using Equation (1) and also given in Table 1. Analysis of variance (ANOVA) was used for the statistical analysis. The model equation representing the dependent variable was determined by using the multiple regression method. An empirical model that correlated the dependent variable to the independent variables can be represented with a second-degree polynomial equation as follows [37]:
Y ¼ b0 þ
k X i¼1
bi xi þ
k1 X k X i¼1 j¼2
bij xi xj þ
k X
bii x2i þ ε
(2)
i¼1
where Y is the predicted dependent variable, Xi and Xj are the independent variables, b0 is the constant coefficient, bi, bii, bij are the linear, quadratic and interaction coefficients, k is the variable number and ε is experimental error. 3. Results and discussion 3.1. Determination of equilibrium adsorption time The equilibrium adsorption experiments were carried out for the determination of equilibrium time by using MWCNT of 0.01 g
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
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Fig. 3. 3D response surface plots for the adsorption capacity (Y) (a) the effect of initial formic acid concentration (X1) and MWCNT amount (X2) at the intermediate value (35 C) of temperature. (b) the effect of initial formic acid concentration (X1) and temperature (X3) at the intermediate value (0.02 g) of MWCNT amount (c) the effect of MWCNT amount (X2) and temperature (X3) at the intermediate value (6%, w/w) of initial formic acid concentration.
€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312
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Table 3 The optimum adsorption conditions. Initial acid concentration (%,w/w) MWCNT amount (g) Temperature C Predicted adsorption capacity (mg/g) Experimental adsorption capacity (mg/g) Desirability 10
0.01
25
1206.84
and formic acid solution of about 10% (w/w) at 25 C. The acquired experimental results of equilibrium time for formic acid adsorption were illustrated in Fig. 1. As shown in Fig. 1, the adsorption process reached the equilibrium in about 120 min for formic acid adsorption on MWCNT. 3.2. Response surface methodology The adsorption of formic acid by MWCNT was investigated under diverse conditions according to the experimental runs as proposed by FCCCD. The analysis of obtained experimental data was carried out by ANOVA. Table 2 shows ANOVA data for the adsorption capacity (Y). F-value in ANOVA implies that the model is significant for dependent variable. As can be seen in Table 2, F-value is 272.77 for adsorption capacity that indicates the model is significant. Prob> F or p-value less than 0.05 demonstrates that these model terms are important [38]. In present study, six model terms (X1, X2, X3, X1X2, X1X3, X21) were most important because of their p-values less than 0.05. These model terms must be shown in the model equation. Based on ANOVA results, the empirical model equation in terms of coded variables was developed for the adsorption capacity as follows:
Y ¼ 165:54 þ 320:95 X1 151:92 X2 98:72 X3 137:11 X1 X2 99:43 X1 X3 þ 218:58 X21
(3)
As can be seen in this model equation, all independent variables, initial formic acid concentration (X1), adsorbent amount (X2) and temperature (X3), had an effect on the adsorption capacity (Y). The initial formic acid concentration (X1) that having the highest coefficient in the equation was the most effective variable on the adsorption capacity (Y). Taking into account the coefficients of other model terms, adsorbent amount (X2) was more effective variable than temperature (X3). The validity of model is checked by some statistical parameters including the determination coefficient (R2), adjusted R2, predicted R2, adequate precision (AP) and coefficient of variation (CV). R2 is defined as a measure of the degree of fit. As R2 approaches to unity, the degree of fit increases. The similarity between R2 and adjusted R2 shows the compatibility of the model to predict the dependent variable. The difference between predicted R2 and adjusted R2 must be less than 0.2. It is indicated that predicted R2 is in acceptable agreement with adjusted R2. AP is defined as a measure of the ratio of the signal to noise. This ratio greater than 4 is desirable [39,40]. In our current study, all findings in Table 2 were acceptable, which confirmed the fitness of the model with the experimental results. Other checkpoints for the verification of experimental results are the analysis of the predicted vs. actual plot or residuals vs. predicted plot. Predicted vs. actual plot shows that the points must be aligned a straight line [36]. Further, residuals vs. predicted plot shows whether the points are in a normal distribution [39]. Fig. 2 a displays predicted vs. actual plot that illustrated between predicted and actual values. As can be seen in Fig. 2 a, the experimental values were followed as a straight line. It is indicated that the predicted values were well in agreement with the actual values. Fig. 2 b presents residuals vs. predicted plot that it was normally distributed and all points are in the range.
1236.26
0.975
Response surface plots of model equation (Fig. 3) were plotted to explain the effect of variables on the adsorption capacity and the interaction among variables. It can be seen from Fig. 3, the adsorption capacity increased with increase in initial formic acid concentration. Usually, the adsorption capacity (amount of molecule adsorbed per unit mass of adsorbent) increases with increasing the initial adsorbate concentration. The reason for this increasing trend is that more acid molecules present in the adsorbate solution at higher initial concentrations, and more acid molecules are adsorbed by the adsorbent [41,42]. It can be seen from Fig. 3 that there was a decrease in adsorption capacity with increasing MWCNT amount. This result was in agreement with similar adsorption studies in literature [20]. It was also observed from Fig. 3 that the adsorption capacity was decreased as increases in temperature. This clearly showed that adsorption process was € exothermic. Similarly, Ozcan et al. have investigated the effect of temperature on acetic acid adsorption by MWCNT. It was concluded that the adsorption process is exothermic for adsorption of acetic acid by MWCNT [43]. An optimization study for formic acid adsorption by MWCNT was exerted by keeping all the independent variables within in range by using RSM in Design-Expert® software. The dependent variable, the adsorption capacity, was targeted to the maximum value. The optimum adsorption conditions were summarized in Table 3. Under optimized conditions, an experiment was conducted to compare the predicted result. As compared with given results in Table 3, it demonstrated that a good agreement of the values of predicted and experimental adsorption capacity. 4. Conclusion In this work, it has been investigated the separation of formic acid from its aqueous solutions by adsorption method. For this purpose, MWCNT has been used as an adsorbent. Parameters that affect the adsorption of formic acid were examined by FCCCD based on RSM and a second-degree model equation representing adsorption capacity were obtained. The model adequately represented by all independent variables, namely initial formic acid concentration, adsorbent amount and temperature. All independent variables had an effect on the adsorption capacity. It is concluded that the second-degree model equation can be used to explain for formic acid adsorption by MWCNT. Also, the optimal adsorption conditions to obtain the higher adsorption capacity were determined. The higher adsorption capacity value was obtained in system where the initial formic acid concentration of 10% (w/w), MWCNT amount of 0.01 g and adsorption temperature of 25 C. This design study has also showed that MWCNT as an adsorbent can be successfully employed for the uptake of formic acid from aqueous solutions. Acknowledgement _ This study was funded by Istanbul University with the Project Number BYP-2018-31279. References [1] W. Reutemann, H. Kieczka, Formic Acid, Ullmann’s Encyclopedia of Industrial
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€ Çelebican et al., Modeling and optimization of formic acid adsorption by multiwall carbon nanotube using response Please cite this article as: O. surface methodology, Journal of Molecular Structure, https://doi.org/10.1016/j.molstruc.2019.127312