Volume 7, Issue 4 (Autumn 2018)
Arch Hyg Sci 2018, 7(4): 288-294 |
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Moradi R, Ganjali A, Yari A R, Mahmoudian M H. Photocatalytic Degradation of Ciprofloxacin pharmacy pollutant in Batch Photoreactor. Arch Hyg Sci 2018; 7 (4) :288-294
URL: http://jhygiene.muq.ac.ir/article-1-343-en.html
URL: http://jhygiene.muq.ac.ir/article-1-343-en.html
1- Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran
2- Department of Experimental Science, Kahnooj Branch, Islamic Azad University, Kahnooj, Iran
3- Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran.
4- Research Center for Environmental Pollutants and Department of Environmental Health Engineering, Qom University of Medical Sciences, Qom, Iran.
2- Department of Experimental Science, Kahnooj Branch, Islamic Azad University, Kahnooj, Iran
3- Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran.
4- Research Center for Environmental Pollutants and Department of Environmental Health Engineering, Qom University of Medical Sciences, Qom, Iran.
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The color is a reflection of the interaction between visible light and matter. There are three criteria for color assessment such as: shades, brightness and depth (1). Azo dye Acid Red 14 is type’s of acidic dyes. This group of dyes are solutions in water, and when dissolve in water, they are negatively charged, and salts are organic acids (2). Dyes and pigments used in industries for applications such as textiles, leathers, papers, foodstuffs, additives, etc (3,4). Azo dyes are toxic and dangerous because they have aromatic ring in their structure. The advanced oxidation processes (AOPs) methods for degradation of dyes in aqueous solution were studied by many investigators (5-8). The mixed use of CNTs and H2O2 to a certain extent yielded a significant improvement of dye photooxidation compared to that of the H2O2 oxidation alone. H2O2 can be activated on the CNTs surface to generate hydroxyl free radicals. The CNTs is an electron-transfer catalyst. The catalytic decomposition of H2O2 by CNTs involves the exchange of a surface hydroxyl group on CNTs surface with a hydrogen peroxide anion (HO2−) to on the surface, which then decomposes another generate peroxide H2O2 molecule producing oxygen and regenerating the CNTs active site (9-11). Recently, CNTs have attracted great interest as a new type of catalyst for removing environmental pollutants (12,13). The statistical methods are invaluable not only in the analysis of experimental data, but also in designing and optimizing experiments. The experimental design is usually used to describe the stages of (14):
1. Identifying the factors which may affect the result of an experiment.
2. Designing the experiment so that the effects of uncontrolled factors are minimized.
3. Using statistical analysis to separate and evaluate the effects of the different factors involved.
Aims of the study:
In this study, MWCNTs particles were characterized by SEM, TEM and FT-IR. The reaction kinetic was studied. The effects of operational factor such as pH, catalyst amount and H2O2 concentration were studied using a (23) [u1] full factorial design with two levels (low and high) and three factors in order to examine the main effects and the interactions between operational factors in the photocatalytic degradation of AR14.
Materials
The mono azo dye, AR14 was obtained from Alvan Sabet Company (Iran) and was used without further purification. The structure and characteristics of AR14 is shown in Table 1. The pH values were adjusted at desired level using dilute NaOH and H2SO4. MWCNTs (appearance: black powder, length: 5–15 µm, outer diameter: 15–40 nm, purity (carbon): ≥92%, density: 0.03-0.16 g cm-3, and specific surface area (BET, N2): 127 m2 g-1) was received from Research Institute of Petroleum Industry (Iran). Other chemicals used in the paper were purchased from the Merck Company (Germany). Double distilled water was used for preparation of requisite solutions.
Table 1) The structure and characteristics of dye.
Apparatus
Fig.1 shows the schematic diagram of photoreactor which was used for photocatalytic decomposition of AR14. In this equipment, capacity 0.5 L with a mercury lamp Philips 15W (UV-C) was used. UV/V is Spectrophotometer, Jenway (6505) was employed to measure the absorbance using glass cells of path length 1 Cm. The morphologies of the catalyst were taken by SEM model MIRA3 TESCAN and TEM model EM10C–Zeiss. The FT-IR spectroscopy was measured on PerkinElmer Spectrometric Analyzer using KBr pellets.
Figure 1) Schematic diagram of photoreactor
1) tank (Pyrex), 2) pump, 3) temperature controller, 4) condenser, 5) UV lamp, 6) reaction flask.
Full factorial experimental design
The photodegradation efficiency of AR14 by MWCNTs particles were investigated using full experimental design. The experiments were designed considering three variables (Factors) including pH, catalyst amount and H2O2 concentration at two levels (low (–) and high (+)). Factorial designs are usually discussed in terms of coded factor spaces. Variables and levels used in (23) full factorial experimental design are shown in Table 2. The experiments were carried out by varying the pH 3 to 5, catalyst amount 20 to 30 mg L-1 and H2O2 concentration 15 to 20 ppm. In this study, Classical calculations method was used for the design of experiments and analyzes the results in the process.
Table 2) Factors and levels used in the (23) factorial design
Procedures
For the photodegradation of AR14, a solution containing specific concentration of dye (40 ppm) and catalyst was prepared. The suspension pH values were adjusted at the desired level using dilute NaOH 0.1N and H2SO4 0.1N (the pH values were measured with Horiba M12 pH meter) and then were allowed to equilibrate for 30 min in darkness. In order to carry out each experiment (according to Table 3), 0.5 L AR14 solution was made as specified concentration and was transferred to reaction flask (Pyrex). The degradation reaction took place under the radiation of a mercury lamp. The concentration of the samples was determined (at 5 min intervals and centrifuged with centrifuge 4232 ALC) using a spectrophotometer (UV-Vis spectrophotometer, Jenway (6505) at λmax=514 nm. The photodegradation efficiency (Y) as a function of time is given by (Eq.1):
(1)
where C0 and C are the concentration of dye (ppm) at t=0 and t (min), respectively.
The characterization of MWCNTs particles
Fig. 2 shows the SEM image of MWCNTs particles. Regarding to the specified scale in the Fig. 2 size of the particles is nanometer. The size and morphology of MWCNTs powder were characterized.
Figure 2) SEM image of MWCNTs
Fig. 3 shows the TEM image of MWCNTs powder. The TEM was used due to its ability to measure nanotubes diameter. From the TEM image it is possible to determine directly the diameter of one nanotubes and bundle diameter. Due to this information, a number of nanotubes in the bundle can be found.
Figure 3) TEM image of MWCNTs
Fig. 4 shows the FT-IR spectrum of MWCNTs particles in the wave number range from 400 of 4000 cm-1. The bands at 3462 and 1703 cm-1 are due to O-H stretching and O-H bending, respectively. The bands at 2929 and 2873 cm-1 correspond to asymmetric and symmetric aliphatic C-H stretching, respectively. Bands at 1300-1000 cm-1 correspond to C-O and C-C stretching (15).
Figure 4) FT-IR spectrum of MWCNTs
The UV-Vis spectra
The absorbance of AR14 solutions during the process at initial and after 50 min irradiation time versus wavelength (λ) are shown in Fig. 5. The spectrum of AR14 in the visible region exhibits a main band with a maximum at 514 nm. The decrease of absorption peaks of AR14 at λmax=514 nm in this figure indicates a rapid degradation of the azo dye. Complete discoloration of dye was observed after 50 min under optimal conditions.
Figure 5) UV-Vis spectra of AR14 on photodegradation (dye concentration = 40 ppm, catalyst amount = 30 mg L-1, H2O2 concentration= 20 ppm, pH= 3, T=25 °C, irradiation time= 50 min).
The classical mathematical treatment of a full factorial design
In this study, the experimental design used is a two level and three factors (A, B and C). The design is called a (23) factorial design, which has 8 experiments. Using the (– and +) notation to represent the low and high levels of the factors; listed the eight experiments in the (23) design as in Table 3. This is called the design matrix. The first column of Table 3 lists the experiment numbers (1-8). The next three columns list the abbreviated coded factor levels (– and +) for factors A, B and C. Table 4 gives a contrast constants for the (23) design. The first column of Table 4 gives a notation often used to describe the treatment combinations (TC), where the presence of the appropriate lower-case letter indicates that the factor is at the high level and its absence that the factor is at the low level. The number 1 is used to indicate that all factors are at the low level. The next seven columns list the three factors and four interaction of the model: the three two-factor interactions (AB, AC and BC), the one three-factor interactions (ABC). Finally, the last column of Table 4 lists the average responses (photodegradation efficiency (Y)) of the each experiment.
Table 6) Yates’s algorithm for the (23) factorial design
The statistical analysis
The statistically significant variables were tested using analysis of variance (ANOVA). It can be shown that in a two-level experiment, like this one, the sum of squares (SS) for the (23) full factorial design is given by (Eq.3):
SS (factor/interaction) =N/4 (the estimate effect of factor/interaction) 2, (3)
where N is the total number of measurements, including replicates. In this case N is 16 since two replicate measurements were made for each combination of factor levels. For example SSA is:
SSA= 16/4 (1.9375)2=15.0156
It can be shown that each sum of squares has one degree of freedom (DOF). Since the mean square (MS) is given by (Eq.4):
MS= SS/DOF (4)
Each MS is simply the corresponding SS. For example MSA is:
MSA=15.0156/1=15.0156
To test for the significance of an effect, the MS is compared with the error mean square (EMS) or residual mean square (RMS). RMS values are listed in Table 7.
Table 7) RMS values for the (23) factorial design
In Table 7, columns (1) and (2) are the values of each experiment that are repeated. Column (3) is the sum of the values of columns (1) and (2). RMS value is given by (Eq.5):
RMS=((65.35)2+(83.57)2+…+(80.44)2+(67.35)2+(85.57)2+…+(82.44)2–1/2 (132.7)2+(169.14)2+…+(162.88)2)=16. (5)
In order to test whether the difference between MS and RMS is significant, the calculated statistic F is given by (Eq.6):
Fcalculated=MS/RMS. (6)
The critical value is F1,8=5.318 (P=0.05). If be Fcalculated>Fcritical, then the effect is significant.
For example F (A) calculated is:
F(A) calculated=MSA/RMS=15.0156/16=0.9384.
The results of ANOVA are listed in Table 8.
Table 8 shows that the main factor such as: H2O2 concentration is significant. Also interaction between pH×H2O2 concentrations is significant. Thus the factor of H2O2 concentration and interaction between pH×H2O2 concentrations are significant.
Table 8) ANOVA table for the (23) factorial design
The regression model
In a (23) factorial design, it is easy to express the results of the experiment in terms of a regression model. Because the (23) is just a factorial design, can be also use either an effects or a means model. A first order regression model is given by (Eq.7):
=b0+b3X3+b13X1X3, (7)
Where is the predicted photodegradation efficiency, b0 the average response, b3 the regression coefficient and b13 is interaction coefficient. The coded variable X3 is represent H2O2 concentration. The X1X3 term is the interaction between pH×H2O2 concentration. The coded variable X1, X3 take on values between –1 and +1. The at run (1) is:
=78.5487+(7.8325/2)(–1)–(11.5575/2) (–1)(–1)=68.8538
Residuals can be obtained as the difference between experimental and predicted etches rate deviations. The residual is given by (Eq.8):
. (8)
The values of , and are listed in Table 9. Table 9 shows that H2O2 concentration and interaction between pH×H2O2 concentrations are the significant effects and that the underlying assumptions of the analysis are satisfied.
Table 9) Values of , and for the (23) factorial design
The kinetic of photocatalytic degradation of dye
Photocatalytic degradation reaction kinetic of dye completely correspond the kinetic of pseudo first order reaction model (16,17). In the kinetic equation of first order relationship between [dye] and time (t) is in (Eq.9):
, (9)
The integral Eq.9 is in Eq.10:
(10)
In which k is the apparent first order rate constant (that is affected by [dye]) and t is the reaction time.
A plot of versus t for optimum condition of photocatalytic degradation of dye is shown in Fig. 6. The linear plot suggests that the photodegradation reaction approximately follows the pseudo first order kinetic with a rate constant k=0.0696 min-1.
Figure 6) Kinetics of photocatalytic degradation of AR14 (dye concentration=40 ppm, catalyst amount = 30 mg L-1, H2O2 concentration=20 ppm, pH=3, T=25 °C, irradiation time= 50 min)
The half-life time (t1/2) for the first order reaction is given by (Eq.11):
. (11)
So, a first order reaction with half-life time (t1/2=9.956 min−1) was observed for the photocatalytic degradation reaction.
In this study, for degradation of dye as dangerous pollutants used a photoreactor with capacity 0.5 L that shows the schematic diagram in Fig.1. The photodegradation process using full factorial experimental design was studied by many investigators (18-20). The maximum photodegradation efficiency of AR14 obtained in this study was found to be 90.65%, corresponding to the operating conditions of 3, 30 mg L-1 and 20 ppm respectively, for the pH, catalyst amount and H2O2 concentration. The positive signs (run number: 7, Table 3) are related to factors such as: catalyst amount and H2O2 concentration. The increase in catalyst amount increases the surface area available by more photocatalyst particles. So, the number of active sites on the photocatalyst surface increases that leads to the increase of the photocatalytic degradation efficiency (21). The effect of H2O2 concentration on the photodegradation efficiency of dye was performed at a range 15 ppm or 20 ppm. The photodegradation efficiency increases with increase of H2O2 concentration. This can be explained by the effect of the additionally produced hydroxyl radicals (22,23).
The negative sign (run number: 7, Table 3) is related to factor pH. The photodegradation efficiency of AR14 at pH with levels 3 and 5, which the best results obtained in acidic solution, (pH=3, X=90.65%). The charge of catalyst and its surface is presumably positively charged in acidic solution and negatively charged in alkaline solution. For the above reasons, dye that has a sulfunic groups (SO3–) in its structure, which is negatively charged, the acidic solution favors adsorption of dye onto the photocatalyst surface, thus the photodegradation efficiency increases. This effect is in agreement with the results of Sato, Yoneyama and Nikazar (24-26). In a experiments result indicated that the degradation rate of dye in this process fitted by the first order kinetic model (16,17).
Azo dyes have been widely are used in different industries. Wastewater industries cause environmental pollutant problems. So, industries wastewater treatment plan are necessary. MWCNTs particles have been characterized by SEM, TEM and FT-IR. The full factorial experimental design based design matrix and Yates’ algorithm were used in this process. As observed, the most effective factors in the photocatalytic degradation efficiency were H2O2 concentration. pH was the least effect factor on the response. The interaction between pH×H2O2 concentration was the most influencing interaction. The results of ANOVA shows that the main factor such as: H2O2 concentration is significant. Also interaction between pH×H2O2 concentration are significant. Thus the factor of H2O2 concentration and interaction between pH×H2O2 concentration are highly significant. The regression model was studied. Kinetics of photocatalytic decomposition reaction was determined. Pseudo first order model reaction corresponds to the experiment data of photocatalytic degradation of dye was observed.
Acknowledgement:
The authors wish to acknowledge members of the Research Laboratory of Islamic Azad University, Arak Branch, Arak, Iran.
Conflict of Interest:
The Authors have no conflict of interest.
Full-Text: (935 Views)
|
Background
|
The color is a reflection of the interaction between visible light and matter. There are three criteria for color assessment such as: shades, brightness and depth (1). Azo dye Acid Red 14 is type’s of acidic dyes. This group of dyes are solutions in water, and when dissolve in water, they are negatively charged, and salts are organic acids (2). Dyes and pigments used in industries for applications such as textiles, leathers, papers, foodstuffs, additives, etc (3,4). Azo dyes are toxic and dangerous because they have aromatic ring in their structure. The advanced oxidation processes (AOPs) methods for degradation of dyes in aqueous solution were studied by many investigators (5-8). The mixed use of CNTs and H2O2 to a certain extent yielded a significant improvement of dye photooxidation compared to that of the H2O2 oxidation alone. H2O2 can be activated on the CNTs surface to generate hydroxyl free radicals. The CNTs is an electron-transfer catalyst. The catalytic decomposition of H2O2 by CNTs involves the exchange of a surface hydroxyl group on CNTs surface with a hydrogen peroxide anion (HO2−) to on the surface, which then decomposes another generate peroxide H2O2 molecule producing oxygen and regenerating the CNTs active site (9-11). Recently, CNTs have attracted great interest as a new type of catalyst for removing environmental pollutants (12,13). The statistical methods are invaluable not only in the analysis of experimental data, but also in designing and optimizing experiments. The experimental design is usually used to describe the stages of (14):
1. Identifying the factors which may affect the result of an experiment.
2. Designing the experiment so that the effects of uncontrolled factors are minimized.
3. Using statistical analysis to separate and evaluate the effects of the different factors involved.
Aims of the study:
In this study, MWCNTs particles were characterized by SEM, TEM and FT-IR. The reaction kinetic was studied. The effects of operational factor such as pH, catalyst amount and H2O2 concentration were studied using a (23) [u1] full factorial design with two levels (low and high) and three factors in order to examine the main effects and the interactions between operational factors in the photocatalytic degradation of AR14.
|
Materials & Methods
|
Materials
The mono azo dye, AR14 was obtained from Alvan Sabet Company (Iran) and was used without further purification. The structure and characteristics of AR14 is shown in Table 1. The pH values were adjusted at desired level using dilute NaOH and H2SO4. MWCNTs (appearance: black powder, length: 5–15 µm, outer diameter: 15–40 nm, purity (carbon): ≥92%, density: 0.03-0.16 g cm-3, and specific surface area (BET, N2): 127 m2 g-1) was received from Research Institute of Petroleum Industry (Iran). Other chemicals used in the paper were purchased from the Merck Company (Germany). Double distilled water was used for preparation of requisite solutions.
Table 1) The structure and characteristics of dye.
| MW (g/mol) |
λmax (nm) |
C.I. | Structure | Dye |
| 502.423 | 514 | 14720 |
|
AR14 |
Apparatus
Fig.1 shows the schematic diagram of photoreactor which was used for photocatalytic decomposition of AR14. In this equipment, capacity 0.5 L with a mercury lamp Philips 15W (UV-C) was used. UV/V is Spectrophotometer, Jenway (6505) was employed to measure the absorbance using glass cells of path length 1 Cm. The morphologies of the catalyst were taken by SEM model MIRA3 TESCAN and TEM model EM10C–Zeiss. The FT-IR spectroscopy was measured on PerkinElmer Spectrometric Analyzer using KBr pellets.
Figure 1) Schematic diagram of photoreactor
1) tank (Pyrex), 2) pump, 3) temperature controller, 4) condenser, 5) UV lamp, 6) reaction flask.
Full factorial experimental design
The photodegradation efficiency of AR14 by MWCNTs particles were investigated using full experimental design. The experiments were designed considering three variables (Factors) including pH, catalyst amount and H2O2 concentration at two levels (low (–) and high (+)). Factorial designs are usually discussed in terms of coded factor spaces. Variables and levels used in (23) full factorial experimental design are shown in Table 2. The experiments were carried out by varying the pH 3 to 5, catalyst amount 20 to 30 mg L-1 and H2O2 concentration 15 to 20 ppm. In this study, Classical calculations method was used for the design of experiments and analyzes the results in the process.
Table 2) Factors and levels used in the (23) factorial design
| Range and levels | |||
| Variables (Factors) | Coding | Low (–) | High (+) |
| A) pH | X1 | 3 | 5 |
| B) Catalyst amount (mg L-1) | X2 | 20 | 30 |
| C) H2O2 concentration (ppm) | X3 | 15 | 20 |
Procedures
For the photodegradation of AR14, a solution containing specific concentration of dye (40 ppm) and catalyst was prepared. The suspension pH values were adjusted at the desired level using dilute NaOH 0.1N and H2SO4 0.1N (the pH values were measured with Horiba M12 pH meter) and then were allowed to equilibrate for 30 min in darkness. In order to carry out each experiment (according to Table 3), 0.5 L AR14 solution was made as specified concentration and was transferred to reaction flask (Pyrex). The degradation reaction took place under the radiation of a mercury lamp. The concentration of the samples was determined (at 5 min intervals and centrifuged with centrifuge 4232 ALC) using a spectrophotometer (UV-Vis spectrophotometer, Jenway (6505) at λmax=514 nm. The photodegradation efficiency (Y) as a function of time is given by (Eq.1):
(1)
where C0 and C are the concentration of dye (ppm) at t=0 and t (min), respectively.
|
Results
|
The characterization of MWCNTs particles
Fig. 2 shows the SEM image of MWCNTs particles. Regarding to the specified scale in the Fig. 2 size of the particles is nanometer. The size and morphology of MWCNTs powder were characterized.
Figure 2) SEM image of MWCNTs
Fig. 3 shows the TEM image of MWCNTs powder. The TEM was used due to its ability to measure nanotubes diameter. From the TEM image it is possible to determine directly the diameter of one nanotubes and bundle diameter. Due to this information, a number of nanotubes in the bundle can be found.
Figure 3) TEM image of MWCNTs
Fig. 4 shows the FT-IR spectrum of MWCNTs particles in the wave number range from 400 of 4000 cm-1. The bands at 3462 and 1703 cm-1 are due to O-H stretching and O-H bending, respectively. The bands at 2929 and 2873 cm-1 correspond to asymmetric and symmetric aliphatic C-H stretching, respectively. Bands at 1300-1000 cm-1 correspond to C-O and C-C stretching (15).
Figure 4) FT-IR spectrum of MWCNTs
The UV-Vis spectra
The absorbance of AR14 solutions during the process at initial and after 50 min irradiation time versus wavelength (λ) are shown in Fig. 5. The spectrum of AR14 in the visible region exhibits a main band with a maximum at 514 nm. The decrease of absorption peaks of AR14 at λmax=514 nm in this figure indicates a rapid degradation of the azo dye. Complete discoloration of dye was observed after 50 min under optimal conditions.
Figure 5) UV-Vis spectra of AR14 on photodegradation (dye concentration = 40 ppm, catalyst amount = 30 mg L-1, H2O2 concentration= 20 ppm, pH= 3, T=25 °C, irradiation time= 50 min).
The classical mathematical treatment of a full factorial design
In this study, the experimental design used is a two level and three factors (A, B and C). The design is called a (23) factorial design, which has 8 experiments. Using the (– and +) notation to represent the low and high levels of the factors; listed the eight experiments in the (23) design as in Table 3. This is called the design matrix. The first column of Table 3 lists the experiment numbers (1-8). The next three columns list the abbreviated coded factor levels (– and +) for factors A, B and C. Table 4 gives a contrast constants for the (23) design. The first column of Table 4 gives a notation often used to describe the treatment combinations (TC), where the presence of the appropriate lower-case letter indicates that the factor is at the high level and its absence that the factor is at the low level. The number 1 is used to indicate that all factors are at the low level. The next seven columns list the three factors and four interaction of the model: the three two-factor interactions (AB, AC and BC), the one three-factor interactions (ABC). Finally, the last column of Table 4 lists the average responses (photodegradation efficiency (Y)) of the each experiment.
Table 3) Experimental design matrix, experimental results photodegradation efficiency for AR14 (%)
Table 4) Contrast constants for the (23) design
| Run Number | Factors | Treatment Combination (TC) |
Response (Y) | ||
| A | B | C | |||
| 1 | – | – | – | (1) | 66.35 |
| 2 | + | – | – | a | 84.57 |
| 3 | – | + | – | b | 69.42 |
| 4 | + | + | – | ab | 78.19 |
| 5 | – | – | + | c | 83.90 |
| 6 | + | – | + | ac | 73.87 |
| 7 | – | + | + | bc | 90.65 |
| 8 | + | + | + | abc | 81.44 |
Table 4) Contrast constants for the (23) design
| TC | Factors and their interactions | Response (Y) | ||||||
| A | B | AB | C | AC | BC | ABC | ||
| (1) | – | – | + | – | + | + | – | 66.35 |
| a | + | – | – | – | – | + | + | 84.57 |
| b | – | + | – | – | + | – | + | 69.42 |
| ab | + | + | + | – | – | – | – | 78.19 |
| c | – | – | + | + | – | – | + | 83.90 |
| ac | + | – | – | + | + | – | – | 73.87 |
| bc | – | + | – | + | – | + | – | 90.65 |
| abc | + | + | + | + | + | + | + | 81.44 |
In the classical factorial design literature, a factor effect is defined as the difference in average responses between the experiments carried out at the high level of the factor and the experiments carried out at the low level of the factor. Thus, in a (23) full factorial design, the main effect of A would be calculated by (Eq.2):
A=(Average responses at high level of A)–(Average responses at low level of A) (2)
Average responses at high level of A=
=1/4×(84.57+78.19+73.87+81.44)=79.5175.
Average responses at low level of A=
=1/4×(66.35+69.42+83.90+90.65)=77.58.
The main effect of A=79.5175–77.58=1.9375.
The interactions effect can be calculated similarly. Therefore, the effect of each factor/interaction is listed in Table 5. The effect of each factor on response was found to be in the following the order: pH (1.9375), catalyst amount (2.7525) and H2O2 concentration (7.8325). It can be seen that H2O2 concentration has the main effect on response. The least effect factor on the response is pH. The interaction between pH×catalyst amount and pH×catalyst amount×H2O2 concentration have a little effect on response. The interaction between pH×H2O2 concentration and catalyst amount × H2O2 concentration have a great effect on response. Thus the most effect interaction is that between pH×H2O2 concentration (AC).
Table 5) Effect of each factor/interaction on response
The Yates’ algorithm
There is a quick way to calculate the effects of factorial design presented by Yates’. The Yates’ algorithm describe here, and illustrate its use for the (23) full factorial design discussed. To apply the Yates’ algorithm, the results should be placed in a standard order. The results are the numerical values of the estimated factor effects. As shown in Table 6, the Yates calculations start by evaluating as many auxiliary columns as factors being considered, in our example three columns E1, E2 and E3 for a (23) design. The first four elements in column E1 are obtained by adding the pairs together (66.35+84.57=150.92, 69.42+78.19=147.61, etc). The last four elements in column E1 are obtained by subtracting the top response from the bottom response of each pair, thus 84.57–66.35=18.22, 78.19– 69.42=8.77, and so on. In just the same way that column E1 is obtained from the "Response" column, column E2 is obtained from column E1 and column E3 is obtained from column E2. Finally, obtain the "Effect" column have only to divide these values by the appropriate divisor, as before, which is 8 (the number of runs) for the first element and 4 (half of the runs) for the others. Calculations can be made sure that the first value in column E3 (628.39) is the sum of all the responses.
A=(Average responses at high level of A)–(Average responses at low level of A) (2)
Average responses at high level of A=
=1/4×(84.57+78.19+73.87+81.44)=79.5175.
Average responses at low level of A=
=1/4×(66.35+69.42+83.90+90.65)=77.58.
The main effect of A=79.5175–77.58=1.9375.
The interactions effect can be calculated similarly. Therefore, the effect of each factor/interaction is listed in Table 5. The effect of each factor on response was found to be in the following the order: pH (1.9375), catalyst amount (2.7525) and H2O2 concentration (7.8325). It can be seen that H2O2 concentration has the main effect on response. The least effect factor on the response is pH. The interaction between pH×catalyst amount and pH×catalyst amount×H2O2 concentration have a little effect on response. The interaction between pH×H2O2 concentration and catalyst amount × H2O2 concentration have a great effect on response. Thus the most effect interaction is that between pH×H2O2 concentration (AC).
Table 5) Effect of each factor/interaction on response
| Factors/interactions | Effect |
| A | 1.9375 |
| B | 2.7525 |
| AB | –2.1575 |
| C | 7.8325 |
| AC | –11.5575 |
| BC | 4.4075 |
| ABC | 2.5675 |
The Yates’ algorithm
There is a quick way to calculate the effects of factorial design presented by Yates’. The Yates’ algorithm describe here, and illustrate its use for the (23) full factorial design discussed. To apply the Yates’ algorithm, the results should be placed in a standard order. The results are the numerical values of the estimated factor effects. As shown in Table 6, the Yates calculations start by evaluating as many auxiliary columns as factors being considered, in our example three columns E1, E2 and E3 for a (23) design. The first four elements in column E1 are obtained by adding the pairs together (66.35+84.57=150.92, 69.42+78.19=147.61, etc). The last four elements in column E1 are obtained by subtracting the top response from the bottom response of each pair, thus 84.57–66.35=18.22, 78.19– 69.42=8.77, and so on. In just the same way that column E1 is obtained from the "Response" column, column E2 is obtained from column E1 and column E3 is obtained from column E2. Finally, obtain the "Effect" column have only to divide these values by the appropriate divisor, as before, which is 8 (the number of runs) for the first element and 4 (half of the runs) for the others. Calculations can be made sure that the first value in column E3 (628.39) is the sum of all the responses.
Table 6) Yates’s algorithm for the (23) factorial design
| Run Number | TC | Response | E1 | E2 | E3 | Divisor | Effect |
| 1 | None | 66.35 | 150.92 | 298.53 | 628.39 | 8 | 78.5487 |
| 2 | a | 84.57 | 147.61 | 329.86 | 7.75 | 4 | 1.9375 |
| 3 | b | 69.42 | 157.77 | 26.99 | 11.01 | 4 | 2.7525 |
| 4 | ab | 78.19 | 172.09 | –19.24 | –8.63 | 4 | –2.1575 |
| 5 | c | 83.90 | 18.22 | –3.31 | 31.33 | 4 | 7.8325 |
| 6 | ac | 73.87 | 8.77 | 14.32 | –46.23 | 4 | –11.5575 |
| 7 | bc | 90.65 | –10.03 | –9.45 | 17.63 | 4 | 4.4075 |
| 8 | abc | 81.44 | –9.21 | 0.82 | 10.27 | 4 | 2.5675 |
The statistical analysis
The statistically significant variables were tested using analysis of variance (ANOVA). It can be shown that in a two-level experiment, like this one, the sum of squares (SS) for the (23) full factorial design is given by (Eq.3):
SS (factor/interaction) =N/4 (the estimate effect of factor/interaction) 2, (3)
where N is the total number of measurements, including replicates. In this case N is 16 since two replicate measurements were made for each combination of factor levels. For example SSA is:
SSA= 16/4 (1.9375)2=15.0156
It can be shown that each sum of squares has one degree of freedom (DOF). Since the mean square (MS) is given by (Eq.4):
MS= SS/DOF (4)
Each MS is simply the corresponding SS. For example MSA is:
MSA=15.0156/1=15.0156
To test for the significance of an effect, the MS is compared with the error mean square (EMS) or residual mean square (RMS). RMS values are listed in Table 7.
Table 7) RMS values for the (23) factorial design
| Run Number | TC | (1) | (2) | (3) |
| 1 | None | 65.35 | 67.35 | 132.7 |
| 2 | a | 83.57 | 85.57 | 169.14 |
| 3 | b | 68.42 | 70.42 | 138.84 |
| 4 | ab | 77.19 | 79.19 | 156.38 |
| 5 | c | 82.90 | 84.90 | 167.8 |
| 6 | ac | 72.87 | 74.87 | 147.74 |
| 7 | bc | 89.65 | 91.65 | 181.3 |
| 8 | abc | 80.44 | 82.44 | 162.88 |
RMS=((65.35)2+(83.57)2+…+(80.44)2+(67.35)2+(85.57)2+…+(82.44)2–1/2 (132.7)2+(169.14)2+…+(162.88)2)=16. (5)
In order to test whether the difference between MS and RMS is significant, the calculated statistic F is given by (Eq.6):
Fcalculated=MS/RMS. (6)
The critical value is F1,8=5.318 (P=0.05). If be Fcalculated>Fcritical, then the effect is significant.
For example F (A) calculated is:
F(A) calculated=MSA/RMS=15.0156/16=0.9384.
The results of ANOVA are listed in Table 8.
Table 8 shows that the main factor such as: H2O2 concentration is significant. Also interaction between pH×H2O2 concentrations is significant. Thus the factor of H2O2 concentration and interaction between pH×H2O2 concentrations are significant.
Table 8) ANOVA table for the (23) factorial design
| Factor/ interaction |
SS | DOF | MS | F | Result |
| A | 15.0156 | 1 | 15.0156 | 0.9384 | no significant |
| B | 30.3050 | 1 | 30.3050 | 1.8940 | no significant |
| AB | 18.6192 | 1 | 18.6192 | 1.1637 | no significant |
| C | 245.3922 | 1 | 245.3922 | 15.3370 | significant |
| AC | 534.3032 | 1 | 534.3032 | 33.3939 | significant |
| BC | 77.7042 | 1 | 77.7042 | 4.8565 | no significant |
| ABC | 26.3682 | 1 | 26.3682 | 1.6480 | no significant |
| Error | 8 | ||||
| Total | 15 |
The regression model
In a (23) factorial design, it is easy to express the results of the experiment in terms of a regression model. Because the (23) is just a factorial design, can be also use either an effects or a means model. A first order regression model is given by (Eq.7):
=b0+b3X3+b13X1X3, (7)
Where is the predicted photodegradation efficiency, b0 the average response, b3 the regression coefficient and b13 is interaction coefficient. The coded variable X3 is represent H2O2 concentration. The X1X3 term is the interaction between pH×H2O2 concentration. The coded variable X1, X3 take on values between –1 and +1. The at run (1) is:
=78.5487+(7.8325/2)(–1)–(11.5575/2) (–1)(–1)=68.8538
Residuals can be obtained as the difference between experimental and predicted etches rate deviations. The residual is given by (Eq.8):
. (8)
The values of , and are listed in Table 9. Table 9 shows that H2O2 concentration and interaction between pH×H2O2 concentrations are the significant effects and that the underlying assumptions of the analysis are satisfied.
Table 9) Values of , and for the (23) factorial design
| Run Number | TC | |||
| 1 | (1) | 66.35 | 68.8538 | –2.5038 |
| 2 | a | 84.57 | 80.4112 | 4.1588 |
| 3 | b | 69.42 | 68.8538 | 0.5662 |
| 4 | ab | 78.19 | 80.4112 | –2.2212 |
| 5 | c | 83.90 | 88.2436 | –4.3436 |
| 6 | ac | 73.87 | 76.6862 | –2.8162 |
| 7 | bc | 90.65 | 88.2436 | 2.4064 |
| 8 | abc | 81.44 | 76.6862 | 4.7538 |
The kinetic of photocatalytic degradation of dye
Photocatalytic degradation reaction kinetic of dye completely correspond the kinetic of pseudo first order reaction model (16,17). In the kinetic equation of first order relationship between [dye] and time (t) is in (Eq.9):
, (9)
The integral Eq.9 is in Eq.10:
(10)
In which k is the apparent first order rate constant (that is affected by [dye]) and t is the reaction time.
A plot of versus t for optimum condition of photocatalytic degradation of dye is shown in Fig. 6. The linear plot suggests that the photodegradation reaction approximately follows the pseudo first order kinetic with a rate constant k=0.0696 min-1.
Figure 6) Kinetics of photocatalytic degradation of AR14 (dye concentration=40 ppm, catalyst amount = 30 mg L-1, H2O2 concentration=20 ppm, pH=3, T=25 °C, irradiation time= 50 min)
The half-life time (t1/2) for the first order reaction is given by (Eq.11):
. (11)
So, a first order reaction with half-life time (t1/2=9.956 min−1) was observed for the photocatalytic degradation reaction.
|
Discussion
|
In this study, for degradation of dye as dangerous pollutants used a photoreactor with capacity 0.5 L that shows the schematic diagram in Fig.1. The photodegradation process using full factorial experimental design was studied by many investigators (18-20). The maximum photodegradation efficiency of AR14 obtained in this study was found to be 90.65%, corresponding to the operating conditions of 3, 30 mg L-1 and 20 ppm respectively, for the pH, catalyst amount and H2O2 concentration. The positive signs (run number: 7, Table 3) are related to factors such as: catalyst amount and H2O2 concentration. The increase in catalyst amount increases the surface area available by more photocatalyst particles. So, the number of active sites on the photocatalyst surface increases that leads to the increase of the photocatalytic degradation efficiency (21). The effect of H2O2 concentration on the photodegradation efficiency of dye was performed at a range 15 ppm or 20 ppm. The photodegradation efficiency increases with increase of H2O2 concentration. This can be explained by the effect of the additionally produced hydroxyl radicals (22,23).
The negative sign (run number: 7, Table 3) is related to factor pH. The photodegradation efficiency of AR14 at pH with levels 3 and 5, which the best results obtained in acidic solution, (pH=3, X=90.65%). The charge of catalyst and its surface is presumably positively charged in acidic solution and negatively charged in alkaline solution. For the above reasons, dye that has a sulfunic groups (SO3–) in its structure, which is negatively charged, the acidic solution favors adsorption of dye onto the photocatalyst surface, thus the photodegradation efficiency increases. This effect is in agreement with the results of Sato, Yoneyama and Nikazar (24-26). In a experiments result indicated that the degradation rate of dye in this process fitted by the first order kinetic model (16,17).
|
Conclusion
|
Azo dyes have been widely are used in different industries. Wastewater industries cause environmental pollutant problems. So, industries wastewater treatment plan are necessary. MWCNTs particles have been characterized by SEM, TEM and FT-IR. The full factorial experimental design based design matrix and Yates’ algorithm were used in this process. As observed, the most effective factors in the photocatalytic degradation efficiency were H2O2 concentration. pH was the least effect factor on the response. The interaction between pH×H2O2 concentration was the most influencing interaction. The results of ANOVA shows that the main factor such as: H2O2 concentration is significant. Also interaction between pH×H2O2 concentration are significant. Thus the factor of H2O2 concentration and interaction between pH×H2O2 concentration are highly significant. The regression model was studied. Kinetics of photocatalytic decomposition reaction was determined. Pseudo first order model reaction corresponds to the experiment data of photocatalytic degradation of dye was observed.
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Footnotes
|
Acknowledgement:
The authors wish to acknowledge members of the Research Laboratory of Islamic Azad University, Arak Branch, Arak, Iran.
Conflict of Interest:
The Authors have no conflict of interest.
Type of Study: Original Article |
Subject:
Environmental Health
Received: 2018/06/5 | Accepted: 2018/12/27 | Published: 2018/12/29
Received: 2018/06/5 | Accepted: 2018/12/27 | Published: 2018/12/29
References
1. Elmolla ES, Chaudhuri M. Degradation of amoxicillin, ampicillin and cloxacillin antibiotics in aqueous solution by the UV/ZnO photocatalytic process. Journal of hazardous materials. 2010;173(1-3):445-9. Link [DOI:10.1016/j.jhazmat.2009.08.104]
2. Ikehata K, Jodeiri Naghashkar N, Gamal El-Din M. Degradation of aqueous pharmaceuticals by ozonation and advanced oxidation processes: a review. Ozone: Science and Engineering. 2006;28(6):353-414. Link [DOI:10.1080/01919510600985937]
3. Hojat Ansari S, Giahi M. Photochemical Degradation of Fluocinolone Acetonidin Drug in Aqueous Solutions Using Nanophotocatalyst ZnO Doped by C, N, and S. Iranian Journal of Chemistry and Chemical Engineering (IJCCE). 2017;36(3):183-9. Link
4. Carballa M, Omil F, Lema JM. Comparison of predicted and measured concentrations of selected pharmaceuticals, fragrances and hormones in Spanish sewage. Chemosphere. 2008;72(8):1118-23. Link [DOI:10.1016/j.chemosphere.2008.04.034]
5. Halling-Sørensen B, Nielsen SN, Lanzky P, Ingerslev F, Lützhøft HH, Jørgensen S. Occurrence, fate and effects of pharmaceutical substances in the environment-A review. Chemosphere. 1998;36(2):357-93. Link [DOI:10.1016/S0045-6535(97)00354-8]
6. Joss A, Andersen H, Ternes T, Richle PR, Siegrist H. Removal of estrogens in municipal wastewater treatment under aerobic and anaerobic conditions: consequences for plant optimization. Environmental science & technology. 2004;38(11):3047-55. Link [DOI:10.1021/es0351488]
7. Lai WW-P, Lin HH-H, Lin AY-C. TiO2 photocatalytic degradation and transformation of oxazaphosphorine drugs in an aqueous environment. Journal of hazardous materials. 2015;287:133-41. Link [DOI:10.1016/j.jhazmat.2015.01.045]
8. Kolpin DW, Furlong ET, Meyer MT, Thurman EM, Zaugg SD, Barber LB, et al. Pharmaceuticals, hormones, and other organic wastewater contaminants in US streams, 1999− 2000: A national reconnaissance. Environmental science & technology. 2002;36(6):1202-11. Link [DOI:10.1021/es011055j]
9. El-Kemary M, El-Shamy H, El-Mehasseb I. Photocatalytic degradation of ciprofloxacin drug in water using ZnO nanoparticles. Journal of Luminescence. 2010;130(12):2327-31. Link [DOI:10.1016/j.jlumin.2010.07.013]
10. Peng H, Pan B, Wu M, Liu Y, Zhang D, Xing B. Adsorption of ofloxacin and norfloxacin on carbon nanotubes: hydrophobicity-and structure-controlled process. Journal of hazardous materials. 2012;233:89-96. Link [DOI:10.1016/j.jhazmat.2012.06.058]
11. El-Shafey E-SI, Al-Lawati H, Al-Sumri AS. Ciprofloxacin adsorption from aqueous solution onto chemically prepared carbon from date palm leaflets. J Environ Sci. 2012;24(9):1579-86. Link [DOI:10.1016/S1001-0742(11)60949-2]
12. Linares-Hernández I, Barrera-Díaz C, Bilyeu B, Juárez-GarcíaRojas P, Campos-Medina E. A combined electrocoagulation-electrooxidation treatment for industrial wastewater. Journal of hazardous materials. 2010;175(1-3):688-94. Link [DOI:10.1016/j.jhazmat.2009.10.064]
13. Garoma T, Umamaheshwar SK, Mumper A. Removal of sulfadiazine, sulfamethizole, sulfamethoxazole, and sulfathiazole from aqueous solution by ozonation. Chemosphere. 2010;79(8):814-20. [DOI:10.1016/j.chemosphere.2010.02.060]
14. Nawrocki J, Kasprzyk-Hordern B. The efficiency and mechanisms of catalytic ozonation. Applied Catalysis B: Environmental. 2010;99(1-2):27-42. Link [DOI:10.1016/j.apcatb.2010.06.033]
15. Chatzitakis A, Berberidou C, Paspaltsis I, Kyriakou G, Sklaviadis T, Poulios I. Photocatalytic degradation and drug activity reduction of chloramphenicol. Water Research. 2008;42(1-2):386-94. Link [DOI:10.1016/j.watres.2007.07.030]
16. Lin AY-C, Lin C-F, Chiou J-M, Hong PA. O3 and O3/H2O2 treatment of sulfonamide and macrolide antibiotics in wastewater. Journal of hazardous materials. 2009;171(1-3):452-8. Link [DOI:10.1016/j.jhazmat.2009.06.031]
17. Nazari S, Yari AR, Mahmodian MH, Tanhaye Reshvanloo M, Alizadeh Matboo S, Majidi G, et al. Application of H2O2 and H2O2/Fe0 in removal of Acid Red 18 dye from aqueous solutions. Archives of Hygiene Sciences. 2013;2(3):104-10. Link
18. Gonzalez-Olmos R, Martin MJ, Georgi A, Kopinke F-D, Oller I, Malato S. Fe-zeolites as heterogeneous catalysts in solar Fenton-like reactions at neutral pH. Applied Catalysis B: Environmental. 2012;125:51-8. Link [DOI:10.1016/j.apcatb.2012.05.022]
19. Yu L, Chen J, Liang Z, Xu W, Chen L, Ye D. Degradation of phenol using Fe3O4-GO nanocomposite as a heterogeneous photo-Fenton catalyst. Separation and Purification Technology. 2016;171:80-7. Link [DOI:10.1016/j.seppur.2016.07.020]
20. Pérez-Moya M, Graells M, Castells G, Amigó J, Ortega E, Buhigas G, et al. Characterization of the degradation performance of the sulfamethazine antibiotic by photo-Fenton process. Water Research. 2010;44(8):2533-40. Link [DOI:10.1016/j.watres.2010.01.032]
21. Babić S, Zrnčić M, Ljubas D, Ćurković L, Škorić I. Photolytic and thin TiO2 film assisted photocatalytic degradation of sulfamethazine in aqueous solution. Environmental science and pollution research. 2015;22(15):11372-86. Link [DOI:10.1007/s11356-015-4338-5]
22. Kang S-F, Liao C-H, Po S-T. Decolorization of textile wastewater by photo-Fenton oxidation technology. Chemosphere. 2000;41(8):1287-94. Link [DOI:10.1016/S0045-6535(99)00524-X]
23. Safarzadeh-Amiri A, Bolton JR, Cater SR. The use of iron in advanced oxidation processes. Journal of Advanced Oxidation Technologies. 1996;1(1):18-26. Link [DOI:10.1515/jaots-1996-0105]
24. Gogate PR, Pandit AB. A review of imperative technologies for wastewater treatment II: hybrid methods. Advances in Environmental Research. 2004;8(3-4):553-97. Link [DOI:10.1016/S1093-0191(03)00031-5]
25. Lima MJ, Silva CG, Silva AM, Lopes JC, Dias MM, Faria JL. Homogeneous and heterogeneous photo-Fenton degradation of antibiotics using an innovative static mixer photoreactor. Chemical Engineering Journal. 2017;310:342-51. Link [DOI:10.1016/j.cej.2016.04.032]
26. Bobu M, Yediler A, Siminiceanu I, Schulte-Hostede S. Degradation studies of ciprofloxacin on a pillared iron catalyst. Applied Catalysis B: Environmental. 2008;83(1-2):15-23. Link [DOI:10.1016/j.apcatb.2008.01.029]
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