Scholarly article on topic 'Factorial experimental design for the optimization of catalytic degradation of malachite green dye in aqueous solution by Fenton process'

Factorial experimental design for the optimization of catalytic degradation of malachite green dye in aqueous solution by Fenton process Academic research paper on "Chemical sciences"

CC BY-NC-ND
0
0
Share paper
Academic journal
Water Resources and Industry
OECD Field of science
Keywords
{"Experimental design" / Optimization / "Fenton process" / "Catalytic degradation" / "Malachite green"}

Abstract of research paper on Chemical sciences, author of scientific article — A. Elhalil, H. Tounsadi, R. Elmoubarki, F.Z. Mahjoubi, M. Farnane, et al.

Abstract This work focuses on the optimization of the catalytic degradation of malachite green dye (MG) by Fenton process “Fe2+/H2O2”. A 24 full factorial experimental design was used to evaluate the effects of four factors considered in the optimization of the oxidative process: concentration of MG (X1), concentration of Fe2+ (X2), concentration of H2O2 (X3) and temperature (X4). Individual and interaction effects of the factors that influenced the percentage of dye degradation were tested. The effect of interactions between the four parameters shows that there is a dependency between concentration of MG and concentration of Fe2+; concentration of Fe2+ and concentration of H2O2, expressed by the great values of the coefficient of interaction. The analysis of variance proved that, the concentration of MG, the concentration of Fe2+ and the concentration of H2O2 have an influence on the catalytic degradation while it is not the case for the temperature. In the optimization, the great dependence between observed and predicted degradation efficiency, the correlation coefficient for the model (R2=0.986) and the important value of F-ratio proved the validity of the model. The optimum degradation efficiency of malachite green was 93.83%, when the operational parameters were malachite green concentration of 10mg/L, Fe2+ concentration of 10mM, H2O2 concentration of 25.6mM and temperature of 40°C.

Academic research paper on topic "Factorial experimental design for the optimization of catalytic degradation of malachite green dye in aqueous solution by Fenton process"

Author's Accepted Manuscript

n Water Resources

& INDUSTRY

Factorial experimental design for the optimization of catalytic degradation of malachite green dye in aqueous solution by Fenton process

A. Elhalil, H. Tounsadi, R. Elmoubarki, F.Z. Mahjoubi, M. Farnane, M. Sadiq, M. Abdennouri, S. Qourzal, N. Barka

PII: S2212-3717(16)30067-1

DOI: http ://dx. doi.org/ 10.1016/j .wri .2016.07.002

Reference: WRI70

To appear in: Water Resources and Industry

Received date: 15 December 2015 Revised date: 6 July 2016 Accepted date: 22 July 2016

Cite this article as: A. Elhalil, H. Tounsadi, R. Elmoubarki, F.Z. Mahjoubi, M. Farnane, M. Sadiq, M. Abdennouri, S. Qourzal and N. Barka, Factoria experimental design for the optimization of catalytic degradation of malachit green dye in aqueous solution by Fenton process, Water Resources and Industry, http://dx.doi.org/10.10167j.wii.2016.07.002

This is a PDF file of an unedited manuscript that has been accepted fo publication. As a service to our customers we are providing this early version o the manuscript. The manuscript will undergo copyediting, typesetting, an< review of the resulting galley proof before it is published in its final citable form Please note that during the production process errors may be discovered whic could affect the content, and all legal disclaimers that apply to the journal pertain

Factorial experimental design for the optimization of catalytic degradation of malachite green dye in aqueous solution by Fenton process

A. Elhalil1*, H. Tounsadi1, R. Elmoubarki1, F.Z. Mahjoubi1, M. Farnane1, M. Sadiq1, M.

Abdennouri1, S. Qourzal2, N. Barka1

1Univ. Hassan 1, Laboratoire des Sciences des Matériaux, des Milieux et de la Modélisation

(LS3M), BP.145, 25000 Khouribga, Morocco.

2Equipe de Matériaux, Photocatalyse et Environnement, Département de Chimie, Faculté des Sciences, Université Ibn Zohr, B.P. 8106 Cité Dakhla, Agadir, Morocco

^Corresponding author: Tel.: +212 678 83 19 28; fax: +212 523 49 03 54. elhalil.alaaeddine@gmal.com

Abstract

This work focuses on the optimization of the catalytic degradation of malachite green dye (MG) by Fenton process ''Fe2+/H2O2M. A 24 full factorial experimental design was used to evaluate the effects of four factors considered in the optimization of the oxidative process: concentration of MG (X1), concentration

of Fe (X2), concentration of H2O2 (X3) and temperature (X4). Individual and interaction effects of the factors that influenced the percentage of dye degradation were tested. The effect of interactions between the four parameters shows that there is a dependency between concentration of MG and concentration of Fe2+; concentration of Fe2+ and concentration of H2O2, expressed by the great values of the coefficient of interaction. The analysis of variance proved that, the concentration of MG, the concentration of Fe2+ and the concentration of H2O2 have an influence on the catalytic degradation while it is not the case for the temperature. In the optimization, the great

dependence between observed and predicted degradation efficiency, the correlation coefficient for the model (R = 0.986) and the important value of F-ratio proved the validity of the model. The optimum degradation efficiency of malachite green was 93.83%, when the

operational parameters were malachite green concentration of 10mg/L, Fe concentration of 10mM, H2O2 concentration of 25.6mM and temperature of 40°C.

Graphical abstract

Pollutant

OH "OH

1er' *OH

"OH r*J

f^- "OH 'OH

MG)OH ( MG) OH

h,o+co2

products

Feutou process

Keywords: Experimental design; Optimization; Fenton process; Catalytic degradation; Malachite green.

1. Introduction

Dye wastewater, is one of the major industrial water pollution sources in developing countries. Industries such as textiles, leather, paper-making, plastics, food, rubber and cosmetics use different types of dyestuffs which also appear in the effluents discharged from

some of these industries. Synthetic dyes are toxic as well as noxious, hence they must be

removed immediately from aquatic sources, and otherwise they will lead to severe detrimental effect on the individual health and on the sustaining diversified flora as well as aquatic fauna. For example, malachite green (MG) is the most commonly used dye for cotton, silk, paper, leather and also in manufacturing of paints and printing inks. Malachite green has properties that make it difficult to remove from aqueous solutions. It belongs to the same group of triphenylmethane dyes. A lot of studies have reported its teratogenic [1], carcinogenic [2] and reproductive abnormalities [3] spanning its effect from various fish to mammals [4].

Dyes in wastewater can be treated by different processes like: adsorption [5-10], membranes processes [11-14], coagulation/flocculation [15-17], combined coagulation/flocculation and adsorption on activated carbon [18], biological processes [19,20] etc. Most of these methods are non-destructive and/or they generate secondary pollution, because the dyes are transferred to another phase and this phase has to be regenerated.

It was necessary to develop novel and cost-effective technologies to treat the dye wastewater. Recently, advanced oxidation technologies have been accepted as efficient ways for the degradation of toxic and refractory organics [21-29]. Advanced oxidation processes, in

which oxygen-based radicals (°OH, HO°2, and O-°2) are generated in situ from water and O2, have been applied to dye degradation. These species take part in different reactions to degrade dye molecules completely. The processes are cleaner because dyes totally decompose to low-molecular-weight compounds, CO2 and H2O, and no significant or solid secondary pollution is generated. Especially, Fenton processes have been proved as one of the best methods for the control of organic pollution, in which cheap and environmentally friendly reagents are employed [30].

Fenton's reaction is a homogeneous catalytic oxidation process using a mixture of hydrogen peroxide (H2O2) and ferrous ions (Fe2+) in an acidic medium, which was firstly

discovered by Fenton in the 1890s [31]. In the last decades, Fenton's reaction has been introduced into wastewater treatment processes, and it has been well proven that a variety of refractory organics could be effectively degraded through Fenton's reaction without producing any toxic substances in water environment [32,33].

The generation of hydroxyl radicals in Fenton process is described in the following equations [34]:

Fe2+ + H2O2 -> Fe3+ + °OH + OH-

°OH + Pollutants -> Degradation by products

Several parameters influence the Fenton process, in particular the pH of solution, the concentration of ferrous ions, the concentration of hydrogen peroxide, the stirring speed, the initial concentration of the element to deteriorate, the volume of the solution, temperature, and contact time. Studying of the effect of each and every factor is quite tedious and time consuming. Thus, a factorial design can minimize the above difficulties by optimizing all the affecting parameters collectively at a time.

Factorial design is employed to achieve the best overall optimization of a process [35,36]. The design determines the effect of each factor on the response as well as how the effect of each factor varies with the change in level of the other factors [37]. Interaction effects of different factors could be attained using design of experiments only [35,36]. This technique was used to reduce the number of experiments, time, overall process cost and to obtain better response. The advantages of factorial designs over one-factor-at-a time experiments are that they are more efficient and they allow interactions to be detected [38]. The studies using the experimental designs showed the relevance of this methodology [39,40].

In our work, the optimization of the catalytic degradation of malachite green in aqueous solution by fenton process, using a 24 factorial experimental design was performed.

Four factors were chosen to build the full factorial design with two levels. The effects of factors and their interaction and compatibility of the chosen model with the response have been studied.

2. Materials and methods

1.1. Reagents

In all experiments, we used analytical grade chemicals. The malachite green oxalate form: C23H25N2, C2HO4, 0.5C2H2O4, of molecular weigh 463.5g/mol, was supplied by Sigma-Aldrich (United Kingdom). Ferrous sulfate FeSO4.7H2O, the sulfuric acid H2SO4 (95%-97%) and sodium hydroxide NaOH were purchased from Sigma-Aldrich (Germany). The hydrogen peroxide H2O2 (30%) was obtained from Soparma (Morocco).

1.2. Experimental procedures

A stock solution of 20 mg/L was prepared by dissolving required mass of MG dye in deionized water and the other solutions were prepared by dilution. The degradation tests were performed in a beaker containing 50mL of malachite green solution at designed concentration. The pH of the solution was adjusted to 3 by addition of H2SO4 (1M). Thereafter, the required mass of ferrous sulfate was added. The Fenton reaction was initiated by adding the required volume of hydrogen peroxide (H2O2). The mixture was kept at a constant stirring of 300 rpm at the temperature of the experiment.

1.3. Analysis

Concentration of MG was determined by measuring absorbance at 618 nm using a TOMOS V-1100 spectrophotometer. Prior to the measurement, a calibration curve was obtained by using standard MG solution with known concentrations. Because the reaction

continued after sampling, the measurement of absorbance of reaction solution was done within 1 min. The pH measurements of the different solutions were performed using an EZODO PL-600 pH meter.

The degradation efficiency (De%) was defined as follows:

Ci - Cf De(%) = C *100 Ci

where De is the degradation efficiency (%)after 1h of reaction, Cf is the concentration of dye after reaction, and Ci is the initial concentration in solution.

1.4. Experimental design and statistical analysis

A statistical methodology was adopted to optimize the Fenton process. A factorial model is composed of a list of coefficients multiplied by associated factor. In a 2k factorial experimental design k factors are varied over 2 levels. For a given combination of the k factors, more then one test can be performed. These are referred as replicates, r. Therefore, the total number of tests is given as: N= r x 2k +C.

where C represents the number of center-point measurements used to test for quadratic terms in the low-to-high range. Center points are used to estimate pure error and curvature in the model. In this study N=17 (r=1, k=4, C=1).

The polynomial equation based on the first-order model with four parameters (X1, X2, X3 and X4) and their interaction terms can be given in the form of the following expression:

De = bo + b1X1 + b2X2 + b3X3 + b4X4 + ^2X1X2 + ^3X1X3 + ^4X1X4 + b23X2X3 + b24X2X4 + b34X3X4 + ^23X1X2X3 + b 124X1X2X4 + ^34X1X3X4 + b234X2X3X4+ b1234X1X2X3X4 (1)

where b0 is the average value of the result; b1, b2, b3 and b4 are the linear coefficients; and b12, b13, b14, b23, b24, b34, b123, b124, b134, b234 and b1234 represent the interactions coefficients. The letters X1, X2, X3 and X4 represent the factors in the model. Combinations of factors (such as X1X2) represent interactions between the individual factors in that term.

In this study, the influence of four main factors has been investigated: Concentration of MG (X1), Concentration of ferrous ions (X2), Concentration of hydrogen peroxide (X3) and temperature (X4). The degradation efficiency of MG was considered as dependents factors (response). Table 1 illustrates the four parameters and their chosen levels for the experiment. The factors levels were coded as -1 (low), 0 (central point) and +1 (high). A total of sixteen experiments were carried and another in the center of the experimental field. The results were analyzed with 95% confidence intervals using the JMP 12.0.1 Statistical Discovery Software from Statistical Analysis System (SAS).

3. Results and Discussions

3.1. Modeling of the degradation efficiency

Table 2 shows the experimental design matrix and the results of the response studied. The table indicates that response varied an important ways in the considered experimental domain. This last could likely contain the required optimal zone. The exploitation of experimental results allowed us to estimate the main effects and interaction effects that are grouped in the Table 3.

By substituting the coefficients bi in Eq. (1) by their values we get:

De = 78.51 - 4.67X1 + 5.24X2 - 6.85X3 + 2.29X4 + 2.8X1X2 + 1.1X1X3 - 0.34X1X4 + 2.53X2X3 - 1.05X2X4 + 0.44X3X4 + 0.19X1X2X3 - 1.29X1X2X4 + 0.40X1X3X4 - 0.74X2X3X4 -1.16X1X2X3X4

3.2. Main effects

From the equation of the model, it was noted that the effects of the concentration of ferrous ions and the temperature are positive, we can affirm that X2 and X4 have a positive effect on the response, which is important for the X2 and lower for X4. This result means that the degradation efficiency increases when the two factors changes from low level to high level. On the other hand the concentration of MG and the concentration of hydrogen peroxide have a negative effect. We can confirm that X1 and X3 have a negative effect on the response. This means that, the degradation efficiency falls when the factors passed from the low levels to the high levels. However an excess of hydrogen peroxides can have a behavior of limiting factor because it can become a trap for the hydroxyl radicals and so cause a decrease of the kinetics of degradation by inhibition of the Fenton reaction. Literature studies confirm that the increase in the concentration of Fe2+ ions always leads to an increase in the speed of reaction [40-43].

3.3. Interaction effects between factors

Fig. 1 shows the interaction effects of the factors in the low level and the high level of another factor. The figure indicates that X1X2 and X2X3 interactions are the most important interactions because the lines of its effects are not parallel. Other interactions have straight effects which are practically parallel. Subsequently, Figs. 2 and 3 showed the response surface

presented as a function of [Fe2+] concentrations and [H2O2] at a temperature of 40°C and hydrogen peroxide concentrations of 25mM and 10mg/L, respectively. Fig. 2 shows that the degradation efficiency increases with the increase in concentration of Fe2+ and rating decrease

concentration of MG. In Fig. 3, it can also be observed that the degradation efficiency

increases with the increase in concentration of Fe2+ and the diminution in concentration of hydrogen peroxide H2O2.

3.4.Analysis of variance (ANOVA)

In this study the analysis of variance was performed by the Student test (Table 4). The main and interaction effects of each factor having Prob <0.05 are considered as potentially significant. In other words, the Prob. whose values estimated are located outside the limits (blue line) correspond for active effects coming with significant factors, such as [H2O2], [Fe2+] and [MG]. As against that the temperature did not greatly influence the other factors (X1, X2, and X3). This can be explained by the fact that the initiation step of radical mechanism requires low activation energy [44].

3.5.Validity of the model

The fit of the model was further checked by the coefficient of determination R2. The

2 2 R2 value is always between 0 and 1. The closer the R2 value is to 1, the better the model

predicts the response [45]. Fig.4 presents the variation of the observed efficiency depending to

the predicted efficiency. It is found that there is a correlation between the two performances

with a coefficient of about 0.986. That is to say that 98% of results are explained by the

model, in addition, the F-ratio is significant, so the model adopted in this study (full factorial

design) is acceptable and validated.

In summary, the optimal reaction conditions to degrade the malachite green were: pH = 3; [MG] = 10mg/L; [Fe2+] = 10 mM; [H2O2] = 25.6 mM and T = 40°C. Under these conditions and with a 60 min treatment, it was possible to reduce 93.83% of the dye by Fenton treatment.

4. Conclusion

Optimization of the degradation of malachite green by the Fenton process using full factorial design allowed us to determine the optimal conditions to have a better degradation of malachite green. According to this study, we find that the main parameters influencing the Fenton process are: concentration of MG, concentration of ferrous ions and concentration of hydrogen peroxide. The interaction between concentration of malachite green and concentration of ferrous ions, and the interaction between concentration of ferrous ions and concentration of hydrogen peroxide were the most important interactions. The experimental results obtained during this study show that the Fenton process is effective for the degradation of textile dyes. The excellent correlation between predicted and observed degradation efficiency, high and significant R2= 0.986 and R2adj= 0.889 values, giving good accordance between the model and experimental data which confirmed the validity and practicability of the adopted model.

References

[1] S.J. Culp, L.R. Blankenship, D.F. Kusewitt, D.R. Doerge, L.T.Mulligan, F.A. Beland, Toxicity and metabolism of malachite green and leucomalachite green during short-term feeding to Fischer 344 rats and B6C3F1 mice, Chem. Biol. Interact. 122 (1999) 153-170.

[2] K. Lee, J. Wu, Z. Cai, Determination of malachite green and leucomalachite green in edible goldfish muscle by liquid chromatography-ion trap mass spectrometry, J. Chromatogr. B. 843 (2006) 247-251.

[3] C. Cha, D.R. Doerge, C.E. Cerniglia, Biotransformation of malachite green by the fungus Cunninghamella elegans, Appl. Environ. Microbiol. 67 (2001) 4358-4360.

[4] S, Srivastava, R, Sinha, D, Roy, Toxicological effects of malachite green, Aquat. Toxicol. 66 (2004) 319-329.

[5] M. Rajabi, B. Mirza, K. Mahanpoor, M. Mirjalili, F. Najafi, O. Moradi, H. Sadegh, R. Shahryari-ghoshekandi, M. Asif, I. Tyagi, S. Agarwal, Vinod Kumar Gupta, Adsorption of malachite green from aqueous solution by carboxylate group functionalized multi-walled carbon nanotubes: Determination of equilibrium and kinetics parameters, J. Ind. Eng. Chem. 34 (2016) 130-138.

[6] R. Elmoubarki, F.Z. Mahjoubi, H. Tounsadi, J. Moustadraf, M. Abdennouri, A. Zouhri, A. El Albani, N. Barka. Adsorption of textile dyes on raw and decanted Moroccan clays: Kinetics, equilibrium and thermodynamics, Water Resour. Ind. 9 (2015) 16-29.

[7] N. Barka, S. Qourzal, A. Assabbane, A. Nounah, Y. Ait-Ichou, Removal of Reactive Yellow 84 from aqueous solutions by adsorption onto hydroxyapatite, J. Saudi Chem. Soc. 15 (2011) 263-267.

[8] H. Tounsadi, A. Khalidi, M. Abdennouri, N. Barka, Biosorption potential of Diplotaxis harra and Glebionis coronaria L. biomasses for the removal of Cd(II) and Co(II) from aqueous solutions, J. Environ. Chem. Eng. 3 (2015) 822-830.

[9] C. Djilani, R. Zaghdoudi, F. Djazi, B. Bouchekima, A. Lallam, A. Modarressi, M. Rogalski, Adsorption of dyes on activated carbon prepared from apricot stones and commercial activated carbon, Journal of the Taiwan Institute of Chemical Engineers 53 (2015) 112-121.

[10] O. Njoku, K.Y. Foo, M. Asif, B.H. Hameed, Preparation of activated carbons from rambutan (Nephelium lappaceum) peel by microwave-induced KOH activation for acid yellow 17 dye adsorption, Chem. Eng. J. 250 (2014) 198-204.

[11] A.J. Kajekar, B.M. Dodamani, A.M. Isloor, A.K. Zulhairun, N.B. Cheer, A.F. Ismail, S.J. Shilton, Preparation and characterization of novel PSf/PVP/PANI nanofiber nanocomposite hollowfiber ultrafiltration membranes and their possible applications for hazardous dye rejection, Desalination. 365 (2015) 117-125.

[12] X. Chen, Y. Zhao, J. Moutinho, J. Shao, A.L. Zydney, Y. He, Recovery of small dye molecules from aqueous solutions using charged ultrafiltration membranes, J. Hazard. Mater. 284 (2015) 58-64.

[13] T. Chidambaram, Y. Oren, M. Noel, Fouling of nanofiltration membranes by dyes during brine recovery from textile dye bath wastewater, Chem. Eng. J. 262 (2015) 156-168.

[14] J. Lin, W. Ye, H. Zeng , H. Yang, J. Shen, S. Darvishmanesh, P. Luis, A. Sotto, B. Van der Bruggen, Fractionation of direct dyes and salts in aqueous solution using loose nanofiltration membranes, J. Membr. Sci. 477 (2015) 183-193.

[15] A.K. Verma, R.R. Dash, P. Bhunia, A review on chemical coagulation/flocculation technologies for removal of colour from textile wastewaters, J. Environ. Manage. 93 (2012) 154-168.

[16] S. Sadri Moghaddam, M.R. Alavi Moghaddam, M. Arami, Coagulation/flocculation process for dye removal using sludge from water treatment plant: Optimization through response surface methodology, J. Hazard. Mater. 175 (2010) 651-657.

[17] Y.Y. Lau, Y.S. Wong, T.T. Teng, M. Norhashimah, M. Rafatullah, S.A. Ong, Coagulation-flocculation of azo dye Acid Orange 7 with green refined laterite soil, Chem. Eng. J. 246 (2014) 383-390.

[18] F.R. Furlan, L. Graziela de Melo da Silva, A.F. Morgado, A. Augusto Ulson de Souza, S.M.G. Ulson de Souza, Removal of reactive dyes from aqueous solutions using combined coagulation/flocculation and adsorption on activated carbon, Resour. Conserv. Recy. 54 (2010) 283-290.

[19] B. Bonakdarpour, I. Vyrides, D.C. Stuckey, Comparison of the performance of one stage and two stage sequential anaerobiceaerobic biological processes for the treatment of reactive-azo-dye-containing synthetic wastewaters, Int. Biodeterior. Biodegrad. 65 (2011) 591-599.

[20] A.R. Khataee, G. Dehghan, A. Ebadi, M. Zarei, M. Pourhassan, Biological treatment of a dye solution by Macroalgae Chara sp.: Effect of operational parameters, intermediates identification and artificial neural network modeling, Bioresour. Technol. 101 (2010) 22522258.

[21] N. Barka, S. Qourzal, A. Assabbane, A. Nounah, Y. Ait-Ichou. Photocatalytic degradation of an azo reactive dye, Reactive Yellow 84, in water using an industrial titanium dioxide coated media, Arab. J. Chem. 3 (2010) 279-283.

[22] N. Barka, S. Qourzal, A. Assabbane, A. Nounah, Y. Ait-Ichou. Triphenylmethane dye, patent blue V, photocatalytic degradation on supported TiO2 : Kinetics, mineralization and reaction pathway, Chem. Eng. Commun. 198 (2011) 1233-1243.

[23] M. Abdennouri, M. Baalala, A.Galadi , M. El Makhfouk, M. Bensitel, K. Nohair, M. Sadiq, A. Boussaoud, N. Barka. Photocatalytic degradation of pesticides by titanium dioxide and titanium pillared purified clays. Arab. J. Chem. (2011), doi:10.1016/j.arabjc.2011.04.005.

[24] M, Abdennouri, A, Galadi, N, Barka, M, Baalala, K, Nohair, M, Elkrati, M, Sadiq, M, Bensitel, Synthesis, characterization and photocatalytic activity by para-chlorotoluene photooxidation of tin oxide films deposited on Pyrex glass substrates, Phys. Chem. News. 54 (2010) 126-130.

[25] K. Ayoub, E.D. Hullebusch, M. Cassir, A. Bermond, Application of advanced oxidation processes for TNT removal: a review, J. Hazard.Mater. 178 (2010) 10-28.

[26] A. M. Asiri , M. S. Al-Amoudi, T. A. Al-Talhi, A. D. Al-Talhi, Photodegradation of Rhodamine 6G and phenol red by nanosized TiO2 under solar irradiation, J. Saudi Chem. Soc. 15 (2011) 121-128.

[27] B.A. Wols, C.H.M. Hofman-Caris, Review of photochemical reaction constants of organic micropollutants required for UV advanced oxidation processes in water, Water Res. 46 (2012) 2815-2827.

[28] S. Saha, A. Pal, Microporous assembly of MnÜ2 nanosheets for malachite green degradation, Sep. Purif. Technol. 134 (2014) 26-36.

[29] E.S. Baeissa, Photocatalytic degradation of malachite green dye using Au/NaNbO3 nanoparticles, J. Alloys Compd. 672 (2016) 564-570.

[30] C. Bouasla, M.E. Samar, F. Ismail, Degradation of methyl violet 6B dye by the Fenton process, Desalination. 254 (2010) 35-41.

[31] H.J.H. Fenton, Oxidation of tartaric acid in the presence of iron, J. Chem. Soc. 65 (1894) 899-910.

[32] E. Neyens, J. Baeyens, A review of classic Fenton's peroxidation as an advanced oxidation technique, J. Hazard. Mater. 98 (2003) 33-50.

[33] M. Pera-Titus, V. Garc ía-Molina, M.A. Baños, J. Giménez, S. Esplugas, Degradation of chlorophenols by means of advanced oxidation processes: a general review, Appl. Catal. B: Environ. 47 (2004) 219-256.

[34] H.L. Wang, W.Z. Liang, Q.A. Zhang, W.F. Jiang, Solar-light-assisted Fenton oxidation of 2,4-dinitrophenol (DNP) using Al(2)O(3)-supported Fe(III)-5- sulfosalicylic acid (ssal) complex as catalyst, Chem. Eng. J. 164 (2010) 115-120.

[35] D.C. Montgomery, Design and Analysis of Experiments, fourth ed., John Wiley and Sons, New York, 1997.

[36] J.L. Brasil, L.C. Martins, R.R. Ev, J. Dupont, S.L.P. Dias, J.A.A. Sales, C. Airoldi, E.C. Lima, Factorial design for optimization of flow injection preconcentration procedure for copper(II) determination in natural waters, using 2-aminomethylpyridine grafted silica gel as adsorbent and spectrophotometric detection, Int. J. Environ. Anal. Chem. 15 (2005) 475-491.

[37] L.T. Arenas, E.C. Lima, A.A.D. Santos, J.C.P. Vaghetti, T.M.H. Coasta, E.V. Benvenutti, Use of statistical design of experiments to evaluate the sorption capacity of 1,4 diazoniabicycle[2,2,2]octane silica chloride for Cr(VI) adsorption, Colloids Surf. A: Physiochem. Eng. Aspects. 297 (2006) 240-248.

[38] I. Erper, M.S. Odabas, M. Turkkan, The mathematical approach to the effect of potassium bicarbonate on mycelial growth of Sclerotinia sclerotiorum and Rhizoctonia solani AG 4 HG-I in vitro, Zemdirbyste-Agriculture 98 (2011) 195-204.

[39] N. Barka, M. Abdennouri, A. Boussaoud, A. Galadi, M. Baalala, M. Bensitel, A. Sahibed-Dine, K. Nohair and M. Sadiq, Full factorial experimental design applied to oxalic acid photocatalytic degradation in TiO2 aqueous suspension, Arabian J. Chem. 7 (2014) 752757.

[40] F. Torrades, J. García-Montaño, Using central composite experimental design to optimize the degradation of real dye wastewater by Fenton and photo-Fenton reactions, Dyes Pigm. 100 (2014) 184-189.

[41] W. Gernjak, T. Krutzler, A. Glaser, S. Malato, J. Cáceres, R. Bauer, A.R. Fernández-Alba, Photo-Fenton treatment of water containing natural phenolic pollutants, Chemosphere. 50 (2003) 71-78.

[42] W. Gernjak, M. Fuerhacker, P. Fernández-Ibañez, J. Blanco, S. Malato, Solar photoFenton treatment - Process parameters and process control, Appl. Catal. B: Environ. 64 (2006) 121-130.

[43] T. Krutzler, H. Fallmann, P. Maletzky, R. Bauer, S. Malato, J. Blanco, (1999), Solar driven degradation of 4-chlorophenol, Catal. Today. 54 (1999) 321-327.

[44] J.F. Rivas, J. F. Beltran, O. Gimeno, J. Frades, Treatment of olive oil mill wastewater by Fenton's reagent, J. Agric. Food. Chem. 49 (2001) 1873-1880.

[45] J. F. Fu, Y. Q. Zhao, Q. L. Wu, Optimising photoelectrocatalytic oxidation of fulvic acid using response surface methodology, J. Hazard. Mater. 144 (2007) 499-505.

90 80 70 60 50

90 80 70 60 50

90 80 70 60 50

90 80 70 £ 60 50

\Fe*+]

\H2O1]

Temperature

~i—r

■■-n 30

~l-1 I-r

o rM iJ"l vD

~i—I—T

r^ 30 {Ti

~l-1-r

t U*l IUI

Fig. 1. Effects of interactions between the four factors

^1(5,10)

fMG](10.20)

Fig.2. Response surface as a function of [MG] and [Fe2+] ([H2O2] = 25mM; T= 40°C)

|TC+|(5,10)

6 7 8 9

|H>0>|(25.6,51.2)

40 4S SO

•90 KJ

8S 0L>

80 •0

Fig.3. Response surface as a function of [Fe2+] and [H2O2] ([MG] = 10mg/L; T= 40°C)

Predicted degradation efficiency [%)

Fig. 4. Variation of the observed efficiency depending to the predicted efficiency.

Table 1. Experimental ranges and levels of independent variables used in the optimization.

Coded variable Description Experimental field

(Xi) Min. value Central point Max. value

(-1) (0) (+1)

X1 Concentration of malachite green (mg/L) 10 15 20

X2 Concentration of ferrous ions (mM) 5 7.5 10

Table 2. Design matrix in coded, real units and the experimental responses.

Experiment Coded variables Real variables De (%)

X1 X2 X3 X4 [MG] [Fe2] [H2O2] Temperature

1 - - - - 10 5 25.6 27 88.26

2 + - - - 20 5 25.6 27 72.72

3 - + - - 10 10 25.6 27 88.81

4 + + - - 20 10 25.6 27 84.25

5 - - + - 10 5 51.2 27 68.44

6 + - + - 20 5 51.2 27 50.29

7 - + + - 10 10 51.2 27 76.67

8 + + + - 20 10 51.2 27 80.30

9 0 0 0 0 15 7.5 38.4 33.5 76.07

10 - - - + 10 5 25.6 40 93.83

11 + - - + 20 5 25.6 40 75.82

12 - + - + 10 10 25.6 40 93.62

13 + + - + 20 10 25.6 40 85.57

14 - - + + 10 5 51.2 40 72.46

15 + - + + 20 5 51.2 40 64.37

X3 Concentration of hydrogen 25.6 38.4

peroxide (mM)

X4 Temperature (°C) 27 33.5

16 - + + + 10 10 51.2 40 83.35

17 + + + + 20 10 51.2 40 77.39

Table 3. Main and interaction coefficients of the factors

bi Interactions coefficients

b 0 78.37

b 1 -4.67

b 2 5.24

b 3 -6.85

b 4 2.29

b 12 2.80

b 13 1.10

b 14 -0.34

b 23 2.53

b 24 -1.05

b 34 0.44

b 123 0.19

b 124 -1.29

b 134 0.40

b 234 -0.74

b 1234 -1.16

Table 4. ANOVA test

Standard

Terme Estimation error t ratio

[H202](25.6,51.2) -6.850625 0,923526 -7,4179*

[Fe2"1"] (5,10) 5,235625 0,923526 5,6692

[MG] (10,20) -4.670625 0,923526 -5,0574*

[MG] * [Fe2^] 2,803125 0,923526 3,0352

[F^+]*\R202] 2,533125 0,92 3526 2,7429

Tem p era tu re(27.40) 2,291875 0,923526 2,4817

[MG] * [Fe2*] *Temp era tu re -1,291875 0r923526 -1,3989*

[MG]*[H202] 1,099375 0,92 3526 1,1904

[ Fe2"1"] * Tem p era ture -1,054375 0,923526 -1,1417*

[Fe2*] * \H202] *Tem p era tu re -0,736875 0,923526 -0,7979*

[H:02]*Temperature 0,441875 0,923526 0,4785

[MG] * \H202] *Tem p era tu re 0,401875 0,923526 0,4352

[MG] * T em p era ture -0,343125 0,923526 -0,3715*

[MG] * [Fe2*] * [H^ O2 ] 0,185625 0,92 3526 0,2010

Prob. > |t]

0,0177*

0,0297*

0,0369*

0,0936

0,1112

0,1312

0,2968

0,3560

0,3719

0,5086

0,6795

0,7059

0,7459

0,8593

Table 5. Analysis of variance

Source

F-ratio

1931.611 137.972 10.110

Residual 2

27.293 13.646

1958.904

R2= 0.986 R2adj= 0.889

Highlights

• Fenton process for the catalytic degradation of malachite green dye.

• Optimal conditions for better degradation were evaluated by experimental design.

• Proposed model correctly evaluates the effect of the investigated variables.

• Maximum degradation efficiency of 93.83% was obtained.