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Optimization of the experimental parameters in the treatment of textile effluents (INDIGO) by adsorption
T. Lakdioui
(a)*, A. EL Harfi
(a)(a) Laboratoryof Polymer, Radiation and Environment (LPRE), Team of Chemistry Organic and Macromolecular (TCOM), Department of Chemistry, Faculty of Sciences, University Ibn Tofail.B.P.133, 14000, Kenitra, Morocco.
*Corresponding author. E-mail : lakdiouitarik@gmail.com
Received 17 Fev 2015, Revised 20 Fev 2015, Accepted 05 Mars 2015
Abstract
The presence of micro-pollutants organic, inorganic and organometallic in effluents produced in the processes of the dye and/or the textile ennoblement establishes a major-problem in the procedure. During this study, we treated the textile effluents (indigo) by the method of adsorption, by using powder activated charcoal (PAC) as a substratum. This was taken from the values obtained during the experimental studies and it was the way we were able to optimize this treatment process by the adsorption by basing itself on the software NEMRODW. The objective of our work consists of studying the influence of the experimental parameters (pH, temperature, the support mass and time) on the capacity of eliminating a coloring agent of tank which is the Indigo. The optimization of these parameters by the software indicated previously allowed us to determine the optimal conditions for a better treatment.
Keywords: Adsorption,-Indigo,-Optimization,-Activated-charcoal, The NemrodW software.
1. Introduction
In Morocco the textile industry presents 31 % of all the activities of manufacturing [1]. This means there is an important consumption of quantity of water, by generating afterwards an important quantity of liquid rejections [2] considered to be around 8,85 millions m3/year [1]. The elimination of these discharges leads to different types of micro-pollutant [3] sometimes with a biodegradable difficulty [4, 5] and presents a major problem in the processing procedure [6]. The chemical complexity and the diversity of the coloring agents [7] make the treatments insufficient to be effective depending on the values which are experimentally obtained from every case.
The objective of our work consists in studying the influence of all the parameters (pH, temperature, the mass of the medium and the time of adsorption) on the capacity of adsorption [8, 9] of Activated charcoals [10, 11] and to optimize these parameters by the adequate software (NOMRDW) [12, 13, 14], whose purpose is to determine the optimal conditions for a better adsorption [15, 16, 17]
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2. Materials and methods
2.1. Method of the plans of experience (MPE)
This method allows defining the experiments, in reduced number, allowing a complete study of some influence of all the parameters on a given process and their optimization. This is based on the search for a simple mathematical model giving a good representation of the studied phenomenon.
2.2. The Mathematical model
The answers are described by a polynomial model of the following shape:
Y = b0 + b1 * X1 + b2 * X2 + b3 * X3+ b4 * X4 + b12 * (X1*X2) + b13 * (X1*X3) + b14 * (X1*X4) + b23 * (X2*X3) + b24 * (X2*X4) + b34 * (X3*X4)
By applying this model, we tested four factors at two levels. The limit values of the studied parameters are defined in table 1:
Table 1: domains of variations of factors
Parameters Mass PAC (g) [X1] pH Temperature(°C) Time (s) Factors X1 X2 X3 X4
Intervals [0.1 – 0.2] [6.5 – 10.5] [20 – 100] [0 – 120]
3. Results and Discussions
During this work, we based ourselves on the matrix of factorial experiments enough to illustrate the best parameters influencing the process of adsorption by combining the studied interactions.
3.1. Study schedules interactions between factors 3.1.1. Study schedules interactions X1X2:
In this case study, the following parameters: mass PAC (X1) and the pH (X2). They are shown in Figure 1;
According to this system of interaction, it emerges that the best adsorption is determined in the case of interaction where X1= 0,2g (PAC) and X2 = 6,5 (pH).
Figure 1: graphical System of interactions X1X2
282 3.1.2. Study schedules interactions X2X3:
In this case, the parameters, the pH (X2), and the temperature (X3) are mentioned in the figure 2. According to this system of interaction, it emerges that the best adsorption is determined in the case of interaction where X3= 0°C (temperature) and X2 = 6,5 (pH).
Figure 2: graphical System of interactions X2X3 3.1.3. Study schedules interactions X2X4:
In this graphic study, the parameters X2 (pH) and time (X4) is illustrated in figure 3. According to this system of interaction, it emerges that the best adsorption is determined in the case of interaction where X2=
6,5 (pH) and X4 = 120 min (Time).
Figure 3: graphical System of interactions X2X4
3.1.4. Study schedules interactions X1X3:
In this graphic study, the parameters of mass PAC (X1) and the temperature (X3) are illustrated in figure 4.
According to this system of interaction, it emerges that the best adsorption is determined in the case of interaction where X1=0,2g (mass PAC) and X3 = 80°C (temperature).
Figure 4: graphical System of interactions X1X3
283 3.1.5. Study schedules interactions X1X4:
In this graphic study, the parameters of mass PAC (X1) and time (X4) is illustrated in figure 5. According to this system of interaction, it emerges that the best adsorption is determined in the case of interaction where X1=0,2 g (mass PAC) and X4 = 120 min (time).
Figure 5: graphical System of interactions X1X4 3.1.6. Study schedules interactions X3X4:
In this graphic study, the parameters of the temperature (X3) and the time (X4) is illustrated in figure 6.
According to this system of interaction, it emerges that the best adsorption is determined in the case of interaction where X4=120 min (Time) and X3 = 80°C (Temperature).
Figure 6: graphical System of interactions X3X4 3.2. The diagram of baton- drawn average effects
Figure 7 represents the diagram of baton-drawn which would allow releasing the most influencing parameters on the process of adsorption. This is the way we notice that the most active factors are given in the decreasing order of influence. The mass PAC (b2), pH (b1), Time (b4) and the temperature (b3).
3.3. Diagram of Pareto:
The diagram of Pareto consists in completing the results obtained by means of the diagram of baton-drawn obtained previously. This would allow determining the factors influencing the order of decreasing the contribution. The results obtained by the diagram of Pareto are represented in figure 8. The first three factors (pH, mass and temperature) explain more than 98 % of the variation of answer.
284 Figure 7: Study of the influence of the effects on the amount of adsorption of indigo
Figure 8: diagram of Pareto for the adsorption of the indigo
3.4. Study of screening: processing the experimental answers 3.4.1. Validation of the model:
The treatment of various parameters by the diagrams baton- drawn and Pareto chart, helped us out that the regression coefficient is in the vicinity of 90.1% (Table 3), based on the analysis of variance represented in Table 2.
Table 2: analysis of the variance Source of the
variation
sum of squares means
Degrees of freedom
Mean square Report Meaning % Regression 425.1078 4 106.2770 1.233 90.1
Residue 258.5193 3 86.1731 ** **
Grand total 638.6272 7 *** ** **
After the Table 2, it shows that the correlation coefficient equal r to 0.901 [13, 14], we can say that it is very close to 1. This allowed us to say that there is some correlation between the values experimental obtained and those calculated by the proposed mathematical model. And therefore obtaining regression equation is significant in our study.
285 3.4.2. Theoretical correlation of the values with experimental results
The ultimate result of this study is illustrated by the diagram in figure 9. According to figure 9, we can say that the results obtained by the modeling coincide with the experimental results. This shows that the model chooses is adequate for this accomplished work.
Figure 9: theoretical Correlation of the values with experimental results
4. Conclusion
The modeling of the experimental parameters in the treatment of textile effluents (INDIGO) by the adsorption will thus have to take into account at the same time parameters pH, Mass PAC and the temperature, because they are key variables. A perfect model of the adsorption would take into account all the variables susceptible to influence the adsorption of the indigo.
References
[1] F. Harrleka, Thèse en cotutelle. Université Cadi Ayyad, Marrakech (Maroc), Institut National Polytechnique, Lorraine (France), 2008.
[2] C.M. Du, T.H. Shi, Y.W. Sun, X.F. Zhuang, J. Hazard. Mater., 154 (2008) 1192-1197.
[3] R.L. Valladares, V.Y. Quintanilla, Z. Li, G. Amy, J. Recher. Eau., 45 (2011) 6737-6744.
[4] C. L. Jenkins., J. Arch. Environ. Health., 40 (1978) 7-12.
[5] R. A. Damodar, K. Jagannathan, T. Swaminathan, J. Sol. Energy., 81 (2007) 1-7.
[6] G. Annadurai, R. S. Juang, D. J. Lee, J. Hazard. Mater., 92 (2002) 263-274.
[7] F. P. Van Der Zee, G. Lettinga, J. A. Filed, J. Chemosphere, 44 (2001) 1169-1176.
[8] M. Morad, M. Hilali, L. Bazzi, A. Chaouay, Mor. J. Chem, 5 (2014) 475-485.
[9] I. Ismi, E.H. Rifi, A. Lebkiri, Mor. J. Chem, 2 (2014) 403-414 [10] S. Wang, Z. Zhu, J. Dyes and Pigmen., 75 (2007) 306-314.
[11] L. Mouni, L. Belkhiri, M. Tafer, F. Zouggaghe, Y. Kadmi, 5 (2014) 452-456.
[12] R. L. Plackett, J. P. Burman, Biometrica,.33(1946)305–332.
[13] M. Berradi, A. El Harfi, Mor. J. Chem. 3 (2015) 142-146 [14] Y. Elrhayam, A. El Harfi. J. Envir. Sci. Engi. B 3 (2014) 18-23.
[15] B. Benguella, A. Yacouta-Nour, J. Compt. Rend. Chim., 12 (2009) 762–771.
[16] T. Lakdioui, M. Berradi, J. El Azzaoui, R. Ghdiga, A. El Harfi, Internat. J. Enginee. Resaer. Technolo., 3(2014) 2412-2416.
[17] T. Lakdioui, A. El Harfi, Interna. J. Innova. Appli. Stud., 7(2014) 875-882.
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