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Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres
Journal of Cleaner Production xxx (2013) 1e9
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Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres Wafa Haddar a, *, Noureddine Baaka a, Nizar Meksi a, b, Imen Elksibi a, M. Farouk Mhenni a a b
Research Unit of Applied Chemistry and Environment, Faculty of Sciences of Monastir, 5000 Monastir, Tunisia Higher Institute of Fashion of Monastir, 5019 Monastir, Tunisia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 November 2012 Received in revised form 6 November 2013 Accepted 9 November 2013 Available online xxx
The olive oil industry releases considerable amounts of wastewater which contains huge reserves of natural dyes. Such wastewater could successfully be used for the dyeing of acrylic fibers. The influence of the main dyeing conditions (material/liquor ratio, dye bath pH, dyeing duration, dyeing temperature) on the performances of this dyeing process were studied. The dyeing performances of this process were appreciated by measuring the color yield (K/S) and the fastness properties of the dyed samples. A 24 full factorial design method was employed in order to study the interactions between the selected dyeing process parameters and to evaluate the optimal dyeing conditions. The optimization of these dyeing process factors to obtain maximum color yield was carried out by incorporating effect plots, normal probability plots, interaction plots, analysis of variance (ANOVA) and Pareto charts. A regression model was formulated using Minitab software and fitted the experimental data very well. In addition, it was found that dyeing of acrylic enables to reduce the concentration of polyphenols so that it reduced the Chemical oxygen demand COD. Furthermore, the biodegradability ratio (COD/BOD5) decreases but it was always superior to 3 which means that this aqueous waste still not biodegradable. It was also found that reusing the residual bath allowed to obtain a depth of shade very similar to the first dyeing and reduced considerably the environmental parameters (the concentration of polyphénols and COD). Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Olive mill wastewater Acrylic fibers dyeing Green process Full factorial design Optimization Reusing dyeing bath
1. Introduction Increasing awareness of the environmental and health hazards associated with the synthesis, processing, and use of synthetic dyes have generated a renewed worldwide interest in natural dyes (Bulut and Akar, 2012). During the last decade, the use of natural dyes gained momentum due to the increased demand of food, pharmaceutical and cosmetic industries. Natural dyes comprise colorants that are obtained from animal or vegetable substances without any chemical processing. This group is mostly known to be eco-friendly, biodegradable and less allergenic compared to synthetic dyes (Ghouila et al., 2012). Besides, it can have a higher compatibility with the environment. For these raisons, a considerable number of research works are being undertaken around the world concerning the production and the application of natural dyes (Vankar et al., 2007; Shaukat et al., 2009). The textile processing industry is one of the major users of dyes (Wesenberg et al., 2003). However, until now the application of the
* Corresponding author. E-mail address: [email protected] (W. Haddar).
natural dyes in this field does not meet the expectations of all the consumers because their use caters mainly to niche products for special markets such as the eco-friendly textiles. Unfortunately, these remain as up-market products which target specific clientele who is fascinated by the sustainability and the green chemistry. The low availability and the high processing cost of natural coloring substances are the most important reasons for preventing these products from being more popular. The attempts to overcome these problems and reduce prices have mainly focused on the discovery of newer sources especially from by-products of farming and forestry as well as the valorization of several wastes from food and beverage industry in order to extract from them their coloring substances and using those colorants as natural dyes for textile dyeing. This idea is very interesting because the raw material is abundant, cheap and renewable (Prusty et al., 2010). Another problem which may also limit the application of these dyes in the textile industry is the low dye exhaustion of the majority of natural colorants and the poor fastness of dyed fabrics on synthetic fibers compared to wool and others natural fibers because synthetic fibers are hydrophobic, highly crystalline, non-polar polymers as well as the absence of active chemical groups in their polymer structure (Gupta et al., 2011). These limitations
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generate several technical problems for producing natural-dyed synthetic textile fibers in order to fulfill the demands of more eco-friendly textile products (Sriumaoum et al., 2012). Recently, a variety of projects about the possible use of natural dyes in synthetic fibers dyeing especially polyester and acrylic fibers have been performed by various researchers. Different techniques such as chemical modification to include amidoxime groups on polymer surface (Guesmi et al., 2012) and air atmospheric plasma treatment as well as ultraviolet (UV) excimer lamp irradiation (Kerkeni et al., 2012) were used to improve the dyeing performances of these synthetic textile fibers. In these papers, the authors described encouraging results with regard to color yield, shade, and fastness properties. Olive mill wastewater (OMW) is a dark brown to black effluent produced during olive oil extraction. This effluent is characterized by a high organic load including sugars, tannins, pectin, lipids, polyphenols and polyalcohols (Efthalia et al., 2013). The annual OMW production of the Mediterranean olive-growing countries is estimated to over than 30 M m3 (Tezcan Ün et al., 2008). This wastewater has a considerable pollution that occurs as a result of seasonal OMW production. In fact, it is one of the most crucial environmental issues in the Mediterranean area. Therefore, it constitutes a serious problem with severe negative impact on the soil and the water quality. Several techniques have been reported in literature to treat or valorize this effluent (El-Abbassi et al., 2013). However, for technical and economic reasons, supplementary studies and researches are still required to develop efficient systems which could be really applied in a large scale to resolve this ecological problem. The construction of artificial big ponds into which OMW is stored, waiting for its natural evaporation is until now the most common practice in the Mediterranean region. Unfortunately, this method, besides being very slow, causes subsequent unpleasant environmental pollution linked to generation of bad odors due to anaerobic activity (Saez et al., 1992). In the previous part of this work (Meksi et al., 2012), it was found that OMW contains valuable resource of abundant natural coloring substances which could be successfully exploited as natural dye for textile coloration. The application of such wastewater as a source of natural dyes can help in the preservation of environment and also decrease the cost of natural dyeing. The objective of the present work is to investigate the dyeing of acrylic fibers by natural dyes extracted from olive mill wastewater. The modeling and the optimization of some experimental dyeing conditions were investigated using full factorial design methodology in order to study the exhausting dyeing process. The effect of mordanting on dyeing of acrylic fibre by OMW natural matters was also studied. The environment impact of this dyeing on the OMW dye bath characteristics (Total polyphenols, Chemical oxygen demand COD, Biological oxygen demand BOD5 and the biodegradability ratio COD/BOD5) was also studied with the effect of multi-using of the residual dye bath on these parameters. 2. Experimental 2.1. Textile material Acrylic fabric (Plain weave and weight, 200 g/m2) was procured commercially. Before being used, it was soaped with 2 g L1 of nonionic detergent at 60 C for 30 min, thoroughly rinsed and air dried.
2.3. Olive mill wastewaters (OMW) used The OMW used in this study was obtained from an arranged evaporating pond which is located in the region of Monastir (Menzel Hareb) in Tunisia. 2.4. Preparation of dye bath from OMW The used dye bath from OMW was prepared accordingly to the procedure described in Meksi et al. (2012). 2.5. Dyeing procedures In a dye bath with various liquor ratio (10:1 - 60:1), acrylic fabric was dyed in a laboratory dyeing machine (Ahiba Datacolor International, USA) at different pH values (2e8) for different durations (15e115 min) and at different temperatures (50e110 C). The dyed fabrics were then rinsed with cold water and washed then followed by soaping with 2 g L1 of a nonionic soap, Cotoblanc RS (Bezema AG, Switzerland) at 60 C. Finally the fabric samples were washed thoroughly with cold water, squeezed and dried. The pH values were recorded with Eutech Instruments pH 510 (Singapore). In case of mordanting, the three known methods: premordanting, meta-mordanting and post-mordanting were used. Mordant concentration of 3% (w/w with respect to the fabric) and a material to liquor ratio of 1:40 were used for all experiments. The acrylic fabrics were treated at 30 C for 45 min. 2.6. Color evaluation The spectral reflectance of the dyed samples was measured using SpectroFlash SF300 spectrophotometer with dataMaster 2.3 software (Datacolor International, USA). The color yield (K/S) values were calculated by Kubleka-Munk equation (Kubelka, 1948, 1954):
. ð1 RÞ2 ð1 R0 Þ2 K S ¼ 2R 2R0 where R is the decimal fraction of the reflectance of dyed fabric, R0 is the decimal fraction of the reflectance of undyed fabric, K is the absorption coefficient and S is the scattering coefficient. In case of dyeing with mordants, the shades may vary. So, the dyeing performances of the dyed samples were appreciated by measuring the Sum(K/S) which is calculated as follow:
. 700 . X K S Sum K S ¼ l ¼ 400
l
CIELab coordinates (L*, a*, b*, C*, h*, where L* defines lightness; a* denotes the red/green value; b* the yellow/bleu value; C* the saturation value and h* is the hue) were calculated from the reflectance data for 10 observer and illuminant D65. To estimate the color differences between samples dyed with the residual dye bath and the first samples dyed with the aqueous extract of olive wastewater, the color difference was calculated as follow:
DE ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi DL* 2 þ Da* 2 þ Db* 2
2.7. Fastness testing 2.2. Chemicals used Alum, ferrous sulphate and stannous chloride were used as mordants without further purification.
The dyed samples were tested according to standard methods. The specific tests were for color fastness to washing ISO 105-C06, color fastness to rubbing ISO 105-X12, color
Please cite this article in press as: Haddar, W., et al., Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres, Journal of Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.11.017
W. Haddar et al. / Journal of Cleaner Production xxx (2013) 1e9 Table 1 Effect of the liquor ratio to fabric on the color yield. (Dyeing conditions: pH 5, 60 min and 80 C). Liquor ratio to fabric
Color yield (K/S)
1:10 1:20 1:30 1:40 1:50 1:60
2,48 4,73 5,02 4,91 4,74 4,42
fastness to light ISO 105-B02 and color fastness to perspiration ISO 105-E04. 2.8. Design of experiment (DOE) The high and low levels defined for the 24 factorial design were listed in Table 2. The low and high levels for the factors were selected according to the first part of this work describing the effects of dyeing parameters. The factorial design matrix and (K/S) value measured in each factorial experiment are shown in Table 3, with low (1) and high (þ1) levels as specified in Table 2. The color yield (K/S) was determined as an average of three parallel experiments. The results were analyzed with the statistical software package Minitab version 15 (State College, PA, USA), and the main effects and interactions between factors were determined. The analysis of regression and of the variance (ANOVA) as well as the graphical optimizations were performed in order to determinate the optimal conditions of the critical variables. 2.9. Residual dye baths analyses Determination of total polyphenols concentration in the dye baths before and after dyeing was carried out with a CECIL 2021 Instruments UV/Vis spectrophotometer at 765 nm using the FolinCiocalteau reagent and the gallic acid as a standard (Singleton and Rossi, 1965). Total polyphenols removal was determined using the following equation:
C Cfin TPremoval ð%Þ ¼ in 100 Cin where Cin and Cfin are the total polyphenols concentrations in dye bath before and after dyeing, respectively. Analysis of Chemical Oxygen Demand COD analyses in dye baths before and after dyeing were determined by the procedure described in Rodier (1997). COD removal was determined using the following equation:
CODremovalð%Þ ¼
3
Table 3 Factorial design experimental data. Experiments
A
B
C
D
K/S
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
4.43 5.07 5.93 6.43 3.43 3.97 4.41 5.31 4.23 4.82 5.71 5.86 3.26 3.72 3.68 4.54
BOD5 removal was determined using the following equation:
BOD5 removalð%Þ ¼
ðBOD5 Þin ðBOD5 Þfin 100 ðBODÞin
where (BOD5)in and (BOD5)fin are the Biological Oxygen demand of dyebath before and after dyeing, respectively. 3. Results and discussion 3.1. Effect of dyeing conditions on dyeing quality 3.1.1. Effect of the dye bath pH Fig. 1 show the effect of the dye bath pH on the color yield which was investigated in the range of 2e8. In this figure, it can be clearly observed that the best result is obtained for pH 3. When the dye bath pH exceeded this value, the color yield decreased considerably. 3.1.2. Effect of dye bath temperature The effect of dyeing temperature on the depth of shade (K/S) of dyed acrylic fabric is shown in Fig. 2. It was found that the color yield increases with the dyeing temperature until 100 C. Generally, the increase in dye-uptake can be explained by fibre swelling which enhanced dye diffusion. The color yield became important notably when temperature exceeded the glass transition temperature of acrylic fibres due to the increase of dye accessibility.
CODin CODfin 100 CODin
where CODin and CODfin are the Chemical Oxygen Demand of dye bath before and after dyeing, respectively. The Biological Oxygen Demand BOD5 was analyzed according to (Rodier, 1997).
Table 2 Factors and levels used in the factorial design. Factors
Code
Low level (1)
Low level (þ1)
Dyeing duration (min) Dyeing temperature ( C) Dye bath pH Liquor ratio to fabric
A B C D
15 70 3 1:50
105 100 7 1:20
Fig. 1. Effect of the dye bath pH on the color yield. (Dyeing conditions: 60 min, 80 C, and LR 40:1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article in press as: Haddar, W., et al., Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres, Journal of Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.11.017
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3.1.3. Effect of dyeing duration Fig. 3 shows the effect of dyeing time on the color yield obtained for the dyed fabrics. This figure indicates that the color yield increases when the time increases up to 60 min, then a plateau is attained after 60 up to 105 min. It seems that acrylic fabrics reached the saturation and did not absorb more OMW natural dyes. 3.1.4. Effect of liquor ratio to fabric The effect of the liquor ratio to fabric on the depth of shade (K/S) of dyed acrylic fabric is also investigated. The obtained results are given in Table 1. It is clear that the color yield increased with the dyeing liquor ratio up to 1:20 and then it remained slightly constant. The lower relative color yield value at low dyeing ratio may be explained by the overcrowding of dye molecules at lower dyeing ratio resulting in reduced dye exhaustion on the fabric. However, the quasi stability of the color yield after a liquor ratio of 1:20 can be explained by the important quantity of natural dyes present in the olive wastewaters. It appears that the fibre attained its saturation on colorant with only 1:20 of liquor ratio to fabric and almost no further increase with further colorant concentration in the dyeing bath can be obtained. 3.2. Modeling and optimisation of the dyeing process In this work, the factorial experimental design methodology was employed to estimate the influence of the different variables of dyeing process. Factorial designs are widely used to investigate the effects of experimental factors and the interactions between those factors, that is, how the effect of one factor varies with the level of the other factors in a response. The advantages of factorial experiments include the relatively low cost, the reduced number of experiments, and the increased possibilities to evaluate interactions among the variables. The most common first-order designs are the two-level full factorial design and the two-level full fractional design. In these methodologies, each factor is experimentally studied at two levels that are coded as: 1 for low level and þ1 for high level. The full factorial design consists of a 2k experiments (k factors, each experiment at two levels), which is very useful for either preliminary studies or for initial optimization steps, while fractional designs are almost mandatory when the problem involves a large number of factors (Bingol et al., 2010). According to the previous part, the dyeing process of fibers using natural dyes extracted from olive mill wastewater involves many factors. So, a 24 full factorial design was chosen for the purpose of evaluating the importance of dyeing duration, dyeing
Fig. 3. Effect of the dyeing duration on the color yield. (Dyeing conditions: 60 min, 80 C, and LR 40:1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
temperature, pH of the dye bath and the liquor ratio to fabric on the dyeing process. These factors were evaluated by using factorial plots: main effect, interaction effect, the Pareto chart plot and the normal probability plots. ANOVA and P-value significant levels were used to check the significance of the effect on the color yield (K/S) (the response). 3.2.1. Variance analysis (ANOVA) Identifying areas of variations of each factor are set from the previous study of the effects of dyeing parameters. The definition of high and low levels is illustrated in Table 2. This experimental design gave 24 experiments, which were carried out in the random order given in Table 3. The response considered in the analysis is color yield (K/S). All our statistical analyses were performed using the statistical software package Minitab 15. The effects, regression coefficients, standard errors and T values (standardized effects) are reported in Table 4. From the data given in Table 4, a mathematical model can be formulated as follow:
K=S ¼ 4:675 þ 0:290 A þ 0:55875 B 0:635 C 0:1975 D þ 0:01125 AB þ 0:055 AC 0:0325 AD 0:11375 BC 0:08875 BD 0:0425 CD (I) Where K/S is the color yield, A is the dyeing duration, B is the dyeing temperature, C is dye bath pH and D is the liquor ratio to fabric. In order to determine the significant main and the interaction effects of the factors influencing the performance of this dyeing
Table 4 Estimated effects and coefficients for the color yield (K/S).
Fig. 2. Effect of the dye bath temperature on the color yield. (Dyeing conditions: pH 5, 60 min, and LR 40:1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Term
Coef
SE Coef
T
P
Constant Dyeing duration (A) Dyeing temperature (B) Dye bath pH (C) Liquor ratio to fabric (D) Dyeing duration-dyeing temperature (AB) Dyeing duration-dye bath pH (AC) Dyeing duration-liquor ratio (AD) Dyeing temperature-dye bath pH (BC) Dyeing temperature-liquor ratio (BD) Dye bath pH-liquor ratio (CD)
4.67500 0.29000 0.55875 0.63500 0.19750 0.01125
0.04511 0.04511 0.04511 0.04511 0.04511 0.04511
103.633 6.429 12.386 14.076 4.378 0.249
0.000 0.001 0.000 0.000 0.007 0.813
0.05500 0.03250 0.11375 0.08875 0.04250
0.04511 0.04511 0.04511 0.04511 0.04511
1.219 0.720 2.522 1.967 0.942
0.277 0.504 0.053 0.106 0.389
S ¼ 0.180444, R-sq ¼ 98.84%, R-sq(adj) ¼ 96,51%.
Please cite this article in press as: Haddar, W., et al., Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres, Journal of Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.11.017
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process, an analysis of variance (ANOVA) was performed using Student’s t-test p < 0.05 (Carmona et al., 2005). P-value is the probability value that is used to determine the statistically significant effects in the model. The importance of the data can be judged by its P-value, with values closer to zero denoting greater significance. For a 95% confidence level the P-value should be less than or equal to 0.05 for the effect to be considered statistically significant (Srinivasan and Viraraghavan, 2010). According to the obtained Pvalue (Table 4), it seems that the dyeing duration (A), the dyeing temperature (B), the dye bath pH (C), liquor ratio to fabric (D) are statistically significant. However, the interactions .AB (P ¼ 0.813), AC (P ¼ 0.277), AD (P ¼ 0.504), BC (P ¼ 0.053), BD (P ¼ 0.106) and CD (P ¼ 0.389) are statistically not significant with 95% confidence level. In addition, the model presented an R2 ¼ 98.84% and an 2 R (adjusted) ¼ 96.51%. So, it could be deduced that the resulted model obtained has a very good predictability within the range of the chosen variables. This model (I) describes how the experimental variables and their interactions influence the dyeing process. The dye bath pH (C) had the greatest effect on the color yield, followed by the dyeing temperature (B), the dyeing duration (A) and the liquor ratio to fabric (D). Absolute values of the effects of main factors and the interaction of factors are also provided in the Pareto chart (Fig. 4) and they are in accordance with the previous results. In Fig. 4, for tvalue equal to 0.342, a reference was dressed which corresponds to the 95% confidence level. The values that exceed this reference line, i.e., those corresponding to the 95% confidence interval, are significant values (Mathialagan and Viraraghavan, 2005). According to Fig. 4, the main significant factors are: (C) > (B) > (A) > (D). 3.2.2. Main and interaction effects The main effects of each dyeing parameter on the color yield are shown in Fig. 5. Analyzing the graphs of Fig. 5 and the coefficients of the Equation (I), it can be inferred that the dye bath pH (C) is the most important variable on the color yield since its coefficient was the largest (i.e. 1.27). The negative sign of this coefficient meant that the increase of this parameter decreased the color yield (K/S). That is also observed in the case of the effect of liquor ratio to fabric
5
(D). However, the effects of dyeing duration (A) and dyeing temperature (B) factors are positive, that is, an increase of (K/S) is observed when these factors change from low to high. Dyeing duration (A) and dyeing temperature (B) are two factors which result in a higher (K/S) values at their high level, compared to dye bath pH (C) and liquor ratio to fabric (D) which have higher (K/S) values at their low level. The interaction effects plots are also studied and they are shown in Fig. 6. The non-parallel lines in this figure are indications of interaction between the two factors (Mathialagan and Viraraghavan, 2005). From graphs of Fig. 6 and the Equation (I), it can be seen that there is no significant interaction between all others factors. 3.2.3. Normal probability plots To determine whether the experimental data set is normally distributed or not, the normal probability plot of residual values was dressed and reported in Fig. 7. From this figure, it can be seen that the experimental points follow a straight line suggesting normal distribution of the data. The selected model adequately described the observed data, explaining approximately 98.84%% (due to R2 ¼ 0.9884) of the variability of (K/S). 3.2.4. Response optimization Response optimization was carried out also by Minitab software. The obtained results indicate that optimal experimental conditions are a dyeing duration of about 105 min, a dyeing temperature of about 100 C, a dye bath pH of about 3 and a liquor ratio to fabric of about 1:50 in order to obtain a color yield equal to 6.43. 3.2.5. Model validation To validate the theoretical model obtained, three samples were performed from dyeing of acrylic with the aqueous extract of OMW in the optimal conditions given by the curves of response optimization. An average value of 6.48 of color yield (K/S) was found which indicates that the experimental result is quite close to the theoretical optimum. Hence, the model is validated.
Fig. 4. Pareto chart of the standardized effects.
Please cite this article in press as: Haddar, W., et al., Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres, Journal of Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.11.017
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Fig. 5. Main effects plot for color yield.
3.3. Effect of mordanting conditions 3.3.1. Effect of mordanting conditions on dyeing properties Three mordant methods: pre-mordanting, simultaneous mordanting and post-mordanting are used for dying acrylic fabrics with the optimum condition obtained. Ferrous sulphate, alum and stannous chloride are used as mordants. Table 5 shows the effect of
mordanting methods for acrylic fabric dyeing with OMW on the color yield (K/S) and the colorimetric data (L*, a*, b*, C* and h*). It was observed that brownish black and brownish green shades were obtained. The fluctuation of colorimetric data shows that the apparent color change with varying the kind of mordant as well as the method of mordanting, so that the dyeing performances of the dyed samples were appreciated by measuring the Sum(K/S).
Fig. 6. Interaction plot for color yield. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article in press as: Haddar, W., et al., Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres, Journal of Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.11.017
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Fig. 7. Normal probability plot of residuals for color yield. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 5 The dyeing results, the Sum(K/S) and the colorimetric data (L*, a*, b*, C* and h*) for the dyed samples with and without metallic salts. Method
Mordant
Sum (K/S) L*
Without mordanting Pre-mordanting
e
85.18
50.32 2.23 5.64 6.05 68.41
76.21 77.61 62.01 60.93 94.41 59.64 73.13 72.01 69.24
52.38 53.70 54.14 59.92 46.43 62.14 50.62 46.48 53.54
Alum Ferrous sulfate Stannous chloride Meta-mordanting Alum Ferrous sulfate Stannous chloride Post-mordanting Alum Ferrous sulfate Stannous chloride
a*
2.24 2.07 2.32 2.49 1.28 2.61 2,19 1.77 2.25
b*
5.67 6.16 5.03 8.61 3.19 6.03 6.81 8.65 5.69
C*
6.07 6.48 5.52 8.95 3.42 6.57 7.14 8.82 6.10
h*
68.43 71.40 65.21 73.86 68.12 66.57 72.16 78.42 68.42
Table 5 shows that in the case of the pre-mordanting method, ferrous sulfate gave higher yield of shade value (Sum(K/S) ¼ 77.61) than the other used mordants. The (Sum(K/S)) values of the dyed fabrics increased in the following order:
FeSO4 > Alum > SnCl2 The same order was obtained in the case of meta-mordanting method where the ferrous sulphate has shown the highest
brownish black shade obtained. These results show clearly that ferrous sulphate gave generally higher yields of shade when it was used as mordants (especially in the case of meta-mordanting when the Sum of (K/S) was the best value obtained (Sum(K/S) ¼ 94.41). However, the stannous chloride has shown the clearest brownish shade by the same method of mordanting. Table 6 shows also that only stannous chloride in the pre-mordanting method and ferrous sulphate in the meta-mordanting method have decreased the chroma or purity (C*) of color. The obtained shade became duller. For the meta-mordanting method, it was found that Alum gave the highest yield of shade and the (Sum(K/S)) values of the dyed fabrics increased in the following order:
Alum > FeSO4 > SnCl2 3.3.2. Effect of mordanting conditions on fastness properties of dyed fabrics The rating of fastness (washing, rubbing, light, and perspiration fastness) of mordanted and unmordanted acrylic are shown in Table 6. It was found that the fastness of unmordanted acrylic fabrics was considerably good. In Table 6, it can be seen that only post-mordanting method has slightly improved wash fastness in the case of stannous chloride. Perspiration and rubbing fastnesses were good and they are also slightly improved after being
Table 6 Fastness properties of the dyed samples with and without metallic salts. Method
Without mordanting Pre-mordanting
Meta-mordanting
Post-mordanting
Mordant
e Alum Ferrous sulfate Stannous chloride Alum Ferrous sulfate Stannous chloride Alum Ferrous sulfate Stannous chloride
Wash ISO 105-C06
4 4 4 4 4 4 4 4 4 4e5
Light ISO 105-B02
5 5 5 3 5 3 3 4 4 4
Rubbing ISO 105X12
Perspiration ISO 105-E04
Dry
Wet
Acidic
Alkaline
4e5 4e5 5 5 4e5 5 4e5 4e5 5 4e5
3e4 3e4 4 3e4 3e4 4 3e4 3e4 4 3e4
4 4 4 4 4 4 4 4 4 4e5
4 4 4 4 4 4 4 4 4e5 4
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mordanted. However, the light fastness of unmordanted fabrics was medium in the case of acrylic dyed with the aqueous extract of OMW.
Total COD BOD5 COD/BOD5 polyphenols (g L1) (g L1) (g L1)
3.4. Effect of reusing the dye bath 3.4.1. Effect of reusing the dye bath on the dyeing quality After every dyeing process of the acrylic fibre, it is noticed that the residual dyeing bath is still very concentrated with colored substances. So the effect of reusing the dye bath on the color yield (K/S) and the reproducibility of color (DE) were studied. Results are represented in Table 7. It seems clear according to this table that the color yield (K/S) decreases slightly every time of reusing the residual dye bath was reused in a new dyeing process. It can be seen otherwise that for a single dye bath reuse, there is a slight decrease of this parameter (which is in the order of 19%) with an acceptable DE (equal to 1.23). However, when the bath was used several times the color yield decreases but it still generally high ((K/S) is equal to 4.04). This can be attributed to the decrease of colored substances in the dye residual dye bath. In the other hand and according to Table 9, although the total polyphenols removal decreased from one use to another (for seven uses of the dye bath, TP removal is in the order of 41.39%), the bath still concentrated with polyphénols responsible of the dyeing quality (for seven uses the total polyphenols concentration is in the order of 5.28 g L1). 3.4.2. Effect of reusing the dye bath on the environmental parameters The effect of reusing the dye bath of acrylic dyed with olive wastewater on the environmental parameters was studied. The quality of the corresponding residual dye baths wastewater was evaluated by measuring the total polyphenols concentration, the Chemical Oxygen Demand COD and the Biological Oxygen Demand BOD5 in the dye baths before and after dyeing as well as the biodegradability ratio (COD/BOD5). The obtained results are reported in Table 8. The polyphenols removal, the COD removal and the BOD5 removal were also determined after dyeing of acrylic with OMW extract. These results are given in Table 9. Table 8 reveals that the OMW aqueous extract used as dye bath containes high load of organic matter. This was confirmed by the high values of COD (66.24 g L1) and BOD5 (8.5 g L1) obtained. Besides, it was found that the initial dye bath is also characterized by high content of polyphenols (9.01 g L1). These data show clearly that the direct disposal of this effluent may pollute both land and aquatic environments. From Tables 8 and 9, it can be seen that the elaborated dyeing process enables to decrease significantly the polyphenols concentration in the dye bath. The polyphenols removal obtained for one use and for seven uses of the dye bath are respectively about 2.55% and 41.39%. These results show, firstly, that polyphenols are the major coloring substances absorbed by acrylic fibers. On the other hand, the developed dyeing process enables also to decrease
Table 7 Effect of reuse number of the dye bath on the color yield (K/S) and the color difference (DE) parameters.
After After After After After After After
one use of dye bath two uses of dye bath three uses of dye bath forth uses of dye bath five uses of dye bath six uses of dye bath seven uses of dye bath
Table 8 Total polyphenols, COD, BOD5 and the diodegradability ratio (COD/BOD5) of the dye baths before and after their reusing on the dyeing of modified acrylic fibers.
Color yield (K/S)
DE
6.4 5.18 5.14 5.05 4.75 4.63 4.04
e 1.23 1.33 1.48 1.78 2.74 6.63
Before dyeing After one use of dye bath After two uses of dye bath After three uses of dye bath After forth uses of dye bath After five uses of dye bath After six uses of dye bath After seven uses of dye bath
9.01 8.78 8.55 7.93 7.01 6.60 5.92 5.28
66.24 58.88 53.36 47.84 42.32 36.80 33.12 25.76
8.5 8.3 8.4 8.4 8.2 8.4 8.3 8.4
7.79 7.09 6.35 5.69 5.16 4.38 3.99 3.06
considerably the pollutant load of the OMW aqueous extract (the initial dye bath). From Table 9, it can be observed that the values of the COD removal were very important passing from 11.11 to 61.11% for the seven use. However, the values of their BOD5 removal remained neglegtable (2.35% for one use and 1.17% the seven). Furthermore, the biodegradability rate of OMW represented by the ratio of (COD/BOD5) was almost > 3 what means that the effluent is not biodegradable after dyeing. But this factor of biodegradability decreased from 7.79 for the first use to 3.06 for the seventh use of the same dye bath, which demonstrates that this new way of valorization of OMW allows reducing its pollution load and subsequently facilitating its treatment. To conclude, from all these values, it appears that dyeing of acrylic fiber can reduce considerably the environmental impact of this organic waste. So this process of natural dyeing, not only utilizes a priceless and abundant natural dyes, but it can also reduce its polluting effect with generating an effluent with low pollutant load as the reusing the dye bath enables to regenerate an effluent containing the minimum amount of organic substances. So the process is green, economic and eco-friendly as well. 4. Conclusion The project reported in this paper presents a study about the optimization of effectiveness factors on synthetic fiber dyeing by using the full factorial design approach. The R2 value of 98.84% indicates a very good fit of the model with experimental data. Optimized conditions obtained for dyeing are: pH ¼ 3, RdB ¼ 1: 50, temperature ¼ 100 C and time ¼ 105 min. Furthermore, natural dye contained in OMW could provide brownish green hues and considerable color fastness properties for acrylic fibre. Although, this developed process of natural dyeing with the aqueous extract of OMW is very attractive because it is considered as an ecological process. It also contributes in decreasing remarkably the pollution generated by this effluent. After dyeing, the residual dye baths can be obviously used again to color acrylic fibers and the polyphenols load can be consequently decreased which directly contribute to reduce the Chemical Oxygen Demand COD and the biodegradability rate represented by the ratio of (COD/BOD5) of OMW. Table 9 Polyphenols. COD removal and BOD5 removal values obtained after reusing of the dye bath on the dyeing of modified acrylic fibers.
After After After After After After After
one use of dye bath two uses of dye bath three uses of dye bath forth uses of dye bath five uses of dye bath six uses of dye bath seven uses of dye bath
Polyphenols removal (%)
COD removal (%)
BOD5 removal (%)
2.55 5.10 11.98 22.19 26.74 34.29 41.39
11.11 19.44 27.77 36.11 44.44 50 61.11
2.35 1.17 1.17 3.52 1.17 2.35 1.17
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Acknowledgments The authors are particularly grateful to Dr. Sonia Dridi Dhaoudi from the Unity of applied chemistry and environment of the Faculty of Sciences in Monastir for her help and her cooperation. References Bingol, D., Tekin, N., Alkan, M., 2010. Brilliant yellow dye adsorption onto sepiolite using a full factorial design. Appl. Clay Sci. 50, 315e321. Bulut, M.O., Akar, E., 2012. Ecological dyeing with some plant pulps on woolen yarn and cationized cotton fabric. J. Clean. Prod. 32, 1e9. Carmona, M.E.R., Da Silva, M.A.P., Leite, S.G.F., 2005. Biosorption of chromium using factorial experimental design. Proc. Biochem. 40, 779e788. Efthalia, C., Spyros, F., Dionissios, M., Theocharis, T., 2013. Life cycle assessment of advanced oxidation processes for olive mill wastewater treatment. J. Clean. Prod. 54, 229e234. El-Abbassi, A., et al., 2013. Application of ultrafiltration for olive processing wastewaters treatment. J. Clean. Prod.. http://dx.doi.org/10.1016/j.jclepro.2013. Gouila, H., Meksi, N., Haddar, W., Mhenni, M.F., Jannet, H.B., 2012. Extraction, identification and dyeing studies of Isosalipurposide, a natural chalcone dye from Acacia cyanophylla flowers on wool. Ind. Crop. Prod. 35, 31e36. Guesmi, A., Ladhari, N., Ben Hamadi, N., Sakli, F., 2012. Isolation, identification and dyeing studies of betanin on modified acrylic fabrics. Ind. Crop. Prod. 37, 342e 346. Gupta, M.L., Cook, F.L., Etters, J.N., 2011. Commercially adaptable coloration processes for generic polypropylene fiber. In: Hauser, P. (Ed.), Textile Dyeing. InTech, Shanghai, pp. 155e172. Kerkeni, A., Behary, N., Perwuelz, A., Gupta, D., 2012. Dyeing of woven polyester fabric with curcumin: effect of dye concentrations and surface pre-activation using air atmospheric plasma and ultraviolet excimer treatment. Color. Technol. 128, 223e229.
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Kubelka, P., 1948. New contributions to the optics of intensely light-scattering materials. Part I. J. Opt. Soc. Am. 38, 448e457. Kubelka, P., 1954. New contributions to the optics of intensely light-scattering materials. Part II: nonhomogeneous layers. J. Opt. Soc. Am. 44, 330e334. Mathialagan, T., Viraraghavan, T., 2005. Biosorption of pentachlorophenol by fungal biomass from aqueous solutions: a factorial design analysis. Environ. Technol. 6, 571e579. Meksi, N., Haddar, W., Hammami, S., Mhenni, M.F., 2012. Olive mill wastewater: a potential source of natural dyes for textile dyeing. Ind. Crop. Prod. 40, 103e109. Prusty, A.K., Das, T., Nayak, A., Das, N.B., 2010. Colourimetric analysis and antimicrobial study of natural dyes and dyed silk. J. Clean. Prod. 32, 1750e1756. Rodier, J., 1997. L’analyse de l’eau. Eaux naturelles, eaux résiduaires, eau de mer (Analysis of the Water, Natural Waters, Wastewater, Seawate). Dunod, Paris, pp. 557e762. Saez, L., Perez, J., Martinez, J., 1992. Low molecular weight phenolics attenuation during simulated treatment of wastewaters from olive oil mills in evaporation ponds. Water Res. 26, 1261e1266. Shaukat, A., Tanveer, H., Rakhshanda, N., 2009. Optimization of alkaline extraction of natural dye from Henna leaves and its dyeing on cotton by exhaust method. J. Clean. Prod. 17, 61e66. Singleton, V.L., Rossi Jr., J.A., 1965. Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. Am. J. Enol. Vitic. 16, 144e 158. Srinivasan, A., Viraraghavan, T., 2010. Oil removal from water by fungal biomass: a factorial design analysis. J. Hazard. Mater 175, 695e702. Sriumaoum, V., Suesat, J., Suwanruji, P., 2012. Dyeing and spectroscopic properties of natural dyes on Poly (Lactic acid) and Poly (Ethylene Terephthalate) fabrics. Int. J. Biosci. Biochem. Bioinfo. 2, 155e158. Tezcan Ün, Ü., Altay, Ü., Koparal, A.S., Bakır Ögütveren, Ü., 2008. Complete treatment of olive mill wastewaters by electrooxidation. Chem. Eng. J. 139, 445e452. Vankar, P.S., Rakhi, S., Verma, A., 2007. Enzymatic natural dyeing of cotton and silk fabrics without metal mordants. J. Clean. Prod. 15, 1441e1450. Wesenberg, D., Kyriakides, I., Agathos, S.N., 2003. White-rot fungi and their enzymes for the treatment of industrial dye effluents. Biotech. Adv. 22, 161e187.
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Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres Wafa Haddar a, *, Noureddine Baaka a, Nizar Meksi a, b, Imen Elksibi a, M. Farouk Mhenni a a b
Research Unit of Applied Chemistry and Environment, Faculty of Sciences of Monastir, 5000 Monastir, Tunisia Higher Institute of Fashion of Monastir, 5019 Monastir, Tunisia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 November 2012 Received in revised form 6 November 2013 Accepted 9 November 2013 Available online xxx
The olive oil industry releases considerable amounts of wastewater which contains huge reserves of natural dyes. Such wastewater could successfully be used for the dyeing of acrylic fibers. The influence of the main dyeing conditions (material/liquor ratio, dye bath pH, dyeing duration, dyeing temperature) on the performances of this dyeing process were studied. The dyeing performances of this process were appreciated by measuring the color yield (K/S) and the fastness properties of the dyed samples. A 24 full factorial design method was employed in order to study the interactions between the selected dyeing process parameters and to evaluate the optimal dyeing conditions. The optimization of these dyeing process factors to obtain maximum color yield was carried out by incorporating effect plots, normal probability plots, interaction plots, analysis of variance (ANOVA) and Pareto charts. A regression model was formulated using Minitab software and fitted the experimental data very well. In addition, it was found that dyeing of acrylic enables to reduce the concentration of polyphenols so that it reduced the Chemical oxygen demand COD. Furthermore, the biodegradability ratio (COD/BOD5) decreases but it was always superior to 3 which means that this aqueous waste still not biodegradable. It was also found that reusing the residual bath allowed to obtain a depth of shade very similar to the first dyeing and reduced considerably the environmental parameters (the concentration of polyphénols and COD). Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Olive mill wastewater Acrylic fibers dyeing Green process Full factorial design Optimization Reusing dyeing bath
1. Introduction Increasing awareness of the environmental and health hazards associated with the synthesis, processing, and use of synthetic dyes have generated a renewed worldwide interest in natural dyes (Bulut and Akar, 2012). During the last decade, the use of natural dyes gained momentum due to the increased demand of food, pharmaceutical and cosmetic industries. Natural dyes comprise colorants that are obtained from animal or vegetable substances without any chemical processing. This group is mostly known to be eco-friendly, biodegradable and less allergenic compared to synthetic dyes (Ghouila et al., 2012). Besides, it can have a higher compatibility with the environment. For these raisons, a considerable number of research works are being undertaken around the world concerning the production and the application of natural dyes (Vankar et al., 2007; Shaukat et al., 2009). The textile processing industry is one of the major users of dyes (Wesenberg et al., 2003). However, until now the application of the
* Corresponding author. E-mail address: [email protected] (W. Haddar).
natural dyes in this field does not meet the expectations of all the consumers because their use caters mainly to niche products for special markets such as the eco-friendly textiles. Unfortunately, these remain as up-market products which target specific clientele who is fascinated by the sustainability and the green chemistry. The low availability and the high processing cost of natural coloring substances are the most important reasons for preventing these products from being more popular. The attempts to overcome these problems and reduce prices have mainly focused on the discovery of newer sources especially from by-products of farming and forestry as well as the valorization of several wastes from food and beverage industry in order to extract from them their coloring substances and using those colorants as natural dyes for textile dyeing. This idea is very interesting because the raw material is abundant, cheap and renewable (Prusty et al., 2010). Another problem which may also limit the application of these dyes in the textile industry is the low dye exhaustion of the majority of natural colorants and the poor fastness of dyed fabrics on synthetic fibers compared to wool and others natural fibers because synthetic fibers are hydrophobic, highly crystalline, non-polar polymers as well as the absence of active chemical groups in their polymer structure (Gupta et al., 2011). These limitations
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generate several technical problems for producing natural-dyed synthetic textile fibers in order to fulfill the demands of more eco-friendly textile products (Sriumaoum et al., 2012). Recently, a variety of projects about the possible use of natural dyes in synthetic fibers dyeing especially polyester and acrylic fibers have been performed by various researchers. Different techniques such as chemical modification to include amidoxime groups on polymer surface (Guesmi et al., 2012) and air atmospheric plasma treatment as well as ultraviolet (UV) excimer lamp irradiation (Kerkeni et al., 2012) were used to improve the dyeing performances of these synthetic textile fibers. In these papers, the authors described encouraging results with regard to color yield, shade, and fastness properties. Olive mill wastewater (OMW) is a dark brown to black effluent produced during olive oil extraction. This effluent is characterized by a high organic load including sugars, tannins, pectin, lipids, polyphenols and polyalcohols (Efthalia et al., 2013). The annual OMW production of the Mediterranean olive-growing countries is estimated to over than 30 M m3 (Tezcan Ün et al., 2008). This wastewater has a considerable pollution that occurs as a result of seasonal OMW production. In fact, it is one of the most crucial environmental issues in the Mediterranean area. Therefore, it constitutes a serious problem with severe negative impact on the soil and the water quality. Several techniques have been reported in literature to treat or valorize this effluent (El-Abbassi et al., 2013). However, for technical and economic reasons, supplementary studies and researches are still required to develop efficient systems which could be really applied in a large scale to resolve this ecological problem. The construction of artificial big ponds into which OMW is stored, waiting for its natural evaporation is until now the most common practice in the Mediterranean region. Unfortunately, this method, besides being very slow, causes subsequent unpleasant environmental pollution linked to generation of bad odors due to anaerobic activity (Saez et al., 1992). In the previous part of this work (Meksi et al., 2012), it was found that OMW contains valuable resource of abundant natural coloring substances which could be successfully exploited as natural dye for textile coloration. The application of such wastewater as a source of natural dyes can help in the preservation of environment and also decrease the cost of natural dyeing. The objective of the present work is to investigate the dyeing of acrylic fibers by natural dyes extracted from olive mill wastewater. The modeling and the optimization of some experimental dyeing conditions were investigated using full factorial design methodology in order to study the exhausting dyeing process. The effect of mordanting on dyeing of acrylic fibre by OMW natural matters was also studied. The environment impact of this dyeing on the OMW dye bath characteristics (Total polyphenols, Chemical oxygen demand COD, Biological oxygen demand BOD5 and the biodegradability ratio COD/BOD5) was also studied with the effect of multi-using of the residual dye bath on these parameters. 2. Experimental 2.1. Textile material Acrylic fabric (Plain weave and weight, 200 g/m2) was procured commercially. Before being used, it was soaped with 2 g L1 of nonionic detergent at 60 C for 30 min, thoroughly rinsed and air dried.
2.3. Olive mill wastewaters (OMW) used The OMW used in this study was obtained from an arranged evaporating pond which is located in the region of Monastir (Menzel Hareb) in Tunisia. 2.4. Preparation of dye bath from OMW The used dye bath from OMW was prepared accordingly to the procedure described in Meksi et al. (2012). 2.5. Dyeing procedures In a dye bath with various liquor ratio (10:1 - 60:1), acrylic fabric was dyed in a laboratory dyeing machine (Ahiba Datacolor International, USA) at different pH values (2e8) for different durations (15e115 min) and at different temperatures (50e110 C). The dyed fabrics were then rinsed with cold water and washed then followed by soaping with 2 g L1 of a nonionic soap, Cotoblanc RS (Bezema AG, Switzerland) at 60 C. Finally the fabric samples were washed thoroughly with cold water, squeezed and dried. The pH values were recorded with Eutech Instruments pH 510 (Singapore). In case of mordanting, the three known methods: premordanting, meta-mordanting and post-mordanting were used. Mordant concentration of 3% (w/w with respect to the fabric) and a material to liquor ratio of 1:40 were used for all experiments. The acrylic fabrics were treated at 30 C for 45 min. 2.6. Color evaluation The spectral reflectance of the dyed samples was measured using SpectroFlash SF300 spectrophotometer with dataMaster 2.3 software (Datacolor International, USA). The color yield (K/S) values were calculated by Kubleka-Munk equation (Kubelka, 1948, 1954):
. ð1 RÞ2 ð1 R0 Þ2 K S ¼ 2R 2R0 where R is the decimal fraction of the reflectance of dyed fabric, R0 is the decimal fraction of the reflectance of undyed fabric, K is the absorption coefficient and S is the scattering coefficient. In case of dyeing with mordants, the shades may vary. So, the dyeing performances of the dyed samples were appreciated by measuring the Sum(K/S) which is calculated as follow:
. 700 . X K S Sum K S ¼ l ¼ 400
l
CIELab coordinates (L*, a*, b*, C*, h*, where L* defines lightness; a* denotes the red/green value; b* the yellow/bleu value; C* the saturation value and h* is the hue) were calculated from the reflectance data for 10 observer and illuminant D65. To estimate the color differences between samples dyed with the residual dye bath and the first samples dyed with the aqueous extract of olive wastewater, the color difference was calculated as follow:
DE ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi DL* 2 þ Da* 2 þ Db* 2
2.7. Fastness testing 2.2. Chemicals used Alum, ferrous sulphate and stannous chloride were used as mordants without further purification.
The dyed samples were tested according to standard methods. The specific tests were for color fastness to washing ISO 105-C06, color fastness to rubbing ISO 105-X12, color
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W. Haddar et al. / Journal of Cleaner Production xxx (2013) 1e9 Table 1 Effect of the liquor ratio to fabric on the color yield. (Dyeing conditions: pH 5, 60 min and 80 C). Liquor ratio to fabric
Color yield (K/S)
1:10 1:20 1:30 1:40 1:50 1:60
2,48 4,73 5,02 4,91 4,74 4,42
fastness to light ISO 105-B02 and color fastness to perspiration ISO 105-E04. 2.8. Design of experiment (DOE) The high and low levels defined for the 24 factorial design were listed in Table 2. The low and high levels for the factors were selected according to the first part of this work describing the effects of dyeing parameters. The factorial design matrix and (K/S) value measured in each factorial experiment are shown in Table 3, with low (1) and high (þ1) levels as specified in Table 2. The color yield (K/S) was determined as an average of three parallel experiments. The results were analyzed with the statistical software package Minitab version 15 (State College, PA, USA), and the main effects and interactions between factors were determined. The analysis of regression and of the variance (ANOVA) as well as the graphical optimizations were performed in order to determinate the optimal conditions of the critical variables. 2.9. Residual dye baths analyses Determination of total polyphenols concentration in the dye baths before and after dyeing was carried out with a CECIL 2021 Instruments UV/Vis spectrophotometer at 765 nm using the FolinCiocalteau reagent and the gallic acid as a standard (Singleton and Rossi, 1965). Total polyphenols removal was determined using the following equation:
C Cfin TPremoval ð%Þ ¼ in 100 Cin where Cin and Cfin are the total polyphenols concentrations in dye bath before and after dyeing, respectively. Analysis of Chemical Oxygen Demand COD analyses in dye baths before and after dyeing were determined by the procedure described in Rodier (1997). COD removal was determined using the following equation:
CODremovalð%Þ ¼
3
Table 3 Factorial design experimental data. Experiments
A
B
C
D
K/S
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
4.43 5.07 5.93 6.43 3.43 3.97 4.41 5.31 4.23 4.82 5.71 5.86 3.26 3.72 3.68 4.54
BOD5 removal was determined using the following equation:
BOD5 removalð%Þ ¼
ðBOD5 Þin ðBOD5 Þfin 100 ðBODÞin
where (BOD5)in and (BOD5)fin are the Biological Oxygen demand of dyebath before and after dyeing, respectively. 3. Results and discussion 3.1. Effect of dyeing conditions on dyeing quality 3.1.1. Effect of the dye bath pH Fig. 1 show the effect of the dye bath pH on the color yield which was investigated in the range of 2e8. In this figure, it can be clearly observed that the best result is obtained for pH 3. When the dye bath pH exceeded this value, the color yield decreased considerably. 3.1.2. Effect of dye bath temperature The effect of dyeing temperature on the depth of shade (K/S) of dyed acrylic fabric is shown in Fig. 2. It was found that the color yield increases with the dyeing temperature until 100 C. Generally, the increase in dye-uptake can be explained by fibre swelling which enhanced dye diffusion. The color yield became important notably when temperature exceeded the glass transition temperature of acrylic fibres due to the increase of dye accessibility.
CODin CODfin 100 CODin
where CODin and CODfin are the Chemical Oxygen Demand of dye bath before and after dyeing, respectively. The Biological Oxygen Demand BOD5 was analyzed according to (Rodier, 1997).
Table 2 Factors and levels used in the factorial design. Factors
Code
Low level (1)
Low level (þ1)
Dyeing duration (min) Dyeing temperature ( C) Dye bath pH Liquor ratio to fabric
A B C D
15 70 3 1:50
105 100 7 1:20
Fig. 1. Effect of the dye bath pH on the color yield. (Dyeing conditions: 60 min, 80 C, and LR 40:1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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3.1.3. Effect of dyeing duration Fig. 3 shows the effect of dyeing time on the color yield obtained for the dyed fabrics. This figure indicates that the color yield increases when the time increases up to 60 min, then a plateau is attained after 60 up to 105 min. It seems that acrylic fabrics reached the saturation and did not absorb more OMW natural dyes. 3.1.4. Effect of liquor ratio to fabric The effect of the liquor ratio to fabric on the depth of shade (K/S) of dyed acrylic fabric is also investigated. The obtained results are given in Table 1. It is clear that the color yield increased with the dyeing liquor ratio up to 1:20 and then it remained slightly constant. The lower relative color yield value at low dyeing ratio may be explained by the overcrowding of dye molecules at lower dyeing ratio resulting in reduced dye exhaustion on the fabric. However, the quasi stability of the color yield after a liquor ratio of 1:20 can be explained by the important quantity of natural dyes present in the olive wastewaters. It appears that the fibre attained its saturation on colorant with only 1:20 of liquor ratio to fabric and almost no further increase with further colorant concentration in the dyeing bath can be obtained. 3.2. Modeling and optimisation of the dyeing process In this work, the factorial experimental design methodology was employed to estimate the influence of the different variables of dyeing process. Factorial designs are widely used to investigate the effects of experimental factors and the interactions between those factors, that is, how the effect of one factor varies with the level of the other factors in a response. The advantages of factorial experiments include the relatively low cost, the reduced number of experiments, and the increased possibilities to evaluate interactions among the variables. The most common first-order designs are the two-level full factorial design and the two-level full fractional design. In these methodologies, each factor is experimentally studied at two levels that are coded as: 1 for low level and þ1 for high level. The full factorial design consists of a 2k experiments (k factors, each experiment at two levels), which is very useful for either preliminary studies or for initial optimization steps, while fractional designs are almost mandatory when the problem involves a large number of factors (Bingol et al., 2010). According to the previous part, the dyeing process of fibers using natural dyes extracted from olive mill wastewater involves many factors. So, a 24 full factorial design was chosen for the purpose of evaluating the importance of dyeing duration, dyeing
Fig. 3. Effect of the dyeing duration on the color yield. (Dyeing conditions: 60 min, 80 C, and LR 40:1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
temperature, pH of the dye bath and the liquor ratio to fabric on the dyeing process. These factors were evaluated by using factorial plots: main effect, interaction effect, the Pareto chart plot and the normal probability plots. ANOVA and P-value significant levels were used to check the significance of the effect on the color yield (K/S) (the response). 3.2.1. Variance analysis (ANOVA) Identifying areas of variations of each factor are set from the previous study of the effects of dyeing parameters. The definition of high and low levels is illustrated in Table 2. This experimental design gave 24 experiments, which were carried out in the random order given in Table 3. The response considered in the analysis is color yield (K/S). All our statistical analyses were performed using the statistical software package Minitab 15. The effects, regression coefficients, standard errors and T values (standardized effects) are reported in Table 4. From the data given in Table 4, a mathematical model can be formulated as follow:
K=S ¼ 4:675 þ 0:290 A þ 0:55875 B 0:635 C 0:1975 D þ 0:01125 AB þ 0:055 AC 0:0325 AD 0:11375 BC 0:08875 BD 0:0425 CD (I) Where K/S is the color yield, A is the dyeing duration, B is the dyeing temperature, C is dye bath pH and D is the liquor ratio to fabric. In order to determine the significant main and the interaction effects of the factors influencing the performance of this dyeing
Table 4 Estimated effects and coefficients for the color yield (K/S).
Fig. 2. Effect of the dye bath temperature on the color yield. (Dyeing conditions: pH 5, 60 min, and LR 40:1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Term
Coef
SE Coef
T
P
Constant Dyeing duration (A) Dyeing temperature (B) Dye bath pH (C) Liquor ratio to fabric (D) Dyeing duration-dyeing temperature (AB) Dyeing duration-dye bath pH (AC) Dyeing duration-liquor ratio (AD) Dyeing temperature-dye bath pH (BC) Dyeing temperature-liquor ratio (BD) Dye bath pH-liquor ratio (CD)
4.67500 0.29000 0.55875 0.63500 0.19750 0.01125
0.04511 0.04511 0.04511 0.04511 0.04511 0.04511
103.633 6.429 12.386 14.076 4.378 0.249
0.000 0.001 0.000 0.000 0.007 0.813
0.05500 0.03250 0.11375 0.08875 0.04250
0.04511 0.04511 0.04511 0.04511 0.04511
1.219 0.720 2.522 1.967 0.942
0.277 0.504 0.053 0.106 0.389
S ¼ 0.180444, R-sq ¼ 98.84%, R-sq(adj) ¼ 96,51%.
Please cite this article in press as: Haddar, W., et al., Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres, Journal of Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.11.017
W. Haddar et al. / Journal of Cleaner Production xxx (2013) 1e9
process, an analysis of variance (ANOVA) was performed using Student’s t-test p < 0.05 (Carmona et al., 2005). P-value is the probability value that is used to determine the statistically significant effects in the model. The importance of the data can be judged by its P-value, with values closer to zero denoting greater significance. For a 95% confidence level the P-value should be less than or equal to 0.05 for the effect to be considered statistically significant (Srinivasan and Viraraghavan, 2010). According to the obtained Pvalue (Table 4), it seems that the dyeing duration (A), the dyeing temperature (B), the dye bath pH (C), liquor ratio to fabric (D) are statistically significant. However, the interactions .AB (P ¼ 0.813), AC (P ¼ 0.277), AD (P ¼ 0.504), BC (P ¼ 0.053), BD (P ¼ 0.106) and CD (P ¼ 0.389) are statistically not significant with 95% confidence level. In addition, the model presented an R2 ¼ 98.84% and an 2 R (adjusted) ¼ 96.51%. So, it could be deduced that the resulted model obtained has a very good predictability within the range of the chosen variables. This model (I) describes how the experimental variables and their interactions influence the dyeing process. The dye bath pH (C) had the greatest effect on the color yield, followed by the dyeing temperature (B), the dyeing duration (A) and the liquor ratio to fabric (D). Absolute values of the effects of main factors and the interaction of factors are also provided in the Pareto chart (Fig. 4) and they are in accordance with the previous results. In Fig. 4, for tvalue equal to 0.342, a reference was dressed which corresponds to the 95% confidence level. The values that exceed this reference line, i.e., those corresponding to the 95% confidence interval, are significant values (Mathialagan and Viraraghavan, 2005). According to Fig. 4, the main significant factors are: (C) > (B) > (A) > (D). 3.2.2. Main and interaction effects The main effects of each dyeing parameter on the color yield are shown in Fig. 5. Analyzing the graphs of Fig. 5 and the coefficients of the Equation (I), it can be inferred that the dye bath pH (C) is the most important variable on the color yield since its coefficient was the largest (i.e. 1.27). The negative sign of this coefficient meant that the increase of this parameter decreased the color yield (K/S). That is also observed in the case of the effect of liquor ratio to fabric
5
(D). However, the effects of dyeing duration (A) and dyeing temperature (B) factors are positive, that is, an increase of (K/S) is observed when these factors change from low to high. Dyeing duration (A) and dyeing temperature (B) are two factors which result in a higher (K/S) values at their high level, compared to dye bath pH (C) and liquor ratio to fabric (D) which have higher (K/S) values at their low level. The interaction effects plots are also studied and they are shown in Fig. 6. The non-parallel lines in this figure are indications of interaction between the two factors (Mathialagan and Viraraghavan, 2005). From graphs of Fig. 6 and the Equation (I), it can be seen that there is no significant interaction between all others factors. 3.2.3. Normal probability plots To determine whether the experimental data set is normally distributed or not, the normal probability plot of residual values was dressed and reported in Fig. 7. From this figure, it can be seen that the experimental points follow a straight line suggesting normal distribution of the data. The selected model adequately described the observed data, explaining approximately 98.84%% (due to R2 ¼ 0.9884) of the variability of (K/S). 3.2.4. Response optimization Response optimization was carried out also by Minitab software. The obtained results indicate that optimal experimental conditions are a dyeing duration of about 105 min, a dyeing temperature of about 100 C, a dye bath pH of about 3 and a liquor ratio to fabric of about 1:50 in order to obtain a color yield equal to 6.43. 3.2.5. Model validation To validate the theoretical model obtained, three samples were performed from dyeing of acrylic with the aqueous extract of OMW in the optimal conditions given by the curves of response optimization. An average value of 6.48 of color yield (K/S) was found which indicates that the experimental result is quite close to the theoretical optimum. Hence, the model is validated.
Fig. 4. Pareto chart of the standardized effects.
Please cite this article in press as: Haddar, W., et al., Optimization of an ecofriendly dyeing process using the wastewater of the olive oil industry as natural dyes for acrylic fibres, Journal of Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.11.017
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W. Haddar et al. / Journal of Cleaner Production xxx (2013) 1e9
Fig. 5. Main effects plot for color yield.
3.3. Effect of mordanting conditions 3.3.1. Effect of mordanting conditions on dyeing properties Three mordant methods: pre-mordanting, simultaneous mordanting and post-mordanting are used for dying acrylic fabrics with the optimum condition obtained. Ferrous sulphate, alum and stannous chloride are used as mordants. Table 5 shows the effect of
mordanting methods for acrylic fabric dyeing with OMW on the color yield (K/S) and the colorimetric data (L*, a*, b*, C* and h*). It was observed that brownish black and brownish green shades were obtained. The fluctuation of colorimetric data shows that the apparent color change with varying the kind of mordant as well as the method of mordanting, so that the dyeing performances of the dyed samples were appreciated by measuring the Sum(K/S).
Fig. 6. Interaction plot for color yield. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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7
Fig. 7. Normal probability plot of residuals for color yield. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 5 The dyeing results, the Sum(K/S) and the colorimetric data (L*, a*, b*, C* and h*) for the dyed samples with and without metallic salts. Method
Mordant
Sum (K/S) L*
Without mordanting Pre-mordanting
e
85.18
50.32 2.23 5.64 6.05 68.41
76.21 77.61 62.01 60.93 94.41 59.64 73.13 72.01 69.24
52.38 53.70 54.14 59.92 46.43 62.14 50.62 46.48 53.54
Alum Ferrous sulfate Stannous chloride Meta-mordanting Alum Ferrous sulfate Stannous chloride Post-mordanting Alum Ferrous sulfate Stannous chloride
a*
2.24 2.07 2.32 2.49 1.28 2.61 2,19 1.77 2.25
b*
5.67 6.16 5.03 8.61 3.19 6.03 6.81 8.65 5.69
C*
6.07 6.48 5.52 8.95 3.42 6.57 7.14 8.82 6.10
h*
68.43 71.40 65.21 73.86 68.12 66.57 72.16 78.42 68.42
Table 5 shows that in the case of the pre-mordanting method, ferrous sulfate gave higher yield of shade value (Sum(K/S) ¼ 77.61) than the other used mordants. The (Sum(K/S)) values of the dyed fabrics increased in the following order:
FeSO4 > Alum > SnCl2 The same order was obtained in the case of meta-mordanting method where the ferrous sulphate has shown the highest
brownish black shade obtained. These results show clearly that ferrous sulphate gave generally higher yields of shade when it was used as mordants (especially in the case of meta-mordanting when the Sum of (K/S) was the best value obtained (Sum(K/S) ¼ 94.41). However, the stannous chloride has shown the clearest brownish shade by the same method of mordanting. Table 6 shows also that only stannous chloride in the pre-mordanting method and ferrous sulphate in the meta-mordanting method have decreased the chroma or purity (C*) of color. The obtained shade became duller. For the meta-mordanting method, it was found that Alum gave the highest yield of shade and the (Sum(K/S)) values of the dyed fabrics increased in the following order:
Alum > FeSO4 > SnCl2 3.3.2. Effect of mordanting conditions on fastness properties of dyed fabrics The rating of fastness (washing, rubbing, light, and perspiration fastness) of mordanted and unmordanted acrylic are shown in Table 6. It was found that the fastness of unmordanted acrylic fabrics was considerably good. In Table 6, it can be seen that only post-mordanting method has slightly improved wash fastness in the case of stannous chloride. Perspiration and rubbing fastnesses were good and they are also slightly improved after being
Table 6 Fastness properties of the dyed samples with and without metallic salts. Method
Without mordanting Pre-mordanting
Meta-mordanting
Post-mordanting
Mordant
e Alum Ferrous sulfate Stannous chloride Alum Ferrous sulfate Stannous chloride Alum Ferrous sulfate Stannous chloride
Wash ISO 105-C06
4 4 4 4 4 4 4 4 4 4e5
Light ISO 105-B02
5 5 5 3 5 3 3 4 4 4
Rubbing ISO 105X12
Perspiration ISO 105-E04
Dry
Wet
Acidic
Alkaline
4e5 4e5 5 5 4e5 5 4e5 4e5 5 4e5
3e4 3e4 4 3e4 3e4 4 3e4 3e4 4 3e4
4 4 4 4 4 4 4 4 4 4e5
4 4 4 4 4 4 4 4 4e5 4
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mordanted. However, the light fastness of unmordanted fabrics was medium in the case of acrylic dyed with the aqueous extract of OMW.
Total COD BOD5 COD/BOD5 polyphenols (g L1) (g L1) (g L1)
3.4. Effect of reusing the dye bath 3.4.1. Effect of reusing the dye bath on the dyeing quality After every dyeing process of the acrylic fibre, it is noticed that the residual dyeing bath is still very concentrated with colored substances. So the effect of reusing the dye bath on the color yield (K/S) and the reproducibility of color (DE) were studied. Results are represented in Table 7. It seems clear according to this table that the color yield (K/S) decreases slightly every time of reusing the residual dye bath was reused in a new dyeing process. It can be seen otherwise that for a single dye bath reuse, there is a slight decrease of this parameter (which is in the order of 19%) with an acceptable DE (equal to 1.23). However, when the bath was used several times the color yield decreases but it still generally high ((K/S) is equal to 4.04). This can be attributed to the decrease of colored substances in the dye residual dye bath. In the other hand and according to Table 9, although the total polyphenols removal decreased from one use to another (for seven uses of the dye bath, TP removal is in the order of 41.39%), the bath still concentrated with polyphénols responsible of the dyeing quality (for seven uses the total polyphenols concentration is in the order of 5.28 g L1). 3.4.2. Effect of reusing the dye bath on the environmental parameters The effect of reusing the dye bath of acrylic dyed with olive wastewater on the environmental parameters was studied. The quality of the corresponding residual dye baths wastewater was evaluated by measuring the total polyphenols concentration, the Chemical Oxygen Demand COD and the Biological Oxygen Demand BOD5 in the dye baths before and after dyeing as well as the biodegradability ratio (COD/BOD5). The obtained results are reported in Table 8. The polyphenols removal, the COD removal and the BOD5 removal were also determined after dyeing of acrylic with OMW extract. These results are given in Table 9. Table 8 reveals that the OMW aqueous extract used as dye bath containes high load of organic matter. This was confirmed by the high values of COD (66.24 g L1) and BOD5 (8.5 g L1) obtained. Besides, it was found that the initial dye bath is also characterized by high content of polyphenols (9.01 g L1). These data show clearly that the direct disposal of this effluent may pollute both land and aquatic environments. From Tables 8 and 9, it can be seen that the elaborated dyeing process enables to decrease significantly the polyphenols concentration in the dye bath. The polyphenols removal obtained for one use and for seven uses of the dye bath are respectively about 2.55% and 41.39%. These results show, firstly, that polyphenols are the major coloring substances absorbed by acrylic fibers. On the other hand, the developed dyeing process enables also to decrease
Table 7 Effect of reuse number of the dye bath on the color yield (K/S) and the color difference (DE) parameters.
After After After After After After After
one use of dye bath two uses of dye bath three uses of dye bath forth uses of dye bath five uses of dye bath six uses of dye bath seven uses of dye bath
Table 8 Total polyphenols, COD, BOD5 and the diodegradability ratio (COD/BOD5) of the dye baths before and after their reusing on the dyeing of modified acrylic fibers.
Color yield (K/S)
DE
6.4 5.18 5.14 5.05 4.75 4.63 4.04
e 1.23 1.33 1.48 1.78 2.74 6.63
Before dyeing After one use of dye bath After two uses of dye bath After three uses of dye bath After forth uses of dye bath After five uses of dye bath After six uses of dye bath After seven uses of dye bath
9.01 8.78 8.55 7.93 7.01 6.60 5.92 5.28
66.24 58.88 53.36 47.84 42.32 36.80 33.12 25.76
8.5 8.3 8.4 8.4 8.2 8.4 8.3 8.4
7.79 7.09 6.35 5.69 5.16 4.38 3.99 3.06
considerably the pollutant load of the OMW aqueous extract (the initial dye bath). From Table 9, it can be observed that the values of the COD removal were very important passing from 11.11 to 61.11% for the seven use. However, the values of their BOD5 removal remained neglegtable (2.35% for one use and 1.17% the seven). Furthermore, the biodegradability rate of OMW represented by the ratio of (COD/BOD5) was almost > 3 what means that the effluent is not biodegradable after dyeing. But this factor of biodegradability decreased from 7.79 for the first use to 3.06 for the seventh use of the same dye bath, which demonstrates that this new way of valorization of OMW allows reducing its pollution load and subsequently facilitating its treatment. To conclude, from all these values, it appears that dyeing of acrylic fiber can reduce considerably the environmental impact of this organic waste. So this process of natural dyeing, not only utilizes a priceless and abundant natural dyes, but it can also reduce its polluting effect with generating an effluent with low pollutant load as the reusing the dye bath enables to regenerate an effluent containing the minimum amount of organic substances. So the process is green, economic and eco-friendly as well. 4. Conclusion The project reported in this paper presents a study about the optimization of effectiveness factors on synthetic fiber dyeing by using the full factorial design approach. The R2 value of 98.84% indicates a very good fit of the model with experimental data. Optimized conditions obtained for dyeing are: pH ¼ 3, RdB ¼ 1: 50, temperature ¼ 100 C and time ¼ 105 min. Furthermore, natural dye contained in OMW could provide brownish green hues and considerable color fastness properties for acrylic fibre. Although, this developed process of natural dyeing with the aqueous extract of OMW is very attractive because it is considered as an ecological process. It also contributes in decreasing remarkably the pollution generated by this effluent. After dyeing, the residual dye baths can be obviously used again to color acrylic fibers and the polyphenols load can be consequently decreased which directly contribute to reduce the Chemical Oxygen Demand COD and the biodegradability rate represented by the ratio of (COD/BOD5) of OMW. Table 9 Polyphenols. COD removal and BOD5 removal values obtained after reusing of the dye bath on the dyeing of modified acrylic fibers.
After After After After After After After
one use of dye bath two uses of dye bath three uses of dye bath forth uses of dye bath five uses of dye bath six uses of dye bath seven uses of dye bath
Polyphenols removal (%)
COD removal (%)
BOD5 removal (%)
2.55 5.10 11.98 22.19 26.74 34.29 41.39
11.11 19.44 27.77 36.11 44.44 50 61.11
2.35 1.17 1.17 3.52 1.17 2.35 1.17
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