Adsorption of textile dyes onto iron based waterworks sludge from aqueous solution; isotherm, kinetic and thermodynamic study

Adsorption of textile dyes onto iron based waterworks sludge from aqueous solution; isotherm, kinetic and thermodynamic study

Chemical Engineering Journal 173 (2011) 782–791 Contents lists available at SciVerse ScienceDirect Chemical Engineering Journal journal homepage: ww...

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Chemical Engineering Journal 173 (2011) 782–791

Contents lists available at SciVerse ScienceDirect

Chemical Engineering Journal journal homepage: www.elsevier.com/locate/cej

Adsorption of textile dyes onto iron based waterworks sludge from aqueous solution; isotherm, kinetic and thermodynamic study Birol Kayranli ∗ General Directorate of Iller Bank, International Relations Department, Ankara, Turkey

a r t i c l e

i n f o

Article history: Received 7 June 2011 Received in revised form 16 August 2011 Accepted 17 August 2011 Keywords: Ferric sludge Colour removal Sorption Kinetic Isotherm

a b s t r a c t The use of iron based waterworks sludge as an adsorbent for the removal of cationic (Basic Violet 16), anionic (Direct Blue 71, Acid Blue 40, and Reactive Blue 29) and non-ionic (Disperse Brown 19) was investigated. Initial adsorption studies demonstrated that Direct Blue 71 and Disperse Brown 19 were adsorbed poorly by the sludge. Data obtained from further batch studies were applied to commonly used isotherm models. Among them, Langmuir isotherm model were found to be the best fitted one and based on Langmuir isotherm model adsorption capacities were 625 mg/g for Direct Blue 71, 833.34 mg/g for Acid Blue 40, and 3333.34 mg/g for Basic Violet Blue 16. Kinetic examinations were also performed for all dye tested and it was found that adsorption kinetic was best described by pseudo second-order kinetic model. Gibbs free energy values of the dye-sludge system were calculated and the negative values were found for Direct Blue 71, Acid Blue 40 and Basic Violet 16 indicating the spontaneity of the adsorption process. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Many industries such as textile, pulp mill, paper, leather, cosmetic, pharmaceutical, food, printing and plastic use extensively dye and dyestuffs as colorant. Textile is one of the main industries using dyestuffs, chemicals and water in the process of fibres. Therefore, the industry produces large amount of coloured wastewater [1]. Textile industry wastewater includes dye with various organic compounds, surfactants, pH altering agents, salts and toxicant that can cause damage not only to aquatic life, but also to human life due to their mutagenic, carcinogenic and toxic effect [2,3]. Furthermore, the presence of residual dyestuffs in water streams is highly visible and undesirable even at very low concentration. It is essential to decrease the remaining dye concentration to the limits introduced by the environmental regulations [4]. Synthetic dyes are one of the most common pollutants in textile wastewater. They are categorised as cationic (basic dyes), anionic (direct, acid, and reactive dyes) and non-ionic (disperse dyes) and are produced more than 0.7 million tons yearly worldwide. Furthermore, industries use more than 10,000 different dyestuffs and pigments in their processes. Previous researchers highlighted that approximately 15% of total dyestuffs used in colouring processes can remain as residuals in wastewater [5,6]. Moreover, the residuals tend to be non-biodegradable more than many other

∗ Tel.: +90 312 508 78 55; fax: +90 312 508 78 98. E-mail addresses: [email protected], [email protected] 1385-8947/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.cej.2011.08.051

carbonised materials due to their complex aromatic structures that provide them to resist water, heat, light, oxidizing agents, and also other environmental conditions [7]. Therefore, various treatment methods such as coagulation/flocculation, ion exchange, biological, oxidation, electrochemical, photochemical degradation and adsorption [8–13] have been applied for the removal of dyes from coloured wastewater. Most of these techniques are effective for the removal of dyestuffs from wastewater, however they are quite expensive, require high-technology and generate sludge and other by-products [14,15]. Adsorption process provides an optimum technical alternative to remove the dyestuff, pigments and other chemicals from wastewater due to their simple design, easy operation, low-cost, and high efficiency even in their more concentrated form [16]. Researchers applied, recently, several natural, economic, renewable and locally available adsorbents to remove the various dyestuffs from an aqueous solution. Some of these are as follows: agricultural residues [7], date stones and palm-trees [9], hectorite [12], pomegranate peel [13], fly ash [17], waste materials [18], betonies [19], activated carbon [20], saw dust [21], princess tree leaf [22], rice husk [23], sepiolite and kaolin [24], peat and tree fern [25,26], etc. Waterworks sludge are called as alum, iron and polymeric sludge that depend on which coagulant is applied in water treatment process. The amount of waterworks sludge is 70% of the water treatment plant generated waste. Several million tons of waterworks sludge is spawned annually in Europe and it is predicted that this figure will be increasing by the next decades. Waterworks sludge have been applied for the treatment of some waste

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791

Nomenclature KF 1/n KL KT D ARP BRP G AKC BKC P AFS BFS K k1p k2 kp R2 MPSD ARE NSD P N Ce C0 Ct qe E qm T T R kd V W WWS Q A SS M ˇ ˛

Freundlich constant (mg/g) (L/g)1/n Freundlich exponent Langmuir isotherm constant (L/mg) Tempkin isotherm constant (L/mg) Dubinin–Radushkevich isotherm constant (mol2 kJ−2 ) Redlich–Peterson isotherm constant (L/g) Redlich–Peterson isotherm constant (L/mg)g Redlich–Peterson constant Koble–Corrigan isotherm constant (mg/g) (L/mg)P Koble–Corrigan isotherm constant (L/mg)P Koble–Corrigan constant Fritz–Schlünder isotherm constant Fritz–Schlünder isotherm constant fractional power kinetic model constant (mg/g/h) pseudo-first order kinetic model constant (h−1 ) pseudo-second order kinetic model constant (g mg−1 h−1 ) intraparticle diffusion kinetic model constant (g mg−1 h−0.5 ) correlation coefficient Marquardt’s percent standard deviation average relative error normalized standard deviation number of parameters in isotherm number of experimental measurements equilibrium concentration (mg/L) initial concentration (mg/L) concentration at time t (mg/L) amount of dyestuff adsorbed at equilibrium (mg/g) the mean adsorption energy (kJ/mol) monolayer sorption capacity (mg/g) time (h) temperature (K) ideal gas constant (J K−1 mol−1 ) the distribution constant for Gibbs’ free energy volume of the solution (L) amount of dry waterworks sludge used (g) dry weight of the sludge (kg/day) the flow rate (m3 /s) the amount of used coagulant (mg/L) the amount of suspended solid in raw water (mg/L) the amount of other chemicals such as clay, polymers, etc. (mg/L) Fritz–schlünder and Elovich kinetic model constant Fritz–schlünder and Elovich kinetic model constant

streams such as textile wastewater [8,27], heavy metal [27], phosphate [28]. The aim of this study was to evaluate the adsorption capacity of iron-based waterworks sludge to remove five different dyes from water. Effects of pH, adsorbent dosage and contact time on the adsorption were investigated. The best-fit equilibrium isotherms were determined by applying various adsorption

783

isotherms. Adsorption kinetic models were used to analyse the kinetic and mechanism of dyes adsorption on the sludge. Thermodynamic parameters were also calculated. 2. Materials and methods 2.1. Materials Five different dyestuffs chosen for the present study were as follows: cationic (basic), anionic (direct, acid, and reactive) and non-ionic (disperse). The feature of the dyestuffs is presented in Table 1. The dye stock solution of 25 g/L was prepared by using deionised water. The desired concentration of the test solution was prepared by diluting the stock solution. The pH value of the working solutions was adjusted to the desired values by using 0.1 M HCl or 0.1 M NaOH. All chemicals used in the present work were produced by Merck with Analytical Reagent grade. The iron-based waterworks sludge was taken from a potable water treatment plant where water is provided form river Ceyhan in Adana city. This plant uses FeCl3 as a coagulant (≈800 kg/d) for taking turbidity and colloidal materials away. The sludge was taken from the sedimentation tank as slurry with a total suspended solids concentration of 35.23 g/L, volatile suspended solids concentration of 2.2 g/L and pH of 8.7. The sludge had the iron concentration of 219 mg/L. The sludge is mostly composed of iron, iron hydroxide, clay, and colloidal materials. The amount of waterworks sludge can be calculated according to the equation given below [29]; WWS = 86.4Q (0.44A + SS + M) 2.2. Analytical methods Total suspended solids (TSS), volatile suspended solids, pH, iron concentration were analysed by using American Public Health Association standard methods [30], unless mentioned otherwise. All pH measurements were conducted by using a pH meter (HANNA Instruments, Model HI 8424). Residual dyestuff concentrations were measured with the absorbance of the solution at spectrophotometer (Bausch and Lomb, Spectronic 21) as the absorbance of dyestuff which the wavelength was giving the maximum absorbance. The spectrophotometer is of an absorbance accuracy of ±0.005 at max of the dyestuffs. Isotherm and kinetic parameters were obtained for the non-linear method using the solver add-in with Microsoft Excel. 2.3. Batch sorption tests The batch studies were conducted at 20 ◦ C in conical flasks (1000 mL) with an orbital shaker in a constant room temperature. Stock, 25 g/L, dyes concentration was used and the selected incubation time of the flasks was ranged from 5 to 160 min. Several mL of aqueous solution was taken at different time intervals. The data to derive the isotherms and kinetic models constants were obtained by using a known amount of the sludge and 250 mL aqueous solution containing dyestuff concentrations of 25, 50, 100, 200 400 and 800 mg/L and at fixed pH values. After contacting, the sludge solids

Table 1 Features of dyestuffs. Dyestuff

Type of dyestuff

Colour index number

Company

max (nm)

ABSmax

Reactive Blue 29 Direct Blue 71 Acid Blue 40 Basic Violet 16 Disperse Brown 19

Anionic Anionic Anionic Cationic Non-ionic

– 34140 62125 48013 –

DyStar DyStar DyStar DyStar Basf

593 585 619 348 976

0.21 0.9 0.38 1.25 0.055

784

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791 Disperse Brown 19 Direct Blue 71

100

Acid Blue 40

80

Reactive Blue 29

60 40 20 0

5

6

7

8

8.5

9

pH

were taken away by centrifugation (20 min at 3000 rpm) followed by filtration (Whatman GF/C). The batch studies were carried out in duplicate and then the residual dye concentrations were determined by using spectrophotometer. The results are presented as means. The following equation was used to determine the amount of dyes adsorbed on the sludge; V (Ci − Ce ) W

The dose of the waterworks sludge was quantified by the dry weight of total suspended solids in the slurry at 105 ◦ C. 2.4. Validity of adsorption isotherm Several error analysis method, such as the sum of the error squared, a hybrid error function and Marquardt’s percent standard deviation, the average relative error, the sum of absolute errors, and Chi-square, are applied to confirm the experiment data and the best fitting isotherms equation. In this study, except from the determination coefficient (R2 ), the Marquardt’s percent standard deviation, normalized standard deviation and average relative error was applied to confirm the best fitting. The equations are expressed as follows:

  2 N  exp  1  qei − qcal ei MPSD = 100 N−p

exp

qei

i=1

  2 N  exp  1  qt − qcal  t NSD = 100 N−1



exp

qt

i=1

N

exp

i=1

i



100 

qe − qcal e

ARE =

qexp

N e

Direct Blue 71 Disperse Brown 19 Acid Blue 40 Reactive Blue 29

2

3

4

5

6

7

Amount of Adsorbent (g)

Fig. 1. Effect of pH on removal of dyestuffs.

qe =

Colour Removal (%)

Colour Removal (%)

Basic Violet 16

Basic Violet 16

110 100 90 80 70 60 50 40 30 20 10 0

i

The smaller values of MPSD, NSD and ARE demonstrate more precise estimation of qe value. [31]. 3. Results and discussion 3.1. Effect of pH The pH of the dye solution is an important factor for the sorption capacity of sorbate molecule due to its influence on both characteristic of sorbent surface and ionisation of the dyestuff molecule [32]. The effects of pH on the removal of dyestuffs at 250 mL, 25 mg/L of dyestuff, and 0.47 g/L of adsorbent were investigated and the variety of pH values ranged between 5 and 9 are given in Fig. 1.

Fig. 2. Effect of the sludge dosage on removal of dyestuffs.

Removal rate for Disperse Brown 19 sharply changed when the pH was increased from 7 to 8 and this figure continued for increase in pH (pH = 9), while Direct Blue 71 and Basic Violet 16 were affected slightly with changing pH values and dye removal efficiency fluctuated with increased pH values. Considering Acid Blue 40, its removal rate was mostly affected by changing pH. Increasing value of pH from 5 to 6 was deteriorated for the removal rate of Acid Blue 40 rapidly. However, changing the pH value affected the removal rate of Reactive Blue 29 slightly but colour removal efficiency was changed between 26% and 10%. A deterioration in efficiency was occurred concerning colour removal rate at higher pH (>5.0) values. For Reactive Blue 29, on the other hand, increase in pH values caused only a slight increase in removal efficiency. This increase was negligible (see Fig. 1) and impractical due to add-on more base to adjust pH value to higher pH values. Therefore, value of pH was chosen as 5.0 as an optimum pH value for the further studies and pH value (5.0) was re-adjusted to get maximum colour removal rate during batch sorption tests. 3.2. Effect of adsorbent dosage Adsorption dosage is a critical parameter for the determination of an adsorbent capacity for a known initial concentration of the adsorbate. Effect of adsorbent dose on the removal of the dyes is presented in Fig. 2. The waterworks sludge dosage was varied between 2.0 g and 7.0 g at fixed pH and temperature of 20 ◦ C. The figure demonstrates that the amount of the adsorption increased with increasing adsorbent dosage for the acid blue 40. Considering both Basic Violet 16 and Direct Blue 71, adsorption rates on the sludge was almost 100% of removal at all applied sludge dosages even at the lowest dosage (2.0 g). While reactive blue 29 was affected slightly by increasing dose of the sludge, Disperse Brown 19 adsorption rate on the sludge decreased with increasing adsorbent dosage. This can be explained by the fact that the increase in adsorbent concentration did not provide higher adsorption capacity due to the unsaturation of the adsorption sites through the sorption process, and the other reason could be that particle interaction would result in the diffusion of the dye into the sludge [33]. Thus, while the sludge amount of 2.0 g was chosen in subsequent investigation for Disperse Brown 19, Basic Violet 16, Direct Blue 71 and Reactive Blue 29, the adsorbent dosage was chosen as 7.0 g for Acid Blue 40. 3.3. Effect of contact time The contact time between the dyes and adsorbent is of great importance for the wastewater treatment process. Effect of the contact time on the adsorption rate of the dyestuffs was investigated as given in Fig. 3. As can be seen from Fig. 3, Reactive Blue 29

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791 Basic Violet 16

Specific Adsorption (mg/g)

60

Direct Blue 71

50

Disperse Brown 19 Acid Blue 40

40

Reactive Blue 29

30 20 10 0

5

10

20

40

80

160

Time (min)

785

Blue 71 and Basic Violet 16 were, initially, rapid, then however they gradually declined in time until they reach equilibrium. The increase in removal of the dyestuffs during the initial period may be due to large available amount of surface area of the adsorbent. As the surface area became gradually filled up, the removal rate decreased. It can be observed from Fig. 1 that the sorption capacity of dyestuffs increased in time and at a certain time reached to constant value where no more dyes removed from the solution. For Acid Blue 40, Direct Blue 71 and Basic Violet 16, the most of the dyestuffs were removed within the first 100 min. The contact time of 100 min was, therefore, selected and applied to the subsequent equilibrium experiments. Further equilibrium studies were carried out for dyestuffs; the Acid Blue 40, Direct Blue 71 and Basic Violet 16.

Fig. 3. Effect of contact time on removal of dyestuffs.

3.4. Adsorption isotherms was absorbed by the sludge at specific adsorption values ranging from 8.7 to 13.6 mg/g. Reactive dyes are of anionic characteristic and contains nitrogen-to-nitrogen double bond, azo bonds (N N). Chemical structures of these dyes are much simpler than the other dyestuffs and they link to the fibre and other materials with a covalent bond. The cellulose absorbs the reactive dyes and then reaction occurs with the fibres [34]. In the light of above, it was mainly Reactive Blue 29 that did not form strong covalent bonds on the sludge and these bonds broke in the course of time and fluctuation was occurred for the colour removal rate. Similarly, disperse dyes are a substantially water insoluble non-ionic dyestuff which loosely associate to hydrophobic synthetic fibres. They exist in the dye bath as a suspension or dispersion of microscopic particles, with only a tiny amount in true solution at any time. As can be seen Fig. 3, adsorption–desorption occurred on the sludge in process of time for Disperse Brown 19. Disperse Brown 19 did, firstly, bind to the ferric sludge at a specific adsorption value of 46.1 mg/g and then desorption occurred at a specific adsorption of 34.3 mg/g after 160 min. The removal rate of dyestuffs; Acid Blue 40, Direct

The equilibrium isotherm is crucial to understand the interaction between a sorbate and an adsorbent. The data obtained from batch experiment was applied to commonly used isotherm models; Freundlich, Langmuir, Temkin, Dubinin–Radushkevich, Redlich–Peterson, Koble–Corrigan, and Fritz–Schlünder. The equation and linearised form of these isotherm models are given in Table 2. 3.4.1. Freundlich equation The Freundlich isotherm is an empirical model and describes the heterogeneous adsorption model with different energies of sorption. There is no limitation for the formation of the monolayer with Freundlich isotherm and also the isotherm refers the reversible adsorption [35]. The Freundlich equation foresees the dye concentration bound on the adsorbent surface increases with an increase in dye concentration in the aqueous solution [36]. The value of the 1/n indicates that when the value of 1/n is unity, the adsorption is linear; when the value is lower than unity, that means the

Table 2 Isotherm models and their linearised expression. Isotherms

Equations

Freundlich

qe = KF Ce

Langmuir

Linear expressions

1/n

qe =

log qe = log KF +

qmax KL Ce 1+KL Ce

Ce qe

=

1 qmax KL

Type (II)

1 qe

=

1 qmax KL Ce

qe = qmax − qe Ce

Type (IV)

Dubinin–Radushkevich

qe =

RT AT

ln(KT Ce )

2 qe = qm exp(− ˇε )

ε = RT ln 1 + Redlich–Peterson

Koble–Corrigan

qe =

qe =

ARP Ce

Plot

Parameters

References

log qe vs. log Ce

kF = exp(intercept) n = (slope)−1

[35]

Ce qe

vs. Ce

qe = (slope)−1 KL = slope/intercept

1 qe

vs.

[38]

Type (I)

Type (III)

Tempkin

1 log Ce n

g

p

(AKC Ce )

p (1+BKC Ce )

+



1 qmax



1 qe KL

Ce

= KL qmax − KL qe

qe =

RT AT

ln KT +

RT AT

ln Ce

ln qe = ln qm − ˇε2

1 Ce

1+BRP Ce

Ce qmax

+



ln ARP

Ce qe



− 1 = g ln(Ce ) + ln(BRP )

1 qe

=

p AKC Ce

qe = (intercept) − 1 KL = slope/intercept

qe vs.

qe Ce

qe = intercept

qe vs. ln Ce

KL = −(slope) − 1 qe = (intercept/slope) KL = −slope qe = slope

[39]

ln qe vs. ε2

KT = exp(intercept/slope) qm = exp(intercept)

[40]

qe Ce

vs. qe



ln ARP

Ce qe



−1

D = −slope vs. ln Ce

g = slope

[42]

BRP = intercept/slope ARP a

 1

1 Ce

+

BKC AKC

1 qe

vs.

1 p Ce

AKC = (slope)−1

[43]

BKC = intercept/slope pa Fritz–Schülnder

qe =

AFS Ce˛ ˇ

1+BFS Ce

AFS = (slope)−1 BFS = Intercept/slope ˛ ˇ

a

Optimized using a trial and error method.

[44]

786

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791

Table 3 Isotherm parameters for the adsorption of dyestuff onto waterworks sludge. Isotherms

Freundlich

Parameters Direct Blue 71

Correlation coefficient

Violet Basic 16

Correlation coefficient

Blue Acid 40

Correlation coefficient

KF = 13.907 n = 1.188072

R2 = 0.9943 MPSD = 9.97 NSD = 7.72 ARE = 6.29

KF = 74.299 n = 1.09769

R2 = 0.9155 MPSD = 54.7 NSD = 42.4 ARE = 32.3

KF = 2.646 n = 1.078283

R2 = 0.9994 MPSD = 3.9 NSD = 3.0 ARE = 2.5

qm = 625 KL = 0.021 RL = 0.49

R2 = 0.9778 MPSD = 5.44 NSD = 4.22 ARE = 3.61 R2 = 0.9999 MPSD = 8.70 NSD = 6.74 ARE = 3.29 R2 = 0.3627 MPSD = 58.9 NSD = 45.6 ARE = 37.1 R2 = 0.959 MPSD = 5.30 NSD = 4.10 ARE = 3.27 R2 = 0.8712 MPSD = 57.9 NSD = 44.8 ARE = 34.8 R2 = 0.6974 MPSD = 99.9 NSD = 77.4 ARE = 60.7 R2 = 0.9809 MPSD = 121.9 NSD = 94.4 ARE = 82.9 R2 = 0.9984 MPSD = 35.6 NSD = 27.6 ARE = 14.5 R2 = 0.999 MPSD = 3.78 NSD = 2.93 ARE = 1.73

qm = 3333.3 KL = 0.022 L RL = 0.51

R2 = 0.4824 MPSD = 46.1 NSD = 35.7 ARE = 24.3 R2 = 0.888 MPSD = 107.8 NSD = 83.5 ARE = 55.9 R2 = 0.1286 MPSD = 103.2 NSD = 79.9 ARE = 51.0 R2 = 0.1286 MPSD = 49.7 NSD = 38.5 ARE = 27.4 R2 = 0.9756 MPSD = 115.0 NSD = 89.0 ARE = 51.1 R2 = 0.9016 MPSD = 53.9 NSD = 41.8 ARE = 30.7 R2 = 0.05522 MPSD = 112.5 NSD = 87.1 ARE = 78.8 R2 = 0.9419 MPSD = 104.6 NSD = 81.0 ARE = 55.2 R2 = 0.991 MPSD = 94.91 NSD = 73.52 ARE = 41.07

qm = 833.33 KL = 0.002 RL = 0.76

R2 = 0.9738 MPSD = 2.2 NSD = 1.75 ARE = 1.24 R2 = 0.9996 MPSD = 6.7 NSD = 5.2 ARE = 3.1 R2 = 0.9778 MPSD = 2.26 NSD = 1.7 ARE = 1.4 R2 = 0.9634 MPSD = 2.2 NSD = 1.7 ARE = 1.3 R2 = 0.8481 MPSD = 240.7 NSD = 186.4 ARE = 95.7 R2 = 0.7209 D = 83.8 NSD = 64.9 ARE = 51.5 R2 = 0.9703 MPSD = 141.4 NSD = 109.5 ARE = 99.9 R2 = 0.9999 MPSD = 5.88 NSD = 3.7 ARE = 2.5 R2 = 0.999 MPSD = 35.6 NSD = 1.67 ARE = 1.12

Langmuir Type (I)

Type (II)

Type (III)

Type (IV)

Temkin

qm = 476.1905 KL = 0.029 RL = 0.49 qm = 251.19 KL = 0.094 RL = 0.18 qm = 615.62 KL = 0.0217 RL = 0.49 qm = 74.14 KT = 0.760 L

qm = 119.4 ˇ = 0.0072 E = 8.3

D–R

Redlich–Peterson

Koble–Corrigan

Fritz–Schülnder

g = 2.0039 BRP = 27.36 ARP = 16.39 AKC = 13.40 BKC = −0.008 p = 0.8755 AFS = 16.51 BFS = 0.163 ˛ = 1.0 ˇ = 0.56

qm = −625 KL = −0.077 RL = 0.44 qm = 1110.3 KL = 0.116 RL = 0.17 qm = 5128.7 KL = 0.015 RL = 0.61 qm = 411.41 KT = 0.812 L

qm = 815.9 ˇ = 0.012 E = 6.45 g = 0.3586 BRP = 4.96 ARP = 117.33 AKC = 46.72 BKC = −0.004 p = 1.4771 AFS = 117.33 BFS = 0.05247 ˛ = 1.0 ˇ = 1.0

adsorption process is chemical; when the value is bigger than unity, that refers the process is favourable physical; when the value is close to 0, that implies the adsorbent is of heterogeneous surface. Furthermore, the value of n ranges between 1 and 10, the adsorption process is thought to be acceptable. When the value of n is bigger than 1, chemical adsorption process occurs naturally. The physical adsorption takes place when the values of n is lower than 1 [37]. The value of n for Direct Blue 71, Basic Violet 16 and Acid Blue 40 were found bigger than unity and these were as follows: 1.19, 1.09, and 1.07, respectively (see Table 3). These values indicated that the conditions of adsorption process were favourable and chemical adsorption took place for the removal of dyestuffs. 3.4.2. Langmuir equation Langmuir isotherm model assumes that removal of the sorbate occurs on a specific homogenous surface by monolayer adsorption. Furthermore, when dyestuffs molecules bind a site of surface, others molecules cannot bind at that site. There is no any interaction between the sorbent ions and also all the coverage of sorbate on the adsorbent surface is of equal energy of sorption [38]. This equation can be linearised into four different types. The dimensionless separator factor, RL , is the essential characteristic of this model and defined as; RL =

1 1 + KL Co

qm = 526.31 KL = 0.004 RL = 0.66 qm = 784.06 KL = 0.003 RL = 0.75 qm = 811.31 KL = 0.0029 RL = 0.76 qm = 49.43 KT = 0.220 L

qm = 49.0 ˇ = 0.041 E = 3.47 g = 2.109 BRP = 750.84 ARP = 2.56 AKC = 2.55362 BKC = 0.000 p = 0.9511 AFS = 2.566 BFS = 0.033 ˛ = 1.0 ˇ = 0.54

The value of the RL is a reliable indicator for the type of adsorption isotherm to be irreversible when RL is equal 0; it is favourable when RL is between 0 and 1; linear when RL is equal 1 and unfavourable when RL is bigger than 1 [20]. The values of RL ranges between 0 and 1 for the dyestuffs for all types of Langmuir isotherms apart from type II for Basic Violet 16 (see Table 2). The best fit was obtained by Langmuir type II as compared with the other Langmuir models due to the highest constant of determination that it had. It also demonstrated that high and favourable sorption occurred on the waterworks sludge for the dyestuff except for the Basic Violet 16. The negative value of RL showed that the Langmuir type II isotherm was not suitable for the expression of removal rate of the Basic Violet 16.

3.4.3. Tempkin equation Tempkin equation is based on the effect of some direct adsorbate–adsorbate interactive relation on sorption isotherm and due to these interactions the decrease in the heat of the adsorbent of all the molecules in the adsorbed surface is linear rather than algorithmic [39].

3.4.4. Dubinin–Radushkevich The Dubinin–Radushkevich (R–D) isotherm model does not, unlike the Langmuir isotherm, assume the homogeneous surface

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791

and constant sorption potential [40]. The formula for mean sorption energy is as follows: 1

The value of E gives reliable information for predicting the mean adsorption of energy. The chemical adsorption takes place when the value of E ranges between 1 and 8 kJ/mol, and the physical adsorption occurs at a value of E bigger than 8 kJ/mol [41]. The mean adsorption energy values of Direct Blue 71, Basic Violet 16 and Acid Blue 40 were 8.34, 6.54 and 3.47 kJ/mol respectively. E value of 8.34 kJ/mol showed that the adsorption of Direct Blue 71 on iron based waterworks sludge was physical adsorption, whereas E values of 6.54 and 3.47 kJ/mol for Basic Violet 16 and Acid Blue 40, respectively indicated that the adsorption of these dyes on the waterworks sludge was mostly chemisorptions. 3.4.5. Redlich–Peterson Redlich and Peterson is of an empirical equation integrate into three-parameter isotherm that compose of both features of Freundlic and Langmuir isotherms and can be applied on either homogeneous or heterogeneous adsorbent surface [42]. The constant value of model can be calculated with trial and error analysis since the models is a three-parameter sorption isotherm. The value of g, the constant, ranges between 0 and 1. When the value of g is equal to 1, isotherm equation becomes the Langmuir form [33]; when the value of g is equal to zero, the isotherm becomes the Henry’s law. The g values were as follows: 2.0039 for Direct Blue 71, 0.3586 for Basic Violet 16 and 2.109 for Acid Blue 40. It can be seen that the values of g is zero for Basic Violet 16 and this indicates that adsorption process is close to the Henry’s law. However, the g values demonstrated that Redlich–Peterson model did not fit for the expression of adsorption of Direct Blue 71 and Acid Blue 40 on the sludge due to g values were higher than unity. 3.4.6. Koble–Corrigan The Koble–Corrigan model is another three-parameter isotherm and consists of the combination of both Freundlich and Langmuir isotherm models. This model is generally applied for heterogeneous sorbent surface [43]. 3.4.7. Fritz–Schlünder The Fritz–Schlünder isotherm [44], is of four-parameter and consists of Langmuir and Freundlich isotherms. The isotherm equation is as follows: qe =

AFS Ce˛

400

Calculated q e (mg/g)



300

200

100

0 0

50

100

150

200

250

300

350

Experiment q e (mg/g) Fig. 4. The comparison of the predicted amount of adsorption at equilibrium by different isotherm models and the experimental values for Direct Blue 71.

Freundlich

3500

Langmuir Temkin

3000

Calculated qe (mg/g)



Freundlich Langmiur Temkin D-R Redlich-Peterson Koble-Corrigan experiment Langmuir (II) Langmuir (III) Langmuir (IV) Fritz-Schlunder Linear (experiment)

500

D-R

2500

Redlich-Peterson Koble-Corrigan

2000

Experiment data Langmuir (II)

1500

Langmuir (III) Langmuir (IV)

1000

Fritz-Schlunder Linear (Experiment data)

500 0 0

250

500

750

1000

1250

1500

Experiment q e (mg/g) Fig. 5. The comparison of the predicted amount of adsorption at equilibrium by different isotherm models and the experimental values for Basic Violet 16.

625 mg/g for Direct Blue 71, 833.34 mg/g for Acid Blue 40, and 3333.34 mg/g for Basic Violet 16. The comparison of the maximum monolayer adsorption capacities of iron based waterworks sludge with previous studies for some anionic and cationic dyes are given in Table 4. Hong et al. [19] investigated the equilibrium adsorption of Methylene Blue dye onto bentonite and the adsorption model was well described with Redlich–Peterson isotherm. Similarly, the adsorption of cationic dyestuff including Basic Yellow 28, Methylene Blue and Malachite

ˇ

1 + BFS Ce

where ˛ and ˇ are the equation exponent (˛ and ˇ ≤ 1). The comparison of the predicted amount of adsorption at equilibrium by different isotherm models and the experimental values for Direct Blue 71 is presented in Fig. 4. As shown in Fig. 4, the data calculated values by Langmuir type I, III and IV isotherms is slightly different for the experiment data. These isotherm equations demonstrate the best isotherms for predicting the amount of Direct Blue 71 adsorbed on the iron based waterworks sludge at equilibrium. Considering the Basic Violet 16 and Acid Blue 40, the isotherm data follows the Langmuir type I isotherm due to very low differences between the experiment data and isotherm data (see Figs. 5 and 6). Furthermore, error analysis method including linear regression correlation coefficient, R2 , MPSD, NSD and ARE values, shows that Langmuir isotherms provides a better model for the removal of dyestuff from aqueous solution by the waterworks sludge (see Table 3). Waterworks sludge adsorption capacities are

250

Calculated qe (mg/g)

E=

787

Freundlich langmuir Temkin D-R Redlich-Peterson Koble-Corrigan Experiment Langmuir (II) Langmuir (III) Langmuir (IV) Fritz Schlunder Linear (Experiment)

200

150

100

50

0 0

20

40

60

80

100

120

140

160

180

200

Experiment q e (mg/g) Fig. 6. The comparison of the predicted amount of adsorption at equilibrium by different Isotherm models and the experimental values for Acid Blue 40.

788

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791

Table 4 Comparison of the maximum monolayer adsorption capacities of some cationic and anionic dyes onto various adsorbents.

Cationic dyes Palm-trees waste Princess tree leaf Powder kaolin Polyacrylamide Activated carbon Hevea brasiliensis seed coat Activated Carbon Iron based waterworks sludge Anionic dyes Kaolin Pine Cone Activated red mud Activated carbon, bentonite, sepiolite Egyptian bagasse pith Iron based waterworks sludge Iron based waterworks sludge

Adsorbate

Qe (mg/g)

References

Methylene Blue Basic Red 46 Basic Yellow 28, Methylene Blue, Malachite Green Methylene Violet Basic Blue 9 Basic Blue 3 Methylene Blue, Basic Red, Basic yellow Basic Violet 16

39.47 43.47 16–52 1136 874.68 227.27 270–1001 3333.34

[9] [22] [45] [46] [47] [48] [49] Present study

Congo Red Acid Black 26, Acid Green 25 Congo Red Acid Yellow 194, Acid Blue 349, Acid Red 423 Acid Blue 25 Acid Blue 40 Direct Blue 71

5.44 25–67.11 7.08 25–100 14.40 833.34 625

[20] [46] [50] [51] [52] Present study Present study

Green onto powder kaolin was investigated and the isotherm data of dyes were correlated by the Langmuir adsorption equation. They reported that adsorption capacity of kaolin changed between 16 and 52 mg/g [45]. Furthermore, Rahchamani et al. [46] used polyacrylamide as an adsorbent for the removal of methyl violet. They informed that the adsorbent equilibrium well fitted the Langmuir isotherm and maximum adsorbent capacity was 1136 mg/g. Pine Cone was applied for the adsorption of anionic Acid Black 26, Acid Green 25 and Acid Blue 7. They reported that the isotherm data of Acid Black 26, Acid Green 25 followed Langmuir isotherm, while the isotherm data of Acid Blue 7 followed Freundlich isotherm. The maximum adsorption capacities of Acid Black 26, Acid Green 25 were 67.11 and 43.29 mg/g, respectively [46]. Furthermore, some researchers used natural organic materials including date stones palm-trees waste [9] and the princess tree leaf [22] for the removal of Methylene Blue, Basic Red 46 and Reactive red 24, respectively. The equilibrium data for all adsorbent was well described by Langmuir isotherms and adsorption capacities were as follows: the capacity of 200 mg/g for Reactive red 24, the capacity of 43.10 mg/g for Basic Red 46 and capacity of 43.47 and 39.47 mg/g for Methylene Blue for data stones and palm-trees, respectively. Previous researchers, Vimonses et al. [20] and Tor and Cengeloglu [50], investigated on the removal of Congo Red by using kaolin and activated red mud, respectively. They informed that the maximum adsorption capacity of Congo Red is 5.44 mg/g for kaolin and 7.08 mg/g for activated red mud. Compared with some date in literature (grouped in Table 4), the sludge used in this work is of relatively higher adsorption capacity and ranges within the most efficient and best adsorbent for both cationic and anionic dyes. It can be explained the fact that the sludge consists of clay and Fe(OH)3 provides large number of surface area available of the adsorbent. Clay materials are of high chemical stabilities, high specific surface area and adsorption capacity, several of structural and surface properties [20]. Furthermore, it is well known that metal ions are of an adsorption capacity of dyestuffs. 3.5. Adsorption kinetic The adsorption of compounds on natural materials from aqueous solution is a natural fact with a complex process due to the heterogeneity of adsorbent surface. The residence time of adsorbate is a significant factor for the adsorption rate due to the effect of adsorption rate on the solid–liquid interface [24]. Kinetic analysis also helps to the controlling mechanism of adsorption process such as chemical reaction, mass transfer and diffusion control. Therefore, most commonly used kinetic models including the elovich, pseudofirst order, pseudo-second order type I, II, III, IV, and intraparticle

diffusion applied for the batch experiment. The equation and linearised form of these isotherm models are presented in Table 5. Elovich kinetic model assumes that applicable for the adsorbent is of heterogeneous active sites, and different activation energy occurs during the sorption of organic compounds [53]. The pseudosecond order kinetic model also assumes that chemisorptions of the adsorbate takes place on the adsorbent and a linear form of this equation has been expressed by Ho and MaKay’s, [25,26]. Intraparticle diffusion is known as having limiting steps in the adsorption process due to the liquid phase mass transport and since the intraparticle mass transport rate controls the dyestuff sorption on the adsorbent [13]. The value of C gives the idea about the thickness of boundary. When a plot of qt versus t produces a linear and line that is passing through the origin, intraparticle diffusion is considered to be the rate controlling step for the sorption process [12]. The adsorption of dye from aqueous solution on adsorbent consists of several stages such as transport in the solution, external diffusion or boundary layer diffusion, internal diffusion or intraparticle diffusion, adsorption or desorption on the surface of the interior sites [54]. When the experiment was conducted at rapid shaking condition, the first stage is of no limited effect on the adsorption. Furthermore, previous researchers informed that the last stage is a fast process for the organic molecule adsorption on adsorbents which is porous [55]. Fig. 7 demonstrates that adsorption plots of applied dyes onto iron based waterworks sludge are not linear through whole experiment time and can be categorised in a few linear regions. It demonstrates a two/tri-linearity,

60 50

qt (mg g-1 )

Adsorbent

40

Direct Blue 71 Basic Violet 16 Acid Bleu 40

30 20 10 0 0

1

2

3

4

5

6

7

8

t 1/2 Fig. 7. Intra-particle diffusion plots of the sludge for Direct Blue 7, Basic Violet 16 and Acid Blue 40.

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791

789

Table 5 Features of kinetic models. Kinetic model

Equations

Linear expressions

Elovich

dqt dt dqt dt dqt qt

= ˛ exp(−ˇqt )

qt =

= k1 (qe − qt )

Pseudo-first order Pseudo-second order

Intraparticle diffusion

Plot

Parameters

References

qt vs. ln t

ˇ = slope, ˛ = (slope)−1 exp(intercept/slope)

[12]

ln(qe − qt ) = ln qe − k1 t

ln(qt − qe ) vs. t

qe = exp(intercept), k1p = −(slope)

[1]

Type (I)

1 qt

=

t qt

vs. t

qe = slope−1 , k2 = (slope2 )/intercept

Type (II)

1 qt

=

1 qt

vs.

1 t

qe = intercept−1 , k2 = (intercept2 )/slope

qt vs.

qt t

qe = intercept, k2 = −1/(intercept × slope)

= k2 (qe − qt )

1 ln(˛ˇ) ˇ

+

1 ln t ˇ

2

[25,26] 1 k2 q2 e

+

1 k2 q2 e

Type (III)

qt = qe −

Type (IV) √ qt = kp t + C

t qt



t qe

 1

qt t

1 k2 qe

+

1 qe

t

qt t

= k2 q2e − k2 qe qt √ qt = kp t + C

qt

vs. qt √ vs. t

proposing two/three successive stages of the adsorption happening over the full time range. First linearity is governed by external diffusion or boundary layer diffusion where adsorbate diffuses through the liquid film surrounding the solid particle. The second part may be contributed to the intra-particle states that are the rate control of the adsorption mechanism. The last linearity demonstrates the final equilibrium stage where the intra-particle diffusion begins to slow up, and third one is also related to the adsorption and desorption. The figure demonstrated that intra-particle diffusion plot did not pass through the origin for this study which shows that

qe = −intercept/slope, k2 = (slope2 )/intercept kp = slope

[13]

intra-particle diffusion is involved in the adsorption process however it is not the only limiting step for the sorption of dyestuff onto iron based waterworks sludge. The other mechanism such as ionexchange or complexation may also control the adsorption rate [20]. The kinetic model parameters are presented in Table 6. It was found that pseudo second-order kinetic model describes the waterworks sludge adsorption well for all used dyestuffs. The finding suggested that the removal of dyestuffs by the sludge is complex and includes more than one mechanism. Previous adsorption

Table 6 Kinetic parameters for the adsorption of dyestuff onto waterworks sludge. Kinetic model

Parameters Direct Blue 71

Violet Basic 16

Blue Acid 40

Elovich

ˇ ˛ R2 MPSD NSD ARE qe k1p R2 MPSD NSD ARE

12.701 0.049965 0.6928 0.70 0.54 0.40 3.1434 0.0213 0.1521 137.03 106.14 96.87

␤12.701 0.049965 0.8366 27.89 21.60 17.73 41.359 0.0603 0.6298 57.44 44.49 28.44

4.5614 0.055467 0.8796 38.15 29.55 22.74 13.785 −0.0358 0.8176 7236.8 5605.63 2286.84

qme k2p R2 MPSD NSD ARE qme k2p R2 MPSD NSD ARE qme k2p R2 MPSD NSD ARE qme k2p R2 MPSD NSD ARE kp R2 MPSD NSD ARE

50.5051 0.2800 1.0 0.86 0.67 0.44 0.575 −0.285 0.4447 8889.42 109.26 99.49 365.570 0.00 0.9524 113.30 504.7 439.63 383.685 0.00 0.9524 114.21 99.97 96.63 0.0711 0.1634 104.73 81.12 72.99

35.0877 −0.0078 0.9999 79.99 308.92 168.1 0.575 −0.338 0.4397 8657.02 135.63 116.35 3.251 6.408 0.6062 1360.3 125.95 106.7 4.396 2.870 0.6062 1167.8 93.38 84.95 3.8712 0.3602 54.31 42.07 35.3

22.7273 0.0006 0.6915 54.46 46.02 30.33 0.175 −5.302 0.0213 8343.56 102.67 93.26 0.253 2193.27 0.011 34.32 101.30 91.88 1.703 3.595 0.011 772.7 90.50 80.40 1.3015 0.3318 152.25 117.93 64.93

Pseudo-first order

Pseudo-second order Type (I)

Type (II)

Type (III)

Type (IV)

Intraparticle diffusion

790

B. Kayranli / Chemical Engineering Journal 173 (2011) 782–791

studies related to cationic and anionic dyes with various both inorganic; bentonite [19] and sepiolite [24] and organic; date stones and palm-tress waste [9], princess tree leaf [22], pine cone [56] adsorbents indicated that dye removal followed pseudo second-order kinetic model. The results of the present studies related to cationic and anionic dyestuffs are in agreement with previous research.

due to negative values of Gibbs free energy. Gibbs free energy values for Direct Blue 71, Acid Blue 40 and Basic Violet 16 are −14.7912, −10.79 and −8.8264 kJ/mol, respectively. Further study is required on elemental analysis of the waterworks sludge and on the effect of temperature to better understand the adsorption mechanism of dyestuffs.

3.6. Thermodynamic study The Gibbs’ free energy helps to determine the system spontaneity. Gibbs’ free energy (G◦ ) can be obtained from the followings equations [36]; G◦ = −RT ln kd Kd =

qe Ce

Values for the changing in Gibbs’ free energy are −14.7912, −10.79 and −8.8264 kJ/mol for the sorption of the waterworks sludge of Blue Acid 40, Direct Blue 71, and Violet Basic 16 respectively. This negative value of the Gibbs’ free energy indicates the spontaneity of sorption of dyestuff onto iron based waterworks sludge. These results also suggest that the physical sorption is the leading mechanism for the removal of both anionic and cationic dyestuffs. The results are in agreement with previous studies such as Hong et al. [19] and Bagha et al. [45] reported that cationic dyestuffs adsorption onto bentonite and kaolin was a spontaneous adsorption. Considering the natural organic adsorbent, wheat residue [32], pine cone [56] was used as an adsorbent to remove anionic dyestuffs form aqueous solution and both reported that the sorption process is spontaneous. 3.7. Cost appraisal Chemicals and dealing, disposal of the sludge is an important factor for declining the cost of the treatment process. The use of the waterworks sludge and natural materials for removal of dyestuffs from wastewater has been gained the popularity due to the reduction of the sludge disposal cost at water treatment plant and saving the coagulant in wastewater treatment plant. Most of the organized industries produce their potable water in the treatment plant resulting in generating the waterworks sludge. It is compulsory to dispose the waterworks sludge and therefore the cost of disposal and handling the sludge takes place a significant amount in the operation cost of water treatment plant. Furthermore, the amount of the sludge is likely to increase with increasing demand of potable water and the environmental regulations [57] are quite stringent. Therefore, the use of the waterworks sludge as adsorbent for the removal of coloured wastewater provides the technical advantage in terms of reducing the cost and the concept of sustainable environmental management. 4. Conclusion The results of the study show that iron based waterworks sludge can be used as a practical, effective and low-cost alternative sorbent for the removal of coloured textile wastewater. Initial adsorption studies demonstrated that Disperse Brown 19 and Reactive Blue 29 were removed poorly, on the other hand Direct Blue 71, Acid Blue 40 and Basic Violet 16 bound to the sludge well. Experiment data fitted well to Langmuir isotherm model. The maximum adsorption capacities based on this model were as follows: 625 mg/g for Direct Blue 71, 833.34 mg/g for Acid Blue 40, and 3333.34 mg/g for Basic Violet Blue 16. The sorption of dyestuffs onto iron based waterworks sludge was described well by pseudo second-order kinetic model. The adsorption follows a spontaneous and physical sorption

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