modeling the effects of adsorbent dose and particle...

*Corresponding author.

Modeling the effects of adsorbent dose and particle size on theadsorption of reactive textile dyes by fly ash

S. Karaa, C. Aydinera, E. Demirbasb*, M. Kobyaa, N. Dizgea

aGebze Institute of Technology, Department of Environmental Engineering, 41400, Gebze, TurkeyTel. +90 (262) 6053108; Fax +90 (262) 6053101; email: [email protected]

bGebze Institute of Technology, Department of Chemistry, 41400, Gebze, Turkey

Received 8 July 2006; accepted 18 September 2006

Abstract

The adsorption of three reactive dyes, Remazol Red, Remazol Blue and Rifacion Yellow, from aqueous solutionsusing fly ash as an adsorbent was studied in an agitated batch system to investigate the influence of two parametersviz., adsorbent dosage and particle size on the removal efficiency of the reactive dyes. Firstly, the adsorbent wascharacterized with using several methods such as SEM, XRD and FTIR. The FTIR suggested that the dye on fly ashis probably indicating fly ash/dye complexation. XRD pattern of fly ash consisted of mainly quartz, mullite withsome magnetite and calcite. Surface morphology of fly ash and dye loaded fly ash were obtained with SEM.Secondly, the percentage of dye removal at equilibrium, p, was determined with respect to these two parameterswith a constant initial dye concentration of 100 mg/L, agitation speed of 250 rpm, pH 6 and temperature of 22ºC fora period of 48 h. The experimental data were treated with two simple empirical models used for predicting thepercentage of the dyes adsorbed on the fly ash. Both models showed good correlation coefficients but the bestmodel which determined the p values can be selected on the basis of the standard deviation of the calculated andexperimental values.

Keywords: Reactive dyes; Fly ash; Adsorbent particle size; Adsorbent dosage

1. Introduction

The vinyl sulfone and chlorotriazine dyes arethe most reactive and versatile of the fiber reac-tive dyes, which mean that the dye molecules ac-tually react with fabric molecules. The dyes can

be used on cotton, silk, wool, rayon, paper andwood but not on synthetic fibers [1,2]. These dyesdischarge into water causes environmental pollu-tion. Residues dyes are the major contributors tocolor in wastewaters generated from textile anddye manufacturing industries. Unless properlytreated, these dyes can significantly affect thenature of the water and inhabits sunlight penetra-

Desalination 212 (2007) 282–293

doi:10.1016/j.desal.2006.09.0220011-9164/07/$– See front matter © 2007 Published by Elsevier B.V.

S. Kara et al. / Desalination 212 (2007) 282–293 283

tion into the stream and reduces photosyntheticaction and may also be toxic to certain forms ofaquatic life due to the presence of substituentmetals and chlorine. Dyes can also cause allergicdermatitis, skin irritation, cancer and mutation [3].

The conventional methods for treating dye-containing wastewaters are chemical coagulation,chemical oxidation, photochemical degradation,membrane filtration, including aerobic andanaerobic biological degradation [1,2]. The chemi-cal coagulation process effectively decolorizesinsoluble dyes, but it fails to work well withsoluble dyes. Chemical oxidation is effective, butthe oxidant requirements are very high and thusexpensive. Photochemical degradation in aque-ous solution is likely to progress slowly, as syn-thetic dyes are, in principle designed to exhibithigh stability to light. Although biological treat-ment processes remove BOD, COD, and sus-pended solids to some extent, they are largelyineffective in removing color from wastewater,as most dyes are toxic to the organisms used insuch processes. However, all of these methodssuffer from one or other limitations, and none ofthem were successful in completely removing thecolor from wastewater. These technologies do notalso show significant effectiveness or economicadvantage. Low-cost treatment methods have,therefore, been investigated for a long time.

Adsorption has been used extensively in in-dustrial processes for separation and purification.In wastewater treatment, commercially activatedcarbon has long been used as a standard adsor-bent for color removal. In spite of its widespreaduse in various cleaning procedures, activated car-bon remains expensive; therefore, the develop-ment of low-cost alternative adsorbents has beenthe focus of recent research [4,5]. Contributionsin this regard have been made by many research-ers who have utilized a number of substances suchas agricultural wastes: coir pith, banana pith, sugarcane dust, sawdust, activated carbon fibers andrice hulls [6–10], industrial solid wastes: fly ash,red mud and shale oil ash [11–18], and so forth.

During coal-fired electric power generation,two main types of coal combustion by-productsare obtained, fly ash and bottom ash. The currentannual worldwide production of coal ash is esti-mated about 700 million tons of which at least70% is fly ash [19]. Although, significant quan-tities are being used in a range of applications andparticularly as a substitute for cement in concrete,large amounts are not used and this requires dis-posal. Making a more productive use of fly ashwould have considerable environmental benefits,reducing air and water pollution. About 55 mil-lion tons of coal and lignite is combusted annu-ally in Turkey resulting in more than 15 milliontons of fly ash.

In the present study, the adsorbent was char-acterized with a number of methods includesSEM, XRD and FTIR. Effects of adsorbent dos-age and particle size on the adsorption of threereactive azo dyes [Remazol Blue (RB), RemazolRed RB 133 (RR) and Rifacion Yellow HED(RY)] from aqueous solutions using the fly ashunder equilibrium conditions were investigated.The data were treated with two empirical modelsto predict the percentage of the dyes adsorbed onthe fly ash.

2. Materials and methods

2.1. Materials

The fly ash was obtained from Afsin-ElbistanThermal Power Station in Turkey. The Afsin-Elbistan power plant consumes 18×106 metric tonsof coal per year and generates about 3.24×106

metric tons of fly ashes returning to the dumpingarea of the mine as combustion waste. The par-ticle size distribution of the fly ash was foundbetween 3.6 and 181 µm. Higher percentage ofthe fly ash consists of particles with diameter 40–125 µm (determined by the method of laser beamdispersion using the Malvern 2000 particle sizeanalyzer). The particle size of the adsorbent wasdetermined by sieve analysis. The fly ash was

284 S. Kara et al. / Desalination 212 (2007) 282–293

sieved by using a sieve set and then was collectedin the range of 40–50, 50–60, 60–80, 80–100, and100–125 µm, respectively. The surface area of thefly ash was measured by BET (Brunauer–Emmett–Teller nitrogen adsorption technique).The bulk density of the adsorbent was determinedwith a densitometer. The fly ash was used as re-ceived without any pretreatment in the adsorp-tion experiments.

Three reactive dyes obtained from Dystar andItochu were used in the adsorption study. Theirchemical structures and characterizations are pre-sented in Fig. 1 and Table 1, respectively.

O

O

NH2

NH

SO2CH2CH2OSO3Na

SO3Na

NHOH

SO3NaSO3Na

N=NS

O

NN

N

NH

SO3Na

Cl

ONaSO3O

SO3H

SO3H

N=N

SO3H

NH

NN

NCl

NH

NH

SO3HSO3HSO3H

SO3HN=N

SO3H

NH

NN

N

Cl

SO3H

SO3H

Fig. 1. Chemical structure of the reactive dyes (a) Remazol Brilliant Blue (RB), (b) Remazol Red 133 (RR) and (c)Rifacion Yellow HED (RY).

(a)

(b)

(c)

2.2. Methods

Batch adsorption tests were conducted by vary-ing adsorbent particle sizes and adsorbent doseson a rotary shaker using 100 ml screw-cap coni-cal flasks containing 50 ml of dye solution hav-ing a concentration of 100 mg/L, pH 6, agitationspeed of 250 rpm and temperature of 22°C for aperiod of 48 h. After this period, the final equilib-rium concentrations of the dye in solution weremeasured spectrophotometrically at a wavelengthof 518, 585 and 411 nm for RR, RB and RY, re-spectively using a Perkin-Elmer UV-visible spec-


trophotometer model 550S. The percentage re-moval of dye, p, was calculated using the follow-ing equation

( )0

0

100eC Cp

C−

= × (1)

where C0 and Ce are the initial and equilibriumdye concentration (mg/L), respectively. All ex-periments were replicated and the average resultswere used in data analysis.

3. Results and discussion

3.1. Characterization of the adsorbent material

Since adsorption is a surface phenomenon, therate and extent of adsorption are functions of thespecific surface area of the adsorbent used, i.e.,the portion of the total surface area that is avail-able for adsorption. In fact, the amount of adsorp-tion per unit weight of fly ash depends on its com-position, texture and porosity. The fly ash samplewas characterized through physico-chemical

Table 1Characterizations of the reactive dyes

*MCT — monochlortriazine; ACT — bisaminochlorotriazine, VS — vinylsulfone

Parameters Remazol Brillant Blue (RB)

Remazol Red 133 (RR)

Rifacion Yellow HED (RY)

Color index name Reactive Blue 19 Reactive Red 198 Reactive Yellow 84 Chromophore group Anthraquinone Azo Disazo Reactive anchor systems* VS MCT+VS ACT Molar mass, g/mol 506.5 984.2 1922 Max absorbance, λm, nm 585 518 411 Purity, % ∼50 ∼63 ∼80 Water solubility at 293 K, g/l 100 70 70 Acute oral toxicity LD50, mg/ kg 2000 2000 — Fish toxicity LC0, mg/L — >500 — pH value, 10 g/l water 5–5.5 7 6.5 COD, mg/g — 540 160 BOI5, mg/g — <10 — DOC, mg/g — 120 — Company Dystar Dystar Itochu

analysis together with SEM, XRD pattern andFTIR. Chemical composition of the fly ash bychemical analysis was given as SiO2, 15.14; Fe2O3,3.30; Al2O3, 7.54; CaO, 23.66; MgO, 4.5; SO3,13.22; K2O, 0.28; Na2O, 0.57 and TiO2, 1.03 andlost on ignition, 2.31 wt % [11]. The specific sur-face area and bulk density of the fly ash were de-termined as 3.62 m2/g and 1.05 g/cm3. The sur-face of adsorbent was characterized by scanningelectron microscopy (SEM, Philips XL30S-FEG)before and after the adsorption experiments us-ing RR dye (Fig. 2). The SEM image clearlyshows that the fly ash particles are mainly com-posed of irregular and porous particles. The poresin Fig. 2b are more densely packed with dyes thanthat of Fig. 2a which is no-adsorption of dye onthe adsorbent.

3.2. XRD measurements

X-ray diffraction (XRD) has long been usedas a definitive technique for identifying mineralsand other crystalline phases in a wide range ofmaterials. The XRD for the adsorbents were mea-


sured with an automated Rigaku X-ray diffracto-meter D-Max Rint 2200 Series instrument usingCu K radiation at 40 kV and 40 mA over the range(2 θ of 5–70º). The XRD pattern of fly ash isshown in Fig. 3. The XRD pattern indicates thatthe major phases for the sample are quartz, mul-lite with some magnetite and calcite. Accordingto ASTM standards, the fly ash is divided intotwo types. If the sum of SiO2, Al2O3, and Fe2O3 is70%, it is named type F. If the sum is upto 50%, itis named type C. The fly ash is a C class fly ashwith 23.6% lime CaO as a major constituent.

3.3. FTIR measurements

FTIR technique was used to examine the sur-face groups of the adsorbent and to identify thosegroups responsible for dye adsorption. Adsorp-tion in the IR region takes place because of rota-tional and vibrational movements of the molecu-lar groups and chemical band of a molecule. TheIR spectra of the samples were recorded on a BioRad FTS 175 C spectrophotometer using a pellet(pressed-disk) technique. For this, the adsorbentwas intimately mixed with approximately 100 mgof dry, powdered KBr. The mixture was pressed

(a) Fly ash before adsorption process (b) Fly ash particle with dye adsorbed

Fig. 2. Typical SEM micrograph of fly ash (magnification: 2000×) (a) before dye adsorption (b) with dye adsorbed. Thearrows show no-adsorption of dye on the adsorbent.

under a pressure of 10,000–15,000 psi into a trans-parent disk. The infrared spectra of fly ash anddye-loaded fly ash samples before and after theadsorption process were recorded in the range4000–400 cm–1 (Fig. 4). The adsorption bands inthe region 1637–595 cm–1 were assigned to –SO3and –N=N– groups on the dyes. The strong bandsat 993.3, 1120.9 and 1149.4 cm–1 regions are at-tributed to S=O stretching and the bands at 1626–1637.8 cm–1 to –N=N– stretching. Strong Si–Obands at 875–1121 cm–1 are shifted to higher fre-quencies as a function of chemical interaction ofthe dyes with fly ash. Adsorption band at3645 cm–1 is assigned to free hydroxyl. All thesefindings suggest that the dye on fly ash is held bychemical activation or chemisorption, probablyindicating fly ash/dye complexation [11,20].

3.4. Modeling of the percentage removal

The adsorption experiments were carried outusing various particle sizes (40–125 µm) and ad-sorbent dosages (500–15000 mg/L) of the fly ashat pH 6, initial concentration of 100 mg/L, tem-perature of 22ºC and agitation speed of 250 rpm.A plot of p for any adsorbent dosage and geomet-


Fig. 3. XRD pattern of fly ash.

Fig. 4. FTIR spectra of fly ash and dye-loaded fly ash.


ric mean size of the adsorbent particle as a func-tion of adsorbent dosage for various adsorbentsizes is shown in Figs. 5a and 5b. The figure showsthat the percent dye removal at equilibrium in-creased with increasing adsorbent dosage whiledecreasing with the particle size. The relativelyhigher adsorption with smaller adsorbent particlemay be attributed to the fact that smaller particlesyield large surface areas and the availability ofmore adsorption sites.

The minimum amount of fly ash correspond-ing to the maximum adsorption is declared as theoptimum dosage. The optimum dose observed inthe present study is 10 g/L. The removal efficien-

0.0000 0.0006 0.0012 0.0018 0.00240.00

0.04

0.08

0.12

0.16

dP (µm)

1/WS

p1/

p

(RY)(RB)(RR)

(b)

(a)

0.0000 0.0006 0.0012 0.0018 0.0024

0.009

0.012

0.015

0.018

0.021 d

p (µm) 45 56.5 71.5 90112.5

0.0000 0.0006 0.0012 0.0018 0.00240.00

0.06

0.12

0.18

0.24

0.30 d

p (µm) 45 56.5 71.5 90112.5

dp (µm) 45 56.5 71.5 90112.5

30 60 90 120 150 1800

20

40

60

80

100

WS (mg/L)

500 1000 3000 60001000015000

30 60 90 120 150 18050

60

70

80

90

100

WS (mg/L)

500 1000 3000 60001000015000

30 60 90 120 150 1800

20

40

60

80

100

WS (mg/L)

500 1000 3000 60001000015000

Fig. 5. Plots of (a) the dosage and (b) particle size of the fly ash on the percentage adsorption of RB, RR and RY reactivedyes from aqueous solutions [Eqs. (2) and (4)].

cies of RR, RB and RY reactive dyes with theparticle sizes ranged from 45 to 112.5 µm at theoptimum dosage were calculated as ≥ 97%, 87%and 82%, respectively (Table 2).

Plots in Fig. 5a suggest an empirical modelform as [21,22]:

s

s

Wpa bW

=+

(2)

which can be linearized as

1 1

s

a bp W

⎛ ⎞= +⎜ ⎟

⎝ ⎠(3)


where a and b are model coefficients. The valuesof a and b along with the correlation coefficientsfor various adsorbent sizes can be obtained fromthe regression analysis (Table 3). The values of aand b as a function of mean particle size of theadsorbent (dp) is then substituted in equationsshown in Table 5 to obtain the predictive modelfor p which is illustrated in Table 6. p values cal-culated from the model equation shown in Table6 are plotted against experimental values (Fig. 6a).

Table 2The removal efficiency and adsorbent capacity of the reactive dyes on the adsorbent with respect to particle size atvarious adsorbent dosages

RR RB RY Particle diameter, dP (µm)

Adsorbent dosage, WS (mg/L) Removal (%) qe (mg/g) Removal (%) qe (mg/g) Removal (%) qe (mg/g)

45 500 64.5 129 8.4 17 14.1 28 1000 75.1 75 16.9 17 26.6 27 3000 87.5 29 44.6 15 46.1 15 6000 93.8 16 75.2 13 67.5 11 10000 97.2 10 94.5 9 84.6 8 15000 98.4 7 97.4 6 98.3 7

56.5 500 56.9 114 6.7 13 12.6 25 1000 75.1 75 14.7 15 24.0 24 3000 87.0 29 43.1 14 45.1 15 6000 93.7 16 73.1 12 67.3 11 10000 97.5 10 93.7 9 83.8 8 15000 98.8 7 97.2 6 97.9 7

71.5 500 49.5 99 5.6 11 11.1 22 1000 75.0 75 12.5 12 22.4 22 3000 86.5 29 40.9 14 44.2 15 6000 93.6 16 71.1 12 66.4 11 10000 97.9 10 92.4 9 83.2 8 15000 99.0 7 96.7 6 97.4 6

90 500 43.7 87 4.9 10 9.9 20 1000 74.9 75 11.1 11 21.3 21 3000 86.1 29 41.1 14 43.1 14 6000 93.6 16 70.2 12 65.9 11 10000 98.3 10 90.9 9 82.9 8 15000 99.3 7 95.3 6 96.8 6

112.5 500 35.2 70 3.7 7 7.0 14 1000 74.8 75 9.4 9 17.9 18 3000 85.7 29 37.6 13 41.4 14 6000 93.4 16 68.0 11 64.3 11 10000 98.6 10 88.3 9 81.7 8 15000 99.9 7 94.8 6 96.6 6

The conformity between the experimental dataand the model-predicted values was expressed bythe correlation coefficients (r2). The best fit isobtained for RR dye (r2 = 0.997) as compared therest of the reactive dyes (Fig. 6). In addition tothat, the deviation between the theoretical and theexperimental data is expressed with the standarddeviation. RR dye loaded fly ash has lowest invalue (SD = 4.11).

To provide an alternative predictive model for


Table 3Values of the coefficients a and b from Eq. (2) for various particle sizes, dp

Dye dp (µm) a b r2 45.0 3.021 0.01002 0.991 56.5 3.206 0.01007 0.990 RB 71.5 3.362 0.01011 0.991 90.0 3.591 0.01016 0.991 112.5 3.958 0.01020 0.993 45.0 31.141 0.0096 0.997 56.5 35.548 0.0089 0.999 RR 71.5 40.795 0.0080 0.999 90.0 47.424 0.0069 0.997 112.5 57.191 0.0052 0.994 45 38.214 0.0095 0.961 56.5 42.565 0.0091 0.963 RY 71.5 50.633 0.0079 0.978 90 60.363 0.0049 0.995 112.5 91.063 0.0016 0.997

Table 4Values of the coefficients c and d from Eq. (4) for various adsorbent dosages, Ws

Dye Ws (mg/l) c d r2 500 67.114 –0.1059 0.997 1000 79.837 –0.0749 0.992 RB 3000 89.821 –0.0580 0.976 6000 96.879 –0.0505 0.984 10000 100.390 –0.0370 0.990 15000 101.400 –0.0363 0.987 500 17.620 –0.0866 0.982 1000 26.974 –0.0802 0.987 RR 3000 48.900 –0.0711 0.944 6000 69.828 –0.0474 0.965 10000 86.195 –0.0397 0.990 15000 99.353 –0.0263 0.987 500 17.195 –0.1062 0.989 1000 21.439 –0.1004 0.994 RY 3000 46.830 –0.0896 0.995 6000 78.764 –0.0831 0.999 10000 95.748 –0.0621 0.999 15000 101.250 –0.0491 0.992


p, plots depicted in Fig. 5b were subjected to lin-ear regression analysis, according to a model ofthe form [21,22]

( )pp c d d= + (4)

where c and d are coefficients. The values of cand d along with the regression analysis to obtainequations with respect to the various adsorbentdosages are presented in Tables 4–6. Fig. 6b showsthe best fit between experimental and model pre-dicted values obtained for RR dye. The standarddeviation between calculated and experimentalvalues is 4.94.

Finally, to select the best predictive model, thesimple statistic is to determine the standard de-viation between calculated and experimental val-ues. From this point, model equation provided in

Table 5Correlations between the coefficients a and b from Eq.(2) and c and d from Eq. (4) with respect to adsorbentdosage and particle size

Dye Model equation: p = (Ws/a + bWs) r2 a = 2.4161 + 0.0135 dp 0.994 RB b = 0.00992 – 2.62×10–6 dp 0.978 a = 13.788 + 0.3812 dp 0.998 RR b = 0.01256 – 6.45×10–5 dp 0.997 a = 0.7971 + 1.697 dp 0.973 RY b = 0.0157 – 1.216×10–4 dp 0.964

Dye Model equation: p = c + d (dp) r2 c = 77.459 + 0.002 Ws 0.703 RB d = –0.0834 + 3.9×10–6 Ws 0.692 c = 25.467 + 0.0055 Ws 0.923 RR d = –0.0823 + 4.1×10–6 Ws 0.938 c = 24.16 + 0.0061 Ws 0.877 RY d = –0.1046 + 3.87×10–6 Ws 0.810

0 20 40 60 80 1000

20

40

60

80

100 RR

Experimental

Theo

retic

al

50 60 70 80 90 10050

60

70

80

90

100 RB

b)

a)

0 20 40 60 80 1000

20

40

60

80

100 RY

0 20 40 60 80 1000

20

40

60

80

100 RR

60 70 80 90 10060

70

80

90

100 RB

0 20 40 60 80 1000

20

40

60

80

100 RY

Fig. 6. Plots of the calculated values of p derived from (a) Eq. (2) and (b) Eq. (4) for three reactive dyes against thecorresponding experimental values.


Table 6Results from two alternative generalized predictive models for p

Dyes Model equations

( ) ( )6=2.4161 + 0.0135 + 0.0092 2.62×10

s

p p s

Wpd d W−−⎡ ⎤⎣ ⎦

RB

( )6= 77.459 + 0.002 0.0834 + 3.9 × 10 s s pp W W d−

( ) ( )s

5=13.788 + 0.3812 + 0.01256 6.45×10p p s

Wpd d W−−⎡ ⎤⎣ ⎦

RR

( )6= 25.467 + 0.0055 0.0823 + 4.1 × 10 s s pp W W d−−

( ) ( )4=0.7971 +1.697 + 0.0157 1.216 ×10

s

p p s

Wpd d W−−⎡ ⎤⎣ ⎦

RY

( )6= 24.160 + 0.0061 0.1046 + 3.87 × 10 s s pp W W d−−

Table 5 with a lower standard deviation was themore successful in predicting the p values.

4. Conclusions

Fly ash, a waste residue generated in a sub-stantial amount in during coal-fired electric powergeneration, was used as adsorbent in this study.The adsorbent was characterized with a numberof techniques such as FTIR, XRD and SEM. Thepercentage removals of dyes using predictivemodels as a function of the particle size and ad-sorbent dosage were calculated. Both modelsshowed high values of coefficients (r2) but the bestmodel which determines the p values can be ex-pressed in terms of the standard deviation. Thelowest value of standard deviation determines forpredicting of the p values. The adsorption of thereactive dyes increased with increasing adsorbentdosage, and increased with decreasing the particlesize.

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