Modeling the kinetics of Cd(II) adsorption on Syzygium cumini L leaf powder in a fixed bed mini column

Journal of Industrial and Engineering Chemistry 17 (2011) 174–181

Contents lists available at ScienceDirect

Journal of Industrial and Engineering Chemistry journal homepage: www.elsevier.com/locate/jiec

Modeling the kinetics of Cd(II) adsorption on Syzygium cumini L leaf powder in a fixed bed mini column K.S. Rao a,*, S. Anand a, P. Venkateswarlu b a b

Institute of Minerals and Materials Technology, Bhubaneswar 751 013, India Department of Chemical Engineering, College of Engineering, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India

A R T I C L E I N F O

Article history: Received 9 March 2010 Accepted 15 July 2010 Available online 19 February 2011 Keywords: Mini column Adsorption Modeling Cadmium Desorption

A B S T R A C T

Syzygium cumini L leaf powder was used as a biosorbent for generating adsorption data in a fixed bed mini column. Effect of flow rate, initial Cd(II) concentration and bed height were the experimental parameters chosen to obtain breakthrough curves. The maximum uptake of Cd(II) in a fixed bed adsorption column was 29.08 mg g1 at pH 5.5, initial Cd(II) concentration 100 mg L1, bed height 5 cm and flow rate 40 mL min1. Bohart–Adams, BDST, Thomas and Yoon–Nelson models were applied to the data for predicting breakthrough curves and to determine the characteristic parameters. Prominent and unique characteristics features of the respective models like service time (Hutchins BDST model), adsorption capacity (Thomas model) and time required for 50% breakthrough (Yoon–Nelson model) were determined. The utilization of column data in designing of a commercial column has been discussed. It was possible to desorb 98% of Cd(II) using 0.05 N HCl solution in column. ß 2011 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

1. Introduction Ecosystem is severely affected due to indiscriminate discharge of effluents containing heavy metals into its system by many industries. Therefore, an urgent need to save ground water from these elements was recognized by researchers and planners. World over almost, all Government agencies have set tolerable limits for discharge of heavy metals into its ground and drinking water. WHO has prescribed maximum limits for safe discharge of effluents into ground water. Cadmium is one of the most toxic metals affecting environment. It is extensively used by many industries such as electroplating, painting, and battery manufacture. Effluents discharged from these industries contain undesired amounts of cadmium. It is non-degradable and if taken in excess than the safe limit causes many diseases like, itai-itai, renal damage, hypertension, anemia, etc. Various techniques have been employed to remove and recover heavy metals specially cadmium from effluents and wastewaters. Some of them are precipitation, cementation, ion-exchange, membrane separation, reverse osmosis and adsorption. Adsorption is most studied technique for treatment of waste waters. Adsorption of heavy metals on activated carbon derived from various raw materials has been widely studied [1–7] but it is expensive. Hence efforts are being made by many researchers to

* Corresponding author. Tel.: +91 9438673776; fax: +91 674 2581637. E-mail address: [email protected] (K.S. Rao).

find out cheaper materials. Recently many reviews have been published highlighting use of agricultural low cost waste materials for the removal of heavy metals [8–14]. Some of the adsorption processes developed for removal of cadmium using biosorbents include tea waste [15,16], food waste [17], sugarcane bagasse [18], degreased coffee seeds [19], apple residue [20,21], saw dust [22–24], eucalyptus bark [25] mango peel waste [26], rice husk [27] and leaf powders [28–31]. Although batch laboratory adsorption studies provide useful information on the application of adsorption to the removal of specific waste constituents, continuous column studies provide the most practical application of this process in wastewater treatment. The reason for this is that the high adsorption capacities in equilibrium with the influent concentration rather than the effluent concentration can be achieved [32]. In batch adsorption studies, the same solution remains in contact with a given quantity of the adsorbent. The adsorption process continues, however, till equilibrium between the solute concentration in solution, and the solute adsorbed per unit weight of the adsorbent is reached. This equilibrium established is static in nature, as it does not change further with time. In dynamic column adsorption, solution continuously enters and leaves the column, so that the complete equilibrium is never established at any stage between the solute in solution and the amount sorbed. Equilibrium has to be continuously established, as each time, it meets the fresh concentrations, hence, equilibrium in column mode is termed as dynamic equilibrium. Additional information on efficiency of the treated adsorbent in the column mode has been gathered in order to

1226-086X/$ – see front matter ß 2011 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jiec.2011.02.003

[()TD$FIG]

K.S. Rao et al. / Journal of Industrial and Engineering Chemistry 17 (2011) 174–181

1.2

1.0

0.8

C2/C1

ascertain the practical applicability of the adsorbent for real industrial wastewaters. The current study aims at exploring Cd(II) removal method based on adsorption technique. In the present work, Syzygium cumini L leaf powder (SCL) was used as a biosorbent to remove cadmium from aqueous solutions in a fixed bed mini column. Breakthrough curves were generated at various flow rates, initial cadmium concentration and bed heights. The data was analyzed using different kinetic models namely Bohart–Adams model [33], BDST model [34], Thomas model [35] and Yoon–Nelson model [36]. The desorption behaviour of the Cd(II) loaded biosorbent was also studied in the mini column.

175

0.6

10 mL/min 20mL/min 30 mL/min

0.4

40mL/min 0.2

2. Experimental 0.0

The green leaves of Syzygium cumini L (Jamun) used in the present studies were collected from Institute of Minerals and Materials Technology, Bhubaneswar, Orissa, India. The collected leaves were washed with de-ionized water several times and were completely dried in sunlight for 2 weeks, crushed into small pieces and powdered using domestic mixer. In the present study the powdered leaves of 212 mm particle size were used as biosorbent. The cadmium sulphate hydrate 3CdSO48H2O of Loba-chemie Indoaustranal Co. and Analytical grade (CH3COO)2Pb3H2O from s.d. fine-chem Ltd. were used. All other chemicals used were of Analytical grade. Stock solution of Cd(II) concentration of 1000 mg L1 was prepared by dissolving 4.5637 g of 100% 3CdSO48H2O in 2 L of distilled water. The range of concentration of solutions taken for adsorption studies was varied between 50 and 150 mg L1, and these solutions were prepared after required dilutions of cadmium stock solution. The metal ion concentrations in stock solution and filtrates obtained after adsorption were determined with atomic absorption spectrophotometer (Perkin Elmer AAnalyst 200, USA). Column adsorption studies were carried out using Syzygium cumini L leaf powder (SCL). The various parameters chosen for these studies were: flow rate, initial Cd(II) concentration and bed height. Column studies were carried out using mini glass column having 2.5 cm internal diameter (ID) with 30 cm length. Glass wool was placed at inlet and outlet of the column to avoid any loss of biosorbent material. The cadmium solution was fed through the bottom of the column with the help of a metering pump (Watson Marlow) and out put was collected from the top of the column at regular intervals for analysis. The flow rate was checked regularly. The pumping was continued till there was no further adsorption of cadmium, i.e., inlet and outlet cadmium concentrations became nearly same. Cadmium loaded SCL was prepared by contacting 5 g of biosorbent with 100 mL of 500 mg L1 Cd(II) solution followed by separating, washing and drying. The concentration of cadmium in the loaded SCL was found to be 20.11 mg g1. For desorption experiments 0.05 N HCl solution was pumped through the metering pump at a rate of 15 mL min1 through the column packed with 2 g of Cd(II) loaded biosorbent. Samples were collected at various time intervals and analyzed for cadmium content. The Cd(II) uptake q (mg metal adsorbed/g biosorbent) was calculated from the mass balance as follows: q¼

F m

Z

t

ðC 1  C 2 Þdt

(1)

t¼0

where C1 and C2 are the inlet and outlet concentrations of cadmium at any time, F is the volumetric flow rate of the solution (L min1), t is the time(min) and m is the mass of the biosorbent (g).

0

20

40

60

80

100

120

140

Time, min Fig. 1. Effect of flow rate on the breakthrough curve. Conditions: Cd(II) 100 mg L1, pH 5.5, bed height 5 cm, room temperature.

3. Results and discussion 3.1. Effect of flow rate In evaluating the performance of adsorption process for continuous treatment of wastewater on industrial scale, flow rate plays an important role. To find out the effect of flow rate on Cd(II) adsorption, experiments carried out by varying the flow rate between 10 and 40 mL min1. During these experiments, the initial Cd(II) concentration, pH and bed height were maintained at 100 mg L1, 5.5 and 5 cm, respectively. The effect of flow rate on breakthrough performance at the above operating conditions is shown in Fig. 1. In the present study, the breakthrough times and the exhaust times correspond to C2/C1 = 0.1 and 0.9, respectively [37], where C1 and C2 are the Cd(II) concentration entering and leaving the column, respectively. Some researchers have also used breakthrough points at C2/C1 = 0.5 [38] and 0.05 [42]. It was observed that for the flow rates of 10, 20, 30 and 40 mL min1, the breakthrough times were 16, 12, 11.5 and 7 min, respectively, while the exhaust times were 90, 40, 30 and 22 min, respectively. From these results, if we consider the exhaust time, at low flow rate it takes more time to achieve saturation. This can be explained by the fact that at lower flow rates, the residence time of the adsorbate in the column would increase and metal ions have more time to diffuse into the pores of adsorbent through intra-particle diffusion [39,40]. Similar observations were reported [37,38,40,41]. 3.2. Effect of initial adsorbate concentration The time for the appearance of breakthrough and shape of the breakthrough curve are very important characteristics for determining the operation and the dynamic response of adsorption column. The general position of the breakthrough curve along the time axis depends on the capacity of column with respect to the feed concentration, bed height and the flow rate. The adsorption performance of SCL was tested at various Cd(II) inlet concentrations. The adsorption break through curves were obtained by keeping the inlet Cd(II) concentration as 50, 100 and 150 mg L1. During these experiments, other parameters such as pH, bed height and flow rate were kept constant at 5.5, 5 cm and 30 mL min1, respectively. The adsorption breakthrough curves obtained for adsorbate concentrations of 50, 100 and 150 mg L1 are shown in Fig. 2. The breakthrough time decreased (17–6 min) with the increase in inlet Cd(II) concentration due to binding sites became saturated more quickly [37,38,41,42]. With the increase in initial

[()TD$FIG]

K.S. Rao et al. / Journal of Industrial and Engineering Chemistry 17 (2011) 174–181

176

1.0

0.8

C2 /C1

0.6

0.4 50 mg/L 100 mg/L

0.2

150 mg/L

0.0 0

20

40

60

80

Time, min Fig. 2. Effect of initial adsorbate concentration on breakthrough curve. Conditions: pH 5.5, flow rate 30 mL min1, bed height 5 cm, room temperature.

Cd(II) concentration from 50 to 150 mg L1, the uptake increased from 19.19 to 22.87 mg g1. The percentage of Cd(II) removal decreased with the increase in inlet adsorbate concentration. The increase in uptake capacity of the biosorbent is due to the fact that high inlet Cd(II) concentration provides higher driving force for the transfer process to overcome the mass transfer resistance. As seen in Fig. 2, for low inlet concentration (50 mg L1) of Cd(II), it took longer time (17 min) to achieve the breakthrough whereas at higher Cd(II) concentration (150 mg L1), the breakthrough occurred within a short period of time (6 min). The saturation time for biosorbent decreased from 60 to 26 min when the inlet Cd(II) concentration increased from 50 to 150 mg L1. At lower inlet concentration, the breakthrough was flatter indicating a relatively wide mass transfer zone and film controlled process. On the contrary, the breakthrough curves were sharp at high Cd(II) concentration, implying a relatively smaller mass transfer zone and intra-particle diffusion control. Similar observations have been reported by many researchers [37,42,43]. 3.3. Effect of bed height In order to find out the effect of bed height on the breakthrough curve, the adsorbate solution having Cd(II) concentration of 100 mg L1 at pH 5.5 was pumped through the adsorption column

[()TD$FIG]

1

0.8

3.4. Application of various breakthrough curve models Successful design of a column adsorption process requires prediction of the concentration time profile. Various mathematical models have been used to describe the fixed bed adsorption. Amongst these, many studies have been reported on testing of the kinetics of adsorption in the column using Bohart–Adams model [37,38,42,45,46]. The other models which are tested for fixed bed column adsorption data include BDST [37,41,44,47–49], Thomas and Yoon–Nelson [41,43]. In the present study, attempt was made to find out the best model describing the adsorption kinetics in column. 3.4.1. Application of Bohart–Adams model Bohart–Adams model was applied to the experimental data for description of the initial part of breakthrough curve. This approach focuses on the estimation of characteristic parameters such as saturation concentration (N0, mg L1) and the kinetic constant (kAB, L mg1 min1). The model equation is given below: lnCC 21 ¼ kAB C 1 t  kAB N0 Uz0

0.6

C 2/C1

at fixed flow rate of 30 mL min1 while the bed height was varied. Fig. 3 presents the performance of the breakthrough curves at bed heights of 5, 11 and 15 cm. Cd(II) adsorption uptakes increased from 19.51 to 25.01 mg g1 when the bed height was increased from 5 to 15 cm. With increase in bed height, the throughput volume increased (1159 mL to 2451 mL) to achieve the saturation and this increase in input volume maintaining same flow rate would result in higher contact time, thereby increasing the uptake capacity. The breakthrough time was increased from 11.5 to 37 min with the increase in bed height. At low bed depths, the axial dispersion phenomena predominate in mass transfer and reduce the diffusion of metal ions. The metal ions do not have enough time to diffuse into the whole of the biosorbent mass, due to which reduction in breakthrough time occurred. With the increase in bed depth, the residence time of solution in the column was increased, allowing the metal ions to diffuse deeper inside the biosorbent [40]. Uptake capacities (q) obtained at breakthrough (2– 21.8 mg g1) as well as saturation points (23.12–25.01 mg g1) increased with the increase in bed height from 5 to 15 cm. Such observations have also been made during lead adsorption on saw dust [44]. This increase in the adsorption capacity with that in the bed height is due to the increase in the specific surface of the adsorbent which supplies more fixation binding sites. At any given time, C2/C1 decreased with the increase in bed height as out let concentration decreased due to availability of more amount of adsorbent. The decrease in C2/C1 is the expected trends which has also been reported during column studies by many researchers [37–47].

5cm 11cm 15cm

0.4

0.2

0 0

20

40

60

80

100

120

Time, min Fig. 3. Effect of bed height on breakthrough curve. Conditions: Cd(II) 100 mg L1, flow rate 30 mL min1, pH 5.5 and room temperature.

(2)

where C1 and C2 are the inlet and outlet adsorbate concentrations, respectively, ‘z’ is the bed height (cm), U0 is superficial velocity (cm min1). For evaluation of parameters, the range of time considered is shown in Table 1. The values of ln(C2/C1) were plotted against ‘t’ at different flow rates, initial Cd(II) concentrations and bed heights. The kinetic constant (kAB) and saturation concentration (N0) values were calculated from the slope and intercept of the curves, respectively, and are given in Table 2 which shows that the kinetic constant, kAB, increased with increase in flow rate, decrease in initial concentration and decrease in bed height. This shows that the overall system kinetics was dominated by external mass transfer in the initial part of adsorption in the column [42].

K.S. Rao et al. / Journal of Industrial and Engineering Chemistry 17 (2011) 174–181 Table 1 Time considered (linear portion) for estimation of model parameters. Parameter

3.4.2. Application of the bed depth service time (BDST) model The BDST model is expressed as:

Time (min)

Volume of effluent (L)

60 40 25 20 (mg L1) 45 25 25

0.530 0.837 0.661 0.809 1.16 0.661 0.640

25 35 60

0.661 1.153 1.642

1

Flow rate (mL min ) 10 20 30 40 Initial Cd(II) concentration 50 100 150 Bed height (cm) 5 11 15

kAB  104

Flow rate (mL min1) 10 8.08 20 14.08 30 30.95 40 33.05 Initial Cd(II) concentration (mg L1) 50 29.76 100 30.95 150 8.34 Bed height (cm) 5 30.95 11 22.89 15 16.24

  t ¼ CN10uz  kC11 ln CC 12  1

(3)

where C1 and C2 are inlet and outlet concentrations, respectively, k is the adsorption rate constant (L mg1 min1), N0 is the adsorption capacity (mg L1), z is the bed depth (cm), u is the linear flow rate, mL cm2 min1 and t is the service time (min). Experimental data obtained from column studies were used to plot BDST curve. k and N0 values were calculated from the slope and intercept of the plot between ln(C1/C2  1) versus time t at different adsorption parameters such as flow rate, inlet adsorbate concentration and bed height. The estimated values of characteristic parameters like k and N0 along with other statistical parameters are presented in Table 3.

Table 2 Bohart–Adam model parameters. Parameter

177

N0

R2

9152 12,287 11,931 12,861

0.69 0.82 0.85 0.83

10,436 11,931 17,893

0.84 0.85 0.78

11,931 7625 9700

0.85 0.85 0.78

3.4.3. Application of Thomas model The expression developed by Thomas, calculates the maximum solid phase concentration of the solute on the adsorbent and the adsorption rate constant for a continuous adsorption process in the column. The linearised form of the model is given as:   ln CC 12  1 ¼ kThFq0 m  kTh CF1 V eff

(4)

where kTh is the Thomas rate constant (L mg1 min1), q0 is the maximum solid phase concentration (mg g1) and m is the amount of adsorbent (g) in the column, F is the volumetric flow-rate (L min1) and Veff is the effluent volume (L). The experimental data were fitted to the Thomas model to determine the Thomas rate constant (kTh) and maximum solid

Table 3 BDST model parameters. Parameter

k  104

N0 (mg L1)

R2

qExp (mg g1)

qCal (mg g1)

SSE

SAE

ARE

ARS

0.81 0.95 0.94 0.94

19.51 25.74 23.12 29.08

17.03 25.08 19.76 24.83

35.60

10.71

0.118

0.172

0.93 0.94 0.94

19.19 23.12 22.87

18.53 19.76 21.41

13.86

5.48

0.090

0.115

0.94 0.92 0.94

23.12 22.05 25.01

19.76 20.13 20.46

35.68

9.83

0.135

0.176

1

Flow rate (mL min ) 10 10.69 7297 20 20.36 9634 30 39.69 10,224 40 46.43 10,483 Initial Cd(II) concentration (mg L1) 50 38.00 8830 100 39.69 10,224 150 15.50 11,529 Bed height (cm) 5 39.69 10,224 11 28.69 6601 15 25.60 8328

Table 4 Thomas model parameters. Parameter

kTh  104

q0 (mg g1)

R2

qExp (mg g1)

qCal (mg g1)

SSE

SAE

ARE

ARS

18 28 23 30

0.81 0.92 0.93 0.95

19.51 25.74 23.12 29.08

16.95 25.22 19.71 24.77

37.03

10.8

0.119

0.143

20 23 26

0.91 0.92 0.93

19.19 23.12 22.87

18.27 19.71 21.70

13.84

5.50

0.091

0.116

23 22 22

0.92 0.92 0.92

23.12 22.05 25.01

19.71 19.88 20.88

33.39

9.70

0.134

0.171

1

Flow rate (mL min ) 10 12.02 20 18.26 30 46.17 40 45.14 Initial Cd(II) concentration (mg L1) 50 41.18 100 46.17 150 16.54 Bed height (cm) 5 46.17 11 25.48 15 27.02

K.S. Rao et al. / Journal of Industrial and Engineering Chemistry 17 (2011) 174–181

178 Table 5 Yoon–Nelson model parameters. Parameter

kYN  102

t

R2

qExp (mg g1)

qCal (mg g1)

SSE

SAE

ARE

ARS

41.30 27.31 19.28 14.83

0.81 0.95 0.94 0.94

19.51 25.74 23.12 29.08

17.03 25.08 19.76 24.84

35.35

10.68

0.117

0.140

33.30 19.28 14.49

0.93 0.94 0.94

19.19 23.12 22.87

18.53 19.76 21.41

13.86

5.48

0.090

0.115

19.28 27.38 47.10

0.94 0.92 0.94

23.12 22.05 25.01

19.76 20.13 20.46

35.68

9.83

0.135

0.176

1

phase concentration (q0). The kTh and q0 values were calculated by plotting ln(C2/C1  1) versus effluent volume Veff using values from column experiments (Figs. 1–3). The predicted uptake capacity and the experimental uptake capacity along with kTh and q0 and other statistical parameters are given in Table 4. As the flow rate increased, the value of kTh increased, whereas the value of q0 showed a reverse trend. The bed capacity (q0) increased and the coefficient (kTh) decreased with increase in initial Cd(II) concentration. The well fit of the experimental data on Thomas model indicated that the external and internal diffusion were not the rate limiting steps [42]. 3.4.4. Application of Yoon–Nelson model A simple theoretical model developed by Yoon–Nelson was also tested to investigate the breakthrough behaviour of Cd(II) onto syzygium cumini L leaf powder. The linearised model for a single component system is expressed as: ln



C2 C 1 C 2



as   C1 ln ¼ k1c  k2c t C2  1

(6)

where k1c = N0kz/u = kThq0m/F = tkYN and k2c = kC1 = kThC1 = kYN. From Eq. (6), it is obvious that the characteristic parameters associated with these models vary but all the three models should [()TD$FIG] 1.0

a

0.8

C2/C1

Flow rate (mL min ) 10 10.69 20 20.36 30 39.69 40 46.43 Initial Cd(II) concentration (mg L1) 50 19.00 100 39.69 150 23.25 Bed height (cm) 5 39.69 11 28.69 15 25.60

0.6 0.4 0.2 0.0 0

10

20

30

40

50

60

Time, min ¼ kYN t  t kYN

(5)

Cal.10mL/min

Cal.30mL/min

Cal.20mL/min

Cal.40mL/min

Exp. 10mL/min

Exp. 30mL/min

Exp.20mL/min

Exp.40mL/min

1

3.4.5. Theoretical and experimental breakthrough curves Theoretical breakthrough curves generated based on all four kinetic models tested along with corresponding experimental ones are presented in Figs. 4–7. From these figures, it is seen that Bohart–Adams model is applicable only to the initial period of operation but other three models are valid for the entire range.

1.0

b

C2/C1

0.8 0.6 0.4 0.2 0.0 0

10

20

30

40

Time, min

0.8

Cal. 50mg/L

Cal.100mg/L

Cal. 150mg/L

Exp. 50mg/L

Exp. 100mg/L

Exp. 150mg/L

c

0.6

C2/C1

where kYN is the rate constant (min ), t is the time required for 50% adsorbate breakthrough (min). The values of kYN and t were estimated from the plots between ln(C2/C1  C2) versus time t at different flow rates, initial Cd(II) concentrations and bed heights. The values of kYN were found to decrease with increase in bed height, whereas the corresponding values of t increased (Table 5). With increase in initial Cd(II) concentration as well as flow rate, the kYN values increased whereas the t showed the reverse trend. From the values presented in Table 5, it can be seen that the in most of the cases theoretical uptake capacity is very close to those predicted by the Yoon–Nelson model.

0.4 0.2 0

3.4.6. Comparison between the applied models Bohart–Adams model has been developed for treating the initial part of the breakthrough curve. The researchers have used this model either for the entire breakthrough curve or for 10–50% of the initial adsorbate concentration. Hence this model shows too much deviations if the entire curve up to saturation is considered [42]. If we look into the rest of the three models namely the Hutchins BDST, Thomas and Yoon–Nelson, the linearity can be represented

0

20

40

60

Time, min Cal. 5cm Exp. 5cm

Cal. 11cm Exp. 11cm

Cal. 15cm Exp. 15cm

Fig. 4. Experimental and theoretical breakthrough curves based on Bohart–Adams model: (a) different flow rates, Cd(II) 100 mg L1, pH 5.5, bed height 5 cm, room temperature, (b) different initial cadmium concentrations flow-rate 30 mL min1, pH 5.5, bed height 5 cm, room temperature and (c) different bed heights, Cd(II) 100 mg L1, pH 5.5, flow-rate 30 mL, room temperature.

[()TD$FIG]

[()TD$FIG]

K.S. Rao et al. / Journal of Industrial and Engineering Chemistry 17 (2011) 174–181

1.2

1.2

a C2/C1

0.8

C2/C1

a

1.0

1.0

0.6

179

0.8 0.6 0.4 0.2

0.4

0.0 0.0

0.2

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Veff, L

0.0 0

20

40

60

80

100

120

Cal. 10 mL/min Exp.. 10 mL/min

140

Time, min

1.2

Cal.20mL/min Exp.20mL/min

Cal. 30 mL/min Exp. 30 mL/min

b

1

1.2

Cal.40mL/min Exp.40mL/min

Cal. 30 mL/min Exp.. 30 mL/min

Cal.40mL/min Exp.40mL/min

b

1.0

C2/C1

Cal.10 mL/min Exp. 10 mL/min

Cal.20mL/min Exp.20mL/min

0.8 0.6 0.4

C2/C1

0.2 0.8

0.0 0

0.6

1

1.5

2

Veff

0.4

Cal. 50mg/L Exp. 50mg/L

0.2

1.2

0 0

20

40

60

80

1.2

Cal. 100mg/L Exp. 100mg/L

C2/C1

Cal. 50mg/L Exp. 50mg/L

Cal. 150mg/L Exp. 150mg/L

c

Cal. 100mg/L Exp. 100mg/L

Cal. 150mg/L Exp. 150mg/L

c

1

Time, min

0.8 0.6 0.4 0.2

1

C2/C1

0.5

0 0

0.8

1

2

3

Veff, L

0.6

Cal. 5cm Exp. 5cm

0.4 0.2 0 0

20

40

60

80

100

Time, min Cal. 5cm Exp. 5cm

Cal. 11cm Exp. 11cm

Cal. 11cm Exp. 11cm

Cal. 15cm Exp. 15cm

Fig. 6. Experimental and theoretical breakthrough curves based on Thomas model: (a) different flow rates, Cd(II) 100 mg L1, pH 5.5, bed height 5 cm, room temperature, (b) different initial cadmium concentrations, flow rate 30 mL min1, bed height 5 cm, room temperature and (c) different bed heights, Cd(II) 100 mg L1, flow rate 30 mL min1, room temperature.

Cal. 15cm Exp. 15cm

Fig. 5. Experimental and theoretical breakthrough curves based on BDST model: (a) different flow rates, Cd(II) 100 mg L1, pH 5.5, bed height 5 cm, room temperature, (b) different initial cadmium concentrations, flow rate 30 mL min1, bed height 5 cm, room temperature and (c) different bed heights, Cd(II) 100 mg L1, flow rate 30 mL min1, room temperature.

predict essentially similar uptake capacity and C2/C1 values for a particular data set. Hence they would give similar R2 values and other statistical parameters like sum of square of error (SSE), the sum of absolute error (SAE), average relative error (ARE) and the average relative standard error (ARS) as illustrated in Tables 3, 4 and 5. But the prominent and unique characteristic features of the respective models like service time (Hutchins BDST model), adsorption capacity (Thomas model) and time for 50% breakthrough (Yoon–Nelson model) enable further comparison. Therefore, the unique characteristics of each model were calculated from the predicted model equations. Different statistical parameters such as SSE, SAE, ARE and ARS amongst the experimental and calculated values as well as the unique characteristics of the above three models are given in Table 6. From the statistical parameter ARS, Yoon–Nelson model is the more appropriate model amongst the three tested models. 3.4.7. Evaluation of adsorption column design parameters Laboratory data and pilot plant data form the basis of commercial scale adsorption columns. According to Hutchin

[34] and Kumar and Bandyopadhyay [37], BDST approach requires only three fixed bed tests to collect the necessary data. The equation followed is: t ¼ az þ b

(7)

a ¼ slope ¼ N 0 =C 1u

(8)

and b ¼ intercept ¼ 

  1 C1 ln 1 kC 1 C2

(9)

The data of breakthrough curves plotted for each bed depth of 5, 11 and 15 cm were used to plot a BDST correlation by recording the operating time to reach 10–90% removal at each bed depth (Fig. 8). If the adsorption zone is arbitrarily defined as the adsorbent layer through which the effluent concentration varies from 90% to 10% of the feed concentration, then this zone is defined as the horizontal distance between these two lines in the BDST plot (Fig. 8). The depth of mass transfer zone (MTZ) obtained was 8.2 cm. From the slope (2.51) and intercept (3.80) of the 10% saturation line, design parameters like N0 and k could be found out using Eqs. (8) and (9). Values obtained are 6665 mg L1 and 0.84 L mg1 min1.

[()TD$FIG]

K.S. Rao et al. / Journal of Industrial and Engineering Chemistry 17 (2011) 174–181

180

1.2

Table 6 Comparison of characteristic features of various models.

a

1

Parameter

C2/C1

0.8 0.6 0.4 0.2 0 0

20

40

60

80

100

120

Time, min Cal.10 mL/min Exp.10 mL/min

1.2

Cal. 30 mL/min Exp. 30 mL/min

Cal. 40mL/min Exp. 40mL/min

b

1.0

C2/C1

Cal.20mL/min Exp. 20mL/min

BDST model parameters Flow rate (mL min1) 10 16.0 20.74 20 12 16.52 30 11.5 13.74 40 7 10.09 Initial Cd(II) concentration (mg L1) 50 17.0 21.73 100 11.5 13.74 150 6.0 10.80 Bed height (cm) 5 11.5 13.74 11 17.5 19.72 15 38.0 38.52

SSE

SAE

ARE

ARS

53.75

13.48

0.289

0.363

50.43

11.77

0.399

0.615

12.26

5.98

0.142

0.167

0.8

Parameter

0.6

0.8

Yoon–Nelson model parameters Flow rate (mL min1) 10 34 41.28 20 24 27.31 30 18 19.28 40 14 14.83 Initial Cd(II) concentration (mg L1) 50 14 13 100 19 18 150 33 31 Bed height (cm) 5 19 18 11 26 25 15 47 45

0.6

Thomas model parameters. See Table 4.

0.4 0.2 0.0 0

20

40

60

80

Time, min 50mg/L 50mg/L

1.2

100mg/L 100mg/L

150mg/L 150mg/L

c

1.0

C2/C1

tCal.

tExp.

(t50%) Exp.

(t50%) Cal.

SSE

SAE

ARE

ARS

90.84

16.7

0.182

0.234

6

4

0.067

0.076

6

4

0.049

0.055

0.4 0.2 0.0 0

20

40

60

80

100

Time, min Cal. 5cm Exp. 5cm

Cal.11cm Exp. 11cm

Cal. 15cm Exp.15cm

Fig. 7. Experimental and theoretical breakthrough curves based on Yoon–Nelson model: (a) different flow rates, Cd(II) 100 mg L1, pH 5.5, bed height 5 cm, room temperature, (b) different initial cadmium concentrations, flow rate 30 mL min1, bed height 5 cm, room temperature and (c) different bed heights, Cd(II) 100 mg L1, flow rate 30 mL min1, room temperature.

C1 and C1*, respectively. For example from Fig. 2, the service time obtained for 50 mg L1 was 17 min. Using the values of slope (a1) and intercept (b1) from Fig. 8, the values of a2 and b2 calculated using Eqs. (11) and (12) were 5.03 and 7.61, respectively. From these values the service time calculated was 17.53 min, which is very close to the experimental value (17 min). Similarly, service time obtained for initial Cd(II) concentration of 150 mg L1 was 5.84 against the experimental value of 6 min. Similar calculations can also be made for different flow rates using the presented model. 3.5. Desorption studies

t ¼ a1 z þ b1

(10)

it is possible to predict the equation for concentration C1* as follows: a2 ¼

a1 C 1 C 1

b2 ¼ b1

C 1 lnððC 1 =C 1E Þ  1Þ C 1 lnððC 1 =C 1E Þ  1Þ

(11)

For desorption experiments 0.05 N HCl solution was pumped through the dozing pump at a rate of 15 mL min1 through the same column packed with 2 g of Cd(II) loaded biosorbent. Effect of contact time on cadmium desorption is given in Fig. 9. It shows that desorption kinetics is very fast and within a short period of 5 min 95.6% cadmium present in the biosorbent was desorbed [()TD$FIG] while it required about 15 min to desorb 98% cadmium. First 70

y = 3.3421x + 12.132 2 R = 0.978

60

Service time, min

3.4.8. Design of adsorption column The column design parameters as obtained above can be used for the design of adsorption column in practical use [37]. According to BDST, if the value of a is determined from one flow rate, values for other flow rates can be calculated by multiplying the original slope by the ratio of the original and new flow rates, and the change of b value is insignificant with respect to the changing flow rates. The data collected at one inlet solute concentration can be adjusted by BDST technique and used to design systems for treating other inlet solute concentrations. If a laboratory test is conducted at solute concentration C1 yielding an equation of the form

50 40 Adsorption zone

30

y = 2.5132x - 3.8026 2 R = 0.8181

20 10

(12)

where a1 and a2 are slopes, b1 and b2 are the intercepts and C1E and C1*E are the effluent concentrations at inlet concentrations of

0 0

5

10 Bed depth, cm

15

20

Fig. 8. BDST plot for Cd(II) adsorption on SCL in fixed bed mini column.

[()TD$FIG]

K.S. Rao et al. / Journal of Industrial and Engineering Chemistry 17 (2011) 174–181

100

using different statistical methods. Column desorption studies showed it was possible to desorb 98% of Cd(II) using 0.05 N HCl solution.

80

% Desorption

181

Acknowledgements

60

The authors are thankful to Prof. B.K. Mishra, Director, Institute of Minerals and Materials Technology, for his kind permission to publish this paper. They wish to thank to Dr. R.K. Paramguru, Head, Hydro and Electro Metallurgy Department.

40 20

References

0 0

10

20

30

40

Time, min Fig. 9. Effect of contact time on cadmium desorption. Cd(II) 20.11 mg g1, SCL 2 g, HCl 0.05 M, flow rate 30 mL min1, room temperature.

[()TD$FIG]

2000

Cd(II), mgL

-1

1600 1200 800 400 0 0

100

200

300

400

500

Eluent volume, mL Fig. 10. First cycle desorption profile of Cd(II). Conditions: Cd(II) on loaded SCL 20.11 mg g1, Bed height 5 cm, HCl 0.05 M, flow rate 30 mL min1, room temperature.

cycle desorption profile of Cd(II) is shown in Fig. 10. It reveals that around 100 mL of 0.05 N HCl is enough for 98% removal of loaded Cd(II). 4. Conclusions Effect of different adsorption parameters viz., flow rate, bed height and initial concentration were studied in fixed bed mini column using Syzygium cumini L leaf powder. The %Cd(II) adsorption increased with increase in biosorbent dose and decreased with increase of Cd(II) concentration or bed height. The maximum uptake of Cd(II) in a fixed bed adsorption column was 29.08 mg g1 at pH 5.5, initial Cd(II) concentration 100 mg L1, bed height 15 cm and flow rate 40 mL min1. Bohart–Adams, BDST, Thomas and Yoon–Nelson models were applied to the experimental data obtained from these studies to predict breakthrough curves and to determine the characteristic parameters of the column. Prominent and unique characteristics features of the respective models like service time (Hutchins BDST model), adsorption capacity (Thomas model) and 50% breakthrough (Yoon–Nelson model) were determined. The experimental and predicted unique characteristics of the models were compared

[1] M.A. Ferro-Garcı´a, J. Rivera-Utrilla, J. Rodrı´guez-Gordillo, I. Bautista-Toledo, Carbon 26 (3) (1988) 363. [2] A. Kongsuwan, P. Patnukao, P. Pavasant, J. Ind. Eng. Chem. 15 (4) (2009) 465. [3] K.C. Kang, S.S. Kim, J.W. Choi, S.H. Kwon, J. Ind. Eng. Chem. 14 (1) (2008) 131. [4] T.Y. Kim, H.J. Jin, S.S. Park, S.J. Kim, S.Y. Cho, J. Ind. Eng. Chem. 14 (6) (2008) 714. [5] H.S. Jazeyi, T. Kaghazchi, J. Ind. Eng. Chem., in press. [6] M.G. Lee, J.K. Cheon, S.K. Kam, J. Ind. Eng. Chem. 9 (2003) 174. [7] M. Choi, J. Jang, J. Colloid Interface Sci. 325 (1) (2008) 287. [8] S.E. Bailey, T.J. Olin, R.M. Bricka, D.D. Adrian, Water Res. 33 (1999) 2469. [9] S. Babel, T.A. Kurniawan, J. Hazard. Mater. 97 (2003) 219. [10] T.A. Kurniawan, G.Y.S. Chan, W.H. Lo, S. Babel, Sci. Total Environ. 366 (2006) 409. [11] Y.S.K. Reddy, D. Graybill, R. vonWandruszka, J. Hazard. Mater. 171 (1–3) (2009) 1. [12] S. Qaiser, A.R. Saleemi, M.M. Ahmad, Environ. Biotechnol. 10 (2007) 409. [13] H. Benaissa, J. Hazard. Mater. 132 (2006) 189. [14] V.O. Arief, K. Trilestari, J. Sunarso, N. Indraswati, S. Ismadji, Clean 36 (12) (2008) 937. [15] A.H. Mahvi, D. Naghipour, F. Vaezi, S. Nazamara, Am. J. Appl. Sci. 2 (2005) 372. [16] S. Cay, A. Uyanık, A. Ozasık, Sep. Purif. Technol. 38 (2004) 273. [17] W. Zheng, X.M. Li, F. Wang, Q. Yanga, P. Dengb, G.M. Zenga, J. Hazard. Mater. 157 (2008) 490. [18] S.C. Ibrahim, M.A.K.M. Hanafia, M.A.Z. Yahya, American-Eurrasian J. Agric. Environ. 1 (3) (2006) 179. [19] K. Kaikake, K. Hoaki, H. Sunada, R.P. Dhakal, Y. Baba, Bioresour. Technol. 98 (2007) 2787. [20] S.H. Lee, C.H. Jung, H. Chung, M.Y. Lee, J.W. Yang, Process Biochem. 33 (2) (1998) 205. [21] S.H. Lee, K.R. Kim, G.N. Kim, J.Y. Hyung, H. Chung, J. Ind. Eng. Chem. 4 (3) (1998) 205. [22] S.Q. Memon, N. Memon, S.W. Shaw, M.Y. Khuhawar, M.I. Bhanger, J. Hazard Mater. B139 (2007) 116. [23] T.K. Naiya, P. Chowdhury, A.K. Bhattacharya, S.K. Das, Chem. Eng. J. 148 (2009) 68. [24] C. Jeon, J.H. Kim, J. Ind. Eng. Chem. 15 (6) (2009) 910. [25] I. Ghodbane, L. Nouri, O. Hamdaoui, M. Chiha, J. Hazard. Mater. 152 (2007) 148. [26] M. Iqbal, A. Saeed, S.I. Zafar, J. Hazard. Mater. 164 (2009) 161. [27] U. Kumar, M. Bandyopadhyay, Bioresour. Technol. 97 (2006) 104. [28] K.S. Rao, S. Anand, P. Venkateswarlu, Bioresources 5 (1) (2010) 438. [29] K.S. Rao, S. Anand, P. Venkateswarlu, Korean J. Chem. Eng 27 (5) (2010) 1547. [30] K.S. Rao, S. Anand, P. Venkateswarlu, Adsorp. Sci. Technol. 28 (2) (2010) 163. [31] K.S. Rao, S. Anand, P. Venkateswarlu, Indian J. Chem. Technol. 17 (5) (2010) 329. [32] W.W. Eckenfelder Jr., Industrial Water Pollution Control, McGraw Hill Publication, USA, 1989, p. 273. [33] G. Bohart, E.Q. Adam, J. Am. Chem. Soc. 42 (1920) 523. [34] R.A. Hutchin, Am. J. Chem. Eng. 80 (1973) 133. [35] H.C. Thomas, J. Am. Chem. Soc. 66 (1966) 1664. [36] Y.H. Yoon, J.H. Nelson, Am. Ind. Hyg. Assoc. J. 45 (1984) 509. [37] U. Kumar, M. Bandyopadhyay, J. Hazard. Mater. B129 (2006) 253. [38] J. Goel, K. Kadirvelu, C. Rajagopal, V.K. Garg, J. Hazard. Mater. B125 (2005) 211. [39] G. Yan, T. Viraraghavan, Bioresour. Technol. 78 (2001) 243. [40] S. Qaiser, A.R. Saleemi, M. Umar, J. Hazard. Mater. 166 (2009) 998. [41] M. Zhao, J.R. Duncan, Biotechnol. Lett. 20 (1) (1998) 37. [42] Z. Aksu, F. Gonen, Process Biochem. 39 (2004) 599. [43] N. Sankararamakrishnan, P. Kumar, V.S. Chauhan, Sep. Purif. Technol. 63 (2008) 213. [44] V.C.T. Costodes, H. Fauduet, C. Porte, Y.S. Ho, J. Hazard. Mater. B123 (2005) 135. [45] H.H. Chu, M.A. Hashim, J. Environ. Sci. 19 (2007) 928. [46] X. Liao, M. Zhang, B. Shi, Ind. Eng. Chem. Res. 43 (2004) 2222. [47] E. Malkoc, Y. Nuhoglu, M. Dundar, J. Hazard. Mater. B138 (2006) 142. [48] C. Rongsayamanont, K. Sopajaree, 2007 World Coal Ash (WOCA), Covington, Kentucky, USA, May 7–9, 2007. [49] M. Lehmann, A.I. Zouboulis, K.A. Matis, Environ. Pollut. 113 (2) (2001) 121.