Binary biosorption of phenol and chromium(VI) onto immobilized activated sludge in a packed bed: Prediction of kinetic parameters and breakthrough curves

Separation and Purification Technology 49 (2006) 205–216

Binary biosorption of phenol and chromium(VI) onto immobilized activated sludge in a packed bed: Prediction of kinetic parameters and breakthrough curves Z¨umriye Aksu ∗ , Ferda G¨onen Department of Chemical Engineering, Hacettepe University, 06532 Beytepe, Ankara, Turkey Received 24 May 2005; received in revised form 15 September 2005; accepted 27 September 2005

Abstract Simultaneous biosorption of phenol and chromium(VI) ions to Mowital® B30H resin immobilized activated sludge from binary mixture was studied and compared with single phenol or chromium(VI) biosorption in a continuous packed bed column. The phenol and chromium(VI) binding capacity of biosorbent was shown as a function of single and dual pollutant concentrations at a flow rate of 0.8 ml min−1 and at a pH value of 1.0. The equilibrium uptake (or column biosorption capacity) of each pollutant was determined by evaluating the breakthrough curves obtained at different inlet concentrations changing 50–500 mg l−1 in single and binary systems. The maximum column biosorption capacity of dried activated sludge was 9.0 mg g−1 for phenol and 18.5 mg g−1 for chromium(VI) at single ion situation. The column sorption capacity of immobilized dried activated sludge for phenol (or for chromium(VI)) decreased notably due to the presence of other component. The mono- and multi-component sorptions in packed bed were expressed by the Yoon and Nelson model to determine the kinetic constants and to predict the breakthrough curves of each component. The functional relationship between Yoon and Nelson kinetic constant of each component and concentrations of phenol and chromium(VI) in binary mixture was determined by using Response Surface Methodology. © 2005 Elsevier B.V. All rights reserved. Keywords: Binary biosorption; Phenol; Chromium(VI); Immobilized activated sludge; Packed bed; Yoon and Nelson

1. Introduction It is well recognized that the presence of heavy metals and aromatic compounds in the environment can be detrimental to a variety of living species, including man. Hexavalent chromium (chromium(VI)) has been found together with a variety of aromatic compounds including phenol, naphthalene, and trichloroethylene (TCE) at high concentrations in a number of contaminated sites. Chromium(VI) and its organic copollutants often originate from industrial sources such as leather tanning, photographic-film making, wood preservation, car manufacturing, petroleum refining, and agricultural activity. Chromium(VI) and phenol containing effluents are also discharged from a wide range of industries, including dye, textile, tannery, petrochemical, and many others [1–3]. The main techniques which have been utilized to reduce the

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heavy metal ion and phenolic contents of effluents include precipitation/coagulation, chemical oxidation, biodegradation, adsorption, ion exchange, membrane processing, electrolytic methods, etc. These methods have been found to be limited, since they often involve high capital and operational costs and may also be associated with the generation of secondary wastes which present treatment problems [4,5]. Using microorganisms as biosorbents for heavy metals and organic species offers a potential alternative to existing methods for detoxification and recovery of these components from industrial wastewaters [3–12]. Commercial application of these microbial biomass as a biosorbent, however, has been hindered by problems associated with physical characteristics of these materials such as small particle size with low density, poor mechanical strength and rigidity, and solid–liquid separation. These problems can be solved by immobilization of microbial cells using natural or synthetic polymers. In industrial or technical operations, immobilized microbial cell systems could also provide additional advantages over freely suspended cells. These include ease of regeneration and reuse of the biomass, easier solid–liquid

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separation, and minimal clogging in continuous-flow systems [13–19]. Numerous studies on metal and organics biosorption in batch systems have been reported in the literature. However, in the practical operation of full-scale biosorption processes with immobilized cells, a continuous-flow fixed bed column is often preferred since it is simple to operate, attains a high yield and it can be easily scaled up from a laboratory-scale procedure. The stages in the separation protocol can also be automated and high degrees of purification can often be achieved in a single step process [8,13–19]. While much research has been carried out on the uptake of single species of metal ions and organic species by microbial cells, little attention seems to have been given to the study of organic–metal ion mixtures. Despite the fact that not only single toxic metallic species but organic components also exist in wastewaters and the presence of a multiplicity of metals and organics often gives rise to interactive effects, insufficient attention seems to have been paid to this problem. The examining the effects of metal ions and organics in various combinations is more representative, of the actual environmental problems faced by organisms, than are single metal or organic studies [2,3,8,20]. Probably the most abundant sources of mixed microbial biomass are the aerobic activated sludge wastewater treatment processes used for the purification of some industrial effluents and domestic wastes. Part of the microorganisms over grown in such wastewater systems can be separated and utilized for the removal of pollutants as a biosorbent. The ability of activated sludge biomass to remove and accumulate heavy metals and organics has been recognized and activated sludge systems have been studied to a certain degree for their biosorption capabilities. However, there has been limited research on the simultaneous bioremoval of organic–metal ion mixtures by the activated sludge biomass to date [3,8,10, 11,17,18]. Mowital® B30H resin (a polyvinyl butyral based polymer) is a well-known polymer used extensively in painting and coating industries. While it is abundant, extremely cheap than other immobilizing agents, non-toxic and chemically inert, more suitable for preparation of porous beads, the solid and rigid support, showing little change in volume under any conditions, with dimensional stability under pressure, produced in the desired size and thus highly competitive with ion exchange resins and activated carbon so it can be used for immobilization of microorganisms for biosorption. Activated sludge has been immobilized in Mowital® B30H resin in order to produce a biosorbent material with proper characteristics for use in typical chemical engineering operations such as packed beds [16]. In this study, binary biosorption of phenol and chromium(VI) which are frequently encountered together in wastewaters such as from steel, metal plating, dye, textile, and painting industries [1–3] to Mowital® B30H resin immobilized dried activated sludge was investigated and compared to single component situation in a continuously operated packed bed column. The binding capacity of biomass was shown as a function of single and dual component concentrations.

2. Mathematical description 2.1. Packed bed In the practical operation of full-scale biosorption processes, continuous-flow fixed bed columns are often preferred. In such systems the concentration profiles in the liquid and adsorbent phases vary in both space and time. As a result, design and optimization of fixed bed columns are difficult to carry out a priori without a quantitative modeling approach. From the perspective of process modelling, the dynamic behaviour of a fixed bed column is described in terms of the effluent concentration–time profile, i.e. the breakthrough curve. The time for breakthrough appearance and the shape of the breakthrough curve are very important characteristics for determining the operation and the dynamic response of a biosorption column. The general position of the breakthrough curve along the time (or effluent volume) axis depends on the capacity of the column with respect to the bed height, flow rate and feed concentration. The breakthrough curve would be a step function for favorable separations, i.e. there would be an instantaneous jump in the effluent concentration from zero to the feed concentration at the moment the column capacity is reached [17,21–24]. The breakthrough curves indicate the loading behaviour of immobilized activated sludge biomass for phenol and/or chromium(VI) (i component) to be removed from solution in a fixed bed and is usually expressed in terms of i component concentration adsorbed (Cad,i = inlet i component concentration (Co,i ) − outlet i component concentration (Ci )) or normalized concentration defined as the ratio of effluent concentration of i component to inlet concentration of i component (Ci /Co,i ) as a function of time (t) or throughput volume for a given bed height. The area under the breakthrough curve (Ai ) obtained by integrating the adsorbed concentration of i component (Cad,i , mg l−1 ) versus t (min) plot, can be used to find the total adsorbed quantity of i component (qtotal,i , mg) and for a given inlet concentration and flow rate (ml min−1 ) and it can be calculated from Eq. (1) [16]:
t=ttotal QAi Q Cad,i dt (1) = qtotal,i = 1000 1000 t=0 Total amount of i component sent to column (mtotal,i ) can be found from Eq. (2). mtotal,i

Co,i Qttotal 1000

(2)

Total removal percent of i component (column performance) with respect to flow time can be also found from the ratio of total adsorbed quantity of i component (qtotal,i ) to the total amount of i component sent to column (mtotal,i ) (Eq. (3)). Total removal of i component (%i ) =

qtotal,i × 100 mtotal,i

(3)

Adsorption is also a well-known equilibrium separation process for wastewater treatment. Equilibrium studies on adsorption give information about the capacity of the sorbent or the amount required to remove a unit mass of pollutant under the system

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conditions. Equilibrium uptake of i component (qeq,i ) (or column capacity for i component) in the column is defined by Eq. (4) as the total amount of i component sorbed (qtotal,i ) per gram of sorbent (X) at the end of total flow time. qtotal,i (4) qeq,i = X For binary system, total removal percent of the phenol and chromium(VI) (total column performance) with respect to flow time can be also found from the ratio of total adsorbed quantities of phenol and chromium(VI) (qtotal ) to the total amounts of phenol and chromium(VI) sent to column (mtotal ) ((Eq. (5)).  qtotal,i Total removal (%) =  × 100 (5) mtotal,i Total equilibrium uptake (qeq ) (or total column sorption capacity for two components) in the column is defined by Eq. (6) as the total amount of two components sorbed (qtotal ) per gram of sorbent (X) at the end of total flow time.  qtotal,i qeq = (6) X Developing a model to accurately describe the dynamic behavior of adsorption in a fixed bed system under given specific operating conditions is inherently difficult. Since the concentration of the adsorbate as the feed moves through the bed, the process does not operate at steady state. The fundamental transport equations derived to model the fixed bed with theoretical rigor are differential in nature and usually require complex numerical methods to solve. Various simple mathematical models have been developed to predict the dynamic behaviour of the column. These models are useful for the sizing and optimization of the industrial scale processes. 2.2. The Yoon and Nelson model Yoon and Nelson [24] have developed a relatively simple model addressing the adsorption and breakthrough of adsorbate vapors or gases with respect to activated charcoal. This model assumes the symmetrical nature of breakthrough curve and neglects the effect of axial dispersion. Since the Yoon and Nelson model not only is less complicated than other models, but also requires no detailed data concerning the characteristics of adsorbate(s), the type of adsorbent, and the physical properties of adsorption bed, it is decided that this model can be used to predict the dynamic behavior of single and binary system components. The Yoon and Nelson equation regarding to each component in a single and in a binary system is expressed as: ln

Ci = kYN,i t − τi kYN,i Co,i − Ci

(7)

where kYN,i is the rate constant of i component (min−1 ), τ i the time required for 50% of i adsorbate breakthrough (min), and t is the sampling time (min). The calculation of theoretical breakthrough curves for i component requires the determination of the parameters kYN,i and τ i data. The approach involves a plot of ln Ci /(Co,i − Ci ) versus sampling time (t) according to Eq. (7).

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If the theoretical model accurately characterizes the experimental data of i component, this plot will result in a straight line with slope of kYN,i and intercept τ i kYN,i [24,25]. 2.3. Response Surface Methodology Response Surface Methodology (RSM) is used to find a suitable approximation for the true functional relationship between the dependent variable (response) (Y) and the set of independent variables (factors) (X1 , X2 , . . .). If knowledge concerning the shape of the true response surface is insufficient, first attempts generally try to approximate the shape by fitting a first-order model to the response values. However, if the first-order model suffers from lack of fit arising from existence of surface curvature, the first-order model is upgraded by adding higher order terms to it. The next higher order model is the second-order model and is given by Eq. (8): Y = β0 +

k 

βi X i +

i=1

k  i=1

βii Xi2 +

k  k 

βij Xi Xj + ε

(8)

i=1 j=1

where X1 , X2 , . . ., Xk are the input variables, which influence the response Y; β0 (independent term of regression equation), βi (i = 1, 2, . . ., k) (linear term of regression equation), βii (i = 1, 2, . . ., k) (second-order term of regression equation), and βij (i = 1, 2, . . ., k; j = 1, 2, . . ., k) (interactive term of regression equation) are unknown parameters; ε is a random error. In developing this regression equation, the test variables were coded according to the following equation: x i = Xi −

Xi∗ Xi

(9)

where xi is the coded value of the ith independent variable, Xi the uncoded value of the ith independent variable, Xi∗ the uncoded value of the ith independent variable at the central point, and Xi is the step change value. A 22 full factorial central composite design for two independent variables was employed to fit a second-order polynomial model, which indicate that 13 data were required for this procedure. 3. Materials and methods 3.1. Microorganism The waste activated sludge collected from wastewater treatment system of Meteksan Company-Ankara Paper and Board Mill, Turkey was used in this study. The harvested cells were washed throughly with sterile distilled water, centrifuged at 5000 rpm for 5 min, dried at 60 ◦ C for 24 h and then powdered before immobilization. 3.2. Preparation of Mowital® B30H resin immobilized activated sludge The method used for the immobilization based on solvent evaporation. For each batch immobilization, 1 g Mowital® B30H

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(Hoechst) was solved in 8 ml of chloroform (Merck). When Mowital® B30H resin was completely dissolved in this solution, 1 g of dried and powdered activated sludge biomass was added to this medium mixed continuously by means of an agitator. Four grams of polyvinyl alcohol (Sigma) was solved in 500 ml of distilled water. After that, 0.3 g of sodium dodecyl sulphate (Sigma) was added to this mixture and mixed thoroughly. At the last step, the first prepared mixture was added to this mixture. The mixing operation was continued for 6 h at 750 rpm to obtain the immobilized particles and to evaporate chloroform. Finally, the particles were removed from the immobilization medium and washed with distilled water. These particles had 1.00 mm of mean diameter and contained 50% activated sludge [17,26]. 3.3. Chemicals The test solutions containing single phenol or chromium(VI) ions were prepared by diluting 1.0 g l−1 of stock solutions of phenol and chromium(VI) to the desired concentrations. Stock solutions of phenol and chromium(VI) were obtained by dissolving the exact quantities of 99% purity of phenol (Merck) and potassium dichromate (Merck), in 1 l of double-distilled water, respectively. The ranges of concentrations of both components prepared from stock solutions varied between 50 and 500 mg l−1 . The test solutions containing desired combinations of phenol and chromium(VI) were prepared by diluting 1.0 g l−1 of stock solutions of phenol and chromium(VI) and mixing them in the test medium. As the optimum initial pH for single and simultaneous biosorption of chromium(VI) and phenol on Mowital® B30H resin immobilized activated sludge was found to be 1.0 in the batch system from the previous studies [15,25], this condition was also fixed in the column studies and the pH of each test solution was adjusted to the required value with dilute H2 SO4 solution.

3.5. Analysis of chromium(VI) and phenol The concentration of chromium(VI) ions in the effluent was determined spectrophotometrically by using diphenyl carbazide as the complexing agent. The absorbance of the purple colored solution was read at 540 nm after 10 min [27]. The concentration of residual phenol in the effluent was also determined spectrophotometrically. The absorbance of the colored complex of phenol with p-nitroaniline was read at 470 nm [3]. No interference of chromium(VI) ions and phenol on the analysis method of the other pollutant was observed. 4. Results and discussion Bioremoval of single species of pollutants using microorganisms is affected by several factors. These factors include the specific surface properties of the microorganism and the physicochemical parameters of the solution such as temperature, pH, initial pollutant concentration, and biomass concentration. Many other parameters affect the capacity of microorganisms to bind more than one species simultaneously. The combined effects of two or more components to microorganisms also depend on the number of pollutants competing for binding sites, pollutant combination and levels of pollutant concentration [3,8,16,20,22]. In this study, the simultaneous biosorption of phenol and chromium(VI) ions by Mowital® B30H resin immobilized activated sludge from binary mixture was studied and compared with single phenol or chromium(VI) situation in a continuous packed bed column. The phenol and chromium(VI) binding capacity of microorganism was shown as a function of single and dual pollutant concentrations at a flow rate of 0.8 ml min−1 and at pH 1.0. The equilibrium uptake of each pollutant was determined by evaluating its corresponding breakthrough curve obtained at different inlet concentrations in single and binary systems.

3.4. Sorption studies Continuous fixed bed column studies were performed in a fixed bed mini column with an inside diameter of 0.96 cm, a bed depth of 6 cm and 3 g of immobilized cells containing 1.5 g of dried activated sludge biomass [17,26]. The column was preconditioned to pH 1.0 for phenol and chromium(VI) (by eluting the column with 1 M H2 SO4 ). Solutions at a known concentration and flow rate was passed continuously through the stationary bed of sorbent. The flow rate was regulated with a variable speed pump by a Masterflex L/S digital drive and easy-load pump head. Samples were taken from the effluent at timed intervals and analyzed for chromium(VI) ions and phenol as described below. The experiment was continued until a constant concentration of each component was obtained. The experiments showed that no phenol and chromium(VI) was taken up by Mowital® B30H beads in the absence of entrapped biomass. For the biosorption system, the studies were performed at a constant temperature of 25 ◦ C to be representative of environmentally relevant conditions. All the experiments were carried out in duplicates and the average values were used for further calculations.

4.1. Single and binary biosorption of phenol and chromium(VI) in the packed bed Influent pollutant concentration is one of the most important parameters affecting the operating characteristics of the packed column as well as the general position of the breakthrough curve. The experimental breakthrough curves (along with the predicted ones) of single chromium(VI) and single phenol obtained at their changing inlet concentrations in the range 50–500 mg l−1 are shown in Fig. 1. The breakthrough time increased with a decrease in inlet concentration for both components, resulting in a broadened mass transfer zone (a slow approach of C/Co towards 1), which is commonly observed in liquid phase sorption where intraparticle diffusion is the rate-limiting transport process or slow intraparticle diffusion is occurring within the pores of the immobilized biomass beads. As expected, the treated volume (or exposure time) was the greatest at the lowest inlet concentration since the lower concentration gradient also caused a slower transport due to a decreased diffusion coefficient or decreased mass transfer coefficient. The breakthrough curves

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209

Fig. 1. The experimental and predicted breakthrough curves of single chromium(VI) (a) and single phenol (b) obtained at their changing inlet concentrations in the range 50–500 mg l−1 .

Table 1 Comparison of the individual and total equilibrium uptakes (column capacities; qeq ) and individual and total removal percents (%) of chromium(VI) and phenol at different inlet chromium(VI) concentrations in the absence and in the presence of increasing concentrations of phenol CoCr (mg l−1 )

CoPh (mg l−1 )

qeqCr (mg g−1 )

qeqPh (mg g−1 )

%AdCr

%AdPh

Co(Cr+Ph) (mg l−1 )

qeq(Cr+Ph) (mg g−1 )

%AdTot

51.4 101.2 276.0 503.1

0.0 0.0 0.0 0.0

15.3 16.4 17.0 18.4

0.0 0.0 0.0 0.0

19.4 16.8 15.1 12.7

0.0 0.0 0.0 0.0

51.4 101.2 276.0 503.1

15.3 16.4 17.0 18.5

19.4 16.8 15.1 12.7

51.3 102.2 274.8 501.4

52.1 53.3 53.1 53.2

15.1 15.5 17.5 17.6

5.0 2.8 2.0 1.3

21.9 19.8 15.5 13.7

16.6 13.8 11.4 10.0

103.4 155.5 327.9 554.6

20.1 18.3 19.5 18.9

20.3 18.6 14.4 13.3

52.3 101.4 279.0 501.3

101.9 102.3 102.3 101.4

11.4 12.4 13.2 13.5

5.5 3.8 2.6 1.7

19.0 15.9 13.7 11.5

14.0 11.5 10.8 8.4

154.2 203.7 381.3 602.8

16.9 16.2 15.8 15.6

17.0 14.6 12.6 11.5

53.3 102.7 276.5 501.5

273.0 274.1 273.0 277.1

7.1 8.5 9.6 10.9

6.0 3.9 2.9 1.9

17.4 13.0 11.8 10.5

10.2 9.7 8.6 7.2

326.3 376.8 549.5 778.6

13.1 12.4 12.5 12.8

13.2 11.7 10.3 10.3

51.2 101.2 275.9 503.1

503.3 501.4 500.7 502.8

4.3 6.0 6.9 8.0

6.8 4.6 3.5 2.1

13.1 11.6 10.3 8.9

8.4 7.2 6.9 6.5

554.5 602.6 776.6 1005.9

11.1 10.6 10.4 10.2

9.8 9.1 8.5 8.9

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Table 2 Comparison of the individual and total equilibrium uptakes (column capacities; qeq ) and individual and total removal percents (%) of phenol and chromium(VI) at different inlet phenol concentrations in the absence and in the presence of increasing concentrations of chromium(VI) CoPh (mg l−1 )

CoCr (mg l−1 )

qeqPh (mg g−1 )

qeqCr (mg g−1 )

%AdPh

%AdCr

Co(Ph+Cr) (mg l−1 )

qeq(Ph+Cr) (mg g−1 )

%AdTot

52.3 102.3 278.3 502.3

0.0 0.0 0.0 0.0

6.0 6.8 8.3 9.0

0.0 0.0 0.0 0.0

14.8 13.0 11.0 9.3

0.0 0.0 0.0 0.0

52.3 102.3 278.3 502.3

6.0 6.8 8.3 9.0

14.8 13.0 11.0 9.3

52.1 101.9 273.0 503.3

51.3 52.3 53.3 51.2

5.0 5.5 6.0 6.8

15.1 11.4 7.1 4.3

16.6 14.0 10.2 8.4

21.9 19.0 17.4 13.1

103.4 154.2 326.3 554.5

20.1 16.9 13.1 11.1

20.3 17.0 13.2 9.8

53.3 102.3 274.1 501.4

102.2 101.4 102.7 101.2

2.8 3.8 3.9 4.6

15.5 12.4 8.5 6.0

13.8 11.5 9.7 7.2

19.8 15.9 13.0 11.6

155.5 203.7 376.8 602.6

18.3 16.2 12.4 10.6

18.6 14.6 11.7 9.1

53.1 102.3 273.0 500.7

274.8 279.0 276.5 275.9

2.0 2.6 2.9 3.5

17.5 13.2 9.6 6.9

11.4 10.8 8.6 6.9

15.5 13.7 11.8 10.3

327.9 381.3 549.5 776.6

19.5 15.8 12.5 10.4

14.4 12.6 10.3 8.5

53.2 101.4 277.1 502.8

501.4 501.4 501.5 503.1

1.3 1.7 1.9 2.1

17.6 13.5 10.9 8.0

10.0 8.4 7.2 6.5

13.7 11.5 10.5 8.9

554.6 602.8 778.6 1005.9

18.9 15.6 12.8 10.2

13.3 11.5 10.3 8.9

became steeper as the inlet concentration increased and shifted towards the origin for both pollutants as the binding sites became more quickly saturated in the system at higher dye concentrations. When the inlet chromium(VI) (or phenol) concentration increased from 50 to 500 mg l−1 , the time required for complete column saturation decreased from 2800 min to about 420 min for chromium(VI) and from 1200 min to about 360 min for phenol. These results also indicated that breakthrough of phenol occurred faster than chromium(VI). The column sorption capacity (equilibrium uptake; qeqi ) and adsorption yield values (%i ) obtained for single phenol or single-chromium(VI) ions situation are presented in Tables 1 and 2 along with the binary biosorption data. As seen from the tables, the equilibrium uptake increased, the percentage removal decreased with increasing inlet pollutant concentration and maximum uptakes were determined as 18.5 mg chromium(VI) per gram of biosorbent and as 9.0 mg phenol per gram of biosorbent at 500 mg l−1 influent concentration of each sorbate. The data in these tables also indicated that the dynamic capacity of the column for phenol was generally less than that of the chromium(VI) due to lower affinity of the sorbent for phenol. For binary sorption studies, while inlet chromium(VI) concentration was changed from 50 to 500 mg l−1 , inlet phenol concentration was held constant at 50, 100, 275, or 500 mg l−1 for each experiment set. The breakthrough curves of chromium(VI) obtained in the absence and in the presence of increasing inlet phenol concentrations at two constant inlet chromium(VI) concentrations of 50 and 275 mg l−1 are given in Figs. 2 and 3. As seen in the figures, while the concentration of phenol increased in the feed mixture, the breakthrough curves of chromium(VI) became steeper and the breakpoint time decreased. As a result the chromium(VI) uptake by

immobilized activated sludge was reduced in the presence of phenol indicating the antagonistic effect of phenol. Evaluating the sorption data, the equilibrium uptakes and removal percents of individual chromium(VI), individual phenol and total equilibrium uptake and total removal percents in binary mixtures at different inlet chromium(VI) concentrations in the presence of increasing inlet phenol concentrations are presented in Table 1. At a constant inlet phenol concentration, although equilibrium chromium(VI) uptake increased with increasing inlet chromium(VI) concentration up to 500 mg l−1 , the removal percentage showed opposite trend. The results also indicated that the equilibrium uptake of chromium(VI) decreased regularly with increasing phenol concentration. For example, at a 50 mg l−1 of constant inlet chromium(VI) concentration, in the absence of phenol and in the presence of 500 mg l−1 of phenol, equilibrium uptakes were found as 15.3 and 4.3 mg g−1 , respectively. The decrease in the uptake of chromium(VI) was 71.9% in the presence of 500 mg l−1 of phenol. When inlet chromium(VI) concentration was held constant at 275 mg l−1 , the effect of 500 mg l−1 inlet phenol concentration resulted in a 59.4% reduction in chromium(VI) uptake. The inhibitory effect of phenol on the equilibrium uptake of chromium(VI) ions seems simple to explain. The presence of the other component develops a competition for the adsorption sites on the surface and some sites are occupied by the second component. As a consequence, the uptake of first component is decreased. At this part of column studies, this time, the uptake of phenol in the presence of increasing inlet concentrations of chromium(VI) was investigated at an initial pH value of 1.0. While inlet phenol concentration was changed from 50 to 500 mg l−1 , inlet chromium(VI) concentration was held constant between 50 and 500 mg l−1 for each experiment set. The

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Fig. 2. The breakthrough curves of chromium(VI) at a constant inlet chromium(VI) concentration of 50 mg l−1 in the absence and in the presence of increasing inlet phenol concentrations.

breakthrough curves of phenol obtained at increasing inlet chromium(VI) concentrations at a constant inlet phenol concentration of 50 and of 275 mg l−1 are presented in Figs. 4 and 5. Similar breakthrough curves were obtained both in the single

phenol and phenol–chromium(VI) binary systems; the breakthrough curves of phenol became steeper as the concentration of chromium(VI) in the feed increased. Further, the extent of inhibition in phenol sorption was also enhanced with increasing

Fig. 3. The breakthrough curves of chromium(VI) at a constant inlet chromium(VI) concentration of 275 mg l−1 in the absence and in the presence of increasing inlet phenol concentrations.

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Fig. 4. The breakthrough curves of phenol at a constant inlet phenol concentration of 50 mg l−1 in the absence and in the presence of increasing inlet chromium(VI) concentrations.

chromium(VI) concentration. The data obtained for phenol and chromium(VI) biosorption by immobilized activated sludge in binary mixtures prepared at changing concentrations are presented in Table 2. The results in Table 2 also indicated that

the presence of chromium(VI) has also a lessening effect on the uptake of phenol. At 50 mg l−1 constant inlet phenol concentration, the reduction of phenol uptake in the presence of 50, 100, 275, and 500 mg l−1 chromium(VI) concentrations

Fig. 5. The breakthrough curves of phenol at a constant inlet phenol concentration of 275 mg l−1 in the absence and in the presence of increasing inlet chromium(VI) concentrations.

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column capacity was 20.1 mg g−1 ). An increase in influent concentration of each pollutant appeared to increase the sharpness of the breakthrough curve of each. For 275 mg l−1 phenol and 275 mg l−1 chromium(VI) containing mixture, the column capacity of biosorbent was 9.6 mg chromium(VI) and 2.9 mg phenol per gram of dried biomass (total capacity was equal to 12.5 mg g−1 ) for this binary system. The results showed that the uptake of each component was strongly influenced and was greatly reduced by the presence of other component. Although dried activated sludge had a relatively higher adsorption capacity for phenol and chromium(VI) at single component situation, the adsorption uptake of phenol and chromium(VI) in the binary mixture were found to be decreasing due to the levels of phenol and chromium(VI) concentrations because of the antagonistic interaction between the components.

Fig. 6. Comparison of the individual breakthrough curves of chromium(VI) and phenol obtained in the mixtures containing: (a) 50 mg l−1 phenol and 50 mg l−1 chromium(VI) and (b) 275 mg l−1 phenol and 275 mg l−1 chromium(VI).

was 16.7%, 53.3%, 66.7%, and 78.3%, respectively. When inlet phenol concentration was held constant at 275 mg l−1 , the same inlet chromium(VI) concentrations reduced the phenol uptakes as 27.7%, 56.6%, 65.1%, and 77.1%, respectively. Due to these results it can be said that the inhibitory effect of chromium(VI) ions on the equilibrium phenol uptake is dominant at higher initial chromium(VI) concentrations and the presence of chromium(VI) much more greatly affected the uptake of phenol than that of the presence of phenol in chromium(V) containing solution. Individual breakthrough curves of chromium(VI) and phenol obtained in the mixtures containing 50 mg l−1 phenol and 50 mg l−1 chromium(VI), and containing 275 mg l−1 phenol and 275 mg l−1 chromium(VI) are compared in Fig. 6. For both mixtures the breakthrough curves demonstrated that the breakthrough capacity of the column is different for each component and the data reflect the order of affinity of the dried activated sludge very well. A higher adsorption capacity of dried activated sludge for chromium(VI) in comparison to phenol, may be explained on the basis of difference in ionization of chromium(VI) and phenol in aqueous solution and degree of interaction between components and biomass surface at pH 1.0 [14,17,26,28,29]. For the mixture containing lower inlet concentrations of both components (50 mg l−1 phenol and 50 mg l−1 chromium(VI)), breakthrough curves were more disperse and breakthrough occurred much more later than that of other mixture containing higher inlet concentrations of both components. For 50 mg l−1 phenol and 50 mg l−1 chromium(VI) containing mixture, initially, both phenol and chromium(VI) ions were adsorbed non-selectively due to the competition of both elements for the same binding sites in the biomass and a pollutant-free effluent was produced. With continued treatment, firstly phenol began to leave the column followed by chromium(VI). 15.1 mg chromium(VI) and 5.0 mg phenol was adsorbed per gram of dried activated sludge in this case (total

4.2. Determination of kinetic constants and estimation of breakthrough curves with the application of the Yoon and Nelson model for the single and binary biosorption of phenol and chromium(VI) The Yoon and Nelson model was applied to the column data to investigate the breakthrough behavior of phenol and chromium(VI) in the single and binary mixture systems. Respective values of kYN,i (Yoon–Nelson rate constant) and τ i (the time required for 50% adsorbate breakthrough) were determined from ln[Ci /(Co,i − Ci )] versus t plots of each component (data not shown) in single and binary systems. Inspection of each of the regressed lines indicated that they were all acceptable fits with linear regression coefficients ranging from 0.918 to 0.992. These values were used to calculate the breakthrough curve of individual phenol and individual chromium(VI) in single and binary systems. The kYN,i and τ i values of each component are listed in Tables 3 and 4 along with the correlation coefficients. The rate constant kYN,i , increased significantly with increasing both inlet chromium(VI) and phenol concentrations in both single and binary systems. This is because the driving force of mass transfer in the liquid film is increased. As shown in Tables 3 and 4 the 50% breakthrough time τ i , decreased due to rapid saturation of column with increasing total inlet concentration. The data in the tables also showed negligible or significant differences between the experimental and predicted values of τ i regardless to inlet concentration of each component. These deviations are not surprising, considering that both kinetic parameters of each component depended on assumed breakthrough curve function which was derived for a single component adsorption and used for binary mixture. In addition, this equation predicted a non-zero effluent concentration at t = 0 which contradicts real conditions, especially at lower inlet concentrations of each component. Moreover, the errors coming from the linear plotting should also be considered. The dynamic behavior of the column for each component in both single component system and in binary mixtures predicted with the Yoon–Nelson model are shown in Figs. 1–5 with the superposition of experimental results (points) and the theoretical calculated points (lines). From the experimental results and data regression, the model proposed by Yoon–Nelson provided a

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Table 3 Parameters predicted from the Yoon–Nelson model and model deviations for chromium(VI) biosorption to Mowital® B30H resin immobilized activated sludge at different inlet chromium(VI) concentrations in the absence and in the presence of increasing concentrations of phenol CoCr (mg l−1 )

CoPh (mg l−1 )

kYN,Cr (×102 , min−1 )

τ exp (min)

τ theo (min)

R2

51.4 101.2 276.0 503.1

0.0 0.0 0.0 0.0

0.57 0.67 1.20 1.54

586.7 566.0 225.0 127.5

605.1 496.8 197.7 110.4

0.961 0.970 0.967 0.963

51.3 102.2 274.8 501.4

52.1 53.3 53.1 53.2

1.04 1.18 1.26 1.71

405.0 301.6 174.8 78.6

391.6 288.6 166.4 69.8

0.976 0.963 0.979 0.961

52.3 101.4 279.0 501.3

101.9 102.3 102.3 101.4

1.24 1.31 1.57 2.00

276.4 210.2 119.5 59.4

268.0 193.1 109.9 51.7

0.986 0.979 0.975 0.971

53.3 102.7 276.5 501.5

273.0 274.1 273.0 277.1

1.31 1.48 1.63 2.89

201.8 162.9 77.5 41.2

190.9 150.0 69.3 36.6

0.986 0.969 0.970 0.989

51.2 101.2 275.9 503.1

503.3 501.4 500.7 502.8

1.42 1.55 2.52 3.59

137.5 119.3 46.7 21.9

129.0 113.1 41.6 17.0

0.976 0.984 0.992 0.972

good correlation of the effects of inlet phenol and chromium(VI) concentrations. The deviations between predicted and experimental breakthrough curves observed at higher inlet concentrations were due to the used breakthrough curve equation which was insufficient to indicate the co-component effects [23,24]. Although these discrepancies could lead some error when this equation was used to model single and binary adsorption processes, it was concluded that it can be used successfully to define

such a system since it is less complicated and easier to apply to experimental data than theories developed. 4.3. Application of Response Surface Methodology The results showed that the Yoon–Nelson rate constant of each component, kYN,i , changed notably with increasing both inlet phenol and chromium(VI) concentrations in binary

Table 4 Parameters predicted from the Yoon–Nelson model and model deviations for phenol biosorption to Mowital® B30H resin immobilized activated sludge at different inlet phenol concentrations in the absence and in the presence of increasing concentrations of chromium(VI) CoPh (mg l−1 )

CoCr (mg l−1 )

kYN,Ph (×102 , min−1 )

τ exp (min)

τ theo (min)

R2

52.3 102.3 276.0 502.3

0.0 0.0 0.0 0.0

0.65 0.74 1.37 2.10

240.0 120.0 58.0 30.0

264.9 131.6 63.5 26.0

0.987 0.970 0.918 0.927

51.3 102.2 274.8 501.4

52.1 53.3 53.1 53.2

1.50 2.45 3.67 6.31

224.8 114.6 35.8 23.8

198.9 102.2 33.1 18.8

0.998 0.957 0.967 0.968

52.3 101.4 279.0 501.4

101.9 102.3 102.3 101.4

1.83 3.01 3.83 6.45

102.6 78.5 23.7 13.8

91.5 67.0 18.1 9.1

0.983 0.955 0.968 0.952

53.3 102.7 276.5 501.5

273.0 274.1 273.0 277.1

2.31 3.79 4.10 7.85

56.3 44.5 11.4 5.5

52.3 38.7 8.5 4.4

0.972 0.974 0.964 0.974

51.2 101.2 275.9 503.1

503.3 501.4 500.7 502.8

2.50 4.65 4.69 8.89

26.5 20.2 6.7 7.7

23.8 17.7 7.0 6.5

0.969 0.979 0.973 0.981

Z. Aksu, F. G¨onen / Separation and Purification Technology 49 (2006) 205–216 Table 5 Experimental design variables, range, and levels Independent variables

Design variables

(mg l−1 )

CoPh CoCr (mg l−1 )

X1 X2

Range and levels −1

0

+1

50 50

275 275

500 500

215

software package Design Expert® 6.0 was used. A second-order quadratic equation was generated and then fitted to the data by the algorithm via multiple regression procedure for each component. For a two-factor system, this gave an empirical formula, which related the dependent variable to the independent variables and had the following expression for each component: kYN,Ph = +0.015 + 1.439E − 005 × X1 + 2.224E − 005 × X2 + 1.603E − 007 × X12 − 1.750E

Table 6 Full factorial central composite design matrix of two variables in coded and uncoded values Experiment no.

x1

x2

CoPh (mg l−1 )

CoCr (mg l−1 )

1 2 3 4 5 6 7 8 9 10 11 12 13

1 0 0 0 0 0 0 1 −1 −1 −1 1 0

1 0 0 0 −1 0 0 1 1 0 −1 −1 1

500 275 275 275 275 275 275 500 50 50 50 500 275

275 275 275 275 50 275 275 500 500 275 50 50 500

mixtures. The Response Surface Methodology was used to find a suitable relationship between the Yoon–Nelson rate constant of phenol or chromium(VI) and inlet phenol and chromium(VI) concentrations in binary mixture which may be useful for reactor design. The response (dependent) parameter was the rate constant of each pollutant calculated from the Yoon–Nelson model, while input (independent) variables were inlet phenol and inlet chromium(VI) concentrations in the mixture in this analysis. The two independent variables (inlet phenol and inlet chromium(VI) concentrations) were varied over two levels (50 and 500 mg l−1 ) relative to the center point (275 mg l−1 ) (Table 5). The full factorial central composite design matrix of two variables with respect to their uncoded and coded values is listed in Table 6. To analyze the results, the statistical

− 008 × X22 + 7.802E − 008 × X1 × X2 kYN,Cr = +0.018 − 1.584E − 005 × X1 − 4.803E − 005 × X2 + 1.519E − 008 × X12 + 8.235E − 008 × X22 + 1.096E − 007 × X1 × X2 where X1 and X2 are phenol and chromium(VI) concentrations (mg l−1 ), respectively. These regression equations gave the rate constant of phenol or chromium(VI) as a function of inlet phenol and chromium(VI) concentrations for the binary system. Each of the coefficients in each regression equation showed its contribution to the magnitude of kYN,i . The values of kYN,Ph and kYN,Cr calculated from the Yoon and Nelson model and predicted from RSM listed in Table 7 indicated that for both components the calculated values of kYN,i agreed very well with the predicted values of kYN,i at all concentration combinations studied. The standard analysis of variance (ANOVA) also confirmed the adequacy of the quadratic model since its Prob > F value is 0.0001 for kYN,Ph and 0.0002 for kYN,Cr (Prob > F < 0.05). This result indicated that it is statistically significant at 99.99% and 99.98% confidence level for both kYN,i values. The fit of the model was also checked by the regression coefficient of determination (R2 ). The values of R2 , which is a measure of how well a model can be made to fit the rate constant data, were determined as 0.994 for phenol adsorption rate constant response and 0.952 for chromium(VI) adsorption rate constant response. The closer the R2 value is to 1.000, the stronger the model is and the better it predicts the response. This implied that the sample variation of 99.39% for rate constant of phenol adsorption

Table 7 Comparison of the values of kphenol and kchromium(VI) calculated from Yoon and Nelson model and predicted from RSM CoPh (mg l−1 )

CoCr (mg l−1 )

kYN,Ph calculated (×102 , min−1 )

kYN,Ph predicted (×102 , min−1 )

kYN,Cr calculated (×102 , min−1 )

kYN,Cr predicted (×102 , min−1 )

500.7 273.0 273.0 273.0 273.0 273.0 273.0 502.8 53.2 53.1 52.1 503.3 277.1

275.9 276.5 276.5 276.5 53.3 276.5 276.5 503.1 501.4 274.8 51.3 51.2 501.5

7.85 4.10 4.10 4.10 3.67 4.10 4.10 8.89 2.50 2.31 1.50 6.31 4.69

7.70 4.11 4.11 4.11 3.27 4.11 4.11 8.72 2.38 2.14 1.70 6.46 4.74

2.52 1.63 1.63 1.63 1.31 1.63 1.63 3.59 1.71 1.26 1.04 1.42 2.89

2.22 1.64 1.64 1.64 1.44 1.64 1.64 3.71 1.68 1.21 1.17 1.46 2.67

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and 95.24% for rate constant of chromium(VI) adsorption was attributed to the independent variables and only 0.61% and 4.76%, respectively, of the total variation was not explained by the model. This ensured a satisfactory adjustment of the quadratic model to the rate constant data and was found to be adequate for prediction within the range of variables employed. 5. Conclusion Industrial effluents rarely contain a single component; hence, adsorption systems design must be based on multi-component systems. Moreover, in industrial applications use a fixed bed is more preferable than a batch reactor as it is able to treat wastewater with large quantity. In this study the simultaneous biosorption of phenol and chromium(VI) ions on the Mowital® B30H resin immobilized activated sludge from binary mixture was studied and compared with single phenol or chromium(VI) situation in a packed bed column. It has been demonstrated previously that dried activated sludge offers interesting possibilities as a metal ion and an organic biosorbent, showing rapid binding. The breakthrough curves for column sorption of each component from binary mixture to immobilized activated sludge have been measured at one’s various constant concentrations while the other’s concentration was changed from 50 to 500 mg l−1 at 25 ◦ C. The data obtained in the single and dual systems indicated that the adsorption capacity Mowital® B30H resin immobilized activated sludge for chromium(VI) is generally higher than that of phenol. The data also indicated that uptake capacity of dried activated sludge for chromium(VI) as well as phenol increased with the increasing influent concentration of each component from bisolute aqueous solutions. However, the presence of second component decreased both the capacities. This may be attributed to the fact that both the components are competing for the same adsorption sites on the activated sludge. The mono- and two-component sorption phenomena were expressed by Yoon and Nelson model to predict the breakthrough curves for each component. The model constants belonging to the model were determined by linear techniques and were proposed for the use in column design. Using the method of response surface analysis, the relation between Yoon and Nelson model rate constant of each component and the concentrations of each component in the binary sorption system was determined. Such relationships can be used to find rate constants in a mixture containing unstudied concentrations of phenol and chromium(VI). It seems that although the immobilization process decreased the biosorption properties of the biomass for each com-

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