Biosorptive Abatement of Cd2+ by Water Using Immobilized Biomass

Ind. Eng. Chem. Res. 2011, 50, 247–258

247

Biosorptive Abatement of Cd2+ by Water Using Immobilized Biomass of Arthrobacter sp.: Response Surface Methodological Approach S. H. Hasan* and Preeti Srivastava Water Pollution Research Laboratory, Department of Applied Chemistry, Institute of Technology, Banaras Hindu UniVersity, Varanasi 221 005, India

The present study deals with the successful utilization of bacterial biosorbent Arthrobacter sp. for the removal of Cd(II) ion from water in a batch and a continuous system. The maximum uptake capacity of free and immobilized biomass in batch system was 270.27 and 188.67 mg/g, respectively. The maximum removal percentage Cd2+ of 88.9% was obtained at flow rate of 1.0 mL/min and 19 cm bed height in the column system. The bed depth service time (BDST) model was applied, which was found to be in good agreement with the experimental data with high correlation coefficient (>0.997) and low chi-square value (>1.62). Thomas and Yoon-Nelson models were also applied to the experimental data, and the Yoon-Nelson model provided a better description of experimental kinetic data in comparison to the Thomas model. Column regeneration studies were also carried out using 0.1 mol/L HCl as desorbent for seven sorption-desorption cycles. Furthermore, an attempt has been made to optimize the process conditions for the maximum removal using the central composite design, and the result predicted by optimization plots was 89.64%, which is close to the experimental data, that is, 88.9% at the same process conditions. 1. Introduction A worldwide environmental problem is the contamination of aqueous environments by heavy metals. Among heavy metals, copper, lead, cadmium, mercury, nickel, and zinc have high priority for removal from aqueous environments.1,2 High concentration of Cd is highly corrosion resistant, and therefore it is widely used to plate metal parts used in general industrial hardware, automobiles, electronics, marine and aerospace industries, and nickel cadmium batteries, etc. Cadmium has been a major focus in wastewater treatment because it is associated with many health hazards. Cadmium accumulates in human beings causing erythrocyte destruction, nausea, salivation, diarrhea, muscular cramps, renal degradation, chronic pulmonary problems, skeletal deformity, proteinuria, glucosuria, etc.3 The drinking water guideline recommended by the World Health Organization (WHO) and American Water Works Association (AWWA) is 0.005 mg Cd/L. Thus, it is imperative that cadmium is removed from effluent before being discharged into the sewage system or into the aquatic environment. There are several conventional techniques, utilized for removing heavy metals from aqueous streams, such as chemical precipitation as synthetic coagulants, solvent extraction, ion exchange, and reverse osmosis.4 The application of such traditional treatment techniques, however, needs enormous cost and continuous input of chemicals, which becomes impracticable and uneconomical and causes further environment damage.5 Hence, the search for an easy, effective, economic, and eco-friendly technique is underway, which is required for the fine-tuning of effluent/ wastewater treatment. Biosorption has been suggested as a potential alternative for detoxification and recovery of toxic and valuable metals from wastewaters. Batch experiments are generally done to measure the effectiveness of adsorption for removing specific adsorbates as well as to determine the maximum adsorption capacity. Different types of biomaterials, biomass of microorganisms and agricultural waste, have been used for the removal of cadmium with batch mode of sorption * To whom correspondence should be addressed. Tel.: +919839089919. E-mail: [email protected].

experiments.6,7 However, from an industrial point of view, the continuous adsorption in fixed-bed column is often desired. It is simple to operate and can be scaled-up from a laboratory process. Few attempts have been made for the removal of cadmium from water using continuous mode.8,9 The use of bacteria in this area of biosorption is advantageous.10 However, the use of microbial biomass in its native form for large-scale process utilization is not practicable because of its smaller particle size, poor mechanical strength, and little rigidity. Other drawbacks include difficulty in the separation of biomass after biosorption, possible biomass swelling, inability to regenerate/ reuse, and development of high pressure drop in the column mode.11 Immobilization of microbial biomass within polymeric matrix has been found to be practical for biosorption. It has several benefits including control of particle size, regeneration and reuse of the biomass, and easy recovery of metal from the loaded beads using appropriate desorption techniques, thereby minimizing the possibilities of environmental contamination, high biomass loading, and minimal clogging under continuousflow conditions.12 However, the use of immobilized biomass also has a number of disadvantages. In addition to increasing the cost of biomass pretreatment, immobilization adversely affects the mass transfer kinetics of metal uptake. When biomass is immobilized, the number of binding sites easily accessible to pollutant in solution is greatly reduced because the majority of sites will lie within the bead. So a good support material used for immobilization should be rigid, chemically inert, and cheap, should bind cells firmly, should have high loading capacity, and should have a loose structure for overcoming diffusion limitations.13 Indeed, the choice of immobilization matrix is a key factor in the environmental application of immobilized biomass. The polymeric matrix determines the mechanical strength and chemical resistance of the final biosorbent particles for successive biosorption cycles. In the present Article, the effects of column parameters such as bed height and flow rate have been studied using calcium alginate immobilized beads. Bed depth service time (BDST), Thomas, and Yoon-Nelson models were applied on the experimental data. In addition to this, sorption-desorption studies were also

10.1021/ie101739q  2011 American Chemical Society Published on Web 11/23/2010

248

Ind. Eng. Chem. Res., Vol. 50, No. 1, 2011

carried out to regenerate and reuse the biosorbent. Furthermore, the effect of the variables, that is, flow rate and bed height, which were affecting the abatement of Cd(II) in column system, was evaluated by using a statistical and graphical technique called response surface methodology (RSM).14-16 2. Materials and Methods 2.1. Preparation of Microorganism for Biosorption. The strain used in this study was isolated from the pond situated in Banaras Hindu University Campus, Varanasi, India. The isolation of this strain was done by standard isolation methods (streak plate methods). This isolate was purified by repeated isolation and sub culturing on a nutrient enrichment medium (nutrient agar medium). A pure colony of the strain was identified presumptively on the basis of the following features: colony morphology, cell morphology, Gram-staining reaction, and catalase test. All are given in earlier work.17 Arthrobacter sp. was cultivated aerobically in 250 mL conical flasks containing sterile nutrient broth containing 10 g/L beef extract, 5 g/L NaCl, and 20 g/L peptone on a rotary shaker at 30 °C, 150 rpm for 48 h. The initial pH of the culture was adjusted from 7.0 to 7.5 using 0.1 N NaOH/0.1 mol/L HCl. The growing cells from culture broth were separated from liquid by centrifugation (REMI 24) at 5000 rpm for 10 min and were washed several times with double distilled water. The wet cell biomass was then dried for 24 h at 60 °C in an oven. The dried cells were powdered in uniform size. The surface area of free biomass was calculated by Micromeritics surface area analyzer (model ASAP 2020, U.S.), and it was 1.654 m2/g. 2.2. Immobilization of Arthrobacter sp. in Calcium Alginate. 7.0 g of the dried biomass of Arthrobacter sp. was suspended into 100 mL of hot double deionized water and mixed with 3.0 g of sodium alginate (Himedia Chemical). A 100 mL aliquot of sodium alginate-cell suspension was then extruded into 500 mL of a 2 mmol/L CaCl2 · 2H2O (Qualigens Chemicals) solution in a dropwise manner by syringe of various sizes for polymerization and bead formation. Sodium alginate drops gets precipitated upon contact with solution CaCl2 · 2H2O, forming gel beads of nearly 3, 5, and 10 mm diameter. These beads were then soaked in CaCl2.H2O solution for 8 h for complete gelling, and the beads thus obtained were washed with 5 g/L NaCl (AR grade) solution to remove excessive calcium ions. Blank calcium alginate beads were also prepared without adding biosorbent in a similar way.18 The surface area of blank and immobilized beads was also calculated by a Micromeritics surface area analyzer (model ASAP 2020, U.S.), and it was 1.987 and 2.321 m2/g, respectively. Fifty alginate beads were placed in a shaking flask containing 62.5 mL of 85/L NaCl with five glass beads and kept at 37 °C under shaking at 250 rpm for 6 h. The flask contents were then filtered through stainless steel sieve, and the number of gel beads present was observed. Mechanical resistance was expressed on the basis of fracture frequency, which is given below: fracture frequency % )

N × 100 Nt

(1)

where N is the number of fractured beads, and Nt is the total number of gel beads. 2.3. Preparation of Standards and Reagents. All chemicals and reagents used were of analytical grade and were used without further purification (purchased from E. Merck, India Ltd., Mumbai, India). Stock solutions of Cd(II) 1000 mg/L were prepared from 2.74 g of Cd(NO3)2 · 4H2O in 1000 mL of double

deionized water (DDW) containing a few drops of concentrated HNO3 to prevent the precipitation of Cd(II) by hydrolysis. Required initial concentration of Cd(II) samples was prepared by appropriate dilution of the above stock Cd(II) standard solution. Standards for calibration of the atomic absorption spectrophotometer (AAS) for Cd(II) were prepared from a standard solution of cadmium purchased from E. Merck, India Ltd., Mumbai, India. 2.4. Batch Sorption Experiments. Biosorption experiments were performed in 250 mL conical flasks previously rinsed with HNO3 to remove any metal that remained on the glass wall. The pH of the metal solutions in the conical flask was initially adjusted to desired values by using 0.1 mol/L HNO3 and NaOH; the sorbents (free biomass and immobilized biomass) were added to each flask and were agitated on the shaker until the equilibrium was reached. The sorbent (free biomass and immobilized biomass), separated by centrifugation/filtration at 15 000 rpm for 5 min, was analyzed for residual Cd(II) concentration in the supernatant solution. The biosorption capacity of the metal ion was calculated by the equation: q)

(Co - Ce)V M

(2)

where q is the metal uptake, V is the volume, M is the amount of biomass, and Co and Ce are the initial and equilibrium metal concentrations, respectively. All experiments in this work were conducted in triplicate. 2.5. Column Design and Experimental Procedure. Experiments were carried out in borosilicate glass column of 30 cm height and 2.0 cm internal diameter, filled with different amounts of immobilized biomass, that is, 4.9, 7.0, 9.1, 11.2, and 13.3 g, to achieve different bed heights of 7, 10, 13, 16, and 19 cm, respectively. In the column, 0.5 mm stainless steel mesh and 1.0 cm glass wool were kept at the bottom and the at top of the column, respectively, to support the beads in the column and to ensure a closely packed arrangement. A 3.0 cm layer of glass beads was placed at the column base for providing a uniform inlet flow and good Cd(II) solution distribution into the column. Approximately 5000 mL of the Cd(II) solution having initial concentration 10 mg/L was adjusted to pH 6.0 and pumped through a peristaltic pump (Miclins India, model no. PP 10) connected at the bottom of the column in an upward direction (figure not given). The treated Cd(II) solution was collected from the top with the same flow rate of feed stream and was estimated for the Cd(II) concentration. Operation of the column was stopped when effluent metal concentration exceeded a value of 98% of the initial metal ion concentration. Desorption was carried out by passing selected desorbent through the column bed in an upward direction at a flow rate of 2.0 mL/min. The effluent metal solution was collected and analyzed for Cd(II) content. On the completion of desorption cycle, the column was rinsed with deionized double distilled water in the same manner as for biosorption until the pH of eluting distilled water attains between 4 and 7.0. The desorbed and regenerated column bed was reused for next cycle. Another cycle of sorption-desorption was repeated in the same manner as above-mentioned. All the experiments were performed in triplicates at 30 °C temperature and atmospheric pressure (760 mm). The bed porosity (ε) of the column bed was determined for 0.382 prior to the experiments. The bed porosity, which is the fraction of total volume that is void, is defined as porosity ) volume void/volume of entire bed.19 2.6. Analysis of Cd(II) in Aqueous Solution. The concentration of cadmium in sample solution was determined with a


atomic absorption spectrophotometer (AAS, Shimadzu AA6300, Japan). A hollow cathode lamp for cadmium was used as the light source at 228.8 nm wavelength, a lamp current of 10 mA, and a slit width of 0.7 nm, and with a deuterium lamp for background correction. To generate flame, instrument grade (98%) acetylene, delivered at 4.0 L/min at a pressure of 0.9 kg/cm2, was mixed together with compressed air supplied at 17.5 L/min flow rate and 3.5 kg/cm2 gas pressure. The instrument was calibrated from 0.1 to 10.0 mg/L for Cd(II). Other range samples were diluted until results within the calibration range were obtained for the metal ion. 2.7. Analysis of Column Data. The column data for removal of metal in up-flow fixed bed column system were calculated with the help of the following equations.11,20 The volume of the effluent, Veff (mL), can be calculated through the following equation: Veff ) F · te

(3)

where te is the total exhaustion time in hours, and F is the flow rate that circulates through the column in mL/h. The area above the breakthrough curve (A) represents the total mass of metal biosorbed, qtotal, in mg, for a given feed concentration and flow rate, and it was determined by integration: qtotal )

F FA ) 1000 1000

∫

t)te

t)0

CR dt

Mtotal

CoFte ) 1000

based on Bohart and Adams equation is widely used. This approach, herein after referred to as the BDST approach, based on the surface reaction rate theory, gives an idea of the efficiency of the column under constant operating conditions for achieving a desired breakthrough level. In the fixed bed system, the main design criterion is to predict how long the adsorbent material will be able to sustain removing a specified amount of impurity from solution before regeneration is needed. This period of time is called the service time of the bed. BDST is the simple model for predicting the relationship between bed height (Z) and service time (t) in term of process concentration and adsorption parameters. Hutchins proposed a linear relationship between bed height and service time given by the equation: t)

(5)

qtotal × 100 Mtotal

qtotal m

t ) aZ - b

Md × 100 Mad

(10)

where slope is a)

No Cou

(11)

and intercept is b)

(

Co 1 ln -1 kaCo Cb

)

(12)

The critical bed depth (Zo) is the theoretical depth of the sorbent sufficient to ensure that the outlet solution concentration does not exceed the breakthrough concentration Cb. Zo can be calculated by setting t ) 0 and solving eq 9:22,24 Z)

(7)

where m is the mass of sorbent in g. The metal mass desorption (Md) can be calculated from the desorption curve (concentration versus time), and the desorption efficiency (D.E.) is calculated from the following equation: D.E. (%) )

(9)

.

(6)

The amount of metal sorbed at equilibrium or biosorption capacity, Q (mg/g), was calculated using the following expression: Q)

)

Here, t is the service time, No is the dynamic bed capacity (mg/ L), Z is the bed height of the column (cm), u is the influent linear velocity (cm/h) defined as the ratio of the volumetric flow rate F (mL/h) to the cross-sectional area of the bed Sc (cm2), Cb is the breakthrough concentration of solution (mg/L), Co is the initial concentration of solution in the liquid phase (mg/L), and ka is the rate constant in the BDST model (L/mgh). Equation 9 is rewritten in the form of a straight line.

and the total metal removal (%) was calculated from the ratio of metal mass biosorbed (qtotal) to the total amount of metal ions sent to the column (Mtotal) as %R )

(

NoZ Co 1 ln -1 Cou kaCo Cb

(4)

where CR is the concentration of metal removal in mg/L. The total amount of metal ions sent to the column, in mg, was calculated from the following expression:

249

22,23

(8)

where Mad is the metal adsorbed in mg/g. 2.8. Modeling. Experimental evaluation of the performance of a fixed-bed column in biosorption is generally possible only with small laboratory columns. The data collected during laboratory studies can be very well utilized for predicting and evaluating the performance of practical size column by applying suitable mathematical models developed for such purposes. Several models have been reported for predicting the breakthrough performance in fixed-bed sorption.21 Among various design approaches, the bed depth service time (BDST) approach

(

Co u ln -1 NoKa Cb

)

(13)

Various mathematical models can be used to describe fixed bed sorption. Among these, the Thomas25 and Yoon-Nelson26 models are simple and widely used. The Thomas model assumes the Langmuir kinetics of adsorption-desorption and no axial dispersion and is derived from the adsorption that the rate driving forces obeys secondorder reversible reaction kinetics. This model also assumes a constant separation factor, but it is favorable, and an unfavorable isotherm. The primary weakness of the Thomas model is that its derivation is based on second-order reaction kinetics. Sorption is usually not limited by chemical reaction kinetics, but it is often controlled by interphase mass transfer. This discrepancy can lead to some error when this method is used to model adsorption process. The linearized form of the Thomas model is as follows: ln

(

)

kTHCo kTHqTHm Co Veff -1 ) Ct F F

(14)

250


where kTH is the Thomas rate constant, and qTH is the maximum solid phase concentration of the solute (column capacity). The kinetic coefficient kTH and the adsorption capacity of the bed qTH can be determined from a plot of ln[Co/Ct - 1] against volume effluent at a given flow rate. Yoon and Nelson developed a relatively simple model addressing the sorption and breakthrough of sorbate vapors or gases with respect to activated charcoal. This model is based on the assumption that the rate of decrease in the probability of adsorption for each adsorbate molecule is proportional to the probability of adsorbate adsorption and the probability of adsorbate breakthrough on the adsorbent. The linear form of the Yoon and Nelson equation regarding a single-component system is expressed as:

(

)

Ct ) kYNt - τkYN ln Ct - C o

(15)

where kYN is the Yoon-Nelson rate constant, τ is the time required for 50% sorbate breakthrough, and t is the breakthrough (sampling) time. The parameters kYN and τ may be determined from a plot of ln[(Ct/Co - Ct)] versus sampling time (t). The derivation of eq 15 was based on the definition that 50% breakthrough occurs at t ) τ. Thus, the sorption bed should be completely saturated at t ) 2τ. Because of the symmetrical nature of breakthrough curves due to the Yoon-Nelson model, the amount of metal being sorbed in the fixed bed is one-half of the total metal entering the sorption bed within a 2τ period. Hence, the following equation can be obtained for a given bed: kYN )

(1/2)Co(F/1000)(2τ) CoFτ Mtotal ) ) m m 1000m

(16)

The equation also permits one to determine the adsorption capacity of the column (qYN) as a function of bed height (Z), initial metal ion concentration (Co), flow rate (F), biomass quantity in the column (m), and 50% breakthrough time (τ) by using the Yoon-Nelson model.27 Chi square (χ2) values were used to measure the goodnessof-fit of the better fit of the model. The chi-square test can be defined as: m

χ2 )

∑ i-1

(Xe - Xp)2 Xp

(17)

where Xe is the experimental value and Xp is the predicted value, and m is the number of observations. The smaller values of χ2 indicate the better curve fitting.28 2.9. Response Surface Methodology (RSM). RSM is a combination of mathematical and statistical techniques used for developing, improving, and optimizing the processes and used to evaluate the relative significance of several affecting factors even in the presence of complex interactions. RSM usually contain three steps: (1) design and experiments; (2) response surface modeling through regression; and (3) optimization. The main objective of RSM is to determine the optimum operational conditions of the process or to determine a region that satisfies the operating specifications. The application of statistical experimental design techniques in adsorption process development can result in improved product yields, reduced process variability, closer confirmation of the output response to nominal and target requirements, and reduced development time and overall costs.29,30 Varieties of factorial designs are available to accomplish this task. The most successful and best among them is the central composite design (CCD). It is obtained by adding

two experimental points along each coordinate axis at opposite sides of the origin and at a distance equal to the semidiagonal of the hyper cube of the factorial design. The new extreme values (low and high) for each factor are added in this design.31 If the factorial is a full factorial, then R ) [2k]1/4

(18)

Because in this study two factors such as flow rate and bed height of Cd(II) removal were considered, thus k ) 2. So R ) 1.414. Furthermore, the total number of experiments points (N) in a CCD can be calculated from the following equation: N ) 2k + 2k + Xo

(19)

where N is the total number of experiments, k is the number of variables, and Xo is the number of central points. Thus, for this design, the total number of experimental runs will be 13 (k ) 2; Xo ) 5). Data from the central composite design were subjected to a second-order multiple regression analysis to explain the behavior of the system using the least-squares regression methodology for obtaining the parameter estimators of the mathematical model. Y ) β0 + ∑βiXi + ∑βiiX2i + ∑βijXiXj + ε

(20)

where Y is the response, β0 is the constant, βi is the slope or linear effect of the input factor Xi, βii is the quadratic effect of input factor Xi, βij is the linear by linear interaction effect between the input factor Xi, and ε is the residual term. MINITAB Release 15, developed by Minitab Inc., U.S., a statistical software package,32 was used for regression analysis of the data obtained and to estimate the coefficient of regression equation. Analysis of variance (ANOVA), which is a statistical testing of the model in the form of linear terms, squared terms, and the interaction, was also utilized to test the significance of each term in the equation and the goodness-of-fit of the regression model obtained. This response surface model was also used to predict the result by contour plots to study the individual and cumulative effects of the variables and the mutual interactions between the variables on the dependent variable. Optimization curves were plotted to confirm the experimental results and to achieve the required removal of cadmium by choosing the predicted conditions. 3. Results and Discussion 3.1. Mechanical Strength of Beads. The determination of mechanical strength of the immobilized beads in bioreactors is of crucial importance for the scale-up purpose. The results showed that the smaller beads showed higher stability than larger beads. The beads’ fracture frequency rate obtained was 2%, 4%, and 10% for 3, 5, and 10 mm diameter biomass immobilized beads, respectively. Therefore, for further investigation, 3 mm size immobilized beads were taken. 3.2. Effect of pH and Biosorption Isotherm. Figure 1 shows the biosorption of Cd(II) by free biomass and immobilized beads (3 mm size) as a function of pH (preliminary tests confirmed that the blank beads exhibited negligible Cd(II) uptake (data not shown)). The uptake capacities of the two sorbents generally showed a similar trend: an increase in uptake with pH increase at pH 2.0 to 6.5, with effect leveling off beyond pH 6.5. It was also noted that the immobilized biomass has a much lower uptake capacity than the free biomass at the same pH. The effect


251

Figure 1. Effect of pH. Initial Cd(II) concentration ) 10 mg/L, 30 °C temperature. Vertical bars show the standard deviation of three replicates.

Figure 2. Breakthrough curve for Cd(II) sorption at different bed heights and flow rate. pH ) 6.5, temperature ) 30 °C, initial Cd(II) concentration ) 10 mg/L. Vertical bars show the standard deviation of three replicates.

of pH on the capacity for Cd(II) biosorption by immobilized beads was studied, and the optimum biosorption capacity was obtained at pH 6.5 for both free and immobilized beads. It was observed that the Cd(II) biosorption was relatively less at low pH values (pH 2-3). The less metal uptake at these pH conditions may be explained on the basis of binding sites being protonated, resulting in a competition between H+ and Cd(II) ions for occupancy of binding sites. As the pH increased and surface functional groups got activated, it resulted in increased Cd(II) sorption, and the sharpest increase in Cd(II) uptake was observed in the pH range of 3-6.5. Further increase in pH resulted in a decline in Cd(II) uptake in both cases. When pH is higher than 6.5, the precipitation of insoluble metal hydroxides that takes place thus lowers the biosorption efficiency. In addition to this, at the equilibrium condition, the optimum and initial pH 6.5 of the metal solution slightly increased to 6.68 and 6.79 for free and immobilized biomass, respectively. This could have resulted due to dissolution of certain minerals, such as carbonates from the biomass.33,34 This process became rapid with increase of initial pH from 6.5 to 8.0, and thus uptake capacity was decreased. Therefore, for further investigation, an optimized pH of 6.5 was taken for all the biosorption experiments for both free and immobilized biomass, respectively. The Langmuir isotherm model,35 valid for monolayer sorption onto a surface of a finite number of identical sites, is given by eq 21:

Table 1. Column Data for Packed Bed Immobilized Beads Column for Biosorption of Cd(II) onto Immobilized Arthrobacter sp. at Different Bed Heights (cm) and Flow Rates (mL/min)a

Ce 1 1 ) + 0 Ce qe bQ0 Q

(21)

where Q0 is the maximum metal uptake (mg/g) and b is the Langmuir affinity constant (L/mg). The constant b represents the affinity between the sorbent and the sorbate. The plot of Ce versus Ce/qe (figure not given) of the Langmuir isotherm was found to be linear, and the maximum Cd(II) uptake (Q0) was observed as 270.27 and 188.67 mg/g for free and immobilized biomass, respectively, at pH 6.5, 30 °C temperature, and 10-110 mg/L of Cd(II) concentration range. The constant b was recorded as 0.0037 and 0.0052. The correlation coefficients were 0.997 and 0.995 for free and immobilized biomass, respectively. The Langmuir uptake capacity (Q0) of free biomass was greater than that of the immobilized biomass. This variation could be due to many reasons. First, the binding of cationic and anionic metal species to the bacterial cell wall is assumed to occur predominantly through surface adsorption. Second, the mass transfer

parameter

tb (h)

te (h)

Veff (mL)

Q (mg/g)

%R

Bed Height (cm) Z (Conditions: Co ) 10 mg/L, F ) 1 mL/min) 7 10 13 16 19

7.0 10.1 15.1 18.6 22.3

20.4 30.1 48.6 58.2 65.3

1224 1806 2916 3492 3918

1.70 1.87 2.51 2.60 2.62

68.1 72.5 78.4 83.8 88.9

Flow Rate (mL/min) F (Conditions: Co ) 10 mg/L, Z ) 19 (13.3 g) cm 1 2 3 4 5

22.3 18.1 10.2 7.3 4.5

65.3 35.2 24.0 18.3 14.8

3918 4224 4320 4392 4440

2.62 2.48 2.40 2.02 1.83

88.9 78.4 74.0 61.3 54.7

Co ) initial Cd(II) concentration, te ) exhaustion time (h), tb ) breakthrough time (h), Veff ) volume effluent (mL), Q ) column uptake capacity (mg/g). a

of the metal ions from aqueous phase to the solid sorbent sites is dependent on porosity of the sorbent. Third, the native biomass in contact with the metal ions under the conditions of moderate agitation has its binding sites freely exposed to the sorbate. However, in immobilized systems, the sorbent particles entrapped and retained at the interior may not have accessibility to the metal ions.18 3.3. Effect of Bed Height and Flow Rate. To optimize bed height for maximum Cd(II) removal, experiments were carried out by varying the bed heights of 7.0, 10.0, 13.0, 16.0, and 19.0 cm by the addition of 4.9, 7.0, 9.1, 11.2, and 13.3 g of immobilized beads into the column at flow rate of 1 mL/min and 10 mg/L Cd(II) concentration. The plot of effluent Cd(II) concentration versus time (Ct/Co vs time) at different bed heights was plotted (Figure 2), and data calculated from the breakthrough curves are presented in Table 1. This result indicates that the breakthrough time (7.3-22.3 h), exhaustion time (20.4-65.3 h), uptake capacity (1.70-2.62 mg/g), percentage removal (68.1-88.9%), and volume treated increased (1224-3918 mL) with the rise in bed height from 7.0 to 19.0 cm. This displacement of the front of adsorption with the increase in bed depth can be explained by mass transfer phenomenon that takes place in this process. When the bed depth is reduced, axial dispersion phenomenon predominates in the mass transfer and reduces the diffusion of metallic ions. The solute (metallic ions) has not enough time to diffuse into the whole adsorbent mass.

252


Consequently, an important reduction in the volume treated at breakthrough point was observed. Moreover, an increase in the bed adsorption capacity was noticed at the breakthrough point with the increase in the bed height. This increase in adsorption capacity with increase in the bed height can be due to the increase in the specific surface of the adsorbent, which supplies more fixation binding sites. The effect of flow rate on Cd(II) sorption was studied by varying the flow rate (1.0, 2.0. 3.0, 4.0, and 5 mL/min) at a bed height of 19 cm and 10 mg/L of initial Cd(II) concentration. The plot of Ct/C0 versus time at different flow rate is also shown in Figure 2. The breakthrough time, exhaustion time, treated volume effluent, percentage Cd(II) removal, and Cd(II) uptake capacity with respect to flow rate were evaluated from the sorption data and presented in Table 1. It was found that breakthrough time, exhaustion time, percentage Cd(II) removal, and uptake capacity decrease as the flow rate increased, whereas the volume of treated effluent increased with increase in flow rate from 1.0 to 5 mL/min. At lower flow rate, the contact time of the Cd(II) solution and immobilized biomass in the column was longer, and hence Cd(II) ions got more time to diffuse onto the immobilized biomass, and better adsorption capacity and percentage removal were obtained. Actually when the flow rates were low, the external mass transfer controlled the process, and it was also ideal for intraparticle diffusion systems. Thus, the lower were the flow rates, the more effective was the diffusion process and the higher was the residence time of the sorbate, which ultimately resulted in higher sorption capacity.24,36,37 At a higher flow rate, the biosorbent gets saturated early (certainly because of reduced contact time), a larger amount of ions is adsorbed onto the immobilized biomass, and a weak distribution of the liquid is there into the column. This leads to a lower diffusivity of the solute onto the immobilized biomass. It was also observed that at higher flow rates the immobilized biomass got saturated easily, and this should result in a higher volume of treated effluent at higher flow rate, that is, improper utilization of the sorption capacity of immobilized biomass. 3.4. Application of Bed Depth Service Time Model. The bed depth service time (BDST) model is one of the most widely used models for scaling up the continuous treatment procedure. Thus, this model was utilized for the modeling of column data for the present work. For this purpose, the service time, that is, breakthrough time of the column corresponding to bed heights of 7, 10, 13, 16, and 19 at two flow rates of 1.0 and 3.0 mL/ min at constant initial Cd(II) concentration of 10 mg/L, was recorded. Thereafter, the graph was plotted between service time and bed height and is given in Figure 3. From the slope and intercept of the BDST plot, the BDST parameter, sorption rate constant (ka), and bed sorption capacity (No) were calculated and listed in Table 2. The good values of correlation coefficient showed that the variation of the service time with the bed depth is linear for both flow rates, thus indicating the validity of the BDST model when applied to the continuous column studies. It can be observed from Table 2 that the values of rate constant, ka, were 0.095 and 0.360 L/mg/h for flow rates of 1 and 3 mL/ min, respectively. The rate constant, which is calculated from the intercept of BDST plot, characterizes the rate of solute transfer from the liquid phase to solid phase. It was found that the values of the rate constant were influenced by flow rates and showed an increasing trend with the increase in flow rate, indicating that the overall system kinetics was dominated by external mass transfer in the initial part of the sorption in the column. In general, if ka is large, a short bed is required to avoid breakthrough, but as ka decreases, a progressively longer bed is

Figure 3. Bed depth service time plot for Cd(II) sorption onto Arthrobacter sp.

required to avoid breakthrough. It was also observed from Table 2 that at flow rates of 1 and 3 mL/min the computed values of bed sorption capacity were 249.03 and 247.94 mg/L, respectively. The results given in Table 2 also present the value of the critical bed depth, which represents the sufficient height of the column bed to avoid breakthrough at t ) 0 as was calculated from eq 13. The critical bed depth was found to be 1.77 and 1.35 cm for 1 and 3 mL/min, respectively. These results indicated that the critical bed depth decreased with an increase in the flow rate of the solute through the column. Thus, the finding that with the increase in flow rate the theoretical bed depth (Zo) decreased correlated well with the observed performance in the breakthrough curves and thus explained the experimental results for poor performance of the column at higher flow rates. The simplicity and advantage of using the BDST model is that it can applied for prediction of the slope for any unknown flow rate with a known slope at a given flow rate. Thus, the values of constants obtained from the experimental plot can be extrapolated for alternative flow rates, by modifying the equation. A simplified form of the Bohart-Adams model is t ) aZ - b, where a is the slope, a ) (No)/(Cou), and b is the intercept, b ) (1)/(kaCo) ln(Co)/(Ct) - 1). When a new flow rate, other than the one used in the development of constants, is used in the column system, the equation can be modified by utilizing the new slope: a′ ) a

F u )a u′ F′

(22)

where a and u are the old slope and influent linear velocity, respectively, and a′ and u′ are the new slope and influent linear velocity. As the column used in experiment has the same diameter, the ratio of original (u) and the new influent linear velocity (u′) and original flow rate (F) and new flow rate (F′) will be equal. For the present study, the BDST model parameters were calculated experimentally at two flow rates of 1 and 3 mL/min, and the equations thus obtained are y ) 1.3033x - 2.3233 and y ) 0.45x + 0.61 with r2 values of 0.997 and 0.998, respectively (Figure 3). To show validity of application of BDST model in predicting the column design at a new flow rate, the BDST equations were predicted at the flow rate of 3 mL/min using a sample flow rate of 1 mL/min, so that direct comparison can be done between the experimental and predicted analysis. The result obtained after the prediction at 3 mL/min flow rate is


253

Table 2. Bed Depth Service Time (BDST), Thomas Model, and Yoon-Nelson Model Parameters for Sorption of Cd(II) onto Immobilized Arthrobacter sp.a BDST model

Thomas model 2

2

2

Yoon-Nelson model 2

F

Q(exp)

τ(exp)

No

ka

Zo

r

χ

kTH

qTH

r

χ

kYN

τ

qYN

r2

χ2

1 3

2.62 2.40

55.5 17.0

249.03 257.94

0.095 0.360

1.77 1.35

0.997 0.998

1.24 1.62

0.0156 0.0540

2.51 2.31

0.951 0.968

8.76 11.98

0.157 0.534

58.63 17.26

2.64 2.33

0.976 0.973

6.84 8.64

a F ) flow rate (mL/min), Q(exp) ) experimental column uptake capacity, τ(exp) ) time required for 50% sorbate breakthrough (h), No ) bed sorption capacity (mg/L), ka ) sorption rate constant (L/mgh), Zo ) critical bed depth (cm), Q ) uptake capacity (mg/g), kTH ) Thomas rate constant (mL/mg/ h), qTH ) maximum solid phase concentration of the solute (column capacity) (mg/g), kYN ) Yoon-Nelson rate constant (L/h), τ ) time required for 50% sorbate breakthrough (h) (model predicted), qYN ) sorption capacity of the column (mg/g), r2 ) correlation coefficient, χ2 ) chi squared value.

0.43x + 0.8 with an r2 value of 1.000. It can be seen from the result that the predicted (0.43) and experimental (0.45) values of slopes for a flow rate of 3 mL/min were in good agreement. However, the intercept values showed slight variations, as this term was assumed to be insignificantly affected by changing the flow rates and was not adjusted during prediction. 3.5. Application of the Thomas Model and YoonNelson Model. Thomas and Yoon-Nelson models were also applied to the sorption kinetics data at two flow rates, 1 and 3 mL/min at an initial metal concentration of 10 mg/L and bed height of 19 cm. The Thomas coefficients (kTH and qTH) were determined from the slope and intercepts of linear plot of ln[Co/Ct - 1] versus Veff at 1 and 3 mL/min flow rates (figure not given), and data are presented in Table 2 along with correlation coefficients. It was found by the analysis of the regression coefficients that the regressed lines provided excellent fits to the experimental data with r2 values of 0.951-0.968 (Table 2) for 1 and 3 mL/ min flow rates, respectively. The bed capacity qTH and the coefficient kTh decreased with increasing flow rates. As is evident from Table 2, the difference between the experimental and model predicted values of bed capacity at both flow rates was not much. The simple Yoon-Nelson model was also applied to investigate the breakthrough behavior of Cd(II) onto fixed bed continuous column study. Linear regression was performed, and the values of parameters Yoon-Nelson rate constant (kYN) and τ (the time required for 50% sorbate breakthrough) were determined from the linear plots of ln[Ct/(Co - Ct)] versus time t at two flow rates of 1 and 3 mL/min (figure not given). The values of these calculated parameters were presented in Table 2, along with the correlation coefficients. The experimental data exhibited good fits to the model with linear regression coefficients f 0.977 and 0.974 for 1 and 3 mL/min flow rates, respectively (Table 2). As is evident from the table, the experimental and the calculated τ-values are very close to each other, indicating that the Yoon-Nelson model fits excellently to the experimental data. The bed capacity qYN, which was calculated with the help of eq 16, is also presented in Table 2. Also, it was found that the bed capacity calculated by the Yoon-Nelson model was better correlated with the experimental uptake capacity. Thus, the Yoon-Nelson model was found to well fit to the experimental data as compared to the Thomas model. If data from models are similar to the experimental data, high values will be for r2 and smaller values will be for χ2. It was observed from Table 2 that the high values of r2 and smaller values of χ2 were obtained in case of BDST model. It means the BDST model was adequately fitted to the experimental data in comparison to the Yoon-Nelson and Thomas models. 3.6. Regeneration and Reuse of Biosorbent. Biosorption is a process of treating pollutant-bearing solutions to make it contaminant free. However, it is also necessary to be able to regenerate the biosorbent. This is possible only with the aid of

appropriate eluants, which usually results in a concentrated pollutant solution. Therefore, the overall achievement of a biosorption process is to concentrate the solute, that is, sorption followed by desorption. Desorption is of utmost importance when the biomass preparation/generation is costly, as it is possible to decrease the process cost and also the dependency of the process on a continuous supply of biosorbent. The purpose of desorption is to resolubilize biomass-bound metals in greatly reduced, more manageable volumes, and to recover metals if they are of any economic significance.38 A successful desorption process requires the proper selection of eluants, which strongly depends on the type of biosorbent and the mechanism of biosorption. Also, the elutant must be (i) non damaging to biomass, (ii) less costly, (iii) environmental friendly, and (iv) effective.10 In the present section, an attempt has been made to regenerate the exhausted biosorbent and to use it for various sorption-deorption cycles. For this purpose, the column was initially packed with 13.3 g of immobilized biomass to yield A bed height of 19 cm. For screening of desorbing agent, experiments were carried out by passing different desorbing agents such as 0.1 mol/L H2SO4, HCl, EDTA through the column bed previously loaded by Cd(II) in upward directions at a flow rate of 1 mL/min and (30 °C temperature. The effluent metal solution was collected and analyzed for metal content. It has been observed that maximum desorption of Cd(II) 99.08% occurred with HCl follow decreasing order for different desorbing agent as EDTA (90.28%) > H2SO4 (32.70%). Thus, the 0.1 mol/L HCl solution dilute solution was the most effective and potent desorbent for desorbing cadmium metal ion. The present work indicated that EDTA desorbs metal with great efficiency (90.28%). However, EDTA cannot be used in large scale as its release in nature may lead to serious environmental pollution problems. So, further investigations were carried out by using HCl for Cd(II) desorption. The column regeneration studies wERE carried out for various sorptions-desorption cycles. For this purpose, the column was initially packed with 13.3 g of immobilized beads for an initial bed height of 19.0 cm and flow rate of 1 mL/min at constant cadmium concentration of 10 mg/L and temperature (30 °C. The pH of the solution was initially adjusted at 6.0. The breakthrough curves thus obtained for seven sorption cycles for Cd(II) were represented in Figure 4a. The various parameters, volume of effluent, breakthrough time, exhaustion time, uptake capacity, critical bed height, desorption time, and desorption efficiency, were calculated with the help of the above-mentioned figures, and data therefore are given in Table 3. It observed from Figure 4a and Table 3 that breakthrough time decreases from 20.3 to 12.4 h and exhaustion time increased from 65.3 to 67.5 h as the regeneration cycle progressed from the first to seventh cycle resulting in a broadened mass transfer zone. This behavior may primarily have been due to the gradual deterioration of the biosorbent because of repeated usage and due to previous elution processes, which affected the biomass binding

254

Ind. Eng. Chem. Res., Vol. 50, No. 1, 2011 Table 4. Full Factorial Central Composite Design Matrix of Two Factors along with Experimental and Predicted % Removal of Cd(II)

Figure 4. (a) Sorption breakthrough curve for seven sorption cycles (flow rate ) 1 mL/min, pH ) 6.5, temperature ) 30 °C, initial Cd(II) concentration ) 10 mg/L, bed height ) 19 cm). Vertical bars show the standard deviation of three replicates. (b) Column desorption curve for seven desorption cycles (desorbing agent 0.1 N HCl, flow rate ) 2 mL/min). Vertical bars show the standard deviations of three replicates. Table 3. Sorption-Desorption Process Parameter for Various Sorption-Desorption Cyclesa cycle no. tb (h) te (h) Veff (mL) Q (mg/g) 1 2 3 4 5 6 7 a

22.3 21.7 20.2 18.6 19.2 15.2 12.4

65.3 64.5 63.7 64.1 66.2 67.1 67.5

3918 3370 3822 3846 3972 4026 4050

2.62 2.58 2.51 2.49 2.45 2.42 2.38

%R 88.9 88.1 87.2 86.4 82.0 80.1 78.2

D.T. (h) %D.E. 2.8 2.65 2.60 2.30 2.10 1.95 1.85

99.08 98.87 98.00 97.3 98.4 96.8 97.0

D.T. (h) ) desorption time; %D.E. ) desorption efficiency.

sites.11 The percentage removal was found to be 88.9% to 78.2% for the first to seventh cycle, respectively. Thus, the overall performances of the immobilized beads in all of the cycles were very satisfactory so the removal efficiency was very high. The desorption experiments were performed with already established desorbing agent HCl. The flow rate in the desorption process was maintained at 2.0 mL/min to avoid the over contact of the desorbing agent. Desorption curves for all cycles are presented in Figure 4b. The curves observed in all the cycles exhibited a similar trend: a sharp increase at the beginning, followed by a gradual decrease. The desorbent performed very well, and percent desorption efficiencies of 99.08%, 98.87%, 98.00%, 97.3%, 98.4%, 96.8%, and 97.0% were found for the

run order

flow rate (mL/min)

bed height (cm)

% removal

pred. removal

residual

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

4 3 4 2 3 2 3 3 3 1 3 3 5

10 13 16 10 13 16 13 13 19 13 13 7 13

51.3 66.5 60.2 67.2 66.5 78.2 66.5 66.5 74.8 79.4 66.5 53.2 48.9

52.31 66.20 61.78 67.08 66.20 78.64 66.20 66.20 74.15 79.60 66.20 53.11 47.96

-0.01 0.30 -1.58 0.12 0.30 -0.44 0.30 0.30 0.65 -0.20 0.30 0.09 1.06

first to seventh cycles, respectively (Table 3). The desorption process was carried out for desorption times of 2.8, 2.65, 2.60, 2.30, 2.1, 1.95, and 1.85 h as compared to exhaustion times of 65.3, 64.5, 63.7, 64.1, 66.2, 67.1, and 67.5 h for the first, second, third, fourth, fifth, sixth, and seventh cycles, respectively, for the sorption process, which resulted in highly concentrated Cd(II) solutions in only a small volume of the desorbent and time. For instance, in cycle 1 at t ) 0.2 h, the effluent concentration was 41.74 mg/L. The total volume of Cd(II) bearing solution (10 mg/L) treated during this regeneration study was around 27.50 L in seven cycles, and the total volume of 0.1 N HCl utilized for the desorption process was nearly 1.950 L, which corresponds to approximately 19.77 days of continuous operation. HCl is the most efficient and inexpensive desorbent for Cd(II) metal ion desorption in this study. 3.7. Response Surface Methodological Approach for the Optimization of Process Variable for Biosorption of Cd(II) in Up-Flow Fixed Bed Column System. In the present section, a 22 full-factorial central composite design (CCD) of response surface methodology was applied to predict the effect of flow rate (mL/min) and bed height (cm) on the removal of Cd(II) using MINITAB15 software.32 The parameters were coded at five levels: -R, -1, 0, 1, and R. For flow rate, the different levels studied are 1, 2, 3, 4, and 5 mL/min, and for bed heights, the levels are 7, 10, 13, 16, and 19 cm for cadmium. Because, in the present investigation, there are two variables, 13 experimental runs were required as per 22 full factorial designs. Experiments were performed according to the experimental plan, and the results are given in Table 4 along with the results predicted by the model with the help of software. Significant changes in removal of Cd(II) were observed for all the combinations, implying that all the variables were significantly affecting the sorption of cadmium in the continuous upflow column system. 3.7.1. Interpretation of the Regression Analysis. The response surface regression results, obtained after applying CCD design on the continuous column system, and the experimental results, SE coefficient, T value, and P value, along with the constant and coefficients (estimated using coded values), are given in Table 5 for Cd(II). It was observed from Table 5 representing the constant, which does not depend on any variable and interaction of variables, that the average percentage removal of Cd(II) on Arthrobacter sp. was 66.207 with a T value of 179.513 and a P value of 0.000. The value of constant, which represented the average uptake of Cd(II), was found significant as they have high T and low P values. The effect of the linear term, that is, flow rate

Ind. Eng. Chem. Res., Vol. 50, No. 1, 2011 Table 5. Estimated Regression Coefficients for the Removal of Cd onto Immobilized Biomass of Arthrobacter sp.a term

coef

constant 66.207 flow rate (mL/min) -15.817 bed height (cm) 10.517 flow rate (mL/min)*flow rate -2.423 (mL/min) bed height (cm)*bed height (cm) -2.573 flow rate (mL/min)*bed height -2.100 (cm)

255

Table 6. Analysis of Variance (ANOVA) for the Cd(II) Removal onto Immobilized Biomass of Arthrobacter sp.a

SE coef

T

P

source

DF

seq SS

adj SS

adj MS

F

P

0.3688 0.5128 0.5128 0.7422

179.513 -30.843 20.508 -3.265

0.000 0.000 0.000 0.014

regression linear square interaction residual error lack-of-fit pure error total

5 2 2 1 7 3 4 12

1097.35 1082.30 13.95 1.10 5.52 5.52 0.00 1102.87

1097.35 1082.30 13.95 1.10 5.52 5.52 0.00

219.470 541.151 6.973 1.102 0.789 1.841 0.000

278.18 685.92 8.84 1.40

0.000 0.000 0.012 0.276

0.7422 1.7764

-3.467 0.010 -1.182 0.276

a coef ) coefficient, SE coef ) standard error coefficient, T ) t test, P ) probability value.

and bed height, was found to be highly significant (P < 0.05) on the removal of Cd(II). This also indicates that there was a linear relation of these parameters with the percentage removal of Cd(II) in the up-flow column system. All the quadratic terms were found to be significant (P < 0.05). Quadratic terms are used to evaluate whether or not there is curvature (quadratic) in the response surface. The interaction terms of flow rate*bed height were found to be insignificant (P > 0.05). A positive sign of the coefficient represents a synergistic effect, while a negative sign indicates an antagonistic effect. The linear term flow rate and both the quadratic term bed height and flow rate have a negative relationship with the removal of cadmium. In addition to this interaction term, flow rate*bed height has a negative relationship with the removal of cadmium. The effect of linear term bed height and quadratic term flow rate has a positive effect. On the basis of the obtained values of the regression coefficients, the regression equation was prepared using the coefficients terms, which are as follows: Y ) 66.207 - 15.817*flow rate + 10.517*bed height - 2.423*flow rate2 2.573*bed height2 2.100*flow rate*bed beight

(23)

where Y is the response variable, predicted % removal of Cd(II). To check the goodness-of-fit for the regression equation, R2 and adjusted R2 were computed. The values of R2 and adjusted R2 were found to be 99.5% and 99.1% and indicate a high dependence and correlation between the observed and the predicted values of response for the removal of Cd(II). This also indicates that 99.5% of the result of the total variation can be explained by this model for the removal of Cd(II). The high value of R2 is an indication that the fitted model can be used for prediction with reasonable precision, and the equation adequately represents the actual relationship between the response and significant variables. The adequacy of the equation was also represented by the lower value of standard deviation between the measured and predicted results, and the value was found to be 0.8882 for Cd(II), respectively. 3.7.2. Analysis of Variance (ANOVA). The statistical significance of the ratio of mean square variation due to regression and mean square residual error was tested using analysis of variance (ANOVA). ANOVA is a statistical technique that subdivides the total variation in a set of data into component parts associated with specific sources of variation for testing hypothesis on the parameters of the model.39 The results of ANOVA for fitting the second-order response surface model by least-squares methods are given in Table 6 for removal of Cd(II) in column system. The F value for all regression was higher and found to be 278.18 for removal of Cd(II). The large value of “F” indicates that most of the variation in the response

a DF ) degree of freedom; seq SS ) sequential sum of squares; adj SS ) adjusted sum of squares; adj MS ) adjusted mean of square; F ) F test; P ) probability value.

can be explained by the regression equation. The associated P value is used to estimate whether F is large enough to indicate statistical significance.40 The smaller are the p values for a parameter, the higher is the significance of the parameter; hence, the p value reflects the relative importance of the term attached to that parameter. The p values were used as a tool to check the significance of each coefficient, which also indicates the interaction strength of each cross product. It was observed from ANOVA study that the coefficients for the linear (P < 0.05) and square terms (P < 0.05) were highly significant, which indicates the significant contribution of these predictors in the fitted model, whereas interaction terms (P > 0.05) were insignificant. The ANOVA table also shows no residual error, which means the variation in the response data can be very well explained by the model. 3.7.3. Main Effect Plot. Main effect plots are drawn to compare the changes in the level means to see which factors influence the response the most. A main effect is present when different levels of a factor affect the response differently. It is created by plotting the response mean for each factor level. A line is drawn to connect the points for each factor, and a reference line is also drawn at the overall mean. When the line is horizontal (parallel to the x-axis), there is no main effect present. Each level of the factor affects the response in the same way, and the response mean is the same across all factor levels. When the line is not horizontal (parallel to the x-axis), there is a main effect present. Different levels of the factor affect the response differently. The greater is the difference in the vertical position of the plotted points (the more the line is not parallel to the X-axis), the greater is the magnitude of the main effect.41,42 To visualize the main effects of the variables on the response removal percentage and to compare the relative strength of the effects of various factors on the removal of cadmium, the main effect plots were drawn and are presented in Figure 5 with mean 65.0538. From the figures, it is observed that the levels of both the factors are affecting the response differently for removal of cadmium. It is clear from the figure that the bed height has a positive effect on the removal of Cd(II); that is, the percentage removal of Cd(II) increases with an increase in the bed height. The plots also showed that the percentage removal was more than average at bed height 13.0-19.0 cm and less than average for bed height 7.0 and 10.0 cm. Also, the maximum percentage removal of Cd(II) was achieved at 19.0 cm bed height. In cases of flow rate, the percentage removal was more than average for flow rates of 1 and 3 mL/min and less than average from 4 to 5 mL/min in all of the cases. The maximum removal of Cd(II) was achieved at 1 mL/min. 3.7.4. Interpretation of Residual Graph. The normality of the data can be checked by plotting the normal probability plot (NPP) of the residuals.42 The residual is the difference between

256


Figure 5. Main effect plot.

the observed and the predicted values (or the fitted value) from the regression. If the points on the plot fall fairly close to the straight line, the data are normally distributed. For this purpose, residual plots were drawn. Figure 6a shows the normal probability plot of residual values of Cd(II) in the up-flow column system. It could be seen that the experimental points were reasonably aligned, suggesting normal distribution of the residuals. The results were shown in Figure 6b for Cd(II) with the help of a histogram. A histogram of the residuals shows the distribution of the residuals for all observations. The figure showed an almost bell-shaped symmetrical histogram, implying that the residual terms are normally distributed with mean zero in all cases in the continuous system. Long tails in the plots may indicate skewness in the data. All the bars lie zero, so no outliers are present. Figure 6c plots the residual versus the fitted value for percentage removal of Cd(II). It was observed from these plots that the residuals were randomly scattered about zero, that is, the errors that implied that the variance were independent of the value of the removal of metal ion in all the cases of sorption. The plot shown in Figure 6d for Cd(II) represents the residual value and the order of the corresponding observations. All the points were found to fall in the range of +1 to -1. No evidence of non-normality, skewness, outliers, or unidentified variables exists in any of the cases.41 The residual graph plot is useful when the order of the observations may influence the results, which can occur when data are collected in a line sequence. This plot can be helpful to a designed experiment in

Figure 7. Contour plot of Cd(II) biosorption.

which the runs are not randomized. For percentage removal of Cd(II), the residuals appear to be randomly scattered about zero. 3.7.5. Interpretation of Contour Plots. Contour plots are a graphical representation of the regression equation for the optimization of reaction conditions and the most useful approach in revealing the conditions of the sorption system. Contour plots are useful for understanding the interaction of two test variables and determining their optimum levels when holding other test variables at some constant levels. To investigate the interactive effect of two factors on the removal of Cd(II) in the up-flow column system, the contour plots were drawn and are presented in Figure 7. The hold values of the remaining factors were set at their middle values, and these were bed height at 13 cm and flow rate at 3 mL/min. These plots showed that the percentage removal of metal ions in all the cases increased with increase of bed height from 7 to 19 cm, while it decreased with an increase of flow rate from 1 to 5 mL/min. The lines of contour plots predict the values of percentage removal of Cd(II) for different bed heights at different flow rates. The optimum 89.64% Cd(II) removal was achieved at a flow rate of 1 mL/ min and a bed height of 19.0 cm. 3.7.6. Interpretation of Process Optimization Curve. Response optimization helps to identify the factor settings that optimize a single response or a set of responses. It is useful in

Figure 6. Residual graphs: (a) normal probability plot for residuals, (b) histograms of residuals, (c) residuals versus fitted values, and (d) residuals versus the order of the data.


determining the operating conditions that will result in a desirable response. The optimum condition, that is, the best combination of factor settings for achieving the optimum response, was found to be flow rate of 1 mL/min and bed height of 19 cm for a predicted response of 89.64% with a desirability score of 0.9955. These results are found to be in good accordance with those obtained through experimentation. There are many advantages of the optimization plot as it helps to achieve the predicted response with a higher desirability score, lower-cost factor settings with near optimal properties, to study the sensitivity of response variables to changes in the factor settings, and to get required responses for factor settings of interest. 4. Conclusion The following conclusions can be drawn from this study: (1) The smaller size biomass-immobilized beads’ fracture frequency was minimum, that is 2%. This size of beads was used for the biosorption experiment. (2) Free and immobilized biomass of Arthrobacter sp. was successfully utilized for the removal of Cd2+ both in batch as well as in the continuous system. (3) The Langmuir isotherm model was fitted well to the sorption data, indicating that sorption was monolayer and uptake capacity (Q0) of biomass was 270.27 and 188.67 mg/g for the free and immobilized beads, respectively, at pH 6.5 and 30 °C. (4) The maximum percentage Cd2+ removal of 88.9% was obtained at 1.0 mL/min and 19 cm bed height. (5) The bed depth service time model (BDST) was successfully utilized and in good agreement with the experimental results (r2 > 0.997). The Thomas and Yoon-Nelson models were also applied to the experimental data, and the Yoon-Nelson model provided a better description of experimental kinetic data in comparison to the Thomas model. (6) The sorption performance of immobilized biomass for Cd(II)wassuccessfullyevaluatedforthevarioussorption-desorption cycles by using desorbent 0.1 mol/L HCl. The sorption capacity was good for all the cycles, and desorption efficiencies were greater than 97% for Cd(II) desorption. (7) A 22 full factorial central composite design was utilized with the help of MINITAB Version15 Software for predicting the results with 13 sets of experiments, and a high correlation has been found between the experimental and predicted results (R2 ) 99.5%). (8) In this study, easy availability of the biosorbent, sludge free operation, and low energy input were expected to cut down the operating costs and render the process attractive. The data thus obtained would prove useful in designing an effluent treatment plant for Cd(II)-rich effluents in the up-flow column system. Literature Cited (1) Wang, J.; Chen, C. Biosorbents for heavy metals removal and their future. Biotechnol. AdV. 2009, 27, 195–226. (2) Ahluwalia, S. S.; Goyal, D. Microbial and plant derived biomass for removal of heavy metals from wastewater. Bioresour. Technol. 2007, 98, 2243–2257. (3) Dara, S. S. A Textbook of EnVironmental Chemistry and Pollution Control; S. Chand & Co. LD: New Delhi, 2005; ISBN 81-219-083-3. (4) Hasan, S. H.; Srivastava, P.; Talat, M. Biosorption of Pb(II) from water using biomass of Aeromonas hydrophila: Central composite design for optimization of process variables. J. Hazard. Mater. 2009, 168, 1155– 1162. (5) Viera, R. H. S. F.; Volesky, B. Biosorption: a solution to pollution. Int. Microbiol. 2000, 3, 17–24.

257

(6) Tsekova, K.; Todorova, D.; Dencheva, V.; Ganeva, S. Biosorption of copper(II) and cadmium(II) from aqueous solutions by free and immobilized biomass of Aspergillus niger. Bioresour. Technol. 2010, 101, 1727–1731. (7) Semerjian, L. Equilibrium and kinetics of cadmium adsorption from aqueous solutions using untreated Pinus halepensis sawdust. J. Hazard. Mater. 2010, 173, 236–242. (8) Li, C.; Champagne, P. Fixed-bed column study for the removal of cadmium (II) and nickel (II) ions from aqueous solutions using peat and mollusk shells. J. Hazard. Mater. 2009, 171, 872–878. (9) Vilar, V. J. P.; Santos, S. C. R.; Martins, R. J. E.; Botelho, C. M. S.; Boaventura, R. A. R. Cadmium uptake by algal biomass in batch and continuous (CSTR and packed bed column) adsorbers. Biochem. Eng. J. 2008, 42, 276–289. (10) Vijayaraghavan, K.; Yun, Y. S. Bacterial biosorbents and biosorption. Biotechnol. AdV. 2008a, 26, 266–291. (11) Vijayaraghavan, K.; Yun, Y. S. Polysulfone-immobilized Corynebacterium glutamicum: A biosorbent for Reactive black 5 from aqueous solution in an up-flow packed column. Chem. Eng. J. 2008b, 145, 44–49. (12) Mohan, D.; Pittman, C. U., Jr. Activated carbons and low cost adsorbents for remediation of tri- and hexavalent chromium from water. J. Hazard. Mater. 2006, 137, 762–811. (13) Aksu, Z.; Go¨nen, F. Biosorption of phenol by immobilized activated sludge in a continuous packed bed: prediction of breakthrough curves. Process Biochem. 2004, 39, 599–613. (14) Khuri, A. I.; Cornell, S. A. Response Surface Design and Analysis of Experiment; Marcel Dekker Inc.: New York. 1987. (15) Mason, R. I., Funst, R. F., Hess, J. L. Statistical Design and Analysis of Experiments; Wiley: New York, 1989. (16) Heck, F. J.; Floris, S. H.; Herty, P. F.; Ayub, M. A. Z. Optimization of cellulose-free xylanose activity produced by Bacillus Coagulans BL69 in solid-state cultivation. Process Biochem. 2005, 40, 107–112. (17) Hasan, S. H.; Srivastava, P. Batch and continuous biosorption of Cu2+ by immobilized biomass of Arthrobacter sp. J. EnViron. Manage. 2009, 90, 3313–3321. (18) Bai, R. S.; Abraham, T. E. Studies on Cr(VI) adsorption-desorption using immobilized fungal biomass. Bioresour. Technol. 2003, 87, 17–26. (19) Malkoc, E.; Nuhoglu, Y. Fixed bed studies for the sorption of chromium(VI) onto tea factorywaste. Chem. Eng. Sci. 2006, 61, 4363–437. (20) Hasan, S. H.; Srivastava, P.; Talat, M. Biosorption of lead using immobilized Aeromonas hydrophila biomass in up flow column system: Factorial design for process optimization. J. Hazard. Mater. 2010, 177, 312–322. (21) Pakshirajan, K.; Swaminathan, T. Biosorption of copper and cadmium in packed bed columns with live immobilized fugal biomass of Phanerochaete chrysosporium. Appl. Biochem. Biotechnol. 2009, 157, 159– 173. (22) Sankararamakrishnan, N.; Kumar, P.; Chauhan, V. S. Modeling fixed bed column for cadmium removal from electroplating wastewater. Sep. Purif. Technol. 2008, 63, 213–219. (23) Kumar, U.; Bandyopadhyay, M. Fixed bed column study for Cd(II) removal from wastewater using treated rice husk. J. Hazard. Mater. 2006, 129, 253–259. (24) Taty-Costodes, V. C.; Fauduet, H.; Porte, C.; Ho, Y. S. Removal of Pb(II) ions from synthetic and real effluents using immobilized Pinus sylVestris saw dust: Adsorption on a fixed bed column. J. Hazard. Mater. 2005, 123, 135–144. (25) Thomas, H. Heterogeneous ion exchange in a flowing system. J. Am. Chem. Soc. 1944, 66, 1664–1666. (26) Quintelas, C.; Fernandes, B.; Castro, J.; Figueiredo, H.; Tavares, T. Biosorption of Cr(VI) by three different bacterial species supported on granular activated carbon-A comparative study. J. Hazard. Mater. 2008, 153, 799–809. (27) Lin, S.-H.; Juang, R.-S.; Wang, Y.-H. Adsorption of Acid dye from water onto pristine and acid activated clays in fixed beds. J. Hazard. Mater. 2004, 113, 195–200. (28) Padmesh, T. V. N.; Vijayaraghvan, K.; Sekaran, G.; Velan, M. Biosorption of Acid Blue 15 using Azolla filiculides: Batch and column studies. Dyes Pigm. 2006, 71, 77–82. (29) Can, M. Y.; Kaya, Y.; Algur, O. F. Response surface optimization of the removal of nickel from aqueous solution by cone biomass of Pinus sylVestris. Bioresour. Technol. 2006, 97, 1761–1765. (30) Ravikumar, K.; Krishnan, S.; Ramalingam, S.; Balu, K. Application of response surface methodology to optimize the process variable for reactive Red and Acid Brown dye removal using a novel adsorbent. Dyes Pigm. 2007, 72, 66–74. (31) Kumar, A.; Prasad, B.; Mishra, I. M. Optimization of process parameter for acrylonitrile removal by low cost adsorbent using BoxBehnken design. J. Hazard. Mater. 2008, 150, 174–182.

258


(32) MINITAB Release 15 Statistical Software for Windows; Minitab Inc.: U.S., 2006. (33) Kuyucak, N.; Volesky, B. The mechanism of cobalt biosorption. Biotechnol. Bioeng. 1989, 339, 823–831. (34) Singh, A.; Kumar, D.; Gaur, J. P. Copper(II) and lead(II) sorption from aqueous solution by non-living Spirogyra neglecta. Bioresour. Technol. 2007, 98, 3622–3629. (35) Langmuir, I. The adsorption of gases on plane surfaces of glass, mica and platinum. J. Am. Chem. Soc. 1918, 40, 1361–1403. (36) Zulfadhly, Z.; Mashitah, M. D.; Bhatia, S. Heavy metals removal in fixed bed column by the macro fungus Pycnoporus sanguineus. EnViron. Pollut. 2001, 112, 463–470. (37) Vijayaraghvan, K.; Prabhu, D. Potential of Sargassum wightii biomass for copper (II) removal from aqueous solutions: Application of different mathematical models to batch and continuous biosorption data. J. Hazard. Mater. 2006, 137, 558–564. (38) Atkinson, B. W.; Bux, F.; Kasan, H. C. Consideration for application of biosorption technology to remediate metal contaminated industrial effluents. Water SA 1998, 24, 129–135.

(39) Segurola, J.; Allen, N. S.; Edge, M.; Mahon, A. M. Design of eutectic photo initiator blends for UV/curable curable acrylated printing inks and coatings. Prog. Org. Coat. 1999, 37, 23–37. (40) Kim, H. M.; Kim, J. G.; Cho, J. D.; Hong, J. W. Optimization and characterization of U.V.-Curable adhesives for optical communication by response surface methodology. Polym. Test. 2003, 22, 899–906. (41) Zulkali, M. M. D.; Ahmad, A. L.; Norulakmal, N. H. Oryza sativa L. husk as heavy metal adsorbent: Optimization with lead as model solution. Bioresour. Technol. 2006, 97, 21–25. (42) Pokhrel, D.; Viraraghvan, T. Arsenic removal from aqueous solution by iron oxide coated fungal biomass: A factorial design analysis. Water, Air, Soil Pollut. 2006, 173, 195–208.

ReceiVed for reView August 18, 2010 ReVised manuscript receiVed October 21, 2010 Accepted November 3, 2010 IE101739Q

Biosorptive Abatement of Cd2+ by Water Using Immobilized Biomass

Recommend Documents