Enhanced Biosorptive Remediation of Hexavalent Chromium Using

Article pubs.acs.org/IECR

Enhanced Biosorptive Remediation of Hexavalent Chromium Using Chemotailored Biomass of a Novel Soil Isolate Bacillus aryabhattai ITBHU02: Process Variables Optimization through Artificial Neural Network Linked Genetic Algorithm Devendra Kumar Verma,† Syed Hadi Hasan,‡ Devendra Kumar Singh,‡ Shalini Singh,† and Yogendra Singh*,† †

School of Biochemical Engineering and ‡Water Pollution Research Laboratory, Department of Applied Chemistry, Indian Institute of Technology (Banaras Hindu University) Varanasi 221005 U.P., India ABSTRACT: A modified biomass of a novel bacterium, Bacillus aryabhattai ITBHU02, was investigated for the removal of hexavalent chromium [Cr(VI)] from water. It was found that modification of the biomass had an improved 28.2% higher Cr(VI) removal and 9.4 mg/g greater uptake capacity as compared to unmodified biomass. At ANN-GA optimized condition of parameters, namely, pH 2.61, biomass dose of 2.8 g/L, temperature of 44 °C, and initial Cr(VI) concentration of 112 mg/L, the maximum uptake capacity of biomass was achieved as 31.2 mg/g and removal was 93.6%. The residual 6.4% chromium in water was found in the form of Cr(III) instead of Cr(VI), which clearly illustrated the reduction of toxic Cr(VI) into nontoxic Cr(III) due to the detoxification capability of biomass during the sorption process. Sorption followed pseudo-second order kinetics with a monolayer pattern in an endothermic and spontaneous way. SEM-EDX and FTIR studies were used to confirm the sorption as well as the functional groups involved in the sorption process. waste disposal site.9 In fact, the bacterial cell wall components possess various functional groups such as carboxyl, amine, and hydroxyl groups, which impart the capability of sorbing metal ions from aqueous solution to the bacterial cell wall.10 Chemical modification of these surface groups may increase biosorption capacity of bacterial biomass.6 Unlike living cells, the dead bacterial biomass provides higher flexibility for chemical modification of the cell. In fact, the living cells require different nutritional components including minerals for their growth and cell maintenance. These medium components may themselves reduce the sorption capability of surface functional groups by competitive binding. A variety of factors like biomass dose, initial concentration of toxic metal species, pH, temperature, and contact time can affect the sorption of the metals over the bacterial biomass.11 Therefore, the most important steps in the study of microbial biomass-based sorption systems are the analysis of effects of these independent variables, their modeling, and optimization to maximize the efficacy of the system.12 A wide range of modeling and optimization strategies are prevalent in literature including the simple one factor at a time method (OFAT) and the complex stochastic designs such as response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA).9 Optimization of a biological phenomenon by application of RSM does not only economize the labor and time of the study but also demonstrate the interaction between process parameters.10 ANN is a synergistic representation of mathematical and

1. INTRODUCTION Sorption is an avenue for developing an economic and ecofriendly treatment system for polluted water. Recently, the removal of noxious heavy metals, compounds, and particulates from solution using biological materials has been recognized as an extension to sorption and is termed as biosorption.1 Various biosorbents obtained from agro-waste material and animal biomass have been investigated for this purpose.2,3 However, none of them shows surface uniformity in terms of homogeneous distribution and pattern of active sites, which is due to adhered microscopic impurities such as dust, different microbes, and other polymeric materials. On the other hand, the achieved information about biosorption over the functional groups in the tested biomaterial would not be very authentic because of the contaminated surfaces. In contrast to agricultural waste and animal biomass-based biosorbents, the use of microbial biomass might be a good alternative as it is always produced in pure form as industrial waste and will produce rather reliable sorption data. Water pollution by chromium is of considerable concern, as this metal is extensively used in electroplating, water cooling, leather tanning, metal finishing, ore and petroleum refining processes, textile industries, and chromate preparation.4,5 Although, chromium occurs in two stable oxidation states Cr(III) and Cr (VI), the state Cr(VI) is of meticulous concern due its toxicity.6 Generally, Cr(VI) is more water-soluble and enters in living cells more easily, thus it is more toxic and carcinogenic than Cr(III).7 The recommended limit of chromium in potable water is 0.05 mg/L and in surface water through industrial discharge up to 0.1 mg/L.8 The present investigation deals with an eco-friendly method of Cr(VI) removal using dead biomass of a soil isolate Bacillus aryabhattai ITBHU02, which was previously isolated from a © 2014 American Chemical Society

Received: Revised: Accepted: Published: 3669

December 17, 2013 February 11, 2014 February 14, 2014 February 14, 2014 dx.doi.org/10.1021/ie404266k | Ind. Eng. Chem. Res. 2014, 53, 3669−3681

Industrial & Engineering Chemistry Research

Article

2.4. Analysis of Chromium in Aqueous Solution. The determination of total chromium (Cr VI and Cr III) was done by atomic absorption spectrophotometer (AAS) (Shimadzu AA-6300) at a hollow cathode lamp current of 12 mA, 0.7 nm slit width, 357.9 nm light source wavelength and with deuterium lamp background correction. The instrument was calibrated by using a standard solution of Cr(NO3)3 (Merck, Mumbai). Furthermore, the Cr(VI) concentration in the samples was again analyzed in a UV-1700 PharmaSpec spectrophotometer at 540 nm as the method described by Yang and Chen.15 The concentration of Cr(III) was then evaluated by taking the difference between the concentrations of Cr(VI) and total chromium of the corresponding samples. 2.5. Predictive Modeling and Optimization Methods. 2.5.1. Response Surface Methodology. Using the RSM tool, the relationship among the parameters, namely, biomass dose, metal concentration, pH, and temperature were expressed mathematically in the form of a polynomial model, which exhibited a response as a function of relevant parameters. A popular secondorder experimental design, that is, central composite design (CCD), was employed for generating experimental data, predicting the level of factors, and achieving an optimal response through regression analysis. The statistical software package MINITAB v15.1.0.0 (Minitab Inc., USA) was utilized for designing the experiments and regression analysis. A secondorder polynomial model, considering all the linear terms, square terms, and linear by linear interaction terms for tested factors, was in the following form:

statistical techniques helpful in the modeling of nonlinear multivariate systems.13 ANN hybridized with genetic algorithm (GA) has been implemented very accurately for controlling and optimizing the biological processes for past decades.14 Though, response surface methods-based parameter optimizations for metal are more accurate in terms of average error and empirical fitness, RSM executes the experimental data up to a quadratic level, while the ANN-GA approach furnishes the output value up to the significant nonlinearities. The present manuscript explores this optimization gap through comparative RSM and ANN-GA analyses for the current bacterial biosorption system, as the ANN-GA method is fully based on minimization of the mean square error by iterations and selecting the best individual based on minimal error, which is a rare approach of comparative optimization in the environmental sector.

2. MATERIAL AND METHODS 2.1. Preparation of Standards and Reagents. All chemicals and reagents used were of analytical grade and were used without further purification (purchased from Himedia, Mumbai, India). Stock solution of 1000 mg/L Cr(VI) was prepared by dissolving the corresponding weight of potassium dichromate (K2Cr2O7) in deionized, double distilled water containing a few drops of concentrated HNO3 to prevent the precipitation of Cr(VI) by hydrolysis. The required initial concentration of Cr(VI) samples was obtained by appropriate dilution of the above stock Cr(VI) standard solution. 2.2. Preparation of Biomass and Its Chemo-Tailoring. Bacillus aryabhattai ITBHU02 (Accession no. JQ673559) used in the study was isolated previously from the soil disposal site contaminated with degrading waste.9 The strain was maintained over nutrient agar (NA) slants (pH 7.0) and stored at 4 °C. Stock culture was transferred to fresh NA medium every 3−4 weeks. For biomass production, the strain was aerobically cultivated in presterilized nutrient broth medium containing 1% peptone, 1% beef extract, and 0.5% NaCl, at 37 °C, agitation speed of 160 rpm, and pH 7.0, using an incubator shaker. The growing cells from the culture broth were collected by centrifugation at 6000g for 10 min; the pellet was washed twice with deionized water. The collected cell biomass was dried for 24 h at 80 °C in an oven. The dried cells were powdered to fine particles using a blender and then divided into two equal parts. The first part of the blended biomass was chemo-tailored by acid-treatment, whereas the second was kept untreated and utilized as a control in further experiments. For the chemo- tailoring, 5.0 g of biomass was suspended in 100 mL of 0.1 N HCl. Then the mixture was incubated at a temperature of 80 °C for 6 h at an agitation speed of 100 rpm. The resulting biomass was then washed thoroughly with deionized double distilled water until the pH of the rinsewater stabilized. This product was termed as chemo-tailored bacterial biomass (CBB). The unmodified part was referred, hereafter, as the untreated bacterial biomass (UBB). Both forms of the biomass were stored in a desiccator over CaCl2 for further biosorption experiments. 2.3. Batch Biosorption Studies in Shake Flasks. Batch biosorption experiments were conducted in 250 mL Erlenmeyer flasks by adding a calculated amount of dried cells of B. aryabhattai in 100 mL of aqueous Cr(VI) solution of desired initial concentration and keeping in a rotary shaking incubator at 100 rpm with varying pH and temperature. Samples were taken out at regular time interval for the analysis of residual chromium in the solution. All the experiments were conducted in triplicate and an average value was utilized for the compatibility analysis.

n

Y = βo +

n

n

n

∑ βi Xi + ∑ βiiXi2 + ∑ ∑ βijXiXj i=1

i=1

i=1 j=1

(1)

where, β0 is the constant, n denotes the number of variables, βi is the slope or linear effect of the input variable Xi, βii is the quadratic effect of input factor Xi, and βij is the linear by linear interaction effect between the input variable Xi and Xj. 2.5.2. Artificial Neural Network Hybridized with Genetic Algorithm (ANN-GA). The artificial neural networks (ANNs) had been extensively applied for approximation and simulation of different nonlinear functions; however, the genetic algorithm (GA) was applied to optimize the input space generated by the ANN model.16 Therefore, the feed-forward back-propagation (FFBP) algorithm was employed to build the predictive mathematical model with four inputs (i.e., biomass dose, metal concentration, pH, and temperature), one hidden layer, and one output node (percentage of Cr(VI) removal). The hidden layer acts as a central processing unit, which communicates input data to output neuron by performing two important actions. First, it computes the total inputs to the hidden layer in terms of weight, including bias, as given by the equation n

sum =

∑ xiwi + θ i=1

(2)

Here, xi (i = 1, n) stands for input parameter, wi is the connection weight, and θ represents bias. Second, weighted output was transferred by hidden neurons to output layer using an activation function, which relocated the space in nonlinearity of input data. The sigmoid transform output function was used in this work, shown by the following equation: f (sum) = 3670

1 1 + exp( −sum)

(3)

dx.doi.org/10.1021/ie404266k | Ind. Eng. Chem. Res. 2014, 53, 3669−3681


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3. RESULT AND DISCUSSION 3.1. Effect of pH on Biosorption and Reduction of Cr(VI). The effect of pH on Cr(VI) removal through biosorption was investigated by performing individual experiments at an initial Cr(VI) concentration of 100 mg/L, temperature of 40 °C, biomass dose of 3.0 g/L and varying pH from 1.0 to 10. The results shown in Figure 1a depict that the Cr(VI) removal and uptake capacity of biomass are significantly affected by change in pH. The maximum Cr(VI) metal ions removal were observed at pH 3.0 for both the biosorbents, 84.2% and 56% for CBB and UBB, respectively, while the maximum uptake capacities were 28.0 mg/g and 18.6 mg/g, respectively (Figure 1a). Variation of pH affects these factors by controlling the chromium aqueous chemistry and charge distribution upon active functional groups over biomass surface. Cr(VI) exists as HCrO4−, Cr2O72−, and CrO42− anions in the aqueous solutions in which HCrO4− ions predominate over the acidic range.25 At low pH, the protonation of biosorbent surface groups increases at acidic conditions to an elevated extent leading to stronger electrostatic attraction and bonding between the chromium oxianions and the protonated groups, which favors the metal uptake. At very low pH, biomass surface becomes frail and less stable due to surface oxidation leading to organic leaching and resulting in lower metal uptake.26 With an increase in pH, both the competitive hydroxyl anions and negative biosorbent charge increase, and hence Cr(VI) uptake is decreased. The reduction of predominating HCrO4− ions and influence of pH over the redox potential of biomass could be understood from aqueous chemistry and the Nernst equation:

Table 1. List of Equations for the Calculation of Different Parameters parameters and brief theory uptake capacity or sorption capacitya

(C − Ct ) × V qt = i W

percentage removal

% removal =

sorption isotherms

eq. no.

equations

ref

(4)

(Ci − Ct ) × 100 Ci

(5)

b

17

Langmuir sorption isotherm:

Ce C 1 = o + eo qe Qb Q

Freundlich sorption isotherm:

log qe = log KF +

thermodynamic studiesc Gibbs−Helmholtz equation

ΔG = − RT ln Kc

(8)

equilibrium constant

C Kc = Ae Ce

(9)

van’t Hoff equation

ln Kc = −

1 n log Ce

(6)

18

(7)

19 20

ΔH ΔS + RT R

21

(10)

sorption kineticsd pseudo-first-order model

log(qe − qt ) = log(qe) −

pseudo-second-order model initial sorption rate (h)

ks t 2.303

(11)

22

t 1 1 = + t qt qe k 2′qe 2

(12)

23

h = k 2′qe 2

(13)

24

a

qt = uptake capacity (mg/g) at time t; Ci = initial metal ion concentration in solution (mg/L); Ct = remaining metal ion concentration in solution (mg/L); V = volume of solution (L); W = weight of biosorbent (g); Ce = equilibrium concentration of solute in solution (mg/L). bqe = uptake capacity at equilibrium (mg/g); b = Langmuir constant related to free sorption energy (L/mg); Qo = monolayer sorption capacity of biosorbent (mg/g); KF = Freundlich constant indicative of uptake capacity (mg/g) ; n = Freundlich constant, indicates the intensity of sorption. cΔG = Gibbs free energy change (kcal/mol); R = universal gas constant (8.314 J/(mol K)); Kc = equilibrium constant; CAe = concentrations of adsorbed molecules over sorbent surface (mg/L); ΔH = enthalpy change (kcal/mol); ΔS = change in the entropy (cal/mol/K). dks = equilibrium rate constant of pseudo-first-order sorption (min−1); k2′ = equilibrium rate constant (g/mg/min); h = initial sorption rate (mg/g/min).

HCrO4 − + 7H+ + 3e− ↔ Cr 3 + + 4H 2O

E = E ο + 0.0197 log

[HCrO4 −] − 0.138pΗ [Cr 3 +]

(14)

(15)

Regarding the chemical composition, the dead bacterial biomass mainly consists of cell wall polysaccharides enriched with Nacetylmuramic acid (NAM) and N-acetylglucosamine (NAG) residues, amino sugars, phospholipids, proteins, and other organic materials representing a large diversity of aromatic, hydroxyl, phosphatic, carboxyl, and carbonyl functionalities.27 However, all these components share the capability to reduce Cr(VI) to Cr(III) due to the presence of different chemical subgroups, such as glucosamine or aromatic or amino groups, that act as electron donors.26,28,29 The predominating HCrO4− ion, attached with a protonated surface group present over biomass, is reduced into Cr(III), resulting in subsequent oxidation of the functional groups of biomass. The proposed mechanism for Cr(VI) reduction involves three steps:25,30 (1) electrostatic binding of Cr(VI) anions over biomass surface, (2) reduction of bound Cr(VI) into Cr(III) by electron donating activity of functional groups having lower reduction potential value than that of Cr(VI), (3) complex formation of generated Cr(III) with the biomaterial or released by electronic repulsion. Figure 1b represents the reduction profile and chromium species distribution in aqueous solution, which indicated that chemo-tailoring (acid treatment) of biomass (i.e., CBB) improved the reducing capability of biomass for Cr(VI). Acid treatment of dead biomass leads to better reducing capability in two ways: first, it cleans the biomass cell wall and replaces the natural mix of ionic species prebound on the cell wall with protons and sulfates,30 and, second, acid treatment creates easily protonable purer amino sugars (D-glucosamines) species over

The generated output weight was transferred by hidden neurons as input to the output layer, which further processed the data to the output response. Performance of the network was computed in terms of mean squared error (MSE), which was the difference between output variable and reference external desired signal. The optimum number of neurons in the hidden layer was determined on the basis of minimum achieved value of MSE. Once a generalized form of ANN model had been built, its input space was optimized using GA, which provides a suitable solution of an optimization problem iteratively using three genetic operators: selection, crossover, and mutation. The process of iteration continues until a suitable result is achieved.9,16 The MATLAB (version 7.0, Mathworks, Inc., MA, USA) was used to perform ANN-GA based modeling studies. 2.6. Formulas for Estimation of Parameters. Equations for the calculation of uptake capacity of biomass,percentremoval, sorption isotherms, thermodynamic parameters, and kinetic constants have been enlisted in Table 1 (eq no. 4−13). 3671



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Figure 1. (a) Effect of pH on Cr(VI) sorption capacity andpercentremoval; (b) effect of pH on reduction of Cr(VI) into Cr(III) and distribution of chromium species [Cr(VI), Cr(III) and total chromium].

pH 3.0, biomass dose of 3.0 g/L, temperature of 40 °C, and varying initial sorbate concentration from 25 to 250 mg/L. The results presented in Figure 2b indicated that at very low metal ion concentrations, thepercentremoval was 100% and the corresponding metal uptake achieved over biomass was the smallest. Furthermore, with increasing initial metal ion concentration, the percent removal gradually decreased, while metal uptake increased to a maxima achieving equilibrium state. A further increase in metal ion concentration showed no effect on uptake capacity. The optimum concentration up to which the uptake capacity of biomass increased was found to be 80 mg/L for UBB and 100 mg/L for CBB. From these results, it could be interpreted that UBB has a lesser number of binding sites than CBB and acquires saturation conditions at comparatively lower sorbate concentration. At higher metal concentrations, the biomass becomes fully saturated and does not show further increase in the sorption capacity of biomass. It can be assumed that at the optimum concentration all the available binding sites over the biomass surface got captured by the metal ions and thus, further increase in metal concentration does not improve the metal uptake capacity of the biosorbent. 3.4. Effect of Sorbent Dose. The effect of biomass dose on the biosorption of Cr(VI) within the range of 0.5−5.0 g/L, while keeping pH (3.0), stirring speed (100 rpm), and initial Cr(VI) concentration (100 mg/L) constant at temperature 40 °C represented in Figure 2c. It was found that biomass dose was inversely correlated with the uptake capacity and showed direct

the biomass surface leading to an increased number of binding surface groups and thus, increasing the reducing capability of CBB.6,7,31 The increased reduction potential of CBB with respect to UBB could be explained by the above Nernst equation. CBB represents more binding sites for binding of HCrO4− ions over its surface resulting into comparatively higher [HCrO4−]/[Cr3+] ratio, which finally improves the value of the reduction potential for CBB. 3.2. Effect of Contact Time. The time of equilibrium for the current sorption system was estimated by assaying the residual chromium concentration in solution up to the point until the metal concentration becomes constant in aqueous solution. These experiments were conducted at initial pH 3.0, initial concentration of Cr(VI) 100 mg/L, biomass dose 3.0 g/L, and temperature 40 °C as illustrated in Figure 2a. It was observed that initially rapid sorption takes place and thereafter, equilibrium get established after 240 min in the case of CBB, whereas the condition of equilibrium was achieved after 330 min in the case of UBB. Lesser equilibrium time for sorption over CBB clearly explains the faster rate of sorption over CBB as compared to UBB biomass. The reason might be associated with the better reduction potential and greater number of binding sites over the CBB surface. The contact time of 330 min for UBB and 240 min for CBB was adopted for further sorption experimentation. 3.3. Effect of Initial Metal Ion Concentration. To optimize the effect of initial metal ion concentration on the Cr(VI) uptake of biosorbent, the experiments were performed at 3672



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Figure 2. (a) Effect of time on Cr(VI) biosorption over UBB and CBB (b) effect of initial metal concentration on Cr(VI) sorption capacity and percentage removal; (c) effect of biomass dose on the biosorption of Cr(VI).

correlation with percent removal; that is, with increasing biomass dose, the overall uptake capacity of biomass decreased while percent removal increased. It was also observed that at higher biomass dose, the percent removal curves attained a plateau, while metal uptake curves continued descending. The fact behind this might be that at high biomass dose the solute is insufficient to completely cover the available binding sites over the biomass surface, also the intrusion between binding sites leads to reduction in the uptake capacity of the biosorbent. Percent metal removal increases due to a surplus of binding sites at high biomass dose. Moreover, after the optimum

biomass dose, the biosorption was governed by equilibrium; and hence further increase in the biomass had no effect on the sorption; that is, percent removal remains unaffected. 3.5. Response Surface Method Based Optimization. RSM is a commonly used method for constructing models and determining the optimal process conditions. On the basis of the experimental results of CCD presented in Table 2, a quadratic polynomial equation correlating maximum Cr(VI) removal (Y) with the independent factors, namely, pH (X1), biomass dose (X2), temperature (X3), and initial Cr(VI) concentration (X4), 3673



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Table 2. 24 Full Factorial Central Composite Design Matrix Containing 31 Trials and the Values Obtained through Experimentation, Regression Model, and Neural Network Model Prediction % removal of Cr (VI) predictedpercentremoval pH (X1)

biomass dose (X2)

temperature (X3)

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

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

30 40 30 30 10 20 40 30 20 30 40 30 30 20 40 20 20 50 30 30 40 20 20 30

4 3 4 1 3 2 4

4 3 2 3 3 4 2

20 30 40 30 30 40 40

initial Cr(VI) concentration (X4) data set used for model training 80 60 80 80 80 60 100 80 100 80 60 80 80 100 60 60 60 80 80 80 100 60 100 80 data set used for model testing 100 120 60 80 40 100 100

regression

ANN

63.6 63.2 84.8 55.4 61.5 48.8 65.7 85.8 57.2 85.8 56.2 84.8 85.7 59.4 56.2 48.8 44.5 73.4 84.8 32.1 61.4 53.0 61.4 85.8

60.5 65.2 85.4 53.2 58.6 50.0 66.7 85.4 57.7 85.4 56.8 85.4 85.4 61.6 57.2 49.9 45.7 71.0 85.4 29.7 62.2 55.3 61.4 85.3

63.8 64.1 84.5 55.2 61.8 49.1 65.7 85.6 57.1 86.1 55.9 84.8 85.5 59.4 56.1 48.8 44.7 73.5 85.0 32.1 61.5 53.1 61.2 85.7

54.0 84.8 47.7 44.5 68.9 67.8 59.3

56.5 82.5 49.8 41.6 65.9 69.8 60.2

54.1 84.4 47.8 44.1 69.0 67.8 59.2

compared to RSM because it represents the nonlinearities in a much better way.32 The input data was partitioned as training set and test set to keep the network away from overtraining and overparameterization. Optimization of the number of neurons in the hidden layer was achieved by executing the program at varying numbers of neurons. The network was also optimized for different values of specialized parameters such as learning rate and random initialization. Tangent sigmoid transfer function (tansig) was utilized for connecting the input layer to the hidden layer, whereas linear transfer function (purelin) was implemented for establishing the connection between the hidden layer and the output layer within the network. The network with the topology 4-11-1 (i.e., hidden layer containing 11 neurons; Figure 4a) has shown the least MSE value for the training data set (E = 0.025) and for the test data (Etst trn = 0.043). The model was fully trained after 653 epochs (Figure 4b). The resulting percentage error between the experimental and ANN predicted Cr(VI) removal for the training and test data sets, respectively, were 2.46 and 4.31. The R2 value between the model-predicted and desired Cr(VI) removal with respect to the training set and the test set was 98.8% (Figure 4c). The smaller values of the MSE and average percentage error of prediction as well as the higher values of R2 for both the training and test set outputs depicted that the

was established. The resulting RSM model equation can be represented as follows: Y = 85.36 − 2.98X1 + 1.82X 2 + 3.1X3 + 4.13X4 − 12.42X12 − 7.12X 2 2 − 5.14X32 − 2.79X4 2 − 0.25X1X 2 − 0.66X1X3 + 0.12X1X4 + 0.8X 2X3 − 1.33X 2X4 − 0.4X3X4

experimental

(16)

On the basis of the predicted results, surface plots were generated to understand the interactive effect of selected variables on percent removal of Cr(VI) as shown in Figure 3a−f. Comparative evaluation between the parameters has revealed that the maximum predicted metal removal was 87.5%, while optimized values of parameters were pH 2.86, biomass dose of 3.0 g/L, temperature of 41.2 °C, and metal ion concentration of 106.5 mg/L. The correlation coefficient (R2) value for the regression model was 95.4%, whereas the average percentage error was calculated to be 8.23. Under the optimized conditions, the experimentally obtained Cr(VI) removal was 86.2 ± 0.3%, which was in harmony with the predicted RSM model. 3.6. ANN-GA Based Optimization. Artificial neural network is a superior and more accurate modeling technique as 3674



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Figure 3. Surface plots showing interactive effect of selected variables on percent removal of Cr(VI): (a) biomass dose and pH; (b) metal ion concentration and biomass dose; (c) metal ion concentration and pH; (d) temperature and pH; (e) biomass dose and temperature; (f) metal ion concentration and temperature.

decision vector, x, denotes the input variables (L = 4); W stands for corresponding weight vectors; and xLl and xUl defines the lower and upper bounds on xl. For the estimation of the fitness score of a chromosome (candidate solution) in a population, the following function was utilized, 1 εj = 1 − j ; j = 1, 2, ..., N ypred ̂ (18)

ANN-based model retained better approximation and generalization features. As seen in Table 2, the measured and predicted ANN model results for Cr(VI) removal were almost alike as compared to the estimated levels by the regression model, which reveals that ANN has better prediction accuracy than RSM.16,32 Finally, the GA-based technique was applied to optimize the input space of the ANN model with the objective of maximizing Cr(VI) removal at the following condition of GA parameters: population type, double vector; original population size, 100; cross over probability, 0.8; elite count, 20; crossover function, @crossoversinglepoint; migration direction, forward; selection function, @selectionroulette; mutation function, @mutationgaussian; total generations, 100. The objective function can be defined as following: Maximize Y = f (x , W );

xlL ≤ xl ≤ xlU ,

l = 1, 2, ..., L

where εj denotes the fitness score of the jth candidate solution and ŷ jpred defines the percent Cr(VI) removal predicted by the model in response to a given candidate solution. All the four parameters, during the implementation of GA-based optimization, were restricted with the followings constraints as enlisted in Table 2: 1.0 ≤ pH ≤ 5.0 1.0 (g/L) ≤ biomass dose ≤ 5.0 (g/L) 10 °C ≤ temperature ≤ 50 °C 40.0 (g/L) ≤ glucose ≤ 120.0 (g/L)

(17)

where f represents the ANN model-based objective function; Y defines maximum percent Cr(VI) removal; the L-dimensional 3675



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Figure 4. (a) Schematic representation of a (4-11-1) neural network; (b) mean square error of the network during training and test data till final adaptation; (c) correlations of prediction distribution coefficient (R2) for training and test set of data; (d) best fitness plot showing the progressive performance [% Cr(VI) removal] of GA till the achievement of optimum solution; (e) best experimental individual showing optimum values of all four parameters.

concentration at sorbate−sorbent interface as a function of its concentration in the aqueous phase.33 The study of isotherms was carried out by varying the initial metal ion concentration from 25 mg/L to 250 mg/L for UBB and CBB, keeping other parameters constant at their optimum values. The results indicate that Langmuir isotherms were fitted better with the current sorption system as evident from higher values of correlation coefficients (Figure 5a, Table 3). Considerably good values of the correlation coefficients were also observed in the case of the Freundlich isotherm (Figure 5b, Table 3) From these data, it was clear that major sorption occurred on the biosorbents surface in the monolayer pattern and sorption energy was uniformly distributed over a major portion of biosorbent; however, some parts of biosorbent consisted of the multilayer pattern of biosorption with a little bit of interaction between adsorbed molecules at neighboring sites. The constant values for both Langmuir isotherms and Freundlich isotherms are enlisted in Table 3. Higher values of Qo and lower values of b for CBB indicated that CBB was a better biosorbent for Cr(VI) removal as compared to UBB. Moreover, the same pattern in Qo and b values

The reiterations at different GA inputs have determined that the entire searching space was explored thoroughly to attain a global optimum solution. Accomplishment of identical optimal solutions for most of the input conditions confirmed that it is a global optimal solution. The best fitness plot achieved during the iterations of GA over generations describes the gradual convergence of results toward the optimal solution (Figure 4d). Figure 4e illustrates the GA optimized conditions, namely, pH 2.61, biomass dose of 2.8 g/L, temperature of 44 °C, and initial Cr(VI) concentration of 112 mg/L, at which the maximum Cr(VI) removal was achieved to be 93.8%. This result was crossvalidated by carrying out the shake flask batch study at the aforesaid GA-specified optimum conditions. The removal of Cr(VI) achieved during experimental conditions was 93.6 ± 0.3% which was in close agreement with the predicted value through the hybrid ANN-GA technique. 3.7. Sorption Isotherms. Sorption isotherms depict the equilibrium state of sorbate (metal ions) at a surface−liquid interface at constant temperature and correlate the sorbate 3676



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Figure 5. (a) Langmuir isotherm plot for Cr(VI) removal at different temperatures; (b) Freunlich isotherm plot for Cr(VI) removal at different temperatures.

Table 3. Langmuir and Freundlich Isotherm Model Constants for Biosorption of Cr(VI) over UBB and CBB at Different Temperatures Langmuir constants

Freundlich constants

biomass

temp (°C)

Q° (mg/g)

b (L/mg)

R2

KF (mg/g)

n

R2

UBB UBB UBB CBB CBB CBB

20 30 40 20 30 40

21.276 28.571 33.333 45.454 52.631 55.555

0.060 0.038 0.022 0.008 0.007 0.004

0.977 0.985 0.999 0.979 0.988 0.999

3.81 4.064 5.0468 5.211 5.915 8.57

0.32 0.348 0.344 0.434 0.429 0.381

0.978 0.982 0.991 0.921 0.953 0.942

Table 4. Kinetic Parameters for the Sorption of Cr(VI) over UBB and CBB at Different Temperatures pseudo-first-order

pseudo-second-order

type of biomass (temp)

ks (L/min)

qe (mg/g)

R2

k′2 (g/mg/min)

h (mg/g/min)

qe (mg/g)

R2

UBB (20 °C) UBB (30 °C) UBB (40 °C) CBB (20 °C) CBB (30 °C) CBB (40 °C)

1.061 0.999 1.034 0.863 0.935 0.884

31.0 38.8 41.3 50.4 57.6 59.7

0.781 0.867 0.821 0.890 0.797 0.860

0.0341 0.0171 0.0092 0.0057 0.0027 0.0029

10.869 9.708 9.009 10.869 9.523 11.363

17.85 23.80 31.25 43.48 58.82 62.5

0.992 0.990 0.936 0.942 0.918 0.937

sorbate at the solid−solution interface. Sorption kinetic studies are an indispensable tool in order to design the sorption systems. Kinetic studies were performed at optimum values of variables to evaluate the concentration variation profile of chromium within

at higher temperature indicated that elevation in the temperature had also supported the sorption process. 3.8. Sorption Kinetic Study. Sorption kinetics represents the solute removal rate which controls the residence time of the 3677



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Figure 6. (a) Pseudo-second-order plot for Cr(VI) removal; (b) Van’t Hoff plot for Cr(VI) sorption over UBB and CBB.

Table 5. Thermodynamic Parameters for Cr(VI) Removal through UBB and CBB at Different Temperatures temp (K)

ΔG (kJ/mol)

293 298 303 308 313 318 323

2.942 1.753 1.126 0.204 −0.839 −1.071 −1.661

293 298 303 308 313 318 323

2.299 1.107 −0.607 −2.417 −4.313 −5.023 −5.897

ΔH (kJ/mol) UBB 0.04782

ΔS (kJ/mol/K)

R2 (Van’t Haff Plot)

0.154067

0.977

0.291099

0.982

CBB 0.0875

Figure 7. FTIR spectra of untreated bacterial biomass (UBB, lower, black line) and chemo-tailored bacterial biomass (CBB) before adsorption (middle, red line) and after Cr(VI) adsorption (upper, blue line).

calculated by extrapolating log (qe − qt) vs time and t/qt vs time, respectively, for the pseudo-first-order and pseudo-second-order reaction kinetic model. Relatively good values of correlation coefficients were observed for the pseudo-second-order model, which indicated that Cr(VI) sorption process over UBB and CBB can be explained with the pseudo-second-order kinetics model (Figure 6a). The validation for the pseudo-second-order model

the solution as well as over the biomass surface (UBB and CBB) as a function of time. Two models (pseudo-first-order and pseudo-second-order) were tested out to define the kinetic pattern followed by the current sorption system. Kinetic constant and correlation coefficients for both of the models are presented in Table 4. The kinetic parameters were 3678



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Figure 8. SEM images of Bacillus aryabhattai ITBHU02 biomass at 10 k× magnification and its corresponding EDS analysis (a) before chromium adsorption; (b) after chromium adsorption.

both the cases was accompanied by the assimilation of heat; that is, the sorption process was endothermic in nature. Moreover, the positive value of entropy change (ΔS) represented the increase in degree of freedom of metal ions during the sorption process due to the disorderliness of the sorption at the solid− liquid interface.36 3.10. Characterization of biomass through SEM, EDX, and FTIR. FTIR analysis of biomass was done before and after the chemical treatment as well as after Cr(VI) sorption in order to analyze the groups present over the surface of biosorbent and groups involved in metal sorption. Infrared spectra in the range of 4000 cm −1 to 400 cm −1 were obtained using IR spectrophotometer (Perkin-Elmer FTIR-1600). In Figure 7, IR spectra of the biomass represented various absorption bands. The broad band around 3200−3400 cm−1 was assigned −OH groups of the glucose and −NH stretching of the protein.6 Further, acid treatment sharpened the −NH stretching and polymeric association band (3200−3400 cm−1), which might be due to the appearance of more free amino and hydroxyl groups

indicated that the sorption proportionality would be corresponding to metal ion concentration and the square of the number of free sites over the biosorbent surface.34 The better fitness of the pseudo-second-order model to the empirical data has also indicated that the rate limiting step in biosorption was the chemisorptive valence force generated through the sharing or exchange of electrons between adsorbent and adsorbate, complexation, coordination and/or chelation.35 3.9. Thermodynamic Study. The thermodynamic study of the sorption process was done at different temperatures, keeping other parameters constant at their optimal values. Thermodynamic parameters were calculated through equations and Van’t Hoff plots (ln Kc vs 1/T, Figure 6b). Calculated values of thermodynamic parameters (ΔG, ΔH, and ΔS) are given in Table 5. Negative magnitudes of ΔG indicate the spontaneity of the sorption process, while a decrease in the value of ΔG with a raise in temperature indicated that elevation in temperature favors the sorption process. Positive values of enthalpy change (ΔH) depicted that the biosorption in 3679



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over the biomass surface because of acid-mediated surface cleaning and release of prebounded anionic species from the surface groups.30 The shoulder around 1750 cm−1 was attributed to lipid −CO stretching vibration.37 Absorption bands at 1651 cm−1 and 1546 cm−1 were the representative of amines (1°, 2°), amides of proteins, and N-acetyl glucosamine fraction of peptidoglycan in the bacterial cell wall.38 The peak at 1465 cm−1 was assigned to the presence of −CH2 groups of the cell lipid.38 The peak at 1388 cm−1 corresponded to the −CO symmetric stretching of −COO−, while the peaks around 1237 cm−1 endorsed the −PO2− symmetric and asymmetric stretching vibrations of the phospholipid component of the cell wall.38 After Cr(VI) sorption, a sharpening and decrease in intensity of the −NH stretching band at around 3388 cm−1 was observed. Moreover, a shift of the peak of −NH bending from 1651 to 1623 cm−1 and a shift in the peak of −COO− stretching from 1388 to 1365 cm−1 showed that the amino and carboxylic groups were involved in Cr(VI) sorption. The negatively charged chromate ions attracted electrostatically toward the positively charged amines present in the bacterial cell wall and then coordinately bounded with carboxylic groups after reduction. These findings were in agreement with the results described by other researchers.6,39,40 Further, chromium sorption was confirmed by SEM associated energy dispersive X-ray analysis (SEM-EDX) at 10 kV accelerating voltage, beam current of 1.0 nA, and typical measuring time of 7 s. Figure 8a,b showed the typical energy dispersive pattern of biomass before and after chromium sorption, which clearly illustrated the presence of a characteristic signal of chromium after adsorption. Thus, the study confirmed the sorption of chromium over the bacterial biomass surface during the process.

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4. CONCLUSION The study identified raw and acid pretreated novel Bacillus aryabhattai ITBHU02 biomass as a suitable biosorbent for Cr(VI) removal in batch experiments. Chemo-tailored biomass showed considerably better uptake capacity and percent Cr(VI) removal. ANN-GA provided more accurate predictions than RSM with higher values of R2 and lower values of MSE. The ANN-GA method had improved Cr(VI) removal by 7.4% as compared to RSM. The process followed mainly a monolayer sorption pattern in a spontaneous and endothermic way and achieved equilibrium through pseudo-second-order kinetics. The sorption over biomass was confirmed through SEM-EDX and FTIR analysis. FTIR analysis illustrated the involvement of amino and carboxylic groups in the sorption process.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +91 9450283646. Fax: +91 542 2368428. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS D. K. Verma would like to acknowledge the Indian Council of Medical Research, New Delhi, for financial support in the form of a senior research fellowship. Financial assistance to D.K.S. and S.S. in the form of a research fellowship, respectively, from MHRD and UGC, Government of India, is gratefully acknowledged. 3680



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Enhanced Biosorptive Remediation of Hexavalent Chromium Using

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