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Mar 22, 2015 - The feasibility of base (0.1 M NaOH) treated cone shell of. Calabrian pine as an effective and inexpensive biosorbent was examined for ...
Dye Biosorption from Water Employing Chemically Modified Calabrian Pine Cone Shell as an Effective Biosorbent Fatih Deniz Birecik Anatolian High School, 63400 Birecik, Turkey; [email protected] (for correspondence) Published online 22 March 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ep.12113 The feasibility of base (0.1 M NaOH) treated cone shell of Calabrian pine as an effective and inexpensive biosorbent was examined for removal of C.I. Basic Red 46 as a model azo dye from aqueous solution. Biosorption conditions selected for this study were optimized using Taguchi experimental design. The pseudo-first-order, pseudo-second-order, logistic, and intraparticle diffusion models were used for the evaluation of kinetic data. The logistic model presented the best fit to the experimental results with the most suitable statistical outcomes. The intraparticle diffusion was not the only rate-limiting step for the dye biosorption and also the other mechanism(s) may control the rate of biosorption or all of which may be operating simultaneously. Furthermore, the relationship between the kinetic parameters and the biosorption performance was investigated. The equilibrium data were analyzed using Freundlich, Langmuir, and DubininRadushkevich isotherm models. Langmuir model fitted better to the biosorption data than Freundlich model. The maximum monolayer biosorption capacity of the biosorbent for the dye was found to be 89.76 mg g21. Besides, when compared with the natural cone shell, the chemically modified cone shell has a greater biosorption capacity for C.I. Basic Red 46. The base modification improved the biosorption ability of the biosorbent for the dye. Dubinin-Radushkevich model and the standard Gibbs free energy change presented that the predominant mechanism of the biosorption of dye by the cone shell was likely physical biosorption. Finally, a single-stage batch biosorption system design for the dye removal was outlined based on the equilibrium data C 2015 American Institute of Chemical Engineers obtained. V Environ Prog, 34: 1267–1278, 2015 Keywords: biosorption, Calabrian pine, cone shell, dye, Taguchi experimental design, water treatment INTRODUCTION

A negative consequence of the growth of mankind, society and technology has been environmental disorder as large amounts of xenobiotic compounds are being accumulated. On the other hand, limited water resources and increasing demand for safe water require efficient water treatment methods [1]. In particular, the release of various harmful dyes into the environment has attracted great attention worldwide in recent years because of their extensive use in many industrial applications including textile, leather, food processing, C 2015 American Institute of Chemical Engineers V

dyeing, cosmetics, paper, and dye manufacturing industries [2]. Nowadays, more than 100,000 kinds of commercial dyes are used with a rough estimated production of one million tons annually [3]. Dyes usually have a synthetic origin and complex chemical structure that make them persistence to light, oxidation, and biodegradable process. As well known, the presence of dyes in water sources can cause reduction of light penetration, photosynthetic activity, and gas solubility in addition to visual pollution. Also many dyes and their degradation derivatives are toxic at even carcinogenic in nature [4]. Therefore, the removal of these pollutants from contaminated water is a big challenge and thus it is necessary to develop efficient methods for this purpose. Currently, biosorption using biological materials is emerging as a highly effective, economical, and widely used method for the treatment of dye contaminated wastewater. It is considered as a potential alternative over the traditional costly treatment technologies [5]. Among biological materials, agro-forest lingo-cellulosic residues have shown good biosorption capacities for some dyes [6]. In particular, cones from conifers have been recently applied for removal of dyes, mostly with no further treatment other than crushing and washing with water [7–10]. Though these biosorbents have shown reasonable dye removal capacities, after biosorption process, the water is seen to have high chemical oxygen demand and biological oxygen demand as well as total carbon due to release of soluble organic compounds contained in the plant materials [11]. Some authors have applied different activation techniques to reduce the leaching of organic components and enhance biosorption capacity [5,12,13]. However, no work has been reported on the use of chemically modified Calabrian pine cone shell as an effective biosorbent for certain dye removal from aqueous solution. Pretreatment with dilute sodium hydroxide solution has been the most popular method of improving surface properties and removing soluble organic components of plant residues [14]. On the other hand, an efficient application of dye biosorption at industrial scale requires the optimization of dye removal system conditions (dye concentration, biosorbent particle size, contact time, pH, and biosorbent dosage etc.). Conventional optimization procedures are frequently used in dye biosorption studies. These methods involve altering one independent factor at a time keeping all others remain constant, which enables to assess the impact of those particular factors on the biosorption system performance. These

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traditional one factor at a time techniques are time consuming and cumbersome, and require more experiments [15]. Conversely, the design of experiment methodologies can be employed to minimize the number of experiments, time, and research costs. Artificial neural network (ANN) and genetic algorithm (GA) are well-known methods for multifactor process optimization. On the other hand, Taguchi experimental design is a simple and efficient tool for optimization of process [16]. Taguchi design can be used for process optimization more economically [17]. In this method, experiments are designed according to the orthogonal array technique. An orthogonal array is a fractional factorial design with pairwise balancing property. Using orthogonal array design can estimate how multiple process factors affect the performance characteristic simultaneously while minimizing the number of experiments [18]. However, up to the present, this technique is slightly employed in dye biosorption studies [19,20]. Pines are coniferous trees in the genus Pinus of the Pinaceae family. The pine tree exists in large amounts and in various species across the world [11]. The species Calabrian pine (Pinus brutia Ten.) is a characteristic species of the eastern Mediterranean. It is widely extended in Turkey and far Eastern Greece, secondarily in the Crimea, Caucasus coast, Azerbaijan, Iraq, Syria, Lebanon, Crete, and Cyprus [21]. Calabrian pine is an economically important forest tree in Turkey, providing both timber resources and amenity, used widely in afforestation and reforestation programs [22]. Its forests represent about 27% of the country’s forest area, which totals at 5,854,673 ha in 2012 [23]. Pine tree cones are produced in large quantities at forest industries as a litter. Utilization of these cones has been limited to domestic fuel in some rural areas, extraction of essential oils for therapeutic purposes when they are still unripe, and on seasonal decoration [24]. These forest residues are potential lingocellulosic biomaterials for dye removal. They are cheap, renewable, and abundant materials. Thus, usage of them as biosorbent is an attractive alternative from both environmental and economic point of view. Besides, it can provide additional income for forest landowners. In this framework, the primary aim of present study is to examine the feasibility of base (0.1 M NaOH) treated cone shell of Calabrian pine as an effective and inexpensive biosorbent for removal of C.I. Basic Red 46 as a model azo dye from aqueous solution. Biosorption conditions including biosorbent particle size, dye concentration, and contact time for C.I. Basic Red 46 biosorption by the pine cone shell were optimized using Taguchi experimental design. The pseudofirst-order, pseudo-second-order, logistic, and intraparticle diffusion models were used for the kinetic data analysis. Besides, the relationship between the kinetic parameters and the biosorption performance was investigated. The equilibrium data were analyzed using Freundlich, Langmuir, and Dubinin-Radushkevich models. Finally, a single-stage batch biosorption system design for the dye removal was outlined based on the equilibrium data obtained. MATERIALS AND METHODS

Characterization of Pine Cone Shell An IR analysis was performed within the range 650 to 4000 cm21 using a Fourier Transform-Infrared Spectrometer (Spectrum 100, PerkinElmer) to identify the functional groups present on the cone shell. Besides, a Scanning Electron Microscope (JSM-6390, JEOL) was utilized to disclose the surface morphology of the material. Modified Biosorbent Preparation Calabrian pine cone shells were collected from a plantation in Gaziantep, Turkey. After washing with distilled water to 1268 September 2015

eliminate dust and other residues, the shells were dried at 80 C and then crushed, milled, and sieved. The fractions of particle between 63 and 500 mm were selected for chemical modification. The pine cone shells were suspended in 100 mL of a 0.1 M NaOH solution at a suitable ratio and the mixture was stirred using a magnetic stirrer for 2 h at room temperature. Then, it was thoroughly washed with distilled water to remove residual NaOH. The final product was dried at 80 C. The fractions of particles between 63 and 500 mm were used as modified biosorbents for biosorption experiments. Preparation of Dye Solution C.I. Basic Red 46 was obtained from a local source. It was of commercial quality and used without further purification. C.I. Basic Red 46 dye stock solution at a concentration of 500 mg L21 was prepared by dissolving appropriate amount of the dye in distilled water. The experimental concentrations were obtained by the dilution of this solution. 0.1 M HCl or 0.1 M NaOH was used for pH adjustment of the working solutions. Biosorption Experiments Taguchi experimental design (L9 (33) orthogonal array) was used to find the optimal conditions of biosorption process. The selected factors and their levels for this biosorption study were biosorbent particle size (63–125, 125–250, and 250–500 mm), dye concentration (40, 60, and 100 mg L21) and contact time (30, 75, and 120 min). In order to investigate the effect of these experimental factors on the dye biosorption by the modified biosorbent, batch biosorption experiments were carried out with 0.05 mg of the biosorbent with 50 mL of C.I. Basic Red 46 solutions of desired concentration at pH 8 in a series of 100 mL conical flasks. The samples were agitated at a constant speed in a temperaturecontrolled water bath at 25 C for the required time periods. The flasks were withdrawn from the bath at prefixed time intervals and the residual dye concentrations in the solutions were analyzed by centrifuging the mixtures and then measuring the absorbance of supernatants using a UV-visible spectrophotometer at the maximum wavelength of dye. The concentration of dye was calculated by comparing absorbance to the dye calibration curve previously obtained. Evaluation of Data The biosorption capacity, q (mg g21), was calculated using the following equation [25]. q5

ðCo 2Ct ÞV M

(1)

where Co (mg L21) is the initial dye concentration, Ct (mg L21) is the residual dye concentration at time t (min), V (L) is the volume of dye solution, and M (g) is the amount of biosorbent used. The q value is equal to qt at time t and qe at equilibrium, respectively. In the same way, the Ct value is equal to Ce at equilibrium. In this study, each experiment was repeated twice at the same conditions and the arithmetical average values obtained from these experiments were used to give results. In order to optimize the selected experimental factors based on Taguchi experimental design, the software Minitab (ver. 16.2.1, Minitab Inc.) was used. The parameters of kinetic and isotherm models with statistical evaluation data were defined by nonlinear regressions using the software OriginPro (ver. 8.0, OriginLab Co.). RESULTS AND DISCUSSION

FT-IR Analysis Biosorption capacity of a biosorbent material depends upon porosity as well as chemical reactivity of functional

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Figure 1. FT-IR spectrum pattern for Calabrian pine cone shell.

groups at its surface [8]. Thus, knowledge of surface functional groups can give insight to the biosorption yield of the pine cone shell. IR has played an important part in the investigation of biosorbent surface chemistry. Direct information on the presence of surface functional groups can be obtained from IR studies [26]. Pine cone is composed of epidermal and sclerenchyma cells which contain cellulose, hemicellulose, lignin, rosin, and tannins in their cell walls which contains polar functional groups such as alcohols, aldehydes, ketones, carboxylic, phenolic, and other groups [14]. These groups will form active sites for biosorption on the material surface. The FT-IR spectrum pattern for Calabrian pine cone shell is shown in Figure 1. Several peaks were observed from the spectrum indicating that the shell is composed of various functional groups which might be responsible for biosorption of C.I. Basic Red 46. The spectra bands observed at 3336.30 and 2908.68 cm21 represent AOH and aliphatic CAH groups, respectively [13]. The peak at 1604.76 cm21 corresponds to the C@O stretch [11]. The peak at 1507.07 cm21 may be due to the presence of aromatic rings [26]. The peak at 1245.06 cm21 is indicative of aliphatic acid group vibration due to deformation vibration of C@O and stretching formation of AOH of carboxylic acid and phenol [27]. The peak at 1025.24 cm21 is associated with CAOAC functionalities [28]. On the other hand, the spectrum of the chemically modified pine cone shell (figure is not presented here) showed similar characteristics as the natural pine cone shell except for slight changes in the intensity in the band at 3336.30 cm21, the peak at 2908.68 cm21 and 1604.76 cm21. This result obtained with base treatment may be attributed to extraction of some soluble organic components of the pine cone shell [11]. Analysis of SEM SEM is a primary tool for characterizing the surface morphology and fundamental physical properties of the biosorbent surface. It is useful for determining the particle shape, porosity and appropriate size distribution of the biosorbent [7]. SEM image of the cone shell is shown in Figure 2. As can be seen in the figure, the pine cone shell exhibits a rough multilayer surface morphology. The biomaterial has considerable numbers of cavities and pores. This is a good possibility for the dye molecules to be trapped and biosorbed. On the

Figure 2. SEM image of pine cone shell.

other hand, the base modification increased the smoothness of the pine cone shell surface and openness of the pore spaces on the surface (image is not presented here). This is likely due to the extraction of some plant extractives based on base treatment of the biosorbent [29]. Results of Taguchi-Designed Biosorption Experiments As mentioned previously, the performance evaluation of cone shell of Calabrian pine for C.I. Basic Red 46 biosorption was performed using Taguchi experimental design. Taguchi L9 (33) orthogonal array design containing the selected factors and their levels for this biosorption study is presented in Table 1. Taguchi design employs a generic signal-to-noise (SN) ratio as a quantitative measure for determining the optimum biosorption conditions. There are primarily three categories of SN ratios, namely, “smaller-is-better,” “larger-isbetter,” and “nominal-is-best.” The selection principle of SN ratio depends on the goal of study. In order to maximize the biosorption of dye, the “larger-is-better” approach was adopted, in which the SN ratio was calculated by the following equation [30].

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Table 1. Taguchi L9 (33) orthogonal array design.

Experiment

Table 2. Dye biosorption capacity and SN ratio value obtained for each experiment.

Operating factors Dye concentration (mg L21)

Biosorbent particle size (mm)

Contact time (min)

40 40 40 60 60 60 100 100 100

63–125 125–250 250–500 63–125 125–250 250–500 63–125 125–250 250–500

30 75 120 75 120 30 120 30 75

1 2 3 4 5 6 7 8 9

SN ratio5210 log

1 n

n X i51

1 yi2

! (2)

where n is the number of experiments, and yi is the value of dye biosorption capacity of each experiment. Based on the approach employed, the level of factor maximizing the SN ratio is optimal condition for the dye removal. The dye biosorption capacity as mean response and the value of SN ratio obtained for each experiment are given in Table 2. Besides, Table 3 and Figure 3 show the biosorption efficiencies and SN ratio values of all the levels of factors studied and the effect of each factor on the dye removal, respectively. As can be observed in Figure 3a, the biosorption capacity of cone shell increased with increase in the initial dye concentration from 40 to 100 mg L21. This may be due to the high driving force for mass transfer at a high initial dye concentration. In addition, if the dye concentration in solution is higher, the active sites of biosorbent are surrounded by much more dye molecules and the biosorption occurs more efficiently [31]. According to the data presented in Figure 3b, the dye removal decreased with enhancing the biosorbent particle size. The higher dye biosorption efficiency with smaller particles can be due to the fact that smaller biosorbent particles provide a larger surface area and better accessibility of dye into active pores [32]. The biosorption capacity of pine cone shell increased with increase in contact time as shown in Figure 3c. It may be attributed to more vacant active sites being available on the biosorbent surface for further dye biosorption until equilibrium [33]. Finally, as can be seen in Table 3, the optimum dye biosorption conditions based on the approach adopted were obtained as the dye concentration of 100 mg L21, biosorbent particle size of 63 to 125 mm and contact time of 120 min. After the determination of optimum dye biosorption conditions, a statistical variance analysis (ANOVA) was performed to observe the effective factors and their confidence levels on the dye biosorption performance. From the results of ANOVA as given in Table 4, it was found that the dye concentration was the most effective factor studied on the removal of dye. Its contribution percentage was calculated to be 64.25%. This was followed by the contact time and biosorbent particle size factors, respectively. Conducting a verification experiment is a crucial final step of Taguchi experimental design. Its purpose is to verify that the optimum biosorption conditions suggested by the experimental design [34]. A confirmation experiment was performed based on the optimal dye removal conditions calculated previously. The predicted biosorption capacity for 1270 September 2015

Experiment 1 2 3 4 5 6 7 8 9

Mean response Dye biosorption capacity, q (mg g21)

SN ratio

21.72 30.34 25.14 61.15 54.82 20.67 90.40 55.13 64.87

26.46 30.35 27.51 35.27 34.60 27.07 39.72 34.37 36.08

the pine cone shell from Taguchi design was obtained as 90.40 mg g21 at the best biosorption conditions. The value was found to be 91.15 mg g21 from the confirmation experiment. It is very close to the predicted performance. The results state that the experimental design is very effective. Kinetics of Biosorption The prediction of biosorption mechanism and potential rate-controlling step(s) is an important issue to be considered [35]. The biosorption dynamics of C.I. Basic Red 46 onto the pine cone shell were investigated at the optimized dye biosorption conditions with various kinetic models, namely, the pseudo-first-order, pseudo-second-order, logistic and intraparticle diffusion. The pseudo-first-order kinetic model [36] is frequently used in biosorption studies. It is expressed as: qt 5qe ð12exp2k1 t Þ

(3)

Furthermore, the initial biosorption rate, h1 (mg g21 min21), [37] for the pseudo-first-order kinetics can be defined from the following equation. h1 5k1 qe

(4)

where qt and qe (mg g21) represent dye biosorption amounts for the biosorbent at time t and at equilibrium, respectively. k1 (min21) is the biosorption rate constant of pseudo-firstorder model. As can be shown in Table 5, the pseudo-firstorder was not appropriate model for describing the biosorption kinetics based on the statistical evaluations. The pseudo-second-order kinetic equation based on biosorption equilibrium capacity [38] is represented by: qt 5

k2 qe 2 t 11k2 qe t

(5)

For the pseudo-second-order model, the initial biosorption rate, h2 (mg g21 min21), is calculated as: h2 5k2 qe 2

(6)

where k2 (g mg21 min21) is the pseudo-second-order rate constant. According to the statistical results presented in Table 5, the pseudo-second-order kinetic model provided a better fit to the experimental data obtained than the pseudofirst-order model. This confirms that the biosorption kinetics of C.I. Basic Red 46 onto the cone shell can be accurately described by the pseudo-second-order model.

Environmental Progress & Sustainable Energy (Vol.34, No.5) DOI 10.1002/ep

Figure 3. Effect of (a) dye concentration, (b) particle size and (c) contact time on dye removal.

The logistic model is mainly used for modeling of microbial growth and product formation [39,40]. On the other hand, this model is slightly employed for explaining dye biosorption dynamics. The sigmoidal logistic equation [41] can be expressed as: qe (7) qt 5 11exp2kðt2tc Þ

where k (min21) is the maximum relative biosorption rate and tc (min) represents time t pointing center of qe (qe/2). As displayed in Table 5, the logistic model presented the best fit to the experimental results with the most suitable statistical outcomes. Furthermore, Figure 4 shows that the logistic points were quite close to the experimental points over all the biosorption period. Thus, these results show that the

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Table 3. Biosorption efficiencies and SN ratio values of all levels of factors studied. Factor 21

Dye concentration (mg L

)

Biosorbent particle size (mm) Contact time (min)

Level

Biosorption efficiency, q (mg g21)

SN ratio

40 60 100 63–125 125–250 250–500 30 75 120

25.73 45.54 70.13 57.76 46.76 36.89 32.51 52.12 56.78

28.11 32.31 36.72 33.82 33.10 30.22 29.30 33.90 33.94

Table 4. Results of variance analysis (ANOVA). Factor Dye concentration Biosorbent particle size Contact time Error Total

DF*

SS**

MS†

F ratio††

p value‡

C (%)‡‡

2 2 2 2 8

2968.18 653.71 995.89 1.63 4619.41

1484.09 326.85 497.94 0.81

1824.16 401.75 612.04

0.001 0.002 0.002

64.25 14.15 21.56 0.04

*Degree of freedom. **Sum of squares. † Mean squares. †† Fischer ratio. ‡ p value ( Rw > 0.1 (zone I); called well approaching equilibrium in the range 0.1 > Rw > 0.01 (zone II); and called drastically approaching equilibrium when Rw < 0.01 (zone III). The value of approaching equilibrium factor for the biosorption of C.I. Basic Red 46 by the pine cone shell was found to be 0.089. It lies in zone II under largely curved and well approaching equilibrium level. The characteristic curve obtained from graphical representation of approaching equilibrium factor can provide useful information under various operating conditions for effective biosorption system design [44]. Another parameter in the pseudo-second-order kinetic model which can reflect kinetic performance is the secondorder rate index [44]. The second-order rate index, Ri (min21), can be obtained by the following equation.

Environmental Progress & Sustainable Energy (Vol.34, No.5) DOI 10.1002/ep

Table 5. Biosorption kinetic parameters with statistical evaluations. Pseudo-first-order k1 (min21) 0.0677 Pseudo-second-order k2 (g mg21 min21) 0.0009 Logistic Intraparticle diffusion kp(mg g21 min21/2) 5.928

qe (mg g21) 86.67

h1 (mg g21 min21) 5.863

R2 0.824

v2* 64.927

SD* 8.058

qe (mg g21) 93.70

h2 (mg g21 min21) 7.918

R2 0.987

v2 5.641

SD 2.375

qe (mg g21) 91.63

k (min21) 0.045

R2 0.999

v2 0.443

SD 0.666

R2 0.954

v2 17.028

SD 4.126

C (mg g21) 32.35

Chi-square. *Standard deviation.

Figure 4. Experimental and kinetic model data for kinetic performance of biosorption system.

It is evident that the second-order rate index is the only parameter of the Eq. (11). The value of second-order rate index is equal to the inverse of biosorption half-life. The value of half-life is suitable for facilitating the understanding the operating time of a biosorption system. For the removal of C.I. Basic Red 46 by the cone shell, the half-life was obtained as 11.833 min. The small half-life indicates the fast biosorption of dye onto the biosorbent [47]. On the other hand, the second-order rate index was found to be 0.085 min21. The second-order rate index is more suitable to describe the biosorption kinetics than the approaching equilibrium factor. The relationship between accurate operating time and amount of biosorption is an important factor in engineering practice. The second-order rate index is a key parameter affecting the fractional biosorption amount at any time [44]. The relationship between operating time required and biosorption amount can be described by the following equation. tX 5

Figure 5. Plot for Weber and Morris intraparticle diffusion model.

Ri 5k2 qe

(10)

Besides, the half-life of biosorption process, t1/2 (min), which is the time for half amount of dye to be removed by biosorbent [45,46] is calculated as: t1=2 5

1 k2 qe

(11)

W k2 qe

(12)

where W 5 qt/(qe 2 qt). The fractional biosorption (X) is defined as X 5 qt/qe and thus, W 5 X/(1 2 X). When X is gradually approaching 1, tx increases rapidly. The required times (tx) for the fractional biosorption (X) values of 0.55, 0.65, 0.75, 0.85, 0.95, and 0.97 for the biosorption of C.I. Basic Red 46 onto the pine cone shell were calculated as 14.46, 21.98, 35.50, 67.05, 224.83, and 382.60 min, respectively. This information can be used to make proper decisions on scale up and design purposes [46]. Thus, from economic aspect, it should be specified the most suitable fractional biosorption and operating time values based on actual operating conditions.

Equilibrium Isotherms of Dye Biosorption Biosorption isotherms reveal the specific relation between dye molecules and biosorbent surface and highlight the distribution of dye molecules between the liquid and solid phases, when a biosorption process reaches its equilibrium state [48]. The equilibrium biosorption isotherms are of importance in the design of biosorption systems [49]. Accordingly, it was evaluated the fitness of the equilibrium data obtained from the biosorption experiments at the optimized dye removal conditions with Freundlich, Langmuir, and Dubinin-Radushkevich models.

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Freundlich model [50] which assumes biosorption onto heterogeneous solid surface and biosorption energy sites of exponential type is represented by: qe 5Kf Ce 1=nf

(13)

where Kf (mg g21) (L mg21)1/n and nf are Freundlich isotherm constants related to biosorption capacity and intensity, respectively. Based on the statistical information in Table 6, Freundlich model did not properly characterize the biosorption equilibrium. On the other hand, the value of nf (3.712) represents a suitable biosorption [43]. Langmuir model [51] which proposes monolayer coverage and identical sites with the same biosorption energy on the biosorbent surface is described as: qe 5

qL bCe 11bCe

(14)

For Langmuir-type biosorption system, the effect of isotherm shape on whether a biosorption process is favorable or unfavorable can be predicted by the separation factor (RL) using the following equation [52]. RL 5

1 11bCo

(15)

other hand, Figure 6 reveals that Langmuir model line was quite close to the experimental line during the biosorption period. This shows the monolayer coverage of C.I. Basic Red 46 dye molecules on the pine cone shell surface. In addition, the RL value obtained between 0 and 1 reflects a favorable biosorption for C.I. Basic Red 46 removal by the cone shell [53]. Besides, when compared with the natural cone shell of Calabrian pine (66.02 mg g21), the chemically modified cone shell (89.76 mg g21) has a greater biosorption capacity for C.I. Basic Red 46. The base modification improved the biosorption ability of the biosorbent for the dye. This may be due to increases in the total pore volume and surface area of the biosorbent related to extraction of plant extractives with base treatment [11]. In order to have an idea about the potential of the modified cone shell for C.I. Basic Red 46 removal from aqueous solution, its maximum biosorption capacity obtained from this work was also compared with those of reported for other biosorbents in Table 7 [1,54–60]. As can be seen from the table, the modified pine cone shell has a higher dye biosorption capacity than those of most biosorbents. In this way, it can be considered as a promising biosorbent for the removal of this dye from contaminated water. Dubinin-Radushkevich model [61] is generally applied to express the nature of biosorption as physical and chemical. Dubinin-Radushkevich model can be defined as: 2

where b (L mg21) is the constant related to the energy of biosorption and qL (mg g21) is the maximum monolayer biosorption capacity of the biosorbent. As can be seen in Table 6, with more suitable statistical results, Langmuir model fitted better to the biosorption data than Freundlich model. On the

qe 5qDR exp2BDR e

(16)

Based on Dubinin-Radushkevich model, the mean free energy, E (kJ mol21), can be calculated from the following equation [62].

Table 6. Isotherm parameters of biosorption equilibrium with results of statistical analysis. Freundlich nf R2 v2* SD* Kf (mg g21) (L mg21)1/n 33.01 3.712 0.684 116.240 10.781 Langmuir RL R2 v2 SD qL (mg g21) 89.76 0.654 0.980 8.648 2.941 Dubinin-Radushkevich E (kJ mol21) R2 v2 SD qDR (mg g21) 95.56 2.292 0.933 24.539 4.954 Chi-square. *Standard deviation.

Figure 6. Curves of experimental and isotherm models for biosorption equilibrium.

Table 7. Comparison of cone shell with other biosorbents for removal of C.I. Basic Red 46.

Biosorbent Modified Calabrian pine cone shell Spirogyra sp. Pleurotus mutilus Pine tree leaves Juglans regia shell biomass Calabrian pine cone shell Canola hull Princess tree leaf Fir wood sawdust Beech wood sawdust

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qL, maximum monolayer biosorption capacity, (mg g21)

Reference

89.76 83.30 76.92 71.94 71.43 66.02 49.02 43.10 20.47 19.24

Present study [1] [60] [56] [55] [54] [59] [57] [58] [58]

Environmental Progress & Sustainable Energy (Vol.34, No.5) DOI 10.1002/ep

Figure 8. Biosorbent amount required (M) against volume of dye solution treated (V).

biosorbent changes from qo to qt (mg g21). The mass balance for dye in single-stage batch dye biosorption is given by [65,66]: Figure 7. Single-stage batch dye biosorption system design.

E5

1 ð2BDR Þ1=2

(17)

where qDR (mg g21) is the maximum biosorption capacity of the biosorbent and BDR (mol2 kJ22) is a constant related to the mean free energy of biosorption. e is the Polanyi potential which is equal to RTln (1 1 (1/Ce)). R (J mol21 K21) is the universal gas constant and T (K) is the absolute temperature. In Dubinin-Radushkevich isotherm, the value of mean free energy shows the mechanism by which biosorption takes place. A value of mean free energy below 8 kJ mol21 displays physical biosorption while a value between 8 and 16 kJ mol21 indicates chemical biosorption [63]. As can be shown in Table 6, the mean free energy for C.I. Basic Red 46 biosorption by the pine cone shell was found to be 2.292 kJ mol21. This presents that the predominant mechanism of the biosorption of dye by the cone shell was likely physical biosorption. To support this information, the standard Gibbs free energy change, DG (kJ mol21), is determined by [64]: DG  52RTln Kc

(18)

where Kc is the distribution coefficient (Cs/Ce). Cs and Ce (mg L21) are the equilibrium dye concentrations on biosorbent and in solution, respectively. The standard Gibbs free energy change for C.I. Basic Red 46 biosorption by the pine cone shell was calculated as 25.608 kJ mol21. A value of the change of free energy between 220 and 0 kJ mol21 indicates a physical biosorption [63]. This result agrees well with that from the Dubinin-Radushkevich isotherm model. Design of Dye Biosorption System An empirical design of biosorption system (biosorber) using biosorption isotherm data is a known technique for predicting the biosorber size and performance [13]. Figure 7 shows a schematic diagram for a single-stage batch dye biosorption system design where the starting inflow contains V (L) volume of dye solution and an initial dye concentration, Co (mg L21), which is to be reduced to Ct (mg L21) in the biosorption process. In the treatment phase, a mass of M (g) biosorbent is added to this system and the dye loading on

V ðCo 2Ct Þ5Mðqt 2qo Þ5Mqt

(19)

For the biosorption of C.I. Basic Red 46 by the pine cone shell, Langmuir isotherm presents better fit to the equilibrium data. Thus, the mass balance based on Langmuir model under equilibrium (Ct ! Ce and qt ! qe) can be obtained by rearranging the Eq. (19) as: M Co 2Ce Co 2Ce 5 5 V qe qL bCe =ð11bCe Þ

(20)

The biosorbent amount required to achieve a specific dye removal percentage at a given dye solution volume can be predicted using the Eq. (20). For different removal percentages of C.I. Basic Red 46 dye, a series of plots of M versus V is displayed in Figure 8 at optimum biosorption conditions previously obtained by Taguchi experimental design. For example, the required amount of the pine cone shell for 70% dye removal is 85.393 g for dye solution volume of 15 L. Thus, a design procedure for a single-stage batch dye biosorption system is outlined and this information can be useful for the application of the cone shell on a large scale for the dye removal. On the other hand, the amount of biosorbent required for the biosorption process is critical both in the design of the biosorption equipment and its application on a large scale [46]. A multistage dye biosorption system can reduce the biosorbent consumption. But, an increased number of stages increases the operation cost. The optimum number of stages should be based on economic considerations [67]. CONCLUSIONS

The chemical modification with NaOH (0.1 M) significantly enhanced the dye biosorption potential of the pine cone shell by 35% as compared with the natural cone shell. The optimal biosorption conditions were successfully determined by Taguchi experimental design. The logistic model was found suitable in describing the biosorption kinetics. The intraparticle diffusion was not the only rate-limiting step in the biosorption. The evaluation of dye biosorption performance of the biosorbent based on pseudo-second-order kinetics showed an effective biosorption system by the approaching equilibrium factor and second-order rate index. The biosorption equilibrium was properly represented by Langmuir isotherm model. Dubinin-Radushkevich model and the standard Gibbs free energy change supported the

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physical biosorption mechanism. A single-stage batch dye biosorption system was also designed based on the equilibrium data. Thus, the chemically modified Calabrian pine cone shell can be used as an effective and inexpensive biosorbent for contaminated water with C.I. Basic Red 46.

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