Evaluation of Copper Biosorption onto Date Palm (Phoenix dactylifera

Article pubs.acs.org/IECR

Evaluation of Copper Biosorption onto Date Palm (Phoenix dactylifera L.) Seeds with MLR and ANFIS Models Deniz Bingöl,*,† Melih Inal,‡ and Seda Ç etintaş† †

Science and Art Faculty, Department of Chemistry, Kocaeli University, Kocaeli, Turkey Technical Education Faculty, Department of Electronics and Computer Education, Kocaeli University, Kocaeli, Turkey

‡

ABSTRACT: Date palm (Phoenix dactylifera L.) seeds, a waste product as a new, novel, and natural biosorbent, were used to remove Cu(II) ions from aqueous solutions by a batch sorption process. In this study first the comparison of a Multiple Linear Regression (MLR) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) applied for modeling the sorption process is presented. Results were evaluated using Root Mean Squared Error (RMSE) and coefficient of determination (R2) as performance parameters. The experimental and model outputs displayed acceptable result for MLR and ANFIS; testing RMSE values were 0.6725 and 0.1716, and R2 values were 0.7594 and 0.9843, respectively. It was determined that Adaptive Neuro-Fuzzy Inference System (ANFIS) may be effectively used to predict the sorption of Cu(II) onto date palm seeds.

1. INTRODUCTION Heavy metals (such as lead, mercury, copper, cadmium, zinc, nickel, chromium, etc.) represent a major class of pollutants. Heavy metals are toxic and dangerous even at low concentration, and they are widely present in industrial and household wastewaters. They are nonbiodegradable and tend to accumulate in living organisms causing diseases and disorders. The major sources of environmental copper releases include the mining, smelting, and refining of copper, industries producing products from copper such as wire, pipes, and sheet metal and fossil fuel combustion. It can be found in various foods, drinking water, and air. Therefore, a substantial amount of copper is taken in our bodies eating, drinking, and breathing every day. Absorption of copper is necessary; because copper is a trace element necessary for human health. Exposure to copper may cause health problems ranging from stomach distress to brain damage. The current Environmental Protection Agency (EPA) drinking water standards has proposed a maximum contaminant level of 1.3 mg/L for copper.1 Adsorption/biosorption using low cost adsorbents could be technically feasible and economically a viable and sustainable technology for the treatment of wastewater streams. These techniques employing solid sorbents are widely used to remove certain classes of chemical pollutants from waters. The biosorption process involves a solid phase (sorbent or biosorbent; adsorbent; biological material) and a liquid phase (solvent, normally water) containing a dissolved species to be sorbed (adsorbate, metal/dyes). Recently, attention has been focused on various natural solid supports, which are able to remove pollutants from contaminated water at low cost. Cost is actually an important parameter for comparing the adsorbent materials. Agricultural waste could be assumed to be low-cost adsorbents since they are abundant in nature, inexpensive, require little processing, and are effective materials.2 Many agricultural waste materials have been used as adsorbents for the removal of copper from wastewater. These © 2013 American Chemical Society

materials included Cinnamomum camphora leaves powder (16.76−17.87 mg Cu2+/g),3 Tectona grandis L.f. (teak leaves powder) (95.40 mg Cu2+/g),4 cassava peel (41.77 mg Cu2+/g),5 cashew nut shell (20.00 mg Cu2+/g),6 Ulva fasciata sp. (26.88 mg Cu2+/g),7 coffee husks (7.5 mg Cu2+/g),8 and Enteromorpha prolifera (57.14 mg Cu2+/g).9 Date palm seeds as an agriculture waste have been a problem to the date industry; therefore, their recycling or reutilization is useful. Phoenix dactylifera L. (synonyms Palma major Garsault and Phoenix cycadifolia Hort. Attens ex Regel), commonly known as date palm, is an important plant in hot and dry climate regions of Africa, the Middle East, and Asia. The date palm fruits are well-known as a staple food. It is composed of a seed surrounded by a fleshy pericarp which constitutes between 85% and 90% of date fruit weight. The fruits are a rich source of carbohydrates, dietary fibers, certain essential vitamins, and minerals. The date pits are also an excellent source of dietary fiber and contain considerable amounts of minerals, lipids, and protein. In addition to its dietary use the dates are of medicinal use and are used to treat a variety of ailments in the various traditional systems of medicine.10,11 Some studies using date palm’s wastes as sorbent have been published including palm kernel fiber for anionic dye,12 copper ion,13,14 lead ion,15−17 Methylene blue,18,19 PO43−,20 palm kernel coat for Congo Red (CR) (an anionic dye),21 date palm tree wastes for heavy metals (Cu2+, Cd2+, Zn2+),22 raw date pits for Methylene blue, Cu2+, and Cd2+,23 raw date pits for Methylene blue,24 and date pits for uranium(VI) ions.25 Among the various multivariate statistical methods, Multiple Linear Regression (MLR), explaining the linear relations successfully, can be easily used for adsorption modeling purposes. MLR is one of the commonest techniques for Received: Revised: Accepted: Published: 4429

December 20, 2012 February 27, 2013 February 28, 2013 February 28, 2013 dx.doi.org/10.1021/ie400484c | Ind. Eng. Chem. Res. 2013, 52, 4429−4435

Industrial & Engineering Chemistry Research

Article

were selected according to the initial experiments. The samples were centrifuged (Elektromag M815P model) at 1000 rpm for 5 min to separate biosorbent from the solution and then filtered using Whatman filter paper (No. 42). The Cu(II) ion concentration remaining in supernatant was analyzed. The adsorbed amount of Cu(II), qe (mg/g), was calculated from the equation as follows

calibration and regression both in chemistry and statistics. MLR can be easily applied to model the relationship between two or more independent variables and a response variable by fitting a linear equation to observed data.26 In recent years, Adaptive Neuro-Fuzzy Inference System (ANFIS), as another fitting data method, is used in experimental and chemical processes, such as evaluation of irrigation water quality,27 predicting COD removal in a biological wastewater treatment plant,28 predicting optimum cure time of rubber compounds,29 and predicting the main product yields of heavy liquid thermal cracking.30 The intelligence methodology provides the modeling of complex relationships, especially nonlinear ones that may be investigated without complicated equations.30 However, only one work has been reported yet in the literature on the application of ANFIS in modeling of the biosorption process by Khokhar et al.31 The aim of this study was to investigate biosorption characteristics of Cu(II) onto date palm seeds used as a raw, natural, and abundant material in batch mode investigating the effect of the key process parameters. The biosorption process was optimized and compared by using both ANFIS and MLR. To our knowledge, no work has been reported yet in the literature on the comparison of ANFIS and MLR in modeling of the sorption process.

qe = (Co − Ce)

V W

(1)

where C o and C e are the initial and the equilibrium concentrations of copper (mg/L), V is the volume of copper solution (L), and W is the mass of date palm seeds sample used (g). 2.3. Multi Linear Regression (MLR). MLR analysis is generally used to find the relevant coefficients in the model equations. The goal of MLR is to model the relationship between the variables and response. The model for MLR, given p observations, is yi = βo + β1x1 + β2x 2 + ... + βpxp + ε

(2)

where yi denotes the dependent (predicted) variable, βo is the intercept of this plane, parameters β1 and β2 are referred to as partial regression coefficients, xi (i = 1,...,p) are the predictor/ independent variables (experimental variables), and ε is random or unexplained error.26 2.4. Adaptive Neuro-Fuzzy Inference System (ANFIS). Artificial intelligence systems offer an alternative to the polynomial regression method as a modeling tool. Intelligence analysis is quite flexible with respect to the number and form of the experimental data which makes it possible to use more informal experimental designs than with statistical approaches.30 ANFIS has a feed-forward neural network structure where each layer is a neuro-fuzzy system component which is developed by Roger Jang.32−34 In this study, the performance of the ANFIS and MLR models was statically measured by Root Mean Squared Error (RMSE) and the coefficient of determination (R2) as follows

2. MATERIALS AND METHODS 2.1. Materials. The date palm fruits were purchased from Kocaeli/Turkey main market. First, the samples were separated from seeds; date palm seeds were washed with tap water repeatedly to eliminate surface dirt. Then, they were subsequently dried under the sun for at least 24 h, crushed, ground, and kept in an oven at 373 K for 2 h for the removal of moisture, and then they was stored in desiccators. Fourier Transform Infrared (FTIR) spectra of date palm seeds were obtained from FTIR spectrophotometer (Bruker Tensor 27 model). A stock solution of copper (1000 mg/L) in deionized water was prepared from copper sulfate (CuSO4; AR grade). Cu(II) working standard solutions in the range of 5−100 mg/L were prepared for use in the experiments by dilution of 1000 mg/L stock solution. All the glassware materials were cleaned by soaking them in diluted HNO3 (1 + 9) and were rinsed with distilled water prior to use. 2.2. Biosorption Studies. Batch experiments were performed for the removal of Cu(II) ions from aqueous solutions using date palm seeds in the Cu(II) solutions. The biosorption study was conducted by adding a desired amount of adsorbent (0.05−0.5 g) into several beakers, each of which contained 50 mL of metal solution with an initial Cu(II) concentration from 5 to 100 mg/L. Afterward, the beaker was placed in a stirrer (ARE model magnetic stirrer) and stirred at 300 rpm at a temperature of 293 K until equilibrium condition was achieved. After 60 min, the solution was taken to analyze its Cu(II) content using a Perkin-Elmer model AAnalyst 800 flame atomic absorption spectrophotometer (FAAS) equipped with copper hallow cathode lamp (λ = 324.8 nm) and acetylene−air as the fuel-oxidant and fitted with a deuterium arc background corrector. The pH effect was studied at pH range of 2.0−6.0 since its hydroxide take place at a pH above 6.0. The pH (Hanna pH 211 Microprocessor pH-meter) was adjusted by adding appropriate amounts of HCl solution (0.1 N) to the solution. The equilibrium time (60 min) and the initial solution volume (50 mL)

⎛1 RMSE = ⎜⎜ ⎝n

⎞1/2 2⎟ − ( q q ) ∑ e,pred e,exp ⎟ ⎠ i=1 n

(3)

n

2

R =1−

∑i = 1 (qe,pred − qe,exp)2 n

2 ∑i = 1 (qe,exp − qe,exp ̅ )

(4)

where n is the number of points, qe,pred and qe,exp are the predicted and experimental qe-values, respectively, and qe̅ ,exp is the average of the experimental values.

3. RESULTS AND DISCUSSIONS The chemical composition of date palm seeds on dry weight basis was as follows: carbohydrate 22.61%, lipid 6.43%, fiber 54.35%, ash 0.97%, protein 4.94%, moisture 10.70%, Ca 0.03%, P 0.12%, Mg 0.08%, and the energy was 1.68 kcal/g. Fourier Transform Infrared (FTIR) spectra of date palm seeds showed the presence of several functional groups on the surface of date palm seeds, namely hydroxyl, carboxyl, carboxylate, etc. The peak around 3317 cm−1 indicates the existence of free and intermolecular bonded OH groups. The peaks observed at 2922−2863 cm−1 can be assigned to aliphatic C−H groups. A peak at 1744 cm−1 represents the stretching of carboxyl groups; 4430

dx.doi.org/10.1021/ie400484c | Ind. Eng. Chem. Res. 2013, 52, 4429−4435


Article

Figure 1. Architecture of an ANFIS with three inputs.

Figure 2. Sample of gaussmf of the first input (the parameter of pH).

structure has 3 inputs and each input has 3 gauss membership functions, totally the ANFIS has 27 rules (3inputs3rules = 27). A sample of Gaussian membership function (gaussmf) of the first input (the parameter of pH) is shown in Figure 2. The gaussmf has given the lowest RMSE result. First, the experimental data were portioned into training and test sets as shown in Table 1. These two data sets were used for both MLR and ANFIS models. The experimental data were processed using Minitab 16 Statistical Software and MATLAB R2008b. 3.1. Results of MLR. MLR was used to predict the effects of the experimental variables with the adsorbed amount of Cu(II) (qe). The general regression equation showing the relationship

these groups are associated with carboxylic acids. Carboxylate groups are observed at a peak of 1625 cm−1. The peak present at 1031 cm−1 indicates the presence of OH groups (cellulosic compounds).5 Modeling of the biosorption process was performed in two methods, MLR and ANFIS. According to the traditional onevariable-at-a-time approach, the analysis included a total set of 20 experiments and was employed to assess the statistical significance of pH (2.0−6.0), initial Cu(II) concentration (5−100 mg/L), and biosorbent mass (0.050−0.500 g), affecting the sorption of Cu(II) ions onto date palm seeds. The construction of a Sugeno type of ANFIS for predicting response variables is presented in Figure 1. Since the ANFIS model 4431



Article

3.2. Results of ANFIS. The ANFIS model was also used to predict the effects of the experimental features with the adsorbed amount of Cu(II) (qe). To verify the prediction performance of the proposed ANFIS model, predicted qe values were evaluated for different ways. The ANFIS model is trained with the training set which is shown in Table 1, the data without asterisk rows for 5000 epochs. Finally the RMSE is founded as 1.3108 × 10−6 for the training set. An initial Sugeno type Fuzzy Inference System (FIS) is generated for training ANFIS. Before the training stage, the output of the ANFIS is initially zero for all inputs combination. After the training stage, the output of the ANFIS is changed according to the combination of inputs. These situations can be seen in Figure 3. According to the combination of inputs, the output of the ANFIS has a minima and a maxima value. That means the output converges for each state of the inputs. Three-dimensional plots for the predicted qe using the ANFIS model are shown in Figure 3. pH, initial concentration of copper, and sorbent mass are the studied variables. The nonlinear behavior of the studied system can be clearly seen from the plots. Results show that at lower pH, the sorbed amount of Cu(II) (qe) has little change due to the competition between hydrogen and copper ions for active sites on the date palm seeds surface. At larger pH, qe is always increased due to the negative surface charge of date palm seeds and hence increased electrostatic interactions. In addition, at lower sorbent mass the effect of pH was more pronounced. At a larger mass of date palm seeds, qe is not changed too much even if the initial concentration of copper and pH are increased. With an increase in the mass of date palm seeds, the decrease in qe was due to the concentration difference between copper concentration in the solution and on the surface of date palm seeds divided by the unit weight of date palm seeds (eq 1). Some research studies related to good performance of ANFIS is also given in the literature.27−31 3.3. Comparison of MLR and ANFIS. In order to show the deviations from the observed values of qe, the absolute error of the test data for the models constructed from the experimental values were also calculated, and the resulting graphs are shown in Figure 4. These graphics indicated that the deviation interval (0.0 to 0.3) of the predicted values from ANFIS is smaller than the deviation interval of MLR (0.0 to 1.3). Also, a correlation between the training and the test values by both MLR and ANFIS models was tested. Figure 5 shows the graphical comparison between the predicted test data results (test data) from MLR and ANFIS models and the experimental data (training data). The results reveal that an excellent agreement exists between the training results and the test data. A good fit between ANFIS results and experimental results is obvious. MLR and ANFIS models were compared in terms of their accuracy and predictive ability. The predicted values both of MLR and ANFIS models are given in Table 3 for the sorption of Cu(II) ions. It was found that the values of R2 and RMSE for ANFIS were 0.9843 and 0.1716, respectively, while they are 0.7594 and 0.6725 for MLR. Due to lower R2 value, the MLR model shows greater deviation in fitting to the measured responses than does the ANFIS. The RMSE value for the ANFIS model is comparatively smaller than that for the MLR model. Hence, ANFIS is able to predict the percentage of sorption with lower error. The results also showed that the regression models developed by MLR could not catch the nonlinearity of the system; therefore, it showed a lower accuracy as compared to the

Table 1. Training and Test Data Sets

a

run

pH

m (g)

Co (mg/L)

qe (mg/g)

1 2a 3 4a 5 6a 7 8a 9 10a 11 12a 13 14a 15 16a 17 18a 19 20a

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 6.0

0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.050 0.200 0.300 0.400 0.500 0.100 0.100 0.100 0.100 0.100 0.100 0.100

5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 10.0 20.0 40.0 60.0 80.0 100.0 5.0

0.01 0.10 0.21 0.80 1.45 1.59 1.67 1.66 2.76 0.75 0.52 0.39 0.31 2.65 3.58 4.18 4.14 4.11 4.16 1.62

Separated for test data set.

between the adsorbed amount of Cu(II) and the variables was achieved as follows: qe (mg/g) = − 1.27824 + 0.783457· pH − 6.35335·m + 0.0214773·Co

(5)

High correlation among the observed experimental results and the fitted values by using eq 2 demonstrates that the model is well fitted, considering the determination coefficient (R2 = 92.18%) and only 7.82% (residuals) of total variation was not explained by the model. The regression model was also performed with linear and polynomial (second or third order) terms. Polynomial regression is one method for modeling curvature in the relationship between a response variable (y) and a predictor/independent variable (x) by extending the simple linear regression model to include x2 and x3 as predictors. Although the shape of the regression surface is curvilinear, the regression model is still linear because the model is linear in the parameters. The model order is an important factor in how accurately the model describes the data and predicts a response. According to the type of regression model for standardized data, a comparison of the determination coefficients (R2, R2-adj) and standard deviation (S) values was given in Table 2. All the R-squared of model Table 2. Comparison Based on Regression Model Order model order parameter 2

R R2-adj S

linear

quadratic

cubic

0.9220 0.9120 0.4925

0.9240 0.9020 0.4989

0.9410 0.9120 0.4723

orders were found above 0.90. In this case, the values of the determination coefficients indicated that over 90% of the total variations are explained by the model.35 Therefore, MLR was selected for comparison with ANFIS. 4432



Article

Figure 3. Three-dimensional plots for the predicted qe using ANFIS.

Figure 4. Absolute error of test data for ANFIS and MLR models.

In addition, the nonlinear behavior of the studied system that is modeled by ANFIS can be clearly seen from Figure 3, but MLR does not perform a desired estimation capability.

ANFIS model nonlinear characteristics. For this reason, ANFIS might have better predictive power than MLR. It can be said that the ANFIS model is an alternative to the traditional approach in determining qe for biosorption process. 4433



Article

Figure 5. The variations of the values predicted by MLR and ANFIS models from the observed values.

Table 3. Comparison of the Performance of MLR and ANFIS MLR run

pH

1 2 3 4 5 6 7 8 9 10

2.5 3.5 4.5 5.5 5.5 5.5 5.5 5.5 5.5 6 RMSE R2

m (g) 0.1 0.1 0.1 0.1 0.2 0.4 0.1 0.1 0.1 0.1 MLR 0.6725 0.7594

ANFIS

C0 (mg/L)

qe (mg/g)

predicted

residual

predicted

residual

5 5 5 5 5 5 10 40 80 5

0.10 0.80 1.59 1.66 0.75 0.39 2.65 4.18 4.11 1.62

0.152 0.936 1.719 2.503 1.867 0.597 2.610 3.255 4.114 2.895

−0.052 −0.136 −0.129 −0.843 −1.117 −0.207 0.040 0.925 −0.004 −1.275

−0.1303 0.9122 1.7106 1.5839 0.5556 0.3892 2.3743 3.8779 4.1471 1.5735

0.2303 −0.1122 −0.1206 0.0761 0.1944 0.0008 0.2757 0.3021 −0.0371 0.0465

ANFIS 0.1716 0.9843

4. CONCLUSION In the present study, ANFIS as a new artificial intelligent method and MLR models were developed to predict the adsorbed amount of Cu(II) onto date palm seeds. RMSE and R2 values were used for the evaluation of model performance. It was found that the ANFIS model had an accuracy of more than MLR for predicting qe. In addition, it was proposed as an alternative use of date palm seeds, as untreated sorbents for the removal of Cu(II)

ions as an eco-friendly process. The date palm seeds were effectively used for the removal of Cu(II) ions from aqueous solutions as a potential biosorbent.

■

AUTHOR INFORMATION

Corresponding Author

*Phone: +902623032030. Fax: +902623032003. E-mail: [email protected], [email protected]. 4434



Article

Notes

(24) Banat, F.; Al-Asheh, S.; Al-Makhadmeh, L. Evaluation of the use of raw and activated date pits as potential adsorbents for dye containing waters. Process Biochem. 2003, 39, 193. (25) Saad, E. M.; Mansour, R. A.; El-Asmy, A.; El-Shahawi, M. S. Sorption profile and chromatographic separation of uranium (VI) ions from aqueous solutions onto date pits solid sorbent. Talanta 2008, 76, 1041. (26) Brereton, R. G. Applied Chemometrics for Scientists; Wiley: New York, 2007. (27) Alavi, N.; Nozari, V.; Mazloumzadeh, S. M.; Nezamabadi-pour, H. Irrigation water quality evaluation using adaptive network-based fuzzy inference system. Paddy Water Environ. 2010, 8, 259. (28) Civelekoglu, G.; Yigit, N. O.; Diamadopoulos, E.; Kitis, M. Modeling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network. Water Sci. Technol. 2009, 60 (6), 1475. (29) Karaağaç, B.; Iṅ al, M.; Deniz, V. Artificial neural network approach for predicting optimum cure time of rubber compounds. Mater. Des. 2009, 30, 1685. (30) Sedighi, M.; Keyvanloo, K.; Towfighi, J. Modeling of thermal cracking of heavy liquid hydrocarbon: Application of kinetic modeling, artificial neural network and neuro-fuzzy models. Ind. Eng. Chem. Res. 2011, 50, 1536. (31) Khokhar, Z. H.; Al-Harthi, M. A.; Abdurraheem, A. Investigations of biosorption capacity histories with temperature and other parameters in a given fuzzy universe of discourse. IJMO 2012, 2 (3), 222. (32) Jang, J. S. R. Neuro-Fuzzy and Soft Computing; Prentice-Hall: NJ, 1997. (33) Jang, J. S. R. ANFIS: Adaptive network-based fuzzy inference systems. IEE Trans. Syst. Man. Cybern. 2 1993, 3 (3), 665. (34) Jang, J. S. R. Self-learning fuzzy controllers based on temporal backpropagation. IEE Trans. Neural Networks 1992, 3 (5), 714. (35) Arulsudar, N.; Subramanian, N.; Murthy, R. S. R. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes. J. Pharm. Pharmaceut. Sci. 2005, 8 (2), 243.

The authors declare no competing financial interest.

■

REFERENCES

(1) http://water.epa.gov/drink/contaminants/basicinformation/ copper.cfm (accessed December 1, 2012). (2) Ramachandra, T. V.; Ahalya, N.; Kanamadi, R. D. Biosorption: Techniques and Mechanisms. CES Technical Report 110, Centre for Ecological Sciences; Indian Institute of Science: Bangalore, 2005. (3) Chen, H.; Dai, G.; Zhao, J.; Zhong, A.; Wu, J.; Yan, H. Removal of copper(II) ions by a biosorbent-Cinnamomum camphora leaves powder. J. Hazard. Mater. 2010, 177, 228. (4) King, P.; Srinivas, P.; Kumar, Y. P.; Prasad, V. S. R. K. Sorption of copper(II) ion from aqueous solution by Tectona grandis l.f. (teak leaves powder). J. Hazard. Mater. 2006, B136, 560. (5) Kosasih, A. N.; Febrianto, J.; Sunarso, J.; Ju, Y. H.; Indraswati, N.; Ismadji, S. Sequestering of Cu(II) from aqueous solution using cassava peel (Manihot esculenta). J. Hazard. Mater. 2010, 180, 366. (6) Kumar, P. S.; Ramalingam, S.; Sathyaselvabala, V.; Kirupha, S. D.; Sivanesan, S. Removal of copper(II) ions from aqueous solution by adsorption using cashew nut shell. Desalination 2011, 266, 63. (7) Kumar, Y. P.; King, P.; Prasad, V. S. R. K. Removal of copper from aqueous solution using Ulva fasciata sp.−A marine green algae. J. Hazard. Mater. 2006, B137, 367. (8) Oliveira, W. E.; Franca, A. S.; Oliveira, L. S.; Rocha, S. D. Untreated coffee husks as biosorbents for the removal of heavy metals from aqueous solutions. J. Hazard. Mater. 2008, 152, 1073. (9) Ö zer, A.; Gürbüz, G.; Ç alimli, A.; Körbahti, B. K. Biosorption of copper(II) ions on Enteromorpha prolifera: Application of response surface methodology (RSM). Chem. Eng. J. 2009, 146, 377. (10) Baliga, M. S.; Baliga, B. R. V.; Kandathil, S. M.; Bhat, H. P.; Vayalil, P. K. A review of the chemistry and pharmacology of the date fruits (Phoenix dactylifera L.). Food Res. Int. 2011, 44, 1812. (11) Besbes, S.; Blecker, C.; Deroanne, C.; Drira, N. E.; Attia, H. Date seeds: chemical composition and characteristic profiles of the lipid fraction. Food Chem. 2004, 84, 577. (12) Ofomaja, A. E.; Ho, Y. S. Equilibrium sorption of anionic dye from aqueous solution by palm kernel fibre as sorbent. Dyes Pigm. 2007, 74, 60. (13) Ho, Y. S.; Ofomaja, A. E. Kinetic studies of copper ion adsorption on palm kernel fibre. J. Hazard. Mater. 2006, B137, 1796. (14) Ofomaja, A. E. Equilibrium studies of copper ion adsorption onto palm kernel fibre. J. Environ. Manage. 2010, 91, 1491. (15) Ho, Y. S.; Ofomaja, A. E. Pseudo-second-order model for lead ion sorption from aqueous solutions onto palm kernel fiber. J. Hazard. Mater. 2006, B129, 137. (16) Ho, Y. S.; Ofomaja, A. E. Kinetics and thermodynamics of lead ion sorption on palm kernel fibre from aqueous solution. Process Biochem. 2005, 40, 3455. (17) Ho, Y. S.; Ofomaja, A. E. Effects of calcium competition on lead sorption by palm kernel fibre. J. Hazard. Mater. 2005, B120, 157. (18) Ofomaja, A. E. Sorptive removal of Methylene blue from aqueous solution using palm kernel fibre: Effect of fibre dose. Biochem. Eng. J. 2008, 40, 8. (19) Ofomaja, A. E. Sorption dynamics and isotherm studies of methylene blue uptake on to palm kernel fibre. Chem. Eng. J. 2007, 126, 35. (20) Riahi, K.; Thayer, B. B.; Mammou, A. B.; Ammar, A. B.; Jaafoura, M. H. Biosorption characteristics of phosphates from aqueous solution onto Phoenix dactylifera L. date palm fibers. J. Hazard. Mater. 2009, 170, 511. (21) Oladoja, N. A.; Akinlabi, A. K. Congo red biosorption on palm kernel seed coat. Ind. Eng. Chem. Res. 2009, 48, 6188. (22) Ahmed, L. A. A. Removal of heavy metals from waste water by date palm tree wastes. Eng. Tech. J. 2010, 28 (1), 119. (23) Al-Ghouti, M. A.; Li, J. L.; Salamh, Y.; Al-Laqtah, N.; Walker, G.; Ahamd, M. N. M. Adsorption mechanisms of removing heavy metals and dyes from aqueous solution using date pits solid adsorbent. J. Hazard. Mater. 2010, 176 (1−3), 510. 4435


Evaluation of Copper Biosorption onto Date Palm (Phoenix dactylifera

Recommend Documents