Statistical analysis of Litchi chinensis’s adsorption behavior toward Cr(VI)
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Abstract
The adsorption results of Cr(VI) removal from aqueous solutions on Litchi chinensis have been optimized by the Box–Behnken design of response surface methodology. Three experimental parameters (dose, temperature, and pH) were chosen as independent variables. The maximum Cr(VI) adsorption was obtained at the initial pH of 2. Analysis of variance (ANOVA) of the results was successfully used to check the significance of the independent variables and their interactions. The three-dimensional (3D) response surface plots were used to study the interactive effects of the independent variables on % Cr(VI) removal. These figures successfully interpret the effect of interaction between pH (0.1–1.0), adsorbent dose (0.1–1.0 g.) and temperature (0–50 °C). The second-order polynomial equation was generated for the response. A statistical hypothesis test was conducted to critically analyze the experimental data by applying t test, paired t test, and Chi-square test. The comparison of t-calculated and t-tabulated values showed that the results were in favour of the conducted experiment.
Keywords
Litchi chinensis Box–Behnken design Statistical analysis Hypothesis testIntroduction
From many times, the presence of heavy metals in the water sources is a serious environmental problem and is dangerous to the human health worldwide. These inorganic pollutants are highly toxic because they are not biodegradable; thus, researches are going on for decades to find some methods to adsorb metal ions from aqueous solution. Among them, chromium has become a serious health concern because it causes many severe diseases. Strong exposure to Cr(VI) causes cancer in the digestive tract and may cause epigastric pain, nausea, vomiting, severe diarrhoea, and haemorrhage (Mohanty et al. 2005). Chromium is one of the contaminants, which exist in hexavalent [Cr(VI)] and also in the trivalent form [Cr(III)] (Rao et al. 2015). Chromium and its compounds are widely used in many industries such as metal finishing, dyeing, pigments, inks, glass, ceramics, tanning, textile, wood preserving, electroplating, steel fabrication, and canning industries (Rao et al. 2015). Chromium (VI) is more toxic to human physiology because of its mutagenic and carcinogenic properties, which may even lead to death (Rao et al. 2015). The chromate (CrO _{2} ^{−4} ) and dichromate (Cr_{2}O _{2} ^{−7} ) forms of Cr(VI) are extremely hazardous (Eliodorio et al. 2017), and the maximum acceptable limit by various standards has been set (Uddin 2017). Permissible limits for Cr(VI) in drinking water (mg/L) according to various standards are very low: 0.050 (IS 10500) (FAD 25: Drinking Water 2012), 0.050 (WHO) (Uddin 2017), 0.100 (EPA) (Uddin 2017), 0.050 (EU standard) (Wikipedia).
Many processes like precipitation (Esalah and Husein 2008), chemical oxidation (Kaur and Crimi 2014), reverse osmosis (Çimen 2015), electrochemical treatment (Ruotolo et al. 2006), emulsion (Nosrati et al. 2011), ultrafiltration (Muthumareeswaran et al. 2017), photo-catalysis (Machado et al. 2014), ion exchange (Kononova et al. 2009), pre-concentration (Rao and Kashifuddin 2012a), evaporation (Sachitanand et al. 2013), sedimentation (Vukić et al. 2008), adsorption (Khatoon et al. 2018; Naushad et al. 2015; Alqadmi et al. 2016; Uddin and Bushra 2017) have been developed to remove this toxic Cr(VI) from aqueous solution. Out of these methods, adsorption is a widely used and most efficient method to eliminate heavy metals from contaminated water (Rao and Kashifuddin 2012a, b). This technique is superior because of its low cost, ease of operation, efficiency in treatment, good applicability, high capacity, reliability, less energy consumption, and simplicity (Rao and Kashifuddin 2016; Khan et al. 2014). Various effective and low-cost adsorbents have been used for the removal of Cr(VI) recently (Eliodorio et al. 2017; Suriga 2017; Ali et al. 2016; Eldin et al. 2017; Lee et al. 2017; Mullick et al. 2018; Fan et al. 2017; Qi et al. 2016; Panda et al. 2017; Ali et al. 2016; Gorzin and Abadi 2018).
Red- or pink-red-colored smooth fruit peel of litchi tree (Litchi chinensis) covered with small sharp protuberances was successfully utilized as a low cost, efficient, waste adsorbent for the removal of Cr(VI) from wastewater (Rao et al. 2012; Yi et al. 2017). The results of the research studies investigated by Rao et al. 2012 and Yi et al. 2017 concluded that Litchi peel exhibited remarkable adsorption capacity toward Cr(VI) ions, as investigated by the effect of various parameters such as pH, contact time, temperature, adsorbent amount, and initial Cr(VI) concentration. Different isotherms, thermodynamics, and kinetics parameters also showed the effectiveness of Litchi peel to treat hexavalent chromium containing wastewater (Rao et al. 2012).
To confirm the reliability of experimental data, a statistical optimization process that is known as response surface methodology (RSM) was used (Mondal et al. 2017). RSM is a multivariate, computational statistical technique in which the experimental adsorption data were fitted in a second-order polynomial equation, and finally, it was analyzed by performing tests of variance and lack of fit (Mondal et al. 2017). The Box–Behnken design (BBD) is one of the available designs of response surface methodology that is used to optimize the adsorption process (Simsek et al. 2015). It was used in many successful recent studies to validate the experimental results (Mondal et al. 2017; Loqman et al. 2016; Okwadha and Nyingi 2016; Siva Kiran et al. 2017; Igberase et al. 2017; Kavitha and Thambavani 2016; Ma et al. 2016; Wei et al. 2016; Perez et al. 2017). The objective of the present work is to optimize the efficiency of litchi fruit peel in Cr(VI) removal from electroplating wastewater (Rao et al. 2012). The variables like pH, solution temperature, and adsorbent dose were optimized to evaluate the combined and interactive effects of the variables in the process for the removal of chromium ions from aqueous solution.
Materials and experimental methods
The dried peel of Litchi fruit was used in the form of powder to remove the Cr(VI) using the batch experimental procedure, the same as investigated before by Rao et al. 2012. Cr(VI) adsorption experiments were then conducted by defined concentration and volume of the metal ion with different doses of adsorbent (0.1, 0.5, and 1.0 g) at different pH values (2.0, 6.0, and 10.0) and temperatures (30, 40, and 50 °C). Double-distilled water (DDW) was used in all adsorption experiments.
Statistical analysis of adsorption data
Levels and codes of selected variables for Box–Behnken design
Variables | Symbol | Coded levels | ||
---|---|---|---|---|
Coded | − 1 | 0 | + 1 | |
Dose | X _{1} | 0.10 | 0.55 | 1.00 |
Temperature | X _{2} | 30 | 40 | 50 |
pH | X _{3} | 2 | 6 | 10 |
The applicability of the adsorption process can also be monitored, qualitatively and quantitatively, by applying statistical hypothesis testing (Kaushal and Singh 2016). In this type of statistical analysis, the effect of different factors like pH value, time, and concentration on % adsorption of Cr(VI) was examined. In each case, the null hypothesis (H_{o}: µ_{1} = µ_{2}) was assumed that the factors did not effect on the % adsorption, while the alternative hypothesis (H_{a}: µ_{1} > µ_{2}) was assumed that the experimental parameters were effective and caused an increase in % adsorption. The analysis was tested using t test, paired t test, and Chi-square test within 5% level of confidence.
Results and discussion
Response surface methodology and statistical analysis
Box–Behnken design
Box–Behnken design matrix (BBD) in terms of coded values and the comparison of experimental and predicted % Cr(VI) removal
Run | Dose | Temp. | pH | X _{1} | X _{2} | X _{3} | % Cr(VI) removal (Y) | |
---|---|---|---|---|---|---|---|---|
Experimental | Predicted | |||||||
1 | 0.1 | 30 | 6 | − 1 | 0 | − 1 | 40 | 40.50 |
2 | 1 | 30 | 6 | 0 | − 1 | − 1 | 80 | 75.75 |
3 | 0.1 | 50 | 6 | − 1 | 1 | 0 | 32 | 36.20 |
4 | 1 | 50 | 6 | 0 | 0 | 0 | 85 | 84.50 |
5 | 0.1 | 40 | 2 | 0 | − 1 | 1 | 62 | 60.62 |
6 | 1 | 40 | 2 | − 1 | − 1 | 0 | 96 | 99.37 |
7 | 0.1 | 40 | 10 | 1 | 0 | 1 | 28 | 24.62 |
8 | 1 | 40 | 10 | 0 | 0 | 0 | 68 | 69.37 |
9 | 0.55 | 30 | 2 | 0 | 0 | 0 | 88 | 88.87 |
10 | 0.55 | 50 | 2 | − 1 | 0 | 1 | 95 | 92.12 |
11 | 0.55 | 30 | 10 | 1 | − 1 | 0 | 54 | 56.87 |
12 | 0.55 | 50 | 10 | 1 | 1 | 0 | 59 | 58.12 |
13 | 0.55 | 40 | 6 | 0 | 1 | 1 | 77 | 77.00 |
14 | 0.55 | 40 | 6 | 0 | 1 | − 1 | 77 | 77.00 |
15 | 0.55 | 40 | 6 | 1 | 0 | − 1 | 77 | 77.00 |
The linear term of coefficients \( X_{1} {\text{and}} X_{2} \) showed positive, favorable, and a significant effect on the response, while X_{3} showed a negative effect, which is opposite, if compared, to quadratic terms in which \( X_{1}^{2} {\text{and}} X_{2}^{2} \) showed negative, while \( X_{3}^{2} \) showed a positive effect on the response (Y). Interaction terms (\( X_{1} X_{2} , X_{1} X_{3} \)) of the same parameter indicated positive and favorable effect, while X_{2}X_{3} showed negative and unfavorable effect on percentage removal of Cr(VI).
Analysis of variance in the regression model for the optimization of Cr(VI) ions
Source of variations | Degree of freedom | Sum of square | Mean square | value | P value |
---|---|---|---|---|---|
Regression | 9 | 6500.48 | 722.28 | 44.45 | 0.000 |
Linear | 3 | 5674.25 | 1891.42 | 116.39 | 0.000 |
Dose (X_{1}) | 1 | 3486.13 | 3486.13 | 214.53 | 0.000 |
Temp (X_{2}) | 1 | 10.13 | 10.13 | 0.62 | 0.466 |
pH (X_{3}) | 1 | 2178.00 | 2178.00 | 134.03 | 0.000 |
Square | 3 | 773.98 | 257.99 | 15.88 | 0.055 |
(X_{1})^{2} | 1 | 736.67 | 736.67 | 45.33 | 0.001 |
(X_{2})^{2} | 1 | 48.52 | 48.52 | 2.99 | 0.145 |
(X_{3})^{2} | 1 | 1.44 | 1.44 | 0.09 | 0.778 |
Two-way interaction | 3 | 52.25 | 17.42 | 1.07 | 0.329 |
X _{1} X _{2} | 1 | 42.25 | 42.25 | 2.60 | 0.168 |
X _{2} X _{3} | 1 | 9.00 | 9.00 | 0.55 | 0.490 |
1 | 1.00 | 1.00 | 0.06 | 0.814 | |
Error | 5 | 81.25 | 16.25 | ||
Lack of fit | 3 | 81.25 | 27.08 | ||
Pure error | 2 | 0.00 | 0.00 | ||
Total | 14 | 6581.73 |
Estimated value of coefficient regression for the fitted quadratic polynomial model of Cr(VI) ion
Term | Effect | Coeff. | Standard error | T value | P value |
---|---|---|---|---|---|
Constant | 77.00 | 2.33 | 33.08 | 0.000 | |
X _{1} | 41.75 | 20.88 | 1.43 | 14.65 | 0.000 |
X _{2} | 2.25 | 1.12 | 1.43 | 0.79 | 0.466 |
X _{3} | − 33.00 | − 16.50 | 1.43 | − 11.58 | 0.000 |
X _{1} X _{1} | − 28.25 | − 14.13 | 2.10 | − 6.73 | 0.001 |
X _{2} X _{2} | − 7.25 | − 3.63 | 2.10 | − 1.73 | 0.145 |
X _{3} X _{3} | 1.25 | 0.63 | 2.10 | 0.30 | 0.778 |
X _{1} X _{2} | 6.50 | 3.25 | 2.02 | 1.61 | 0.168 |
X _{1} X _{3} | 3.00 | 1.50 | 2.02 | 0.74 | 0.490 |
X _{2} X _{3} | − 1.00 | − 0.50 | 2.02 | − 0.25 | 0.814 |
\( R^{2} \) | 0.987 | ||||
\( R^{2}\, {\text{adj}} \) | 0.965 |
Hypothesis testing
The hypothesis testing was conducted: (1) to judge the optimum value of pH, concentration and time for maximum Cr(VI) removal from solution, (2) to judge the success of the experiment by checking higher R^{2} value, (3) to infer that the higher adsorbent dosage resulted in higher % removal of chromium ions.
Effect of pH on % removal of Cr(VI) ion
n | pH | % Removal (X_{i}) |
---|---|---|
1 | 2 | 96 |
2 | 4 | 88 |
3 | 6 | 72 |
4 | 8 | 62 |
5 | 10 | 54 |
- (a)
To describe the hypothesis testing process qualitatively and quantitatively, the following statistical assumptions were made:
\( {\text{null }}\,{\text{hypothesis }}\left( {H_{0} } \right): \) the optimum pH for Cr(VI) adsorption was 2, and alternate hypothesis (H_{a}): the optimum pH \( \ne \) 2. It was determined with significance level α = 0.05 and (n − 1) degree of freedom.
Upon using the two-tailed t-test with a degree of freedom 4 using formula \( t = \frac{{\overline{X} - \mu }}{{\frac{S}{\sqrt n }}} \) (Hogg and Craig 2014), it was found that \( t_{\text{calculated}} {\text{was}} - 2.757\, {\text{while}} \,t_{\text{tabulated}} = \pm \,2.776, \) which means that t_{calculated} < t_{tabulated}. This result of hypothesis testing confirms that null hypothesis H_{0} can be accepted (that the optimum pH of Cr(VI) adsorption was 2), which also in accordance with the experimental result. Figure 3 shows probability chart for t distribution for testing. - (b)Paired t test was used to test the hypothesis of matched pairs of concentration effect (before and after the adsorption experiment), as shown in Table 6. It was assumed thatTable 6
Cr(VI) ion concentrations in the solution before (Xi) and after (Yi) the experiment
n
X _{ i}
Y _{ i}
\( D_{i} = X_{i} - Y_{i} \)
1
10
0.86
9.14
2
20
1.80
18.20
3
30
2.79
27.21
4
50
4.54
45.46
5
60
5.38
54.62
6
80
7.31
72.69
7
100
9.28
90.72
H_{0}: no changes in the final concentration of Cr(VI) ions after adsorption experiment.
H_{a}: Successful adsorption was achieved after the experiment.
The calculations have been done using the formula: \( t = \left( {D^{ - } - \mu } \right)/\left( {S/\surd n} \right) \) (Hogg and Craig 2014), at 6 degree of freedom. It was found that t_{calculated} (4.056) > t_{tabulated} (± 2.447), which means that the null hypothesis can be rejected and alternative hypothesis (that the adsorption experiment was successfully achieved) can be accepted. Figure 4 shows the probability chart for t distribution for paired t test. - (c)
Chi-square test was applied to test the effectiveness of time on final concentration and equilibrium capacity. To describe this statistically, the assumptions were
H_{0}: There was no effect of time on initial concentration
H_{a}: Adsorption was rapid, and equilibrium adsorption capacity (qe) increased with the increase in concentration
Under significance level α = 0.05 and using the experimental data (as shown in Table 7), the formula \( \chi^{2} = \mathop \sum \nolimits_{i = 1}^{n} \frac{{\left( {O_{ij} - E_{ij} } \right)^{2} }}{{E_{ij} }} \) (Hogg and Craig 2014) was used to conduct Chi-square test. It was found that \( \chi_{\text{calculated}}^{2} = 0.8220 < \chi_{\text{tabulated }}^{2} = 32.3600 \). Hence, the null hypotheses did not fall in the accepted region and cannot be accepted, which also means that the alternative hypothesis Ha can be accepted at value level of 0.05. It means that adsorption of Cr(VI) was fast and equilibrium capacity (qe) increased with the increase in Cr(VI) concentration and reached equilibrium in short time (Fig. 5).Table 7Observed frequencies on the effect of time (min) and concentration (mg/L) for the Cr(VI) ion equilibrium adsorption capacity (qe)
Time (min)
10 mg/L
20 mg/L
30 mg/L
50 mg/L
60 mg/L
80 mg/L
100 mg/L
qe (mg)
qe (mg)
qe (mg)
qe (mg)
qe (mg)
qe (mg)
qe (mg)
5
0.02
1.31
2.17
3.70
4.52
6.35
8.20
10
0.68
1.42
2.25
3.79
4.62
6.43
8.27
15
0.73
1.64
2.49
3.96
4.7
6.51
8.46
20
0.86
1.82
2.63
4.08
4.97
6.77
8.61
30
0.86
1.80
2.72
4.22
5.08
6.83
8.68
60
0.86
1.80
2.79
4.53
5.36
7.28
9.16
120
0.86
1.80
2.79
4.54
5.38
7.31
9.24
180
0.86
1.80
2.79
4.54
5.38
7.31
9.28
240
0.86
1.80
2.79
4.54
5.38
7.31
9.28
S _{D}
0.34
0.22
0.23
0.21
0.23
0.21
0.20
Conclusion
In total, 96% Cr(VI) from polluted water can be removed using the fruit peel of L. chinensis by a batch process, which demonstrated its efficient analytical applicability. Critical analysis of the interactive effects of independent variables: initial pH of the solution, dose, and temperature for better understanding of Cr(VI) adsorption onto L. chinensis was successfully studied by Box–Behnken design. The results showed that the values of R^{2} and adjusted R^{2} were quite close to each other, indicated that the model analyzed the experimental data quite well. The linear terms (X_{1}, X_{2}, X_{3}), square values (X _{1} ^{2} , X _{2} ^{2} , and X _{3} ^{2} ), and their two-way interaction (X_{1}X_{2}, X_{2}X_{3}) were found to be significant with low P values, suggesting that these variables have important role in Cr(VI) removal. Hypothesis testing was further studied to confirm the fitting of experimental results. Two-tailed t test, paired t test, Chi-square test within 5% level of confidence were tested, and the results showed that the calculated values were inside the acceptance region in probability chart. Experimental and predictable data of adsorption experiments were close to each other which also confirmed that L. chinensis was an excellent adsorbent to bind Cr(VI) ion.
Notes
Acknowledgements
The corresponding author is thankful to Deanship of Scientific Research, Majmaah University, Al-Majmaah, for funding this research. This research work was conducted under research Project Number 37/61.
References
- Ali A, Saeed K, Mabood F (2016) Removal of chromium (VI) from aqueous medium using chemically modified banana peels as efficient low-cost adsorbent. Alexandria Eng J 55:2933–2942CrossRefGoogle Scholar
- Alqadmi A, Naushad Mu, Ahamad T, Abdalla MA, Al-Othman ZA, AlShehri SM (2016) Synthesis and characterization of Fe3O4@TSC nanocomposite: highly efficient removal of toxic metal ions from aqueous medium. RSC Adv 6:22679–22689CrossRefGoogle Scholar
- Box GEP, Behnken DW (1960a) Simplex-sum designs: a class of second order rotatable designs derivable from those of first order. Ann Math Stat 31:838–864CrossRefGoogle Scholar
- Box GEP, Behnken DW (1960b) Some new three level designs for the study of quantitative variables. Technometrics 2:455–475CrossRefGoogle Scholar
- Çimen A (2015) Removal of chromium from wastewater by reverse osmosis. Rus J Phys Chem A 89(7):1238–1243CrossRefGoogle Scholar
- Doke KM, Khan EM (2017) Equilibrium, kinetic and diffusion mechanism of Cr(VI) adsorption onto activated carbon derived from wood apple shell. Arab J Chem 10:S252–S260CrossRefGoogle Scholar
- Drinking water [FAD 25: Drinking Water] (2012) Indian standard drinking water-specification (second revision) ICS 13.060.20 IS 10500Google Scholar
- Eldin MSM, Al-bogami AS, Aly KM, Khan ZA, Mekky AEM, Saleh TS, Hakamy AAW (2017) Removal of chromium (VI) metal ions using Amberlite IRA-420 anions exchanger. Desalin Water Treat 60:335–342CrossRefGoogle Scholar
- Eliodorio KP, Andolfatto VS, Martins MRG, de Sá BP, Umeki ER, de Araújo Morandim-Giannetti A (2017) Treatment of chromium effluent by adsorption on chitosan activated with ionic liquids. Cellulose 24(6):2559–2570CrossRefGoogle Scholar
- Esalah J, Husein MM (2008) Removal of heavy metals from aqueous solutions by precipitation-filtration using novel organo-phosphorus ligands. Sep Sci Technol 43(13):3461–3475CrossRefGoogle Scholar
- Fan S, Wang Y, Li Y, Tang J, Wang Z, Tang J, Li X, Hu K (2017) Facile synthesis of tea waste/Fe_{3}O_{4} nanoparticle composite for hexavalent chromium removal from aqueous solution. RSC Adv 7:7576–7590CrossRefGoogle Scholar
- Gorzin F, Abadi MMBR (2018) Adsorption of Cr(VI) from aqueous solution by adsorbent prepared from paper mill sludge: kinetics and thermodynamics studies. Adsorp Sci Tech 36:149–169CrossRefGoogle Scholar
- Hogg RV, Craig A (2014) Introduction to mathematical statistics, 7th edn. Pearson Education Limited. https://en.wikipedia.org/wiki/Drinking_water_quality_standards. Accessed 18 Jan 2012
- Igberase E, Osifo P, Ofomaja A (2017) Chromium (VI) ion adsorption by grafted cross- linked chitosan beads in aqueous solution—a mathematical and statistical modeling study. Environ Technol 38:3156–3166. https://doi.org/10.1080/09593330.2017.1290152 CrossRefGoogle Scholar
- Kaur K, Crimi M (2014) Release of chromium from soils with persulfate chemical oxidation. Groundwater 52:748–755CrossRefGoogle Scholar
- Kaushal A, Singh S (2016) Critical analysis of adsorption data statistically. Appl Water Sci. https://doi.org/10.1007/s13201-016-0466-4 Google Scholar
- Kavitha B, Thambavani SD (2016) Kinetics, equilibrium isotherm and neural network modeling studies for the sorption of hexavalent chromium from aqueous solution by Quartz/Feldspar/Wollastonite. RSC Adv 6:5837–5847CrossRefGoogle Scholar
- Khan MA, Uddin MK, Bushra R, Ahmad A (2014) Synthesis and characterization of polyaniline Zr(IV) molybdophosphate for the adsorption of phenol from aqueous solution. React Kinet Mech Catal 113:499–517CrossRefGoogle Scholar
- Khatoon A, Uddin MK, Rao RAK (2018) Adsorptive remediation of Pb(II) from aqueous media using Schleichera oleosa bark. Environ Technol Innov 11:1–14CrossRefGoogle Scholar
- Kononova ON, Shatnykh KA, Prikhod’ko KV, Kashirin DM (2009) Ion-exchange recovery of gold(I) and silver(I) from thiosulfate solutions. Rus J Phys Chem A 83(13):2340–2345CrossRefGoogle Scholar
- Lamhamdi A, Lamhamdi A, Razzouki B, Mejdoubi EM, Al Zabadi H, Ellouzi K, Azzaoui K, Hamed O, Bouhlassa S, Jodeh S (2017) Adsorption of chromium (VI) on calcium phosphate: mechanisms and stability constants of surface complexes. Appl Sci 7:1–14Google Scholar
- Lee CG, Lee S, Park JA, Park C, Lee SJ, Kim SB, An B, Yun ST, Lee SH, Choi JW (2017) Removal of copper, nickel and chromium mixtures from metal plating wastewater by adsorption with modified carbon foam. Chemosphere 166:203–211CrossRefGoogle Scholar
- Loqman A, El Bali B, Lützenkirchen J, Weidler PG, Kherbeche A (2016) Adsorptive removal of crystal violet dye by a local clay and process optimization by response surface methodology. App Water Sci. https://doi.org/10.1007/s13201-016-0509-x Google Scholar
- Ma X, Li D, Wu Z, Zhang H, Chen X, Liu Z (2016) Mercury removal by adsorption on pectin extracted from sugar beet pulp: optimization by response surface methodology. Chem Eng Technol 39:371–377CrossRefGoogle Scholar
- Machado TC, Lansarin MA, Matte N (2014) Reduction of hexavalent chromium: photocatalysis and photochemistry and their application in wastewater remediation. Water Sci Technol 70(1):55–61CrossRefGoogle Scholar
- Mohanty K, Jha M, Meikap BC, Biswas MN (2005) Removal of chromium (VI) from dilute aqueous solutions by activated carbon developed from Terminalia arjuna nuts activated with zinc chloride. Chem Eng Sci 60:3049–3059CrossRefGoogle Scholar
- Mondal NK, Samanta A, Dutta S, Chattoraj S (2017) Optimization of Cr(VI) biosorption onto Aspergillus niger using 3-level Box–Behnken design: equilibrium, kinetic, thermodynamic and regeneration studies. J Genet Eng Biotechnol. https://doi.org/10.1016/j.jgeb.2017.01.006 Google Scholar
- Muthumareeswaran MR, Alhoshan M, Agarwal GP (2017) Ultrafiltration membrane for effective removal of chromium ions from potable water. Sci Rep 7:41423CrossRefGoogle Scholar
- Naushad M, Ahamad T, Al-Othman ZA, Shar MA, Al-Hokbany NS, Alshehri SM (2015) Synthesis, characterization and application of curcumin formaldehyde resin for the removal of Cd^{2+} from wastewater: kinetics, isotherms and thermodynamic studies. J Ind Eng Chem 29:78–86CrossRefGoogle Scholar
- Nosrati S, Jayakumar NS, Hashim MA (2011) Extraction performance of chromium (VI) with emulsion liquid membrane by Cyanex 923 as carrier using response surface methodology. Desalination 266:286–290CrossRefGoogle Scholar
- Okwadha GDO, Nyingi PW (2016) Effectiveness of rice husk ash in stabilizing Kenyan red coffee soil for road subgrades construction. Int J Environ Sci Technol 13:2731–2734CrossRefGoogle Scholar
- Panda H, Tiadi N, Mohanty M, Mohanty CR (2017) Studies on adsorption behavior of an industrial waste for removal of chromium from aqueous solution. S Afr J Chem Eng 23:132–138Google Scholar
- Perez JVD, Nadres ET, Nguyen HN, Dalida MLP, Rodrigues DF (2017) Response surface methodology as a powerful tool to optimize the synthesis of polymer-based graphene oxide nanocomposites for simultaneous removal of cationic and anionic heavy metal contaminants. RSC Adv 7:18480–18490CrossRefGoogle Scholar
- Qi W, Zhao Y, Zheng X, Jia M, Zhang Z (2016) Adsorption behavior and mechanism of Cr(VI) using Sakura waste from aqueous solution. Appl Surface Sci 360:470–476CrossRefGoogle Scholar
- Rao RAK, Kashifuddin M (2012a) a Adsorption Properties of Coriander Seed Powder (Coriandrum sativum): extraction and Pre-concentration of Pb(II), Cu(II) and Zn(II) Ions from Aqueous Solution. Adsorpt Sci Technol 30:127–146CrossRefGoogle Scholar
- Rao RAK, Kashifuddin M (2012b) Pottery glaze—an excellent adsorbent for the removal of Cu(II) from aqueous solution. Chin J Geochem 31:136–146CrossRefGoogle Scholar
- Rao RAK, Kashifuddin M (2016) Adsorption studies of Cd (II) on ball clay: comparison with other natural clays. Arab J Chem 9:S1233–S1241CrossRefGoogle Scholar
- Rao RAK, Rehman F, Kashifuddin M (2012) Removal of Cr(VI) from electroplating wastewater using fruit peel of Leechi (Litchi chinensis). Desalin Water Treat 49:136–146CrossRefGoogle Scholar
- Rao RAK, Ikram S, Uddin MK (2015) Removal of Cr(VI) from aqueous solution on seeds of Artimisia absinthium (novel plant material). Desalin Water Treat 5412:3358–3371CrossRefGoogle Scholar
- Ruotolo LAM, Santos-Júnior DS, Gubulin JC (2006) Electrochemical treatment of effluents containing Cr(VI). Influence of pH and current on the kinetic. Water Res 40:1555–1560CrossRefGoogle Scholar
- Sachitanand R, Sattari M, Svensson JE, Froitzheim J (2013) Evaluation of the oxidation and Cr evaporation properties of selected FeCr alloys used as SOFC interconnects. Int J Hydro Energy 38:15328–15334CrossRefGoogle Scholar
- Simsek EB, Tuna AOA, Beker U (2015) A statistical approach for arsenic adsorption onto Turkey clinoptilolite. Environ Sci Pollut Res 22:3249–3256CrossRefGoogle Scholar
- Siva Kiran RR, Madhu GM, Satyanarayana SV, Kalpana P, Rangaiah GS (2017) Applications of Box–Behnken experimental design coupled with artificial neural networks for biosorption of low concentrations of cadmium using Spirulina (Arthrospira) spp. Resour Eff Technol 3:113–123Google Scholar
- Suriga CL (2017) Catalytic oxidation of dye waste water by biomass charcoal loaded multiple rare earth composite material. IOP Conf Ser Mater Sci Eng 167:1–7CrossRefGoogle Scholar
- Uddin MK (2017) A review on the adsorption of heavy metals by clay minerals, with special focus on the past decade. Chem Eng J 308:438–462CrossRefGoogle Scholar
- Uddin MK, Bushra R (2017) Synthesis and characterization of composite cation-exchange material and its application in removing toxic pollutants. In: Anjum N, Gill S, Tuteja N (eds) enhancing cleanup of environmental pollutants. Springer, ChamGoogle Scholar
- Vukić LM, Gvero PM, Maksimović MD (2008) Gravitational sedimentation—an efficient chromium removal method from the tanning industry wastewaters. Acta Periodica Technologica 39:1–212Google Scholar
- Wei F, Wu B, Zhang J, Zhang W (2016) Modification of abandoned fine blue-coke: optimization study on removal of p-nitrophenol using response surface methodology. RSC Adv 6:13537–13547CrossRefGoogle Scholar
- Mullick A, Moulik S, Bhattacharjee S (2018) Removal of hexavalent chromium from aqueous solutions by low-cost rice husk-based activated carbon : kinetic and thermodynamic studies. Indian Chem Eng 60:58–71CrossRefGoogle Scholar
- Yang ZH, Wang B, Chai LY, Wang YY, Wang HY, Su CQ (2009) Removal of Cr(III) and Cr(VI) from aqueous solution by adsorption on sugarcane pulp residue. J Cent South Univ Technol 16:0101–0107CrossRefGoogle Scholar
- Yi Y, Lv J, Liu Y, Wu G (2017) Synthesis and application of modified Litchi peel for removal of hexavalent chromium from aqueous solutions. J Mol Liq 225:28–33CrossRefGoogle Scholar
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