Synthesis of iron oxides impregnated green adsorbent from sugarcane bagasse: Characterization and evaluation of adsorption efficiency

Journal of Environmental Management 249 (2019) 109323

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Research article

Synthesis of iron oxides impregnated green adsorbent from sugarcane bagasse: Characterization and evaluation of adsorption efficiency

T

Archina Buthiyappan, Jayaprina Gopalan, Abdul Aziz Abdul Raman* Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

ARTICLE INFO

ABSTRACT

Keywords: Adsorbent Agro waste Sugarcane bagasse Dye removal Iron oxide SEM-EDX

This present research aims to synthesize and investigate the adsorption potential of sugarcane bagasse (SCB) impregnated with iron oxide (Fe3O4) for dye removal. The surface morphology and functional groups of the newly developed adsorbent (ISCB) were studied using Scanning Electron Microscopy/Energy-dispersive X-ray spectroscopy (SEM/EDX), Fourier transforms infrared spectroscopy (FTIR), and X-ray powder diffraction (XRD) analysis. The effects of the operating parameters, including initial dye concentration, adsorbent dosage, contact time and initial pH of the dye solution on the adsorption efficiency were investigated to identify an optimal condition. The characterization of SEM-EDX and FTIR analyses shows that ISCB has a porous structure and carbon-containing functional groups. The adsorption result revealed that ISCB removed 93.7% of dye, 88.8% of color and had a dye adsorption capacity of 7.2 mg/g within 6 h of contact time using 0.7 g/L of ISCB at pH 8.4. The result obtained fitted well for Langmuir isotherms, and adsorption process followed the pseudo-second-order kinetic model. In conclusion, this study proved that ISCB has the potential to be used as an effective and low-cost adsorbent to remove dyes from wastewater.

1. Introduction Agricultural waste is defined as the by-products generated from various agricultural industries such as animal waste, crops residue and waste from food processing waste. The composition of waste depends on the type of agricultural activities carried out and can be in the forms of liquid, solid or slurries (Deng et al., 2015; Mo et al., 2018). The large amount of waste that generated by various agricultural industries may cause serious health and environmental problems such as food poisoning, unsafe food hygiene, contaminated farmland, surface and ground water pollution if not managed effectively (Anastopoulos et al., 2017). Therefore, the application of engineering and scientific knowledge is important to manage the agricultural waste problem and develop feasible waste management systems. Agricultural waste has an advantage in the wastewater treatment for the adsorption of organic pollutants and inorganic pollutants (Mo et al., 2018; Patil et al., 2019). Their properties such as environmentally friendly, ample sources, chemical stability, low cost, regeneration/ reusability, high porosity and large surface area make it efficient to remove different types of dyes and heavy metals. The organic compounds such as phenol, aldehyde, and carboxyl that present in their polymer chains help in the removal of various pollutants from wastewater (Mo et al., 2018; Omo-Okoro et al., 2018). Different types of *

agricultural wastes like olive stones, rice husk, leaf powder, banana peel, eucalyptus pulp, sugarcane bagasse, tea waste, maize cobs, peach stones, almond shell and sheep manure waste has been developed into a low-cost adsorbent (Duan et al., 2017; Patil et al., 2019). However, nonmodified agricultural wastes have limitations such as low adsorption efficiency, lengthy contact time, excessive dose and high sensitivity to exceptionally low or high pH (Mo et al., 2018; Wen et al., 2018). For these reasons, agricultural waste needs to be modified to improve their adsorption abilities. In recent years, iron oxides impregnated with agricultural waste have attracted much attention because of their super magnetic properties, higher surface area, and greater actives sites, which are able to improvise the adsorption capacity of the adsorbent (Jabasingh et al., 2018; Jiang et al., 2019; Panneerselvam et al., 2011; Rahnama et al., 2014). The application of magnetite in the field of waste water treatment is becoming an interesting area of research (Shah et al., 2015). In the present work, we have demonstrated the preparation of green adsorbent derived from waste sugarcane bagasse impregnated with iron oxides (ISCB) for the degradation of simulated dyes wastewater. Sugarcane bagasse is a heterogeneous fibrous residue that contains substances such as 50% cellulose, 25% hemicellulose and 25% lignin which reacts actively with dyes and heavy metals (Sud et al., 2008; Rezende et al., 2011; Sharma and Bajpai, 2018). The presence of these

Corresponding author. E-mail address: [email protected] (A.A. Abdul Raman).

https://doi.org/10.1016/j.jenvman.2019.109323 Received 23 November 2018; Received in revised form 24 July 2019; Accepted 26 July 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Environmental Management 249 (2019) 109323

A. Buthiyappan, et al.

Fig. 1. Flowchart of the overall methodology. Fe3 O4 + SCB

SCB

(1)

Fe3 O4

polymers causes the bagasse rich in carboxyl, hydroxyl and phenolic groups. Therefore, it can be modified chemically to generate a low cost and efficient adsorbent with new property for heavy metals, dyes, and phenols removals (Alves et al., 2016; Brandão et al., 2010; Luo et al., 2016). The sugarcane bagasse impregnated with iron oxides finds huge applicability in adsorption due to their magnetic separation, specificity, surface chemistry, and enhancement of the rate of reaction (Jabasingh et al., 2018; Jiang et al., 2019). The adsorption efficiency of the green adsorbent was tested using four types of dye including Methylene Blue (cationic dye), Malachite Green (cationic dye), Reactive Red 535 (anionic dye) and Remazol Brilliant Blue R (anionic dye). Therefore, we feel that it is essential to study the simultaneous removals of these dyes as industrial effluents often include more than one type of dye (Rahimi et al., 2018). Besides, to the best of our knowledge, there is no work reported on the adsorption capacity of ISCB in a simulated dye mixture system. The ISCB was characterized using X-ray diffraction (XRD), Scanning Electron Microscopy/Energy-dispersive X-ray spectroscopy (SEM/EDX) and Fourier-transform infrared spectroscopy (FTIR). The factors affecting the adsorption efficiency, including dye concentration, adsorbent dosage, initial pH and contact time were investigated based on the color removal and dye removal percentages. The adsorption isotherm and kinetics of the adsorption process were also identified.

2. Material and methods 2.1. Materials The iron (II, III) oxides (Fe3O4), 33% hydrogen peroxide (H2O2), sodium hydroxide (NaOH), dimethylsulfoxide (DMSO), acid sulfuric (H2SO4), acetic acid (CH3COOH), sodium sulfite (Na2SO3) were purchased from Sigma Aldrich (M) Sdn. Bhd. Methylene Blue, Malachite Green, Reactive Red 535 and Remazol Brilliant Blue R were purchased from Merck. Sdn. Bhd. All the chemicals were reagent grade and were used without further purification. 2.2. Chemically modified bagasse Sugarcane bagasse was collected from the nearest sugar-producing factory in Kuala Lumpur, Malaysia. The sugarcane bagasse was handstripped and dried under the hot sun for three days. The dried bagasse was manually smashed into smaller pieces and dried at 100 °C in an oven for 24 h. The dried sugarcane bagasse was further milled using an electrical grinder. The powdered bagasse was then washed and rinsed with distilled water and further dried in an oven for 24 h. The dried bagasse was bleached with 0.12 M of H2O2 solution at fibre to liquid ratio of 1:70 at pH 4. The pH was adjusted by adding a few droplets of 8 M CH3COOH solution, and the mixture was left to boil for 5 h to remove the lignin. The mixture was centrifuged and washed repeated 2

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Table 1 Experimental range and levels of the independent variables. Variable

Dosage of Adsorbent (A) Dye Concentration (B) Contact time (C) Initial pH (D)

Unit

g mg/l hours –

Range and level Minimum

Maximum

Mean

10 1 6 5

100 10 24 9

55.0 5.5 15.0 7.0

using with distilled water till it reached pH 7. The residue was then boiled with 0.4 M Na2SO3 solution for 5 h and washed with excess distilled water to detach the lignin part completely and hemicellulose part partially. Next, the residue was boiled using 4.0 M NaOH solution for 5 h to remove the remaining hemicelluloses. The cellulose formed was filtered and centrifuged. Then, the cellulosic materials were washed with distilled water and mixed with 50 mL of 0.7 M DMSO and kept in a water bath of 80 °C for 3 h. Finally, the bagasse was centrifuged and washed with excess distilled water and dried. The prepared SCB was then kept inside an airtight container for further use. 2.3. Preparation of iron oxides- sugarcane bagasse The regenerated cellulose (SCB) was immersed into a 0.5 M H2SO4 solution for 10 min and washed with deionized water. Then, it was immersed into 0.08 M Fe3O4 solution for 20 min. Afterwards, the resulting iron impregnated cellulose was washed with excess deionized water to remove excess Fe2+ and Fe3+ ions. Finally, the cellulose nanoadsorbents impregnated with iron oxide were dried in an ambient condition and labelled as ISCB. The reaction involved during the impregnation of iron (II, III) oxide on the SCB is shown in Equation (1). Flowchart of the overall methodology is given in Fig. 1.

Fig. 3. SEM micrograph of ISCB.

Fig. 2. FTIR spectra of ISCB and SCB. 3

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Fig. 4. The EDX graph of ISCB and SCB.

2.4. Design of experiment

2.6. Characterization of sugarcane bagasse adsorbent

Design Expert (Version 8) software was used to design the experiment. Response Surface Methodology (RSM) combined with BoxBehnken design was used to optimize the operating parameters and dye removal efficiency. As more than three operating parameters were investigated in this work, Box-Behnken was chosen as it can give a lesser number of experimental runs as compared to Central Composite Design. The RSM design was used to estimate the interactions between the parameters. The operating parameters had a total of three levels and were determined based on preliminary and literature studies. Four operating parameters, including the initial dye concentration, adsorbent dosage, contact time and initial pH, were selected as an independent variable to analyze the color removal and dye removal efficiencies. Table 1 shows the selected range of operating parameters and the level of independent variables used in this study. The BoxBehnken design required experiments performed outside the experimental range to allow the prediction of the response functions outside the domain. Replicates were used to evaluate the experimental error and to check the adequacy of the model.

The surface morphology of ISCB was identified using SEM/energy dispersive X-ray (SEM-EDX). Fourier transform infrared spectroscopy (FTIR) analysis was analyzed using IR Prestige-21 FTIR spectrophotometer (Shimadzu) to identify the functional groups present in the ISCB and SCB using the range between 4000 cm−1 and 400 cm−1. The changes in the morphology of the adsorbent were also analyze using XRD analysis (Tedesco and Brunelli, 2017). 2.7. Point of zero charge The point of zero charge (pHpzc) was determined by the method of solid addition. Six vials containing simulated dye solutions of pH between 5 and 10 (pH0) and 0.10 g of adsorbent were stirred for 24 h at room temperature, and the final pH was measured. The difference between the initial pH and the final pH (ΔpH = pH0 -pHf) was plotted against the initial pH (pH0). The pHpzc was determined based on the intersection of the curve with the axis that passes through the zero. 3. Result and discussion

2.5. Adsorption experiment

3.1. Characterization of ISCB

All the experiments were carried out at room temperature (25 ± 0.1 °C). The initial pH of simulated dye solution was adjusted using 1 M NaOH and 0.5 M H2SO4. The predetermined amount of adsorbent was then added to a 25 mL dye solution and was kept stirring in a shaker with a speed of 200 rpm. The contact time of the experiments has been varied between 6 and 24 h. At the end of each experiment, the solution was centrifuged at 4000 rpm for 10 min. UV–vis spectrophotometer is used to measure the changes in the concentration of the simulated dye solution at a wavelength of 616 nm. All the experiments were duplicated, and data were presented as averages with standard deviations. The % of dye removal was calculated based on Eq. (2).

% Dye removal =

Ci

Ct Ci

x 100

Fig. 2 shows the FTIR spectrum of SCB and ISCB. The overall % of absorbance transmitted for ISCB was higher than SCB due to the interaction that occurred between ISCB and the OH groups (Ho et al., 2005). The peak between 480 and 450 cm 1 designates a metal-oxygen bond and therefore, proving that iron had successfully impregnated onto cellulose. The strong peak at 3287.38 cm 1 and 3301.72 cm 1of ISCB and SCB respectively indicated the presence of cellulose and hemicellulose. The signal at 1634.17 cm 1 and 1633.99 cm 1 shows the C]C stretching vibration and a conjugated C]O stretching, respectively. The medium signal shows the carboxylic groups were present in the lignin and hemi-cellulose. The peak at 1080.48 cm 1 and 1053.91 cm 1 , explains the presence of the crystal structure (Ass et al., 2006). FTIR spectra show a noticeable change after the modification of iron oxide onto the SCB adsorbents, which make the crystalline structure to exist more than in the SCB. The presence of C–C skeleton vibrations and C–H (substituted alkene) are indicated by the peak at 542.13 cm 1 and 530.96 cm 1. The surface morphology and elemental composition of SCB and ISCB, before and after adsorption process were analyzed using SEMEDX analysis. Fig. 3 shows ISCB has a uniform elongated fibrous shape, which implies that iron nanoparticles have been impregnated on the

(2)

Equation (3) used to calculate the amount of dye adsorbed at time t, qt (mg/g).

qt =

Ci

Ct V m

(3)

where Ci is the initial dye concentration, Ct is the concentration of the solution after adsorption at time t respectively, V is the volume of solution (L), and m is the adsorbent dosage (g). 4

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Fig. 5. The XRD spectra of a) ISCB and b) SCB.

surface of cellulose from sugarcane bagasse effectively. The tapered end with many pits showed that lignin and hemicellulose were successfully removed from the bagasse and a porous structure of ISCB adsorbent was formed to increase the efficiency of adsorbent (Fig. 3B) (Imran and Anwar Khan, 2018). EDX spectrums of both SCB and ISCB explained well the changes that occurred in the bagasse after impregnated with iron oxides (Fig. 4). The EDX images show the existence of C, O and Fe on the surface of ISCB. This confirmed that iron nanoparticle had formed on the layer of SCB. EDX result also proved that SCB contains 100% of carbon while in ISCB it contains 87.6% carbon and 12.4% of iron oxide. Fig. 5 shows the XRD analyses output of the synthesized SCB and ISCB adsorbents. The iron oxide peaks were identified from the XRD patterns at the peak positions of θ: 30.8, 36.1, 44.5, 57.8 and 63.7. This supported the SEM/EDX that the iron magnetic nanoparticles had been successfully coated and incorporated onto the newly developed ISCB

adsorbent. XRD analysis of SCB also indicated the amorphous nature of sugarcane bagasse. 3.2. Modelling and statistical analysis A total of 46 experiments were conducted. The experimental design matrices as proposed by Box-Behnken design and the results obtained are presented in Table 2. The experimental data was found to be best fitted to the quadratic model to explain the empirical interactions of the dependent and independent variables for both responses based on the experimental design (Table 2). The analysis of variance (ANOVA) was used to evaluate the adequacy of the model in this study. The result obtained for ANOVA is shown in Table 3. If the ‘Prob > F’ value was less than 0.05, the corresponding terms are statistically significant at the 95% probability level. From the table, the ‘Prob > F’ value for both % of dye removal 5

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The adequate precision ratios of 15.04 and 15.49 obtained for color removal and % of dye removal indicates an adequate signal for the analyzed variables. A ratio greater than 4 was desirable as this shows that the models could be used to navigate the design space. The experimental and predicted values for color removal and % of dye removal is presented in Fig. 3. The values shows a good correlation between the theoretical and experimental data. Therefore, it was proven that the “R2” values were in good agreement with the ‘Adjusted R2’ value.

Table 2 Experimental design suggested by RSM, and the result obtained. No.

ISCB Dosage (mg)

Simulated Dye Concentration (mg/l)

Contact Time (Hours)

Initial pH

Dye Removal %

Color Removal %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

10 100 55 55 55 10 10 55 100 55 55 55 55 55 10 55 55 55 55 10 55 55 100 10 100 55 55 55 55 100 55 55 10 55 55 55 55 10 55 55 100 55 55 55 100 100

5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 1.0 5.5 1.0 10.0 5.5 5.5 1.0 1.0 10.0 5.5 10.0 1.0 5.5 5.5 1.0 5.5 10.0 5.5 5.5 5.5 5.5 5.5 10.0 5.5 1.0 10.0 5.5 5.5 5.5 5.5 10.0 5.5 5.5 5.5 5.5 10.0 1.0

15 6 24 6 15 15 6 6 15 15 24 15 15 6 24 6 15 15 15 15 24 15 24 15 15 15 15 15 6 15 15 15 15 15 24 15 15 15 24 6 15 15 15 24 15 15

9 7 7 7 7 5 7 5 5 5 7 9 7 9 7 7 7 9 7 7 7 7 7 7 7 5 9 5 7 9 7 7 7 7 7 5 9 7 9 7 7 7 7 5 7 7

93.3 50.0 36.7 98.0 33.3 73.3 40.0 66.7 63.3 40.0 40.0 97.0 85.0 96.7 80.0 90.0 80.0 92.0 33.3 89.0 70.0 33.3 60.0 100 80.0 86.0 97.6. 40.0 98.0 86.7 33.3 85.0 66.7 90.0 92.4 33.3 96.7 73.3 40.0 86.0 73.3 33.3 36.7 50.0 49.0 99.6

77.6 44.1 33.8 83.5 31.4 12.4 42.1 57.6 55.2 15.0 36.9 73.8 78.4 80.5 70.7 92.5 45.8 85.5 32.1 83.0 34.6 31.8 50.7 61.7 64.1 31.8 86.6 41.7 83.1 73.8 31.4 78.0 60.4 47.7 85.5 36.9 83.3 60.7 37.6 78..3 63.1 30.7 34.5 45.5 42.0 49.5

3.3. Effects of operating parameters on the color removal and percentage removal of dye 3.3.1. Effect of dye concentration The initial concentration of the dye solution has a significant effect on the color removal. In order to identify the effect of dye concentration on color removal, the initial dye concentration was varied from 1 to 10 mg/L. Fig. 6a shows the effects of dye concentration and adsorbent dosage on color removal. The color removal % was decreased from 57.3% to 53.9% when the dye concentration was increased from 1 to 10 mg/L by using 0.4 g/L of adsorbent at pH 7 and contact time of 15 h. Higher color removal was observed at the lowest concentration of dye because of a large amount of vacant active surface sites that were available for adsorption during the initial stage (Sahu et al., 2018). The results obtained in this study suited the findings by Saad and others (2010) (Saad et al., 2010). On the other hand, Fig. 6b depicts that higher initial dye concentration required more dosage of adsorbent to remove the dye as compared to lower initial dye concentration. As can be seen from Fig. 6b, increasing the concentration of dye from 1 to 6 mg/L resulted in an increase in dye removal from 71.9% to 85%. However, a further increase in the concentration of dye to 10 mg/L resulted in a decrease in dye removal % to 58.3%. This indicated that the initial dye concentration played a significant role in the color removal % and dye removal % of dyes solution onto ISCB. 3.3.2. Effect of initial pH The initial pH of the reaction medium was one of the parameters that crucially affect the adsorption. Fig. 7 that the dye removal % increased when the initial pH was increased from 5 to 9. This explained that higher pH is favoring the adsorption process of dyes on the ISCB. The similar trend was observed for color removal % as can be seen from Fig. 8. The pH of the solution affected by the type of the dyes used the surface charge of adsorbent and the ionization or dissociation of the dye molecules (Wang et al., 2018). The pHpzc was found to be 8.5, for ISCB. At higher pH than the pHpzc the cation adsorption will be enhanced, while at pH less than pHpzc. for anions adsorption. Generally, at high pH solution, the percentage of dye removal will increase for cationic dye adsorption, while for anionic dyes the percentage of dye removal will decrease. In contrast, at a low pH solution the dye removal % will decrease for cationic dye adsorption and increase for anionic dye adsorption (Bharathi and Ramesh, 2013). Based on the observation of this study, this is most probably because the increases in the pH above the point zero charge tend to increase the adsorption of cationic dyes compared to anionic dyes from the simulated dye solution. The study conducted by Gupta and others (2019) observed that the size difference and type of dye also may possibly affect rate of adsorption (Gupta et al., 2019) The smaller size of MB which is a cationic dyes having two

and color removal was less than 0.0001, which implies both the models were highly significant. In this case, B, C, AD, BD and CD were the significant model terms for color removal; and C, D and AB were the significant model terms for % of dye removal. Additionally, the model's F-values for the color removal and % of dye removal were 10.26 and 13.07, respectively indicates that the two models suggested in this study were significant for the adsorption of dye using ISBC. The R2 value acted as a goodness-of-fit measure of the predicted model, and it expressed how much variation was tolerated in the model. The color removal had the highest R2 value as compared to % of dye removal, as can be seen from Table 3. The color removal and % of dye removal efficiencies represented that 82.3% and 85.5% of the total variation could be represented by the model selected in this work. Table 3 ANOVA result for the removal of dyes using ISBC. Responses

Prob > F

F value

R2

Mean

Sum of squares

Adequate precision

Std. Devision

Color % Dye Removal %

< 0.0001 < 0.0001

10.26 13.07

0.8226 0.8551

56.40 74.81

6625.75 7685.42

15.04 15.49

6.79 6.48

6

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Fig. 6. Effect of dye concentration and adsorbent dosage on a) color removal % and b) dye removal % (Contact time: 15 h, Initial pH: 7)

aromatic rings might be quickly diffused and eventually adsorbed on the ISCB faster than other dyes. The result shows that the rate of adsorption is high at higher pH. This could be explained by increase of the negative charge increases on the surface of adsorbent with increase in the pH value which exhibits a cation exchange capacity on the surface of adsorbent (Ramaraju et al., 2014). The trend was similar to the adsorption of orange -G and methyl violet dyes onto bagasse fly ash (Mall et al., 2006).

3.3.3. Effect of adsorbent dosage The effect of adsorbent dosage on adsorption of dye was studied by varying the dosage of ISCB from 10 to 100 mg. The study of adsorbent dosage was important for selecting an appropriate amount of adsorbent for industrial application. Fig. 9 shows the effects of adsorbent dosage and initial pH on the color removal of ISCB. The color removal increased from 40% to 53.6% when the adsorbent dosage of ISCB was increased from 10 mg to 60 mg at a reaction time of 15 h. A similar result was reported by (Jawad et al., 2016). They studied the adsorption efficiency of methylene blue overs the activated carbon developed from 7

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Fig. 7. Effect of initial pH of the solution and adsorbent dosage on the dye removal % (Dye concentration: 5.5 mg/L, Contact Time: 15 h).

Fig. 8. Effect of initial pH of the dye solution and contact time on the color removal (Dye concentration: 5.5 mg/L, Adsorbent dosage: 55 mg)

biomass waste and reported that the adsorbent dosage plays an essential role in adsorption. The higher dosage heavily influenced the adsorption as more surface area was available for adsorption, allowing the adsorbate to penetrate the adsorption sites easily. However, the color removal was found to decrease to 23.9% when the adsorbent dosage was increased to beyond 60 mg/L. Aggregation of adsorbent that occurs at higher adsorbent dosage may reduce the color removal. Therefore, 16.5 g/25 mL was selected as an optimum ISCB dosage based on adsorption studies. The results showed that more masses of adsorbent had a different effect and led to a decrease in dye adsorption. It seemed that the aggregation of adsorbent occur when the mass of adsorbent is high.

24 h. The color removal using ISBC shows a maximum color removal of 80% within 11 h of contact time. However, increasing the contact time to 24 h decreased the color removal to 50%, as can be seen from Fig. 10. The similar trend was observed in Fig. 11 for % of dye removal. Higher adsorption was observed at the early period of adsorption as there were many unoccupied adsorption sites on the surface of ISCB. However, the adsorption was found to be decreasing at the higher contact time, as the occupied vacant surface-active site by dye molecules caused the adsorption to be complicated. This was possibly due to the repulsive forces that acted between the adsorbed dye molecules and the simulated dye solution (Chingono et al., 2018). On the other hand, Fig. 11 shows the effect of contact time and dye concentration on dye removal %. The result indicates that 1 mg/L of simulated dye solution required only 12 h to remove 90% of dye. In comparison, 10 mg/L of dye solution required 21 h to remove 70% of

3.3.4. Effect of contact time The effect of contact time for the adsorption of simulated dye solution on the ISCB was investigated by varying the time between 6 and 8

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Fig. 9. Effect of adsorbent dosage and initial pH on the color removal l (Contact time: 15 h, Dye concentration: 5.5 mg/L)

Fig. 10. Effect of time and adsorbent dosage on the color removal (Dye concentration: 5.5 mg/L, pH: 7)

dye. It can be concluded from this study that concentrated simulated dye solution required more time as compared to a lower concentration dye solution.

out using the optimum conditions to confirm the suitability of the model predicted by RSM and the results obtained were presented in Table 4. A good agreement between the predicted value and the experimental value confirmed the validity of the model for the adsorption of dyes using ISCB. It can be concluded that the adsorbent developed from the iron-impregnated cellulose obtained from sugarcane bagasse is efficient for the removal of different types of dye containing textile wastewater.

3.4. Optimization study In this study, the operating conditions were set to be ‘in the range’ without taking the factor of operating costs into consideration to identify the optimum conditions to achieve maximum color removal and dye removal %. The optimum conditions given by the design of expert were presented in Table 4. A verification experiment was carried 9

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Fig. 11. Effect of time and initial dye concentration on the dye removal % (Adsorbent dosage: 55 mg, Initial pH: 7). Table 4 Optimized condition for the adsorption of dyes using ISCB. Adsorbent dosage (mg)

16.5

[Dye] (mg/l)

8.6

Initial pH

8.4

Contact time (hour)

6

Color Removal (%)

% Removal of Dye

Adsorption Capacity (mg/g)

Pred.

Exp.

Pred.

Exp.

Pred.

Exp.

95.2

88.8

99.5

93.7

7.7

7.2

Table 5 Result of Langmuir and Freundlich isotherms for ISCB and previous studies. Langmuir

Freundlich

Reference

qm

KL

R2

KF

1/n

R2

12.28 8.26 22.1 30.40 140.85

0.093 0.53 0.45 0.52 0.02

0.9356 0.9320 0.9900 0.9888 0.9960

1.05 2.16 1.55 1.77 2.21

0.494 0.239 0.18 0.13 0.34

0.8386 0.9590 0.7900 0.9755 0.9750

ISCB Chingono et al. (2018) Pehlivan et al. (2013) Ge et al. (2017) (Anoop Krishnan, Sreejalekshmi and Baiju, 2011)

3.5. Adsorption kinetics The pseudo-first-order and second order rate expression were widely employed to determine adsorption efficiency (Saad et al., 2010). The pseudo-first order kinetic model assumed that the reaction rate of adsorption is directly proportional to the first power of the reactant concentration. The pseudo-first order rate equation is represented by Eq. (5).

log10 (qe

qt ) = log qe

k1 t 2.303

(5)

where the total dye absorbed on the ISCB (mg/g) at equilibrium and at time t was represented by qe and qt respectively. A pseudo-second-order rate equation was represented by Eq. (6). This model assumed that the adsorption rate of ISCB is dependent on the square of the number of vacant active sites of the catalyst.

Fig. 12. Pseudo-first order and second order kinetic models for dye adsorption of simulated dye solution overs ISCB.

t 1 1 = + t qt k2 q2 e qe 10

(6)

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Fig. 12 illustrates the pseudo-first and second order adsorption kinetics of simulated dye solution at different initial dye concentrations. The result showed that pseudo-2nd order kinetics model fitting achieved the highest R2 value 0.9555 as compared to pseudo-first order (R2 = 0.6821). The finding confirmed the validity of the pseudosecond-order model for the adsorption of dyes over the ISCB. Srivastava and other (2006) also reported that the adsorption of phenol by bagasse fly ash followed pseudo second order kinetic with an R2 value of 0.9999 (Srivastava et al., 2006).

reaction caused by the iron oxides. The dye adsorption kinetics and isotherm were investigated, and it was concluded that the adsorption process of simulated dye solution over ISCB followed pseudo-secondorder kinetic and the Langmuir isotherms. The results of the present study suggested that the newly developed iron-impregnated sugarcane bagasse can be used to remove dye-containing wastewater efficiently. Acknowledgements The authors are grateful to the University of Malaya Research Grant (UMRG) - Frontier Science (AFR) RG384-17AFR Research Fund from the University of Malaya for financially supporting this research.

3.6. Adsorption isotherms In this study, the experimental data were fitted into Langmuir and Freundlich models. The Langmuir isotherms defined that adsorption occurs only on the homogeneous surface through monolayer adsorption. Freundlich model suggested that the interactions between adsorbent and adsorbate occur during adsorption (Saad et al., 2010). The Langmuir and Freundlich equations are shown in Eqs. (7) and (8), respectively.

qe =

qm bCe 1 + bCe 1

qe = KF Cen

References Alves, M.J., Cavalcanti, Í.V., de Resende, M.M., Cardoso, V.L., Reis, M.H., 2016. Biodiesel dry purification with sugarcane bagasse. Ind. Crops Prod. 89, 119–127. Anastopoulos, I., Bhatnagar, A., Hameed, B.H., Ok, Y.S., Omirou, M., 2017. A review on waste-derived adsorbents from sugar industry for pollutant removal in water and wastewater. J. Mol. Liq. 240, 179–188. Ass, B.A.P., Ciacco, G.T., Frollini, E., 2006. Cellulose acetates from linters and sisal: correlation between synthesis conditions in DMAc/LiCl and product properties. Bioresour. Technol. 97, 1696–1702. Bharathi, K.S., Ramesh, S.T., 2013. Removal of dyes using agricultural waste as low-cost adsorbents: a review. Appl. Water Sci. 3, 773–790. Brandão, P.C., Souza, T.C., Ferreira, C.A., Hori, C.E., Romanielo, L.L., 2010. Removal of petroleum hydrocarbons from aqueous solution using sugarcane bagasse as adsorbent. J. Hazard Mater. 175, 1106–1112. Chingono, K.E., Sanganyado, E., Bere, E., Yalala, B., 2018. Adsorption of sugarcane vinasse effluent on bagasse fly ash: a parametric and kinetic study. J. Environ. Manag. 224, 182–190. Deng, S., Nie, Y., Du, Z., Huang, Q., Meng, P., Wang, B., Huang, J., Yu, G., 2015. Enhanced adsorption of perfluorooctane sulfonate and perfluorooctanoate by bamboo-derived granular activated carbon. J. Hazard Mater. 282, 150–157. Duan, X., Zhang, C., Srinivasakannan, C., Wang, X., 2017. Waste walnut shell valorization to iron loaded biochar and its application to arsenic removal. Resour. Efficient Technol. 3, 29–36. Foo, K.Y., Hameed, B.H., 2010. Insights into the modeling of adsorption isotherm systems. Chem. Eng. J. 156, 2–10. Ge, M., Du, M., Zheng, L., Wang, B., Zhou, X., Jia, Z., Hu, G., Jahangir Alam, S.M., 2017. A maleic anhydride grafted sugarcane bagasse adsorbent and its performance on the removal of methylene blue from related wastewater. Mater. Chem. Phys. 192, 147–155. Gupta, K., Gupta, D., Khatri, O.P., 2019. Graphene-like porous carbon nanostructure from Bengal gram bean husk and its application for fast and efficient adsorption of organic dyes. Appl. Surf. Sci. 476, 647–657. Ho, Y.-S., Chiu, W.-T., Wang, C.-C., 2005. Regression analysis for the sorption isotherms of basic dyes on sugarcane dust. Bioresour. Technol. 96, 1285–1291. Imran, M., Anwar Khan, A.R., 2018. Characterization of agricultural waste sugarcane bagasse ash at 1100°C with various hours. Mater. Today: Proceedings 5, 3346–3352. Jabasingh, S.A., Belachew, H., Yimam, A., 2018. Iron oxide induced bagasse nanoparticles for the sequestration of Cr6+ ions from tannery effluent using a modified batch reactor. J. Appl. Polym. Sci. 135, 46683. Jawad, A.H., Rashid, R.A., Ishak, M.A.M., Wilson, L.D., 2016. Adsorption of methylene blue onto activated carbon developed from biomass waste by H2SO4 activation: kinetic, equilibrium and thermodynamic studies. Desalination Water Treat. 57, 25194–25206. Jiang, W., Zhang, L., Guo, X., Yang, M., Lu, Y., Wang, Y., Zheng, Y., Wei, G., 2019. Adsorption of cationic dye from water using an iron oxide/activated carbon magnetic composites prepared from sugarcane bagasse by microwave method. Environ. Technol. 1–14. Luo, S., Chen, S., Chen, S., Zhuang, L., Ma, N., Xu, T., Li, Q., Hou, X., 2016. Preparation and characterization of amine-functionalized sugarcane bagasse for CO2 capture. J. Environ. Manag. 168, 142–148. Mall, I.D., Srivastava, V.C., Agarwal, N.K., 2006. Removal of Orange-G and Methyl Violet dyes by adsorption onto bagasse fly ash—kinetic study and equilibrium isotherm analyses. Dyes Pigments 69, 210–223. Mo, J., Yang, Q., Zhang, N., Zhang, W., Zheng, Y., Zhang, Z., 2018. A review on agroindustrial waste (AIW) derived adsorbents for water and wastewater treatment. J. Environ. Manag. 227, 395–405. Omo-Okoro, P.N., Daso, A.P., Okonkwo, J.O., 2018. A review of the application of agricultural wastes as precursor materials for the adsorption of per- and polyfluoroalkyl substances: a focus on current approaches and methodologies. Environ. Technol. Innov. 9, 100–114. Patil, C.S., Gunjal, D.B., Naik, V.M., Harale, N.S., Jagadale, S.D., Kadam, A.N., Patil, P.S., Kolekar, G.B., Gore, A.H., 2019. Waste tea residue as a low cost adsorbent for removal of hydralazine hydrochloride pharmaceutical pollutant from aqueous media: an environmental remediation. J. Clean. Prod. 206, 407–418. Panneerselvam, P., Morad, N., Tan, K.A., 2011. Magnetic nanoparticle (Fe3O4) impregnated onto tea waste for the removal of nickel(II) from aqueous solution. J. Hazard Mater. 186, 160–168.

(7) (9)

The separation factor, RL can be calculated using Eq. (10), when the experimental data well fitted to the Langmuir model and the initial concentration were certained (Foo and Hameed, 2010).

RL =

1 1 + KL Ci

(10)

where, KL is the Langmuir constant and Ci (mg/l) is the initial dye concentration. The Langmuir and Freundlich isotherms were evaluated, and the results were presented in Table 5. The results obtained in this study were also compared with the literature, and the result was presented in Table 5 as well. Based on the correlation coefficients values, Langmuir isotherm exhibited a better fitted (R2 =0.9356) than the Freundlich isotherm (R2 =0.8386). Besides, the Langmuir isotherm model also showed a high maximum adsorption capacity and separation factor; qm of 12.28 mg/g and an RL 0.7818, respectively. The value of RL indicated the type of Langmuir isotherm to be irreversible RL = 0 , linear RL = 1 , unfavorable RL > 1, or favorable (0 < RL < 1) . The RL values between 0 and 1 indicated favorable adsorption (Chingono et al., 2018). The RL value in the present study was lesser than 1, indicating that the adsorption of the dye onto ISCB is favorable. Since the Langmuir isotherm model had a better fit, adsorption of simulated dye solution using ISCB is a monolayer adsorption (Ge et al., 2017). Salimpour Abkenar and others (2015) reported that the Langmuir equation is able to describe the mechanism of dye adsorption. They have investigated the adsorption of different types of dyes over a cotton fabrics grafted with two generations of the poly (propylene imine) dendrimers adsorbent (Salimpour Abkenar et al., 2015). 4. Conclusion In this study, low-cost green adsorbent ISCB was developed, characterized and analyzed for the adsorption of simulated dye solution. The effects of the adsorption efficiency were investigated, and it was concluded that the adsorption process was affected by the adsorbent dosage, contact time, initial pH of the solution and dye concentration. The adsorption study shows that ISCB achieved 93.7% of dye removal, 88.8% of color removal and 7.7 mg/g dye adsorption capacity within 6 h of contact time using 0.7 g/L of ISCB at pH 8.4 and 8.6 mg/L of dye solution. The result proved that higher adsorption was achieved using the sugarcane bagasse impregnated with iron oxide magnetic adsorbent. This is most probably due to enhancement in the rate of 11

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