Clean Techn Environ Policy DOI 10.1007/s10098-016-1124-0
Adsorptive removal of 2,4-dichlorophenoxyacetic acid from aqueous solution using bagasse fly ash as adsorbent in batch and packed-bed techniques Sunil K. Deokar1 • Sachin A. Mandavgane1 • Bhaskar D. Kulkarni2
Received: 2 November 2015 / Accepted: 9 February 2016 Springer-Verlag Berlin Heidelberg 2016
Abstract Among the several synthetic herbicides available currently, 2,4-D is a commonly used herbicide to control broadleaf weeds in agriculture and forestry. However, its increasing use in agricultural and nonagricultural activities has resulted in increasing concentrations of 2,4-D being detected in water bodies. Thus, there is a need to identify methods to remove 2,4-D to protect the environment. Among the various methods used for 2,4-D removal, adsorption is found to be effective, and several adsorbents have been studied to remove 2,4-D from aqueous solutions. In this study, we used bagasse fly ash (BFA), a common industrial waste generated in large amount worldwide, for 2,4-D removal from aqueous solution using batch and continuous packed-bed adsorption. In the batch adsorption process, the effects of initial concentration, contact time, temperature, pH, and particle size of BFA were studied. The packed-bed performance of BFA was investigated by varying the influent concentration (50–150 mg/L), flow rate (1.2–4 mL/min), and bed height (4.5–9 cm). Isotherm
and thermodynamic parameters are determined for batch adsorption, whereas the performance of continuous adsorption is evaluated by different packed-bed models. The particle-size effect indicated the higher removal of 2,4D on the bigger particles of BFA due to greater BET surface area and carbon-to-silica ratio than smaller particles. The maximum percentage removal (37.04) is achieved for an influent concentration of 50 mg/L, flow rate of 1.2 mL/min, and bed height of 6.5 cm. For the first time ever, the deactivation kinetic model was applied for the solid– liquid adsorption system and it showed the best fit among the selected models. The bed capacity (m2/g) of BFA is three times greater than synthetic activated carbon for adsorption of 2,4-D. This informs that the BFA can be used as an adsorbent for 2,4-D removal from aqueous solution.
Electronic supplementary material The online version of this article (doi:10.1007/s10098-016-1124-0) contains supplementary material, which is available to authorized users.
& Sachin A. Mandavgane [email protected]
Sunil K. Deokar sunildeo[email protected]
Bhaskar D. Kulkarni [email protected]
Chemical Engineering Department, Visvesvaraya National Institute of Technology (VNIT), South Ambazari Road, Nagpur 440010, India
CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pune 411 008, India
Keywords Adsorption Bagasse fly ash 2,4-D Packed-bed models
Among the several pesticides/herbicides registered under US EPA (2014) and APIB India (2010), 2,4-D is one of the globally used herbicides to control broadleaf weeds in agriculture and forestry. It is a selective herbicide applied in the form of acid, sodium salt, amine, and ethyl ester, and its application is recommended in the farming of crops, vegetables, and fruits such as rice, sugarcane, wheat, maize, sorghum, potato, citrus, and grapes (MA-GOI 2015). Because of the extensive use of herbicides such as 2,4-D, various concentrations of these herbicides are being reported in both surface and ground waters worldwide (Kearns et al. 2014; Salman et al. 2011). Therefore, the
S. K. Deokar et al.
removal of 2,4-D from the environment is of utmost significance. Ova and Ovez (2013) reported several conventional methods to remove 2,4-D from aqueous solutions. Among the various methods reported, adsorption was found to be superior in terms of initial cost, design, and ease of operation (Ova and Ovez 2013; Song et al. 2014). Furthermore, adsorption does not result in the formation of harmful substances and sludge (Ahmaruzzaman and Gupta 2011). Adsorbents used for the removal of pesticides include synthetic activated carbons (Saleh and Gupta 2014), commercial activated carbons, polymeric materials, clays, agricultural waste, and industrial waste (Ahmad et al. 2010). The chars derived from plant and agricultural biomasses are also reported to be effective adsorbents of pesticides (Yavari et al. 2015). Some of the aforesaid adsorbents (e.g., activated carbons) are expensive to prepare and regenerate, whereas a few are not easily available in bulk (Reddy and Lee 2013). Agricultural wastes such as rice husk, bagasse, corn cob, wheat straw, and fruit peels generated in large quantities are found to be effective adsorbents in either original or modified form for the removal of various pollutants (Choudhary et al. 2015; Sadhukhan et al. 2014; Song et al. 2014). The bagasse fly ash (BFA) is an industrial waste generated in sugar-power industries all over the world. In 2012–2013, the global generation of BFA was about 12 million tons, with 2 million tons from India alone (FAO 2014), which comprises about 2.4 % of the total bagasse produced globally (i.e., 26 % of global sugarcane production; Akram et al. 2009). These data indicate that BFA is produced in large quantities, and thus, there is a necessity to reuse it to avoid disposal problems. Previous studies have successfully used BFA as adsorbent for removal of dyes, heavy metals, and phenolic compounds (Ahmaruzzaman 2010). Dube et al. (2014) used BFA as an additive to prepare an adsorbent for desulfurization of dry flue gas. Another important application of BFA is as a partial replacement of cement and sand in construction work (Madurwar et al. 2013). The removal of 2,4-D from aqueous solution is more difficult than removal of other organic micropollutants (e.g., pesticides, pharmaceuticals, fuel and industrial compounds, residues, and degradation products) on carbon adsorbents (Kearns et al. 2014). A previous study reported high percentage of carbon in BFA. Because of the presence of silica and metal oxides, the nature of this carbon is different from that of the other carbonaceous adsorbent materials (Ahmaruzzaman 2010). As discussed earlier, BFA is generated in large quantities worldwide; however, so far, the usage of BFA for pesticide removal has not been explored critically by researchers. Therefore, in this work, BFA is selected as an adsorbent for 2,4-D removal.
The objectives of this study were to determine the 2,4-D adsorption potential of BFA using batch and packed-bed techniques and to study the effects of chemical composition of BFA (i.e., silica and carbon contents in BFA) on adsorption. To achieve these objectives, the effects of initial concentration, contact time, temperature, solution pH, and particle size of BFA were studied in batch adsorption and the effects of influent concentration, flow rate, and bed height were examined in packed-bed adsorption. The isotherm parameters and thermodynamic properties were determined for batch experimental data, and different packed-bed models were applied to study the dynamics of packed column. Along with the conventional packed-bed models, a novel application of the deactivation kinetic model was considered for the solid–liquid adsorption system.
Materials and methods Adsorbent The adsorbent (i.e., BFA) was supplied by m/s Wainganga Sugar and Power Ltd. (Bhandara, India). The BFA was screened using BS sieves to separate the impurities and select only particles with size less than 0.354 mm for our adsorption study. Proximate analysis of BFA was carried out using a standard method (IS:1350, Part-1, 1984). The surface characteristics were measured by the Brunauer– Emmett–Teller (BET) method (Micromeritics, ASAP 2010, USA). The chemical composition was determined using an XRF analyzer (PANalytical, PW 2403, Netherland), and CHNS analysis was carried out in an elemental analyzer (vario MICRO cube; Elementar, Germany). Fourier transform infrared (FTIR) spectroscopy analysis of BFA was performed using an FTIR spectrometer (IRAffinity-1, Shimadzu, Japan), and was carried out using the DRS technique in which 5 mg of sample was mixed with 100 mg of optically pure KBr sample and resultant mixture was analyzed through the FTIR spectrometer. The background spectrum was recorded with optically pure KBr and it was subtracted from sample spectra. The raw diffuse reflectance spectra were converted using the Kubelka– Munk software. The morphology of BFA was examined using a scanning electron micrograph (SEM, Leo 1430 VP, Oxford Instruments, UK). Adsorbate Analytical-grade 2,4-D was purchased from Sigma-Aldrich and a stock solution was prepared in ultrapure water. The molecular formula and weight of 2,4-D are C8H6Cl2O3 and 221 g/mol, respectively.
Adsorptive removal of 2,4-dichlorophenoxyacetic acid from aqueous solution using bagasse fly…
Experimental methods Batch adsorption study In batch experiments, glass vials containing a desired concentration of 2,4-D (pH 3.5) and BFA were shaken in a water bath at constant temperature for a specific period. The samples were withdrawn after some time and filtered using Whatman filter paper 42. The filtrate after centrifugation was analyzed on a UV–VIS spectrophotometer (Model UV 1800; Shimadzu, Japan) at 283 nm (kmax). Each experiment was performed in triplicate and the average value was reported. The percentage removal, adsorption capacity (qt, mg/g) at any time, and adsorption capacity at equilibrium (qe, mg/g) were calculated from the initial and equilibrium concentrations (C0 and Ce, respectively, mg/L). In the preliminary adsorption study, BFA dosages were increased from 0.25 to 1.5 g per 25 mL of 2,4-D solution. Our results indicated 90 % 2,4-D removal for 1 g BFA; however, significant improvement in removal was not observed for additional BFA dosages. Therefore, 1 g BFA per 25 mL solution was chosen as the optimal dosage to study the effects of initial concentration, contact time, temperature, solution pH, and particle size of BFA. The solution pH, varied between 2 and 12 was measured using a pH meter (Eutech; Model-pH 2700). The BFA was separated into four different particle-size fractions— 0.354–0.251, 0.251–0.178, 0.178–0.104, and \0.104 mm—to study its effect on 2,4-D adsorption. Packed-bed adsorption study A glass column (1.2 cm internal diameter and 30 cm height), with four alternative openings at different positions from the bottom, was fabricated for continuous removal of 2,4-D. Based on the requirement of bed height, BFA (g) was packed between two supporting layers of glass wool. The purpose of using the glass wool is to avoid the loss of BFA in effluent flow. The deionized water was pumped upward through the bed to remove entrapped air using a peristaltic pump (PP201V; Electro Lab, India) and then the column was kept idle for 24 h. The influent flow rate of 2,4-D solution was maintained constant by mounting the rotameter between the pump and the column. At regular intervals, the samples were collected from the outlet and the effluent concentration (Ct, mg/L) was measured. The column experiments were performed for different influent concentration (C0), flow rate (Q), and bed height (Z) at constant temperature (303 ± 2 K) and pH (3.5). The breakthrough (BT) curve (Ct/C0 vs. time) was plotted for each experiment. The column breakthrough (tb, min) and column saturation time (ts, min) were considered at Ct/C0 = 0.10 and 0.97, respectively. As described by
Sotelo et al. (2012), the saturation capacity and breakthrough capacity of column were calculated using Eqs. (1) and (2), respectively C0 Q qs ¼ m
Zts Ct 1 dt C0
C0 Q qb ¼ m
Ct 1 C0
where qs and qb (mg/g) are the saturation capacity and breakthrough capacity, respectively, and m (g) is the mass of the adsorbent. The percent removal in column was determined from the quantity (mg) of 2,4-D entered and adsorbed in column. The length of mass-transfer zone was obtained using Eq. (3). qb MTZ ¼ Z 1 ð3Þ qs
Results and discussion Characterization of BFA Proximate analysis of BFA presented the following composition: 63.47 % ash, 19.32 % volatile matter, 12.66 % fixed carbon, and the remaining as moisture. The presence of carbon was also confirmed by CHNS analysis, which indicated the following composition: about 47.37 % carbon, 2.04 % hydrogen, 1.59 % nitrogen, and 0.43 % sulfur. XRF analysis of BFA revealed the following composition: 36.14 % SiO2, 1.10 % Al2O3, 2.32 % K2O, 1.62 % Fe2O3, 3.10 % CaO, 2.24 % P2O5, and 1.61 % SO3. The oxides in contact with water form charges on surface of BFA depending on pH of the solution when BFA is added in aqueous solution (Lataye et al. 2008). The BET surface and single-point areas were measured to be 51.71 and 51.93 m2/g, respectively. The Barrett–Joyner–Halenda (BJH) cumulative surface areas for adsorption and desorption were found to be 21.57 and 24.04 m2/g, respectively. BJH adsorption/desorption cumulative pore volume and average pore diameter were observed to be 0.0714/ ˚ , respectively. The 0.0615 cm3/g and 132.42/109.12 A adsorption–desorption analysis indicated that 90.27 % of total surface area and 91.88 % of total pore volume are occupied by mesopores, suggesting the mesoporous nature of BFA. The scanning electron micrograph of BFA presented in Fig. 1 indicates the porous structure where the circular and elongated pores can be observed. The walls of some of the circular pores are broken, resulting in the formation irregularly shaped pores. Besides, few pores look internally
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Fig. 1 Scanning electron micrograph (SEM) of Bagasse fly ash
connected. The irregular shape of pores and their connectivity increase the surface area of BFA and also facilitate the mobility of 2,4-D molecules inside these pores (Leofanti et al. 1998). Overall, the removal of 2,4-D is expected to increase due to enhanced surface area. However, increased diffusion resistance may slow down the equilibration process. The FTIR spectrum of 2,4-D (Fig. 2, spectrum-1) shows a peak at 1731 cm-1, which indicates the presence of –C=O bonding in the carboxyl group (Deokar and Mandavgane 2015). The symmetric vibrations of C–O–C and O–H deformation coupled with C–O stretching are identified by a band at 1095 and 1235 cm-1, respectively (Deokar and Mandavgane 2015). The peak at 1487 cm-1 corresponds to C=C vibrations of the aromatic ring (Pavia
Fig. 2 FTIR spectrums of 1 2,4-D, 2 Bagasse fly ash, and 3 Bagasse fly ash after adsorption of 2,4-D
et al. 2008). Besides, the peak at 693 cm-1 indicates the C–Cl stretching vibration (Arivazhagan and Meenakshi 2011). The peaks at 1096 and 815 cm-1 (Fig. 2, spectrum2) correspond to a strong Si–O–Si and Si–H stretching, respectively (Tailor et al. 2012). The 1360–1420 cm-1 bands in BFA can be attributed to the aromatic C–H and carboxyl-carbonate structures (Srivastava et al. 2008). The weak peak at 688 cm-1 is assigned to the symmetric stretching of internal tetrahedral SiO4 (Tailor et al. 2012). After adsorption of 2,4-D (Fig. 2, spectrum-3), the peak at 1731 cm-1 is disappeared and new peaks are appeared at 1745, 1494, 1405, and 1240 cm-1. The peaks near 1500 and 1410 cm-1 may correspond to asymmetric and symmetric stretching vibrations of anionic 2,4-D species (Kim and Hyun 2014; Hung and McBride 1989) attached to positive metal oxide surface of BFA by weak electrostatic interaction. The peak at 1240 cm-1 may be due to the coulombic interaction between sorbed 2,4-D anion and positively charged surface of the Fe oxide (Celis et al. 1999). The peak observed at 1745 cm-1 may be due to the C=O stretching of 2,4-D molecule adsorbed over BFA surface (Hermosin and Cornejo 1993). The nature of FTIR spectrum and the position of peaks of adsorbed 2,4-D along with pH study reveals that 2,4-D can be adsorbed in both the form (i.e., molecular and anionic) onto BFA surface. In anionic form the acting force is coulombic interaction while in molecular form it is the van der Waal force of attraction, implying that the adsorption of 2,4-D on BFA is merely by weak interaction forces. Batch adsorption studies Effect of initial concentration and contact time The initial concentration of 2,4-D was varied between 50 and 400 mg/L for fixed dosage (1 g/25 mL) of BFA. The experiments were performed for different contact times at constant temperature (303 K) and pH (3.5) of pesticide solution. Results are plotted in Fig. 3, which indicate the reduction in the percentage removal of 2,4-D with initial concentration. However, the amount of 2,4-D adsorbed and thereby equilibrium capacity is increased with concentration. The capacity augmentation is ascribed to the increment in mass-transfer driving force with increase in concentration of 2,4-D (Srivastava et al. 2006). The 4.5time increment in capacity is observed for 8-time increase in concentration. In Fig. 3, the effect of contact time shows that the removal in the initial stage of adsorption is faster for each considered initial concentration. Initially, a large number of vacant sites are available and the concentration gradient is also high, both of which result in faster uptake. In later stage of adsorption, however, the removal rate
Adsorptive removal of 2,4-dichlorophenoxyacetic acid from aqueous solution using bagasse fly…
Fig. 3 Effects of initial concentration and contact time on removal of 2,4-D using 1 g BFA/25 mL
progressively slows down with time until the equilibrium is reached. With the increase in adsorption time, the vacant sites on the surface of BFA decreased gradually. The remaining vacant sites are difficult to occupy due to repulsive forces between 2,4-D molecules on the solid surface and those in liquid phase, which ultimately reduce the adsorption rate. The time to reach equilibrium was found to be *360 (min) and 720 (min) for 50 (mg/L) and 400 (mg/L) initial concentrations, respectively. The equilibrium times for 2,4-D adsorption on activated carbon were previously observed to be *420 (min) and 780 (min) for 50 (mg/L) and 400 (mg/L), respectively (Njoku et al. 2013). Isotherm modeling The distribution of adsorbate molecules between the solid and solution phases is given by adsorption isotherm. The selection of isotherm for the adsorbate–adsorbent system depends on the shape of plot between the solid phase and liquid phase concentrations of adsorbate. In this study, the plot of qe versus Ce in Fig. 4 represents an L-type isotherm. Essington (2005) suggested the use of Langmuir and Freundlich models for L-type isotherm. Therefore Langmuir and Freundlich models are applied here at three different temperatures (303, 318, and 328 K). The Langmuir and Freundlich isotherm models are presented in Eqs. (4) and (5), respectively. qe ¼
KL qmax Ce ð1 þ KL Ce Þ
1=n qe ¼ K F C e ;
where KL (L/g) is the Langmuir constant, which is related to the affinity of binding sites and qmax (mg/g) is the
Fig. 4 Adsorption isotherms for removal of 2,4-D using BFA at 303 K
monolayer adsorption capacity. KF [(mg/g)/(mg/L)1/n] and n are the Freundlich constant and adsorption intensity factor, respectively. The plots of isotherm models at 303 K are presented in Fig. 4. The model parameters and coefficients of determination (R2) determined for different temperatures are listed in Table 1. Seven different error functions are used to judge the best-fit isotherm. The values of error functions in Table 1S indicate that the Langmuir isotherm model better describes equilibrium data than the Freundlich isotherm. The Langmuir adsorption capacity (Table 1) increased with increase in temperature, which reflects the endothermic adsorption of 2,4-D on BFA. The increase in Langmuir constant with temperature is an indication of increased affinity between 2,4-D molecules and BFA. The dimensionless factor, [RL = 1/(1 ? KLC0)], for Langmuir isotherms was calculated and found to be between 0 and 1 for all initial concentrations considered at the three different temperatures. This suggests the favorable adsorption of 2,4-D onto BFA in the temperature range of 303–328 (K). The isotherm equations are empirical; therefore, any particular mechanism cannot be Table 1 Isotherm parameters with coefficient of determination for 2,4-D adsorption on BFA at different temperarures Isotherm models
Temperature (K) 303
KL (L/g) R2
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deduced using them. The isotherms are used explicitly for the purpose of describing adsorption of solute by an adsorbent (Essington 2005). Effect of temperature and thermodynamic properties Adsorption studies were performed at three different temperatures for an initial concentration range of 50–400 (mg/L) using the optimal dosage (1 g/25 mL) of BFA. Experimental results indicated an increase in the uptake of 2,4-D with temperature, suggesting the endothermic adsorption on BFA. This may be due to the generation of new adsorption sites with temperature. In addition, the diffusion rate of 2,4-D molecules within the pores of BFA might be increased owing to the decrease in viscosity with temperature (Sathishkumar et al. 2009). The thermodynamic properties such as enthalpy (DH), entropy (DS), and Gibbs free energy (DG) were investigated by applying Eqs. (6) and (7) to experimental results. ln Kd ¼
DH DS þ RT R
DG ¼ RT ln Kd ;
where Kd is the equilibrium distribution coefficient and it is a ratio of equilibrium concentration of 2,4-D on BFA surface and in the solution. R (8.314 J/molK) is the gas constant and T (K) is the absolute temperature. The values of DH (24.569 kJ/mol) and DS (98.359 J/mol) were obtained from the slope and intercept of the Van’t Hoff plot (lnKd versus 1/T), respectively. The positive value of DS refers to the increased randomness at the solid–solution interface during adsorption, whereas positive DH refers to endothermic adsorption. The DG values were calculated as -5.277, -6.167, and -7.249 (kJ/mol) at 303, 318, and 328 (K), respectively. Negative DG values are indicative of feasibility and spontaneity of the adsorption of 2,4-D on BFA. Earlier study reported physisorption for the values of DG observed between 0 and -20 kJ/mol (Kuo et al. 2008).
between pH and pKa, the molecular form of 2,4-D is expected to be in higher number than that of anionic form. Therefore, 2,4-D is better adsorbed in molecular form on carbonaceous part rather than in anionic form. It is expected that 2,4-D adsorption in molecular form over BFA surface is mainly due to van der Waals type of interaction whereas in anionic form may be due to electrostatic type of interaction with positively charged surface, not by any kind of covalent bond formation. With increase in solution pH, anionic form dominates in number to the molecular form of 2,4-D. As the carbonaceous part in BFA is greater than positively charged metal oxides part, the raise in solution pH results lowering in 2,4-D removal. Therefore the removal of 2,4-D is higher at lower pH. Effect of particle size Four different particle-size fractions of BFA were selected, and using the 1 g/25 mL dosage of each fraction, the experiments were performed for 100 mg/L concentration and 12 h contact time. The characteristics of particle-size fractions of BFA are listed in Table 2 which indicate that the bigger particles have higher BET surface area than smaller particles. Therefore bigger particles adsorbed more than smaller particles as can be seen in Table 2. This implies that the smaller particles of BFA have lower surface charge per unit mass. It can be observed in Table 2 that the BET surface area and carbon content are augmented with enhancement in particle size. Further, the silica percentage is significantly minimized in bigger particles. Therefore, the higher BET surface area and carbon percentage together with lesser silica content are responsible for greater removal on bigger particles. Detail characterization of different size fractions of BFA is reported in the earlier work of the authors (Deokar et al. 2016) Packed-bed adsorption studies Effect of influent concentration
Effect of pH The charges developed on the surface of adsorbent and degree of ionization of adsorbate are functions of pH, therefore the effect of solution pH on uptake of 2,4-D was studied for pH between 2 and 12. Results indicated (Fig. 1S) the reduction in percentage removal of 2,4-D with increase in pH of solution. This is due to combined effect of point of zero charge (pHzpc) and solution pH on charges developed on adsorbent surface. pHzpc of BFA was previously reported to be *9 (Lataye et al. 2008). When pH \ pHzpc, the adsorbent surface is positively charged and at pH [ pHzpc, the reverse will occur. At pH \ 2.8 (i.e., pKa of 2,4-D); according to the relationship
The influent concentration of 2,4-D was increased from 50 to 150 (mg/L) at a constant flow rate (1.2, mL/min) and bed height (6.5, cm). The effluent concentrations (Ct, mg/L) are measured at different time intervals and results are plotted in the form of BT curves (Fig. 2S). The packed-bed parameters calculated using Eqs. (1)–(3) are listed in Table 2S. It can be observed in Table 2S that the breakthrough and saturation times (tb and ts) are reduced, whereas the capacities (qb and qs) are increased with influent concentration. Increasing the influent concentration enhanced the effective bed load, leading to quick saturation of binding sites on BFA, thereby reducing tb and ts. This results in the formation of steeper BT curve at
Adsorptive removal of 2,4-dichlorophenoxyacetic acid from aqueous solution using bagasse fly… Table 2 Characteristics of different particle-size fractions of BFA Particle size (mm)
Physicochemical characteristics BET surface area (m2/g)
higher influent concentration as shown in Fig. 2S. The capacity enhancement and growth in mass-transfer zone (MTZ) are attributed to the relative increase in concentration gradient. The significant effect on bed utilization (FBU) is not found due to only a smaller change in MTZ with C0. However, the percentage removal is notably decreased because of the increase in numbers of 2,4-D molecules at constant dosage (i.e., bed height). The extended BT curve for lower C0 is indicative of greater volume (Vs) of 2,4-D solution treated. Effect of flow rate The nature of BT curves is studied by changing the flow rate between 1.2 and 4 (mL/min) at a fixed influent concentration (100, mg/L) and bed height (6.5, cm). The BT curves presented in Fig. 3S imply early saturation of BFA bed leading to steeper BT curve for a higher flow rate. The values of tb and ts (Table 2S) are reduced by nearly four times and seven times, respectively, when the flow rate (Q) is increased by 3.3 times. Further, bed capacities (qb and qs) and percentage removal are reduced with increase in Q. This is ascribed to the inadequate residence time of 2,4-D in the BFA bed to allow for its diffusion into the pores of the adsorbent at higher Q. As a result, the volume of 2,4-D solution treated is decreased owing to early saturation with increase in Q. However, the length of MTZ is enhanced due to the growth in mass-transfer rate with Q. A similar behavior of MTZ was previously observed by Sotelo et al. (2012) for the adsorption of atenolol and isoproturon pesticides using activated carbon. It can be deduced from Table 2S and Fig. 3S that the performance of BFA column at lower flow rate is better than at a higher flow rate. Effect of bed height The BT curves depicted in Fig. 4S are obtained for variable bed height (Z) at a constant influent concentration (100, mg/L) and flow rate (1.2, mL/min). The extended BT curve for higher bed height is observed due to intraparticle
diffusion. The contact time between BFA bed and 2,4-D molecules is more for higher bed height as the molecules have to travel a longer length of the bed. The breakthrough and saturation times, and the bed capacities are greater for higher bed height. Increasing bed capacities and breakthrough and saturation times (Table 2S) are associated with the augmentation in surface area, because surface area is a function of adsorbent dosage (i.e., bed height). Consequently, the length of MTZ is significantly increased with bed height. With the expansion of MTZ with bed height, the utilization of bed is considerably increased, and the solution volume treated is also increased. However, the narrow MTZ for lower Z suggests the efficient utilization of bed in accordance with regeneration and energy (McCabe et al. 2005). Packed-bed adsorption modeling A number of models have been used previously to study the dynamic behavior of packed-bed column. The most commonly applied packed-bed models (Sotelo et al. 2012) for solid–liquid adsorption systems include bed depth service time, Bohart–Adams, Wolborska, Thomas, Clark, Yoon–Nelson models. In addition to these, the deactivation kinetic model, which was initially derived for gas–solid adsorption systems, is also applied in this study (solid– liquid adsorption). Application of bed depth service time model The bed depth service time (BDST) model assumes negligible intraparticle diffusion and resistance to external mass transfer. According to this model, the rate of adsorption is controlled by surface reaction between adsorbate and unused capacity of adsorbent (Srivastava et al. 2008). In this study, the BDST model (Eq. 8) is applied at 10, 50, and 90 % BT when the bed height of BFA is varied in the range of 4.5–9 (cm) at constant C0 and Q. The plot of BDST model (t vs. Z) is shown in Fig. 5. N0 1 C0 t¼ Z ln 1 ; ð8Þ C0 KBD C0 U0 Ct
S. K. Deokar et al.
diffusion, and therefore, the overall length of MTZ (Table 2S) is significantly increased. Application of Bohart–Adams model and Wolborska model
Fig. 5 Bed depth service time model for adsorption of 2,4-D on BFA (Influent concentration, 100 mg/L; flow rate, 1.2 mL/min)
The Bohart–Adams and Wolborska models (Sotelo et al. 2012) were previously used to describe the initial part of BT curve. Therefore, we applied these models for up to 60 % BT of column. The Bohart–Adams and Wolborska models are expressed by Eqs. (9) and (10), respectively. The predicted BT curves obtained using these models are compared with experimental BT curves in Fig. 6. The kinetic constants and adsorption capacities are presented in Table 3S. Ct Z ln ¼ KAB C0 t KAB N0 ð9Þ U0 C0 ln
The adsorption capacities, N0 (mg/L) for the Bohart– Adams model and QW (mg/g) (=N0/Density of BFA) for the Wolborska model, are directly proportional to C0 and Z, and are inversely proportional to Q. The concentration gradient at higher C0 and surface area of BFA for higher value of Z are the reasons for higher capacity. Increasing Q leads to deficiency in residence time for diffusion, and therefore N0 or QW decrease with Q. The deviation between experimental and predicted values of (Ct/C0) is calculated using Marquardt’s percent standard deviation (MPSD) whereas deviation between tb values is determined using percentage deviation (Srivastava et al. 2008). ”
where U0 (cm/min) is a linear flow velocity. The adsorption capacity (N0, mg/L) is calculated from slope and found to be 1263, 1295, and 5020 for 10, 50, and 90 % BT, respectively. The rate constant [KBD, (L/(mg min)] determined from the intercept is 7.5 9 10-4 and -3.8 9 10-4 at 10 and 90 % BT, respectively. The values of coefficient of determination (R2) for 10 and 50 % BT in Fig. 5 are closer to unity than for 90 % BT. This is because of the significant intraparticle diffusion that occurred in the final stages of adsorption, causing an uneven variation in 90 % BT with respect to change in Z. The linear lines in Fig. 5 for 10 and 50 % BT are nearly parallel (slopes 11.9 and 12.2). The perpendicular distance between these lines corresponds to the length of MTZ, which is mathematically identified to be 2.66 cm. However, the values of MTZ for experiments E7–E9 in Table 2S are greater. Therefore, for experiments E7–E9, it can be inferred that the MTZ moves with constant velocity up to 50 % BT, because the 10 and 50 % BT lines are parallel. However, the expansion of MTZ took place after 50 % BT due to intraparticle
Ct bC0 bZ ¼ t U0 C0 N0
Application of the Thomas model The Thomas model is applied in the range of 0.01 \ (Ct/ C0) \ 0.97 to determine maximum solid-phase concentration and the adsorption rate constant for an adsorption column. The linear form of Thomas model is given by Eq. (11).
Fig. 6 Experimental breakthrough curve and breakthrough curve predicted by Bohart–Adams model and Wolborska model for 2,4-D adsorption using BFA a Q = 1.2 mL/min, Z = 6.5 cm; b C0 = 100 mg/L, Z = 6.5 cm; c C0 = 100 mg/L, Q = 1.2 mL/min
Adsorptive removal of 2,4-dichlorophenoxyacetic acid from aqueous solution using bagasse fly…
Fig. 7 Experimental breakthrough curve and breakthrough curve predicted by Thomas model for 2,4-D adsorption using BFA a Q = 1.2 mL/ min, Z = 6.5 cm; b C0 = 100 mg/L, Z = 6.5 cm; c C0 = 100 mg/L, Q = 1.2 mL/min
The adsorption capacity (q0, mg/g) and kinetic constant [KT, L/(mg min)] listed in Table 3S are the function of C0, Q, and Z. A similar change in q0 and KT with change in C0, Q, and Z was previously reported by Srivastava et al. (2008) using BFA as the adsorbent. The predicted values of tb presented in Table 3S are well in agreement with experimental tb (Table 2S). The higher values of MPSD may be due to the fact that the Thomas model is based on second-order reaction kinetics, whereas the adsorption is governed by both chemical reaction kinetics and interphase mass transfer (Sotelo et al. 2012). The BT curves predicted by the Thomas model are compared with experimental BT curves in Fig. 7. Application of the Clark model The Clark model is based on mass-transfer coefficient and Freundlich isotherm, and is expressed as follows: n1 ! C0 ln 1 ¼ rt þ ln A ð12Þ Ct
The parameters r (min-1) and A for the data 0.01 \ (Ct/ C0) \ 0.97 are investigated from slope and intercept using Freundlich isotherm constant (n = 2.88, batch study). Using the values of r and A, the BT curves are predicted and compared with experimental BT curves in Fig. 8. The change in values of ‘r’ with change in C0, Q, and Z for the Clark model is similar to a previous study on phosphate adsorption (Sun et al. 2014). The molecular diffusion path is diminished in the water layer surrounding the adsorbent, which resulted in higher values of r with increased Q. However, r is reduced for greater Z because of the increase in the number of adsorption sites attributed to higher adsorbent loading. The values of and MPSD for the Clark model are considerably higher, suggesting that the Clark model cannot properly describe the adsorption of 2,4-D in a packed bed of BFA. ”
C0 K T q0 m KT C0 t ln 1 ¼ Q Ct
Application of the Yoon–Nelson model The time required for 50 % saturation of column is predicted using Eq. (13) (Yoon–Nelson model), which is applied to experimental data for 0.01 \ (Ct/C0) \ 0.97. Ct ln ð13Þ ¼ KYN t t0:5 KYN C0 Ct
Fig. 8 Experimental breakthrough curve and breakthrough curve predicted by Clark model for 2,4-D adsorption using BFA a Q = 1.2 mL/min, Z = 6.5 cm; b C0 = 100 mg/L, Z = 6.5 cm; c C0 = 100 mg/L, Q = 1.2 mL/min
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Application of the deactivation kinetic model The deactivation kinetic model (Dahlan et al. 2007) was originally derived for gas–solid (packed bed) adsorption to demonstrate initial adsorption rate constant and deactivation rate constant. The model assumes isothermal, pseudosteady-state conditions; negligible axial dispersion; and mass-transfer resistance. A detailed discussion about the deactivation kinetic model is presented in the
supplementary information. In this model, the effects of factors such as pore structure, active surface area, and activity per unit area of solid are combined into one activity term. The derivation of the deactivation kinetic model is as follows: Ct 1 expðk0 B½1 expðkd tÞÞ expðkd tÞ ¼ exp ð14Þ 1 expðkd tÞ C0 Equation (14) is applied for packed-bed adsorption of 2,4-D on BFA and the model parameters are determined for complete saturation of column. The deactivation rate constant (kd, min-1) and initial adsorption rate constant [k0, mL/(g min)] are given in Table 3S. A comparison of predicted BT curves with experimental BT curves at different conditions is shown in Fig. 10. Figure 10 and the deviations ( and MPSD) in Table 3S suggest the similarity between experimental results and results predicted by the deactivation kinetic model. As can be observed in Table 3S, the constant kd increases with C0 and Q at constant Z because the number of 2,4-D molecules adsorbed is more on a fixed number of adsorption sites. However, kd is reduced because of increasing number of sites with increases in Z at constant C0 and Q. The change in k0 values is more significant for variables Q and Z than C0 due ”
The kinetic constant (KYN, min-1) and predicted t0.5 (min) for the Yoon–Nelson model are listed in Table 3S. The constant is increased due to the driving force for mass transfer with increase in C0 and Q. A longer path traveled by the 2,4-D molecules through the packed bed at higher Z is the reason for reduced KYN values (Aziz et al. 2014). The experimental and predicted BT curves obtained for different values of C0, Q, and Z for the Yoon–Nelson model are indicated in Fig. 9. The intraparticle diffusion after 50 % saturation of column leads to the formation of nonsymmetrical BT curves, which ultimately delay the saturation. Therefore, the values of t0.5 predicted (Table 3S) by the Yoon–Nelson model are greater than the experimental values.
Fig. 9 Experimental breakthrough curve and breakthrough curve predicted by Yoon–Nelson for 2,4-D adsorption using BFA a Q = 1.2 mL/ min, Z = 6.5 cm; b C0 = 100 mg/L, Z = 6.5 cm; c C0 = 100 mg/L, Q = 1.2 mL/min
Fig. 10 Experimental breakthrough curve and breakthrough curve predicted by deactivation kinetic model for 2,4-D adsorption using BFA a Q = 1.2 mL/min, Z = 6.5 cm; b C0 = 100 mg/L, Z = 6.5 cm; c C0 = 100 mg/L, Q = 1.2 mL/min
Adsorptive removal of 2,4-dichlorophenoxyacetic acid from aqueous solution using bagasse fly…
to simultaneous change in effluent concentration and weight–time factor (B = m/Q). Comparison of packed-bed models 2
The R value (*1) (Fig. 5) informs the applicability of the BDST model for packed-bed adsorption of 2,4-D on BFA. According to the BDST model, the length of MTZ is significantly increased after 50 % BT. The experimental tb values and those predicted by the Bohart–Adams and Wolborska models are closer than those predicted by the Thomas and Clark models; however, the former models are applied only to the initial part of BT curves. Therefore, the initial part of BT curves is well described by the Bohart– Adams and Wolborska models. The Thomas model provides better fit than the Clark model because it is based on monolayer adsorption (i.e., Langmuir isotherm). The values of deviations ( and MPSD) are significantly smaller for the deactivation kinetic model, which demonstrates good accordance between experimental and predicted results. ”
Capacity comparison: BFA versus activated carbon and 2,4-D versus phenol The 2,4-D adsorption capacity (predicted by the Thomas model) of activated carbon having a surface area of 1237.13 m2/g was calculated to be 0.03 mg/m2 (Salman et al. 2011; Salman and Hameed 2010) whereas the same for BFA in this study was 0.09 (mg/m2). Therefore, a square meter of BFA has three times higher adsorption potential than activated carbon for 2,4-D removal. The BFA adsorption (bed) capacity based on the BDST model for phenol removal was found to be 0.06 (mg/m2) (Srivastava et al. 2006, 2008), whereas it is 0.46 mg/m2 for 2,4-D removal in our study. Thus, BFA has 7.7 times higher adsorption capacity for 2,4-D removal in comparison with phenol removal. Thus, BFA is a better adsorbent than activated carbon for 2,4-D removal in packed bed. Further, BFA is a better adsorbent of 2,4-D than phenol. Kearns et al. (2014) has reported the adsorption capacity of wood biochars in the range 0.17–8.06 (mg/g) and that of bamboo biochars in the range 0.2–13.75 (mg/g) for 100 (lg/L) 2,4-D concentration whereas in present study, adsorption capacity of BFA was observed to be 7.14 (mg/g) for 50–400 (mg/L) 2,4-D concentration. Adsorption mechanism The physicochemical characteristics indicated the presence of various oxides in BFA. When BFA is added in aqueous solution, the oxides develop charges on the surface of BFA
depending on the pH of the solution. This may be explained by following reactions. H2 O $ Hþ þ OH
M þ OH ! M OH þ
M OH þ H ! M
ðbÞ OHþ 2
M OH þ OH ! M O þ H2 O;
where M represents Al, Ca, Fe, and Si etc. All oxides other than silica acquire positive charge at the natural pH (*3.5) of 2,4-D because the point of zero charges of SiO2, Al2O3, CaO, and Fe2O3 are reported to be 2.2, 8.3, 11.0, and 7.2, respectively (Srivastava et al. 2006). At lower pH, 2,4-D is adsorbed on the BFA surface due to the electrostatic force of attraction between the opposite charge species (i.e., anionic 2,4-D and metal oxides). M OHþ 2 þ P ! MOH2 P;
where P- indicates the anionic species of 2,4-D. The H? ions are neutralized by negative sites on the BFA surface contributed by silica. As the pH is increased, the number of negative sites on BFA is increased following the reaction Eq. (d). This reduces the adsorption due to the repulsion between pesticide ions and surface charges. In addition to adsorption of anionic 2,4-D, the molecular form of 2,4-D is also adsorbed on the BFA surface via van der Waals type of interaction.
Conclusion The BFA is an efficient and cost-effective adsorbent for batch and packed-bed adsorption of 2,4-D. The physicochemical characteristics of different particle-size ranges of BFA show the more BET surface area and carbon content for bigger particles, while more silica percentage in smaller particles. Therefore, there is greater adsorption of 2,4-D on bigger particles than on smaller particles of BFA. The application of isotherms to batch data revealed the best fitting of Langmuir model, which indicates monolayer adsorption of 2,4-D on BFA. The effect of temperature demonstrated the endothermic nature of adsorption. According to BDST model, the length of MTZ is significantly increased due to intraparticle diffusion after 50 % BT. The Adams–Bohart and Wolborska models indicated that the kinetics in the early stages of adsorption is dominated by external mass transfer. Results predicted by the Thomas model are better than those predicted by the Clark model; however, the deactivation kinetic model provides the best fitting among all the packed-bed models. Therefore, the deactivation kinetic model can be proposed to explain the solid–liquid adsorption system without the need for evaluating the structural properties of the
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adsorbent. Thus, (i) a combination of Langmuir isotherm (in batch adsorption) and Thomas model (in packed-bed adsorption), and (ii) application of deactivation kinetic model to the solid–liquid adsorption can be concluded from the considered packed-bed models. Acknowledgments The authors thank the Science and Engineering Research Board, India, for providing research grant (SB/S3/CE/077/ 2013) to undertake the work. Sophisticated characterization facilities provided by IBM, Nagpur, and CSMCRI, Bhavnagar (India) are acknowledged. The authors also thank Dr Sayaji Mehtre, Scientist BARC, Mumbai, for his assistance in characterization. Authors thank Raju Dutta for his valuable discussion on IR spectra and thank K Anand Kumar for editing the manuscript.
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