Introduction

Arsenic is a metalloid and has other allotropes and forms. It has different applications in electronics, agriculture, wood preservatives, medicine and metallurgy. It is used in car batteries, ammunition, production of pesticides, herbicides, insecticides, and treated wood products (Nicomel et al. 2015). No doubt, arsenic has different uses in different fields (Shen, 1997), but due to its toxicity, its applications are declining (Fig. 1).

Fig. 1
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Sources of arsenic

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Most research studies have been published on the equilibrium study to remove metal ions using the adsorption process (Pholosi et al. 2020). A limited volume of adsorbate can be treated using the equilibrium method, i.e., batch study, while a wide range of adsorbate can be utilized in the dynamic study (Ahmad and Hameed 2010).The different techniques available for separation of ions from aqueous solution include advanced oxidation (Kurian 2021; Zaw and Emett 2002), electrochemical separation (Gilhotra et al. 2021), coagulation- flocculation (Francisca and Carro Pérez 2014; Han et al. 2002), reverse osmosis (Arslana, et al. 2011),nanofiltration (Maher et al. 2014; Noel et al. 2021) etc. These have been commonly utilized for abatement of arsenite and other heavy metal ions from aqueous or synthetic solutions (Nurul et al.2006). Adsorption (Yu et al. 2021; Jung et al. 2019; Shipley et al. 2009; Mohan et al. 2019; Golberg 2002) is the most widely used technique in water pollution abatement (Sahin et al. 2021; Kutluay et al. 2020a, b) due to its operational flexibilities, availability of low-cost effective adsorbents, and its simplicity. Various adsorbents such as coal derived char ( Batur et al., 2021), modified peanut shell Kutluay et al. 2020a, b), oxide-coated natural materials (Asere et al. 2019; Maji et al. 2012; Siddiqui and Chaudhry 2017), laterite soil (Maji et al. 2008), sludge (Byambaa et al. 2021), carbon-based adsorbents (Islam et al. 2021; Chen et al. 2007), waste by-products (Mukherjee et al. 2021; Senthilkumar et al. 2020), biochar (Liu et al. 2012; Lin et al. 2017), nanocomposites (Siddiqa et al. 2019), Fe(III) loaded cotton cellulose (Zhao et al. 2009), and sirnak coal-derived char (Batur et al. 2021) have been utilized by different researchers. Few researchers have used different activating agents like KOH (Islam et al. 2017), H3PO4 (Abatan et al. 2019; Baytar et al. 2021), and NaOH (Giri et al. 2020) for adsorbents for different bio adsorbents. This study focused on agriculture waste, i.e., rice husk (RH) to remove arsenite ions using dynamic or column study. Raw rice husk can be utilized as an adsorbent to remove arsenic as a potential adsorbent (Nurul et al. 2006; Asif and Chen 2017). In 2019–20 “The United States Department of Agriculture” estimated 496.22 million metric tons of rice production in the World. About 20% of the entire rice grain mass is rice husk. Few properties of rice husk are shown in Fig. 2 (Kalderis et al. 2014).

Fig. 2
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Properties of rice husk

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Several researchers have examined the potential of modified and unmodified rice husk to be used as an adsorbent in equilibrium and dynamic mode for abatement of heavy metal contaminants from wastewater (Ahmaruzzaman 2011; Dang et al. 2009). Dang et al. (2009) reported the adsorption capacity (2.24 mg/g) of iron-modified RH for abatement of arsenic ions using batch mode. The arsenic ions adsorption by RH-Fe was affected by pH and other parameters. Pehlivan et al. (2013) observed the adsorptive removal of As(V) ions on Fe (III)-coated RH in batch mode using various parameters such as pH, As(V) concentration, contact time, ionic strength, and adsorbent amount. At pH 4, maximal removal of As(V) ions was 94% and adsorption capacity obtained was 2.5 mg/g. Mostly the adsorption was carried out using batch adsorption. As very few studies have been conducted for the removal of As ions using fixed bed column. A few of these studies only reported calculated adsorption capacity/percent removal of rice husk, but none has determined the different adsorption zone parameters for the fixed bed columns. Some recent studies have incorporated magnetic nanoparticles for removal of volatile organic compounds like benzene and toluene and reported their adsorption–desorption and recyclability studies (Ece et al. 2020; Sahin et al. 2021). The recyclability studies help in ascertaining the use of adsorbent for repeated cycles and its improving its efficiency. The studies utilized for adsorption of arsenite and other ions using rice husk are summarized in Table 1. The present study deals with the calculation of different column bed design factors such as the time taken for adsorptive zone to move its depth down the column, total time taken by adsorptive zone to form, height of the adsorptive zone, percentage bed saturation etc. which are not reported in the literature. The results show that waste rice husk is a potent sorbent for removal of arsenite ions from wastewater using dynamic mode and these mathematical models can be implemented for the designing of the adsorption column.

Table 1 Studies reported in literature on removal of different metal-ions from water and/or wastewater using RH
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The prime aim of research study was optimization of column adsorption, based on removal of arsenite ion using rice husk.

Materials utilized and methods

Adsorbate and reagents

All the materials (chemicals) utilized in this research were of analytical grade (AR). The standard solution of arsenite ions was made by dissolving 0.132 g of arsenic trioxide salt in 1L deionized water (DI). Then, this solution was mixed and prepared well and stored in the dark. The aqueous solution of different concentrations of arsenite ions was prepared using this stock solution. All chemicals used in this investigation were of the analytical standard of purity. Alkaline chemical ‘sodium-hydroxide’ and acidic chemical ‘hydrochloric acid’ were used to set the pH values of the samples.

Rice husk as an adsorbent

Rice husk was taken from Kohinoor Foods Ltd., Murthal, Sonepat, India. It was then cleaned thoroughly with deionized water to separate impurities like dust particles and followed drying for 24 h at 100 °C in an oven (El-Shafey 2007).

Dynamic study

A Borosil make glass column having an inner diameter of 30 mm and an altitude of 720 mm was utilized for the different experiments in this study. The column was fixed vertically as shown in Fig. 3 (Ayoob et al. 2007) and the arsenite ion solution was delivered through the delivery line from the storage tank to the column. Inside the column, 0.4 mm of the screen was adjusted at bottom and top to support the adsorbent media (Jain et al., 2013).

Fig. 3
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Experimental setup of dynamic study

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The effect of the bed depth of rice husk, the flow rate of arsenite ion solution, and influent concentration were studied. The color intensity of arsenite is directly proportional to its concentration. By varying the concentration of arsenite solution calibration curve was constructed and its absorbance was measured at λmax = 194 nm by using a UV–Visible spectrophotometer (RIGOL Ultra-3660) (Ayoob et al. 2007; Shahlae and Pourhossein 2014).

Results and discussion

Effect of bed depth

The adsorption capacity of rice husk was observed by varying the bed depth 15, 30, 45, and 60 mm (Mohan et al. 2017; Dotto et al. 2015; Song et al. 2016, Fallah and Taghizadeh 2020). The breakthrough curve was obtained by keeping the arsenite ions concentration constant at 50 mg/L and 20 mL/min of flow rate (FR) in 30 mm of inner and 32 mm outer diameter column. In Fig. 4 (a), it was observed that the breakthrough time at 10% effluent concentrations increased on increasing the bed depth (Golie and Upadhyayula 2016; Singh and Pant 2006; Hummadi et al. 2022) as there is an increase in the surface area of the adsorbent bed on increasing the bed depth. The breakthrough time at 10% effluent concentration (tb10%) increased from 7 to 20 min on increasing the bed depth from 15 to 60 mm. Hence, the optimum bed depth was found out to be 60 mm.

Fig. 4
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(a) Breakthrough curves at different bed depths of rice husk. (b). Breakthrough curves at the different flow rates of arsenite ion. (c) Influence of influent concentration of arsenite ions on the breakthrough curves

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Influence of different flow rates

The analysis was done at different flow rates of 20, 35, 45, and 60 mL/min (Mohan et al. 2017; Dotto et al. 2015; Song et al.2016, Fallah and Taghizadeh 2020; Hummadi et al. 2022). As shown in Fig. 4 (b), the breakthrough curves obtained at different flow rates were observed, keeping the concentration of arsenite at 50 mg/L and bed depth at 60 mm. The breakthrough time decreases on increasing the flow rate (Golie and Upadhyayula 2016; Singh and Pant 2006; Fallah and Taghizadeh 2020). The breakthrough time at 10% effluent concentrations (tb 10%) decreased from 20 to 6 min by increasing the flow rate from 20 mL/min to 60 mL/min [Fig. 4(b)]. At lower flow rate, the adsorbate is in contact with the adsorbent for a longer time and results in higher adsorption. In other words, at lower flow-rate, adsorbate pass slowly through the column and takes much time to achieve saturation whereas higher flow-rate, adsorbate gets lesser time to get adsorbed on to the surface of adsorbent, i.e., the residence time of the adsorbate decreased; hence, removal of arsenite ions decreased with increasing flow-rates (Asif and Chen2017). Hence, flow rate of 20 mL/min was adjudged to be the optimum flow rate.

Influence of different arsenite ion concentrations

The adsorption efficiency of rice husk was also investigated with varying influent concentrations in the range of 15–50 mg/L (Mohan et al. 2017; Dotto et al. 2015; Song et al. 2016, Fallah and Taghizadeh 2020). The behavior of the breakthrough curve was studied by keeping the bed depth at 60 mm and 20 mL/min of flow rate in the column. As shown in Fig. 4 (c), the breakthrough time (at 10% effluent concentration) decreased (from 50 to 20 min), with increasing the influent concentration from 15 to 50 mg/L. It can be attributed to the fact that on increasing the influent concentration, there is an increase in the number of arsenite ions to be adsorbed but the number of adsorption sites available on the surface of the adsorbent remains same as the bed depth was kept constant for these experiments. Hence, the amount of adsorbed ions decreased with increased influent concentration of arsenite ions (Liao et al. 2013; Fallah and Taghizadeh 2020; Singh and Pant 2006; Hummadi et al. 2022).

Bohart-Adams model

The Bohart-Adams model is applied to find depth of mass transfer zone. This kinetic model depends upon the surface reaction rate. Therefore according to this model, the service time was calculated by using the equation.

$$\ln \left( {\frac{{C_{o} }}{{C_{e} }} - 1} \right) = \ln \left( {e^{{kN_{o} \frac{x}{v}}} - 1} \right) - kC_{o} t$$
(1)

Since the exponential term, \({e}^\frac{kNo}{v}\)> > 1; hence, unity term was neglected. Then, the equation becomes

$$\ln \left( {\frac{{C_{o} }}{{C_{e} }} - 1} \right) = \ln e^{{kN_{o} \frac{x}{v}}} - kC_{o} t$$
(2)

where:

Co&Ceare the influent and effluent concentration in mg/L

x: bed height in mm

k: adsorption rate constant in mL/mg/min

No: indicate amount adsorbed (mg/cm3)

v: linear flow velocity of the influent into the column(mL/min/cm2)

and t is the service time in min (Pholosi et al. 2020; Goyal et al. 2009; Yan et al. 2001)

Since,

$$\ln e^{{kN_{o} \frac{x}{v}}} = kN_{o} \frac{x}{v}$$
(3)

We have

$$\ln \left( {\frac{{C_{o} }}{{C_{e} }} - 1} \right) = kN_{o} \frac{x}{v} - kC_{o} t$$
(4)

Which is the same as

$$\left( v \right)\ln \left( {\frac{{C_{o} }}{{C_{e} }} - 1} \right) = kN_{o} x - kC_{o} t\left( v \right)$$
(5)

Multiplying both sides by \(\frac{{N_{o} }}{{N_{o} }}\) and dividing both sides by \(kN_{o}\) yields

$$\frac{v}{{kN_{o} }}\ln \left( {\frac{{C_{o} }}{{C_{e} }} - 1} \right) = x - \frac{{vC_{o} t}}{{N_{o} }}$$
(6)

Which can be solved for t

$$t = \frac{{N_{o} }}{{C_{o} v}}x - \frac{1}{{C_{o} k}}\ln \left( {\frac{{C_{o} }}{{C_{e} }} - 1} \right)$$
(7)

the N0, C0 and k, values can be calculated from column experiments, which operated with linear velocity values (Goyal et al. 2009; Baral et al. 2009; Ahmad and Hameed 2010; Abdolali et al. 2017; Zhang et al. 2017).

Hutchins (Hutchins 1973) proposed changes in the Bohart-Adams model equation, which only requires three fixed-bed column runs to acquire the required essential data. The procedure was termed as “bed depth service time” (BDST) model. The Bohart-Adams model Eq. (7) can be depicted in linear form as follows.

$$t = ax + b$$
(8)
$$\mathrm{Slope }a=\frac{{N}_{o}}{{C}_{o}V}=0.4$$
(9)
$$\mathrm{Intercept }b=\frac{1}{{kC}_{o}}\mathrm{ln}\left(\frac{{C}_{o}}{{C}_{e}}-1\right)=-1.5$$
(10)

as shown in Fig. 5 (a).

Fig. 5
figure 5

(a) BDST plot for arsenite ions at a flow rate for 10% breakthrough concentration. (b) BDST plot at two different effluent concentrations arsenite ions. (c) Influence of volumetric flow rate on adsorption capacity of adsorbent

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If the time value is zero, Eq. (7) reduces to:

$${\mathrm{x}}_{0}=\frac{V}{K{N}_{0}}\mathrm{ln}\frac{{C}_{0}}{{C}_{e}}$$
(11)

x0 = minimum/critical adsorbent bed depth, at which effluent concentration passes through it and Adsorption Zone/Mass Transfer Zone Depth = 51 mm as shown in Fig. 5 (b). These results obtained were in agreement with the literature available on calculated parameters using the Bohart-Adams model (Table 2) (Marzbali and Esmaieli 2017; Charola et al. 2018; Chowdhury et al. 2015; Patel and Vashi 2015).

Table 2 Bohart-Adams parameters at particular flow-rate using BDST plots
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At different values of flow rate, an effective adsorbed amount was obtained using breakthrough time (tb) at 10% breakthrough concentration (saturated) using the following equation:

$$\mathrm{Effective amount adsorbed }\left({\mathrm{q}}_{\mathrm{e}}\right)=\frac{{t}_{b}.Q.({C}_{0}-{C}_{e})}{w}$$
(12)

where:

Q: flow- rate (mL/min).

C0 and Ce: the inlet and outlet concentration in mg/L.

w: represents weight of Rice Husk (g) in column.

qe: is an amount of arsenite ions adsorbed.

Figure 5 (c) shows a plot of the amount adsorbed at 10% effluent against the flow rate. The adsorption increases gradually with flow rate, but tends to become slower at a higher flow rate due to mass transfer effect. In the fixed-bed column, maximum adsorption capacity (qm) 5.315 mg/g occurred at 45 mL/min flow rate as shown in Fig. 5 (c).

Adsorption column design parameters

Hutchins (Hutchins 1973) recommended to use column design parameters of one experiment to calculate parameters at other concentration values and flow rates. As per BDST model, the value of a* can be obtained at any feed concentration as well flow rate by the equation given as follows (Han et al. 2008; Srivastava et al. 2008; McKay 1979).

$${\mathrm{a}}^{*}=a\frac{{Q}_{1}}{{Q}_{2}}$$
(13)
$$\mathrm{As};\mathrm{t}=\mathrm{ax}+\mathrm{b}$$
(14)
$$\mathrm{For }{\mathrm{t}}^{*}={\mathrm{a}}^{*}\mathrm{x}+\mathrm{b}$$
(15)

Similarly, for influent concentration

$${\mathrm{a}}^{*}=a\frac{{C}_{1}}{{C}_{2}}$$
(16)
$${\mathrm{b}}^{*}=\mathrm{b}\left(\frac{{C}_{1}}{{C}_{2}}\right)\frac{\mathrm{ln}(\frac{{C}_{2}}{{C}_{F}}-1)}{\mathrm{ln}(\frac{{C}_{2}}{{C}_{B}}-1)}$$
(17)
$${\mathrm{t}}^{*}={\mathrm{a}}^{*}\mathrm{x}+{\mathrm{b}}^{*}$$
(18)

tb values, i.e., experimental run values and those obtained using Eq. (18) at different feed concentrations are enlisted in Table 3. The experimental and calculated values are quite comparable.

Table 3 tb values, i.e., experimental run values and those obtained using Eq. (18) for different feed concentrations. Bed depth = 60 mm. Flow rate = 20 mL/min
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Column adsorption zone parameters

Adsorption zone formation and dynamics have been calculated (Table 4) using the rice husk column data obtained in this study using Eqs. (19)–(25).

Table 4 Adsorption zone parameters for the column. Flow rate = 20 mL/min. Feed concentration = 50 mg/L
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$${\mathrm{t}}_{\mathrm{z}}=\frac{{\mathrm{V}}_{\mathrm{Z}}}{{\mathrm{Q}}_{\mathrm{f}}}=\frac{{\mathrm{V}}_{\mathrm{E}}-{\mathrm{V}}_{\mathrm{B}}}{{\mathrm{Q}}_{\mathrm{f}}}$$
(19)

where:

tz: represents time required for adsorptive zone to move its depth down the column after becoming established

Vz: represents actual volume of inlet aqueous solution used in between the break through point and exhaust point

VE: represents volume of aqueous solution to exhaust point whereas VB represents volume of aqueous solution to breakthrough point (mL)

Qf : depicts solution flow rate (mL/min)

Also, tE represents time needed for the adsorptive zone to become fully confirmed and traverse fully out of the adsorptive bed, expressed by following equation;

$${\mathrm{t}}_{\mathrm{E}}=\frac{{V}_{E}}{{Q}_{f}}$$
(20)

Movement rate of adsorptive zone down the bed is expressed as;

$${\mathrm{V}}_{\mathrm{z}}=\frac{{h}_{z}}{{t}_{z}}=\frac{x}{{t}_{E}-{t}_{o}}$$
(21)

where hz represents adsorptive zone height (mm), x represents bed depth (mm), tF depicts time needed for adsorptive zone to form initially (min) so that,

$${\mathrm{h}}_{\mathrm{z}}=\frac{x{t}_{z}}{{t}_{E}-{t}_{F}}$$
(22)

The value of tF cannot be measured directly but ascertained mathematically using Michaels’ method as given by the following equation:

$${\mathrm{t}}_{\mathrm{F}}=\left(1-\mathrm{F}\right) {\mathrm{t}}_{\mathrm{z}}$$
(23)

where F represents fraction of RH available in adsorptive zone.

$$\mathrm{F}=\frac{{S}_{z}}{{S}_{m}}=\frac{{\int }_{VE}^{Vz}{(C}_{o}-C)dv}{{C}_{0}({V}_{E}-{V}_{B})}$$
(24)

Sz: depicts amount of solute separated by adsorptive zone between breakthrough point to exhaustive point

Sm: represents solute withdrawn by adsorptive zone at exhaustion point.

F value or amount was utilized to obtain percentage saturation of adsorptive column, as follows:

$$\% \mathrm{Saturation}=\frac{x+(1-F){h}_{z}}{h}\times 100$$
(25)

Characterization of rice husk

Fourier-transform infrared spectroscopy

FTIR spectroscopy is an effective method used to identify the characteristic functional groups present on the adsorbent surface. FTIR analysis of natural rice husk before and after adsorption was carried out and has been shown in Figs. 6 and 7. The adsorption peak at 3434.6 and 3417.3 cm−1 referred to the presence of free –OH groups. The CH stretching height around 2925.2, 2923.4, 2856.2, and 2854.5 cm−1 attributes to the presence of a carboxylic acid group. The peak at 2063.0 and 2060.9 cm−1 indicates the alkynes group. The peaks around 1634.5–1636.8 cm−1 correspond to the CO group. Alkenes and aromatic functional groups are shown by the C = C stretching vibrations from range 1546.80–1652.88 cm−1. The presence of CH2 andCH3 groups indicates at the peak of wave number 1458.0 cm−1 while at wave number 1380 cm−1 indicative of CH3. Rice husk at 1374.4–1384.3 cm−1 referred to the aromatic hydrocarbon CH group. The peak range of 1100–1250 cm−1 indicates the alcohol CO group. The peaks range of 400–600 cm−1 means alkyl halides (Bansal et al. 2009; Daffalla et al. 2012; Deshmukh et al. 2012). Table 5 gives the summary of functional groups present in the FTIR spectra.

Fig. 6
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FTIR spectra of rice husk before adsorption

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Fig. 7
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FTIR spectra of rice husk after adsorption

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Table 5 FTIR spectra of rice husk before and after adsorption
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XRD characterization

In Fig. 8, the XRD image of raw rice husk is shown. The XRD image of rice husk exhibited broadband at 2θ = 21.68°~22° (approximately), which indicated the characteristic peak of amorphous silica. A small peak observed at 2θ = 28° stated the presence of carbon. The XRD image analyses of rice husk indicated the unstructured nature (amorphous) of Rice Husk (Liu et al. 2014; Mohamed et al. 2015).

Fig. 8
figure 8

XRD image of rice husk

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Scanning electron microscopy

Rice husk has good adsorption capacity due to the presence of silicon dioxide (SiO2). To analyze the surface of rice husk or adsorption power, the scanning electron microscopy technique was used. Scanning electron microscopy images provide the clear morphology of the rice husk, the structure of the surface. In scanning electron microscopy, a focused beam of electrons was used to produce complex, high-magnification images of a rice husk’s surface topography. According to the scanning electron microscopy (SEM) micrograph of rice husk, the morphological characteristics of the rice husk structure are spherical in size with irregular geometry in the form of agglomerates in micron-scale operating at 10 kV and at different micron size in Fig. 9. In addition, the uneven, rough, and highly porous structure indicated more surface area (Ahiduzzaman and Islam 2016). Figure 9 illustrates the morphology of the outer surfaces of RH, which is uneven and highly roughened. Silica was found out to be the main inorganic component of RH existing in nanoparticle form. The nano-scale roughness comes from the morphology of silica nanoparticles dispersed within the bulk (Askaruly et al. 2020).

Fig. 9
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SEM images of raw rice husk (RRH)

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Transmission electron microscopy

For further modification, transmission electron-based microscopy was used. Size and shape of SiO2 acquired from RH were analyzed using transmission electron microscopy (TEM); the TEM graph and images of size distribution (50–500 µm) of SiO2 are mentioned in Fig. 10. From the image, it can be identified the spherical SiO2 (silica) particles formed the clusters. Particle size of silicon was measured from 500 to 50 µm (Nguyen et al. 2017). As per the TEM images, the rice husk structure is irregular and amorphous in nature.

Fig. 10
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TEM images of raw rice husk (RRH)

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Conclusions

The experimental study of fixed bed column using rice husk is an appropriate and suitable approach for arsenite ions removal from synthetic wastewater owing to its simplicity, ease in operation, and maintenance. As per this study, the breakthrough time increases on increasing bed depth but decreases on increasing initial concentration and flow rate. The different column design parameters were calculated using the Bohart-Adams theoretical approach and Hutchins equation. The critical bed depth (4 mm) and depth of mass transfer zone calculated using the Bohart-Adams equation agreed closely with the experimental and literature values respectively. The optimized parameters obtained for column study were 60 mm of bed depth, 20 mL/min flow rate, and 50 mg/L of influent-concentration. The tb value of 20 min at optimized feed concentration (50 mg/L) also matched closely with the value calculated using the Hutchins equation (Table 3). The maximum adsorption capacity obtained at optimized parameters was (qm) 4.5 mg/g. The adsorption zone parameters were also evaluated using Michaels’ equations including % bed saturation, time required for the adsorptive zone to become fully established (tz), and adsorptive bed height (hz) and were found out to be 95.26%, 19 min, and 28.43 mm at optimized conditions. Hence, column study to separate arsenite ions utilizing rice husk was found to be a potential and efficient method. The future research will need to focus on recycling of heavy metals and eco-friendly waste management of used adsorbents.