BIBECHANA Vol. 21, No. 1, April 2024, 51–62 ISSN 2091-0762 (Print), 2382-5340 (Online) Journal homepage: http://nepjol.info/index.php/BIBECHANA Publisher:Dept. of Phys., Mahendra Morang A. M. Campus (Tribhuvan University)Biratnagar Potential removal of arsenite from contaminated water using a fixed bed column packed with TiO2-impregnated pomegranate peel powder Bhoj Raj Poudel1,3, Ram Lochan Aryal2, Kedar Nath Ghimire3, Hari Paudyal3,∗ Megh Raj Pokhrel3 1Department of Chemistry, Tri-Chandra Multiple Campus, T. U., Kathmandu, Nepal 2Department of Chemistry, Amrit Campus, Tribhuvan University, Kathmandu, Nepal 3Central Department of Chemistry, Tribhuvan University, Kathmandu, Nepal ∗Corresponding author. Email: haripaudyal9@gmail.com Abstract A dynamic biosorption of arsenite in a fixed bed column packed with TiO2 impregnated pomegranate peel (PP@TiO2) has been investigated in this work, which is important to identify the effectiveness and affordability of an adsorbent in actual practice. To create an active adsorption site for As (III) ions, pomegranate peel powder (PP) was impregnated with TiO2. Under various operating parameters, the performance of a column packed with PP@TiO2 for adsorbing As (III) ions was evaluated. Breakthrough curve modelling showed that the bed depth service time (BDST) and Thomas models agreed well with the experimental data. The maximum column capacity of PP@TiO2 using the Thomas model was found to have resembled experimental value with high values of coefficient of determination. Therefore from these results, we may anticipate that PP@TiO2 can be a strong contender for the treatment of wastewater that has traces amount of the As (III) ion in a fixed bed system. Keywords Pomegranate peel; Removal; PP@TiO2; As(III); Fixed bed column. Article information Manuscript received: November 20, 2023; Revised: December 20, 2023; Accepted: December 25, 2023 DOI https://doi.org/10.3126/bibechana.v21i1.60048 This work is licensed under the Creative Commons CC BY-NC License. https://creativecommons. org/licenses/by-nc/4.0/ 51 http://nepjol.info/index.php/BIBECHANA haripaudyal9@gmail.com https://doi.org/10.3126/bibechana.v21i1.60048 https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/ Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 52 1 Introduction Arsenic contamination in an aquatic environment has caused risk to aquatic plants and animals, and serious hazardous effects to humans even in very low concentrations [1, 2]. It can exist in several various oxidation states in aquatic environments, but the main states are As (III) and As (V) [3]. Arsenic is entered into groundwater not only by natural processes such as volcanic eruptions, dissolution of minerals ore, geochemical reaction, biological activ- ities, etc., but also via anthropogenic sources such as wood preservatives, industrial effluents of metal processing, semiconductor, electroplating, mining, battery, pigments, dyestuff, and paints [4,5]. Addi- tionally, acid mine drainage wastewater contains a significant amount of arsenic [6]. Studies reported that long-term exposure to arsenic concentrations above 100 ppb may cause Blackfoot disease, hyper- keratosis, and cancers [7]. Its contamination in food and water is the main way that humans are exposed to it. The maximum permitted limit (MCL) of 10 ppb for arsenic in drinking water has been estab- lished by the USEPA and WHO because of its ex- posure concern and severe noxiousness [8–10]. As a result, effective and affordable arsenic removal tech- niques from polluted water are of emerging concern. Conventional methods for the elimination of pollutants from contaminated water are ion ex- change, electrocoagulation/co-precipitation, lime softening, reverse osmosis, ultrafiltration, nanofil- tration, and resin chelation [6]. Nevertheless, most approaches have practical drawbacks, includ- ing high costs, insufficient metal removal, the pro- duction and disposal of hazardous metal sludge, and unsuitability for water with traces of contami- nants [11]. Currently, adsorption is the most effec- tive process for the removal of contaminants from water compared to other methods due to its simplic- ity, cost-effectiveness with high efficiency, poten- tial for regeneration, and sludge-free operation [12]. Recently, various low-cost non-conventional adsor- bents have been utilized to sequester arsenic from contaminated water [2, 3, 13–17]. To date, most of the research on the adsorption of arsenic from aqueous solutions has been done in batch mode experiments. A sorbent utilized in this study is TiO2-impregnated pomegranate peel, abbreviated as PP@TiO2 hereafter. In our previous work, we focused on the As(III) removal using PP@TiO2 as an adsorbent and photocatalyst using batch mode experiment [4]. The PP@TiO2 was proven to be an effective, ecological, profitable, and reusable biosor- bent for sequestering arsenic from contaminated water. It was also observed that the adsorbed As(III) was partly oxidized to As(V) on the adsor- bent’s surface. The efficient application of biomass- based adsorbents for removing As(III) in continu- ous flow fixed-bed column adsorption systems is not well documented [18–20]. Therefore, careful atten- tion to these details is needed to design the practical implementation of this investigated PP@TiO2 and the design of industrial columns. For an industrial application or real wastewater refining, biosorption in a fixed-bed column packed with adsorbent is de- sirable [21–25]. Thus, the laboratory size fixed-bed column’s experimental findings justify the design of an adsorption column for industrial use. As an extension of our earlier study [4], this work extensively investigated the effectiveness of PP@TiO2 in As(III) removal in a fixed-bed col- umn mode. The biosorption capacity of SPP@TiO2 for As(III) concerning some operating variables like flow rate, influent concentration, and bed depth was assessed. Experimental data were used in a variety of models, including the Yoon Nelson, Bed Depth Service Time (BDST), and Thomas models, to as- sess design parameters. 2 Materials and Methods 2.1 Chemicals and instruments Analytical grade chemicals were utilized without further purification in the present work. The flow of the feed solution is controlled by using a peristaltic pump (EYELA MP-1000-MP-1000-H, Japan), whereas that of effluent samples was col- lected each hour using a fraction collector (Advan- tec SF-2120, Advantec Tokyo Kaish, Ltd., Japan). 2.2 Synthesis of the adsorbent (PP@TiO2) The local juice trader in Kathmandu, Nepal, gra- ciously provided the pomegranate peel waste. First, distilled water was used to thoroughly wash the pomegranate peels. It was then dried for 48 h at 343 to 353 K in an oven. The dry bulk was crushed and put through a copper sieve with a mesh size of 150 microns. The biopolymer’s hydroxyl groups were cross- linked by a condensation reaction using Conc. H2SO4 as a dehydrating agent, preventing the ad- sorbent from dissolving in aqueous solutions. The cross-linked pomegranate peels were made accord- ing to Paudyal et al. (2017). In a round bottom flask, 15 g of raw peel powder was combined with 30 mL of Conc. H2SO4, which was then heated to 100°C and stirred for 24 h before cooling to room temperature. After being neutralized with sodium bicarbon- ate, the charred mass was once again stirred in 1 M HCl solution. It was rinsed repeatedly in distilled water until it reached neutrality, and then it was dried at 70°C in a convection oven. The term PP refers to the powder made from pomegranate peels in this manner. Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 53 Then, PP@TiO2 was prepared using a modified sol-gel method. About 10 mL of titanium (IV) n- butoxide was mixed with 15 mL of ethanol. Af- ter stirring for 30 min at room temperature, 2 g of powdered PP was added to it. Then, 15 mL of a so- lution of 1:1:1 ethanol, deionized water, and acetic acid were added dropwise from the burette to the magnetically stirred mixture. The whole content was further stirred for 4h until sol was obtained. The sol was dried overnight at 80°C after aging at 40°C for 2 h. Finally, the dried gel was ground into a powder and calcined at 400°C in a muffle furnace. The re- sulting PP@TiO2 was stored in a plastic container before being used in the characterization and ad- sorption experiment. The detailed methodology for preparing PP@TiO2 is shown in Scheme 1. 2.3 Characterization As a continuation of our previous study, the methodology and results of the characterization of PP@TiO2 before and after adsorption have been described in detail elsewhere [4]. In brief, EDX spectra showed peaks associated with Ti after TiO2 impregnation (PP@TiO2), in- dicating that the biomass had been effectively im- pregnated with TiO2. The EDX spectra of As(III) absorbed PP@TiO2 showed an additional peak as- sociated with arsenic. This demonstrated that ar- senic had been effectively adsorbed from an aqueous solution by PP@TiO2. In XRD analysis, the diffraction peaks associated with crystalline TiO2 in the case of PP@TiO2 emerge at 2θ = 25.2◦, 38.1◦, 48◦, 55◦, 63◦, 70◦, and 75◦. The (1 0 1), (0 0 4), (2 0 0), (1 0 5), (2 0 4), (2 2 0), and (2 1 5) planes, respectively, are represented by these peaks. This demonstrates that TiO2 was success- fully embedded in biomass in the form of crystalline anatase. These peaks demonstrate excellent agree- ment with JCPDS card No. 00-021-1272 of the TiO2 anatase phase. In FTIR spectra of PP@TiO2, the Ti-O vibra- tion, which represents the contact of TiO2 with PP, is attributed to absorption peaks between 420 and 700 cm-1. This offers convincing proof of TiO2 im- pregnation with the creation of the Ti-O-C bond. In the FTIR spectra of As(III) adsorbed PP@TiO2, an extra peak related to As-O vibrations was discov- ered at 825 cm-1 following arsenic biosorption. This supports the biosorption of As(III) onto PP@TiO2. Using SEM coupled with an EDX spectrom- eter, the morphology and elemental composition of as-synthesized biosorbent were examined. The crystallinity of the PP@TiO2 was determined us- ing XRD patterns from an X-ray diffractometer. Using FTIR spectroscopy, the surface functional- ities of the biosorbents before and after biosorp- tion of As(III) were examined. The XPS investiga- tions were performed by using an XPS spectrome- ter (Thermo Fisher Scientific, UK) to identify the elemental bonding and chemical states. 2.4 Dynamic adsorption test in fix bed col- umn As seen in Figure 1, a glass column with an inner di- ameter of 0.8 cm and a height of 20 cm was used for the continuous adsorption test of As(III) in a fixed bed column. For this, the PP@TiO2 was soaked in DI water before being packed into the column. Following that, the wet adsorbent was packed into a column. The column was first filled with glass beads (5 cm), then with a layer of cotton (2 cm), and then PP@TiO2 (1.4 to 4.1 cm). The column was once more packed with a 2 cm cotton layer trailed by a 5 cm glass bead layer. Before the biosorption test, the column was con- ditioned by passing DI water for 5 h at the same pH as the test solution. After this, As(III) solution (10 mg/L) was passed into the column at the required flow rate by using a peristaltic pump. The efflu- ent solutions for the measurement were collected at each regular interval with the help of a fraction col- lector. Alkali solution (0.1 M NaOH) was used to elute the loaded As(III) since they had been suc- cessfully used in earlier batch tests. The effluent samples were then analyzed to measure the final concentrations of arsenic by using ICP-MS. 2.5 Analysis of breakthrough curve pa- rameters It is crucial to study the breakthrough curve to eval- uate a column’s biosorption performance. Calculat- ing the breakthrough curve parameters can do this. The ratio of As(III) concentration from the effluent to the intake, Ce Ci , is presented against time (h) fol- lowing the commencement of the flow. Equation 1 provides the total quantity of adsorbate ion sorbed onto the packed column, qtotal, and the dynamic biosorption capacity, qe [21, 23]. qtotal = QA 1000 = Q 1000 ∫ t=total t=0 Cadsdt (1) qe = qtotal M (2) where ttotal, Q, A, M, and Cads stand for, respec- tively, the total flow period for the column to grasp exhaustion, volumetric flow rate, the area under the breakthrough curve, the quantity of biosorbent (g), the difference in the initial and the effluent adsor- bate concentration. Equation 3 may be used to de- termine the mass transfer zone [26]: MTZ = Z tE − tB tE (3) Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 54 where Z denotes the bed height (cm); tB denotes the breakthrough time, and tE is the exhaustion duration. 2.6 Breakthrough curve modeling The breakthrough curve must be predicted to de- sign and optimize the column for the biosorption process. For this, three mathematical models: the Yoon-Nelson model, and the Bed depth service time (BDST) model, were employed in this work to examine the dynamic biosorption efficiency of PP@TiO2. The column experiment conditions are presented in Table 1. 2.6.1 Thomas model This model assumes that mass transfer at the in- terface rather than chemical interactions restricts adsorption, and experimental results exhibit Lang- muir isotherms and second-order kinetics [27]. This is appropriate for illustrating the entire break- through curve [26]. The following Equation 4 may be used to express the Thomas model in linear form [21,28]: ln ( Ci Ce − 1 ) = kTh q0M Q = kThCit (4) where kTh denotes Thomas rate constant (mL/min.mg), qe represents the equilibrium biosorption capacity (mg/g), M represents the mass of biosorbent (g), and Q denotes the feed rate (mL/min). The linear plot of ln (Ci/Ce– 1) vs t allowed for the evaluation of the values kTh and qe. Scheme 1: Flowsheet detailing the synthesis of PP@TiO2 from biomass derived from pomegranate peels. Figure 1: Schematic diagram for the column experiments (Figure adapted with permission from Biswas et al. 2008 [25], Copyright, Elsevier). Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 55 2.6.2 Yoon-Nelson model This model presumes that the probability of biosorption for each adsorbate ion decreases at a rate that is proportionate to its probability of both biosorption and breakthrough of biosorbent from the biosorbent [29]. In the later phases of the breakthrough curve, the Yoon-Nelson model, like the Thomas model, may reduce the shortcomings of the Adams-Bohart model. The Yoon-Nelson model’s linear expression is presented by the fol- lowing Equation 5 [23,30]: ln ( Ce Ci − Ce ) = kY N − τkY N (5) where τ denotes the amount of time required for a 50% adsorbate breakthrough in minutes and kY N denotes the Yoon-Nelson rate constant (min−1). 2.6.3 Bed depth service time (BDST) model This model predicts that bed depth and service time will be linearly related for a certain breakthrough concentration. Past studies state that this model does a good job of explaining the first 10 to 50% of the breakthrough curve [7]. Equation 6 provides the linear expression of the BDST model [21]: tB = N0Z CiU0 − 1 kbCi ln ( Ci Cb − 1 ) (6) where N0 is the column adsorption capacity (mg/L), tB denotes the service period of column (in hours), Cb denotes the outflow concentration at breakthrough point (mg/L), and kb denotes the rate constant [L/(mg.h)]. The BDST parameters, N0, and kb, are computed from the time versus bed depth graphs. 2.6.4 Adams Bohrt model This model works better in situations where the ef- fluent concentration is lower. Adams-Bohrts model predicts that the biosorption rate is proportional to the biosorbent concentration and the residual ca- pacity of the solids. In the linear form, it can be written as [31], ln ( ct ci ) = kABCi ×−N0 U0 kABZ (7) where Ci is the initial concentration (mg/L) of As(V); Ct is the concentration (mmol/L) of As(V) at time t; kAB is the Bohart–Adams model rate constant (L/min); N0 is the column saturation con- centration (mg/L); Z is the height of the bed (cm) in a column. 3 Results and Discussion 3.1 Characterization As a continuation of our previous study, the ad- sorbents used in this study were thoroughly char- Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 56 acterized in previous research [4]. Their relevant characteristics are briefly mentioned here. 3.1.1 EDX spectra adsorbent before and af- ter As(III) adsorption The EDX spectra of PP, PP@TiO2, and As(III) ad- sorbed PP@TiO2 are revealed in Figure 2(a), Fig- ure 2(b), and Figure 2(c), correspondingly. The EDX spectra showed peaks associated with Ti after TiO2 impregnation (PP@TiO2), indicating that the biomass had been effectively impregnated with TiO2. the EDX spectra of As(III) absorbed PP@TiO2 showed an additional peak associated with arsenic. This demonstrated that arsenic had been effectively adsorbable from an aqueous solu- tion by PP@TiO2. 3.1.2 XRD pattern of biosorbent Figure 3 displays the XRD pattern of PP and PP@TiO2. The absence of sharp peaks in the PP signals its amorphous nature. The diffraction peaks associated with crystalline TiO2 in the case of PP@TiO2 emerge at 2θ = 25.2◦, 38.1◦, 48◦, 55◦, 63◦, 70◦, and 75◦. The (1 0 1), (0 0 4), (2 0 0), (1 0 5), (2 0 4), (2 2 0), and (2 1 5) planes, respectively, are represented by these peaks. This demonstrates that TiO2 was successfully embedded in biomass in the form of crystalline anatase. These peaks demonstrate excellent agreement with JCPDS card No. 00-021-1272 of the TiO2 anatase phase. 3.1.3 Functional group analysis FTIR spectra were used to confirm the functional groups on the biosorbents, as given in Figure 4. In the FTIR spectra of PP@TiO2, the Ti-O vibration, which represents the contact of TiO2 with PP, is at- tributed to absorption peaks between 420 and 700 cm-1. This offers convincing proof of TiO2 impreg- nation with the creation of the Ti-O-C bond. In the FTIR spectra of As(III) adsorbed PP@TiO2, an ex- tra peak related to As-O vibrations was discovered at 825 cm-1 following arsenic biosorption. This sup- ports the biosorption of As(III) onto PP@TiO2. 3.2 Effect of the flow rate The impact of the flow rate of the inflowing solu- tion on the As(III) biosorption by PP@TiO2 was investigated at different flow rates (72, 150, and 240 mL/h) and a fixed bed height (4.1 cm), and prelimi- nary As(III) concentration (10.0 mg/L). The break- through data presented in Figure 5(a) indicates that a lesser time is sufficient for column breakthrough, whereas its value is increased with the decrease of flow rate. This is attributed to the possibility of channeling and short contact between the As(III) ions and active sites of PP@TiO2 bed at a higher flow rate. Moreover, the treated bed volumes are determined to be 297.1, 327.7, and 349.5 at flow rates of 72, 150, and 240 mL/h, respectively. An- other reason for an earlier breakthrough might be because a greater volume of solution containing a greater number of As(III) ions passed across the bed at a higher flow rate. Consequently, more As(III) ions became in contact with the adsorbent sites of PP@TiO2, making them get saturated more rapidly. Likewise, a higher biosorption capacity is achieved at a lower flow rate as expected. As the flow rate increased, the amount of As(III) passed through the PP@TiO2 bed containing a fixed num- ber of active sites increased; however, the contact time between the adsorbate and adsorbent poten- tially reduced, which increases the possibility of channeling. Because of this, the limited number of active sites are colloids or adsorbed with As(III) ion resulting in a decrease in column adsorption ca- pacity. The longer contact time and lesser channel- ing led to more efficient adsorption of As(III) onto PP@TiO2, and thus, a higher biosorption capacity was attained at a lower flow rate. Figure 2: EDX spectra of (a) PP, (b) PP@TiO2 and (c) As(III) adsorbed PP@TiO2. Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 57 Figure 3: XRD spectra of PP and PP@TiO2. Figure 4: FTIR spectra of PP, PP@TiO2 and As(III) adsorbed PP@TiO2. 3.3 Effect of initial As(III) concentration The breakthrough curves at varying initial As(III) concentrations for a flow rate of 150 mL/h and a bed height of 4.1 cm are shown in Figure 6(a). The treated bed volumes were 436.9, 327.7, and 145.63 for initial As(III) concentrations of 5.0, 10.0, and 15.0 mg/L, respectively. The figure clearly shows that with increasing initial As(III) concentra- tion, the biosorption approached saturation more quickly, and the breakthrough time was decreased. A similar trend was reported by Paudyal et al., 2013 [21]. The decrease in breakthrough time at higher concentrations is due to the contact of a large num- ber of As(III) at high concentrations of arsenic with active sites in the PP@TiO2 bed. The biosorption capacity of PP@TiO2 for As(III) also increased (2.31 to 3.14 mg/g) with increasing initial As(III) concentration, which can probably be attributed to the higher concentration offering more driving force for the transfer process. 3.4 Effect of bed height Figure 7(a) depicted the impact of bed height on the breakthrough curves regarding As(III) biosorp- tion onto the PP@TiO2. The treated bed volumes of As(III) solution increased from 515.5 to 625.0, and 687.8 with an increasing bed height of 1.4, 2.6, and 4.1 cm, respectively, which may relate to an extended contact duration. The evaluated As(III) removal capacity of PP@TiO2 for the bed heights of 4.1, 2.6, and 1.4 cm are presented in Table 2. 3.5 Modeling of the breakthrough curve This study explores the dynamic biosorption behav- ior of PP@TiO2 using the Thomas, Yoon-Nelson, and Adams-Bohart models. Figures 5(b), 5(c), and 5(d), respectively, show the modeling curve of Thomas, Yoon-Nelson, and Adams-Bohart models at different flow rates. Figures 6(b), 6(c), and 6(d), respectively, show the modeling curve of Thomas, Yoon-Nelson, and Adams-Bohart models at differ- ent As(III) concentrations. Similarly, Figures 7(b), 7(c), and 7(d), respectively, show the modeling curve of Thomas, Yoon-Nelson, and Adams-Bohart models at different bed heights. Table 2 shows the results or dynamic parameters obtained for the ad- sorption of As(III) onto the column of PP@TiO2 using various models. The entire breakthrough curve may be analyzed using the Thomas model. This model presupposes Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 58 that mass transfer at the interface limits biosorp- tion and that the data follows second-order kinetics and Langmuir isotherms. The dynamic biosorption capacity rose from 3.81 to 2.86 mg/g as the flow rate increased from 72 to 240 mL/h, whereas the Thomas rate constant increased from 0.127 to 0.521 L/mg·h. In a packed bed system, homogeneous con- tact decreases due to channeling, which causes q0 to decline at greater flow rates. The Thomas rate con- stant increased from 0.1992 to 0.2567 L/mg·h, and the biosorption capacity increased from 2.31 to 3.13 mg/g as the initial As(III) concentration rose from 5.0 to 15.0 mg/L. The higher concentration gradi- ent provided a greater driving force, and a higher amount of mass transfer occurred through the ad- sorbent bed, causing better biosorption capacity at the higher concentration of As(III). Similar results were observed by Paudyal et al., 2013, in the case of fluoride adsorption using Zr(IV) modified dried orange juice residue [21]. In the case of the Yoon-Nelson model, it was discovered that, with an increase in flow rate from 72 to 240 mL/h, kY N rose from 1.271 to 2.961 h-1, and τ reduced from 4.54 to 1.04 h, respectively. The kY N values fall from 1.194 to 0.047 h-1 when the bed height rises from 1.4 to 4.1 cm, while τ increases from 89.3 to 165.1 h. An increase in the kY N val- ues from 0.996 to 5.472 h-1 and a drop in τ from 4.63 to 1.04 h was brought on by a change in the initial As(III) concentration from 5 to 15 mg/L. The Adams-Bohart model, which is used to eval- uate the initial part of the breakthrough curve (Ct/C0 = 0 to 0.5), assumes that equilibrium is not instantaneous. When the initial As(III) concentra- tion was increased from 5 to 10 mg/L, the kinetic constant kAB reduced from 0.175 to 0.042 L/mg·h, but it rose when the flow rate was increased from 72 to 240 mL/h from 0.063 to 0.181 L/mg·h. Addition- ally, a rise in bed height from 1.4 to 4.1 cm caused kAB to fall from 0.0725 to 0.0552 L/mg·h. With an increase in flow rate from 72 to 240 mL/h, the column saturation concentration of the adsorbent (N0) decreased from 2532 to 2195 mg/L, respec- tively. When the initial As(III) concentration was increased from 5 to 15 mg/L, N0 values increased from 1.63 to 7.91 mg/L, respectively. Figure 5: Biosorption of As(III) onto PP@TiO2 in fixed bed system at different flow rates (a) break- through profile, and modeling using (b) Thomas, (c) Yoon Nelson, and (d) Adams-Bohrats models. Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 59 Figure 6: Biosorption of As(III) onto PP@TiO2 in fixed bed system at different concentrations (a) breakthrough profile, and modeling using (b) Thomas, (c) Yoon Nelson, and (d) Adams-Bohrats models. Figure 7: Biosorption of As(III) onto PP@TiO2 in fixed bed system at different bed height (a) break- through profile, and modeling using (b) Thomas, (c) Yoon Nelson, and (d) Adams-Bohrats models. Bhoj Raj Poudel et al./ BIBECHANA 21 (2024) 51-62 60 4 Conclusion The results of this research show that the novel adsorbent, PP@TiO2, is successful in removing As(III) from water utilizing a fixed bed column. The flow rate, initial As (III) concentration, and bed depth all had an impact on how well As (III) could be absorbed via a fixed-bed column. It was discovered that the design parameters may be accu- rately predicted using the Thomas and the BDST model. To remove As (III) from wastewater, it is anticipated that the PP@TiO2 will be a good choice. Acknowledgments The first author (B.R. Poudel) acknowledges fund- ing support from Ph.D. Research Grant (Grant No. 2078), Office of the Rector, Research Directorate, Tribhuvan University, Kathmandu, Nepal. References [1] B.R. Poudel, R.L. Aryal, S.K. Gautam, K.N. Ghimire, H. Paudyal, and M.R. Pokhrel. Effective remediation of arsenate from con- taminated water by zirconium modified pomegranate peel as an anion exchanger. J. Environ. 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Soc., 42:523–544, 1920. https://doi.org/10.1016/j.jhazmat.2007.11.030 https://doi.org/10.1016/j.jhazmat.2007.11.030 https://doi.org/10.1080/15298668491400197 https://doi.org/10.1080/15298668491400197 Introduction Materials and Methods Chemicals and instruments Synthesis of the adsorbent (PP@TiO2) Characterization Dynamic adsorption test in fix bed column Analysis of breakthrough curve parameters Breakthrough curve modeling Thomas model Yoon-Nelson model Bed depth service time (BDST) model Adams Bohrt model Results and Discussion Characterization EDX spectra adsorbent before and after As(III) adsorption XRD pattern of biosorbent Functional group analysis Effect of the flow rate Effect of initial As(III) concentration Effect of bed height Modeling of the breakthrough curve Conclusion