Modeling of the As (III) adsorption using Fe impregnated polyethylene terephthalate char matrix: A statistical approach

Arsenic (As) has become a major environmental pollutant across the globe. The most common inorganic arsenic sources in the aquatic system include erosion of arsenic laden minerals, industrial effluent, weed control, and drugs (Singh et al. 2022). In water bodies, inorganic arsenic is extensively dissolved and mobilised, and its behaviour is influenced by redox state and pH (Sawood & Gupta 2018). As (III) and arsenate As (V) are the two most common inorganic arsenic forms present in groundwater and industrial effluent. It has a pH range of neutral to slightly acidic or alkaline (Weerasundara et al. 2021). It's been proven that arsenic species are hazardous, with arsenite having a greater negative impact on human health than arsenate (Lee et al. 2022). Arsenic groundwater pollution is a potential issue around the globe due to its high prospective toxicity and carcinogen nature. As a result, the World Health Organisation establishes a maximum permissible arsenic concentration of 10 ppb for potable water as a normative standard (Dettori et al. 2022).

Purification/desalination of potable aquatic system, and facilities related to treatment of waste water, particularly in poor nations, must be modified immediately to address this complex situation (Palansooriya et al. 2020). Highly effective and economic techniques for mitigating arsenic from aquatic aquifers are highly suggested for this application. Numerous methods for mitigation arsenic from the aquatic system have been developed over the last few decades, comprising oxidation, coagulation/flocculation, filtration, precipitation, and adsorption, ion exchange, and membrane separation (Pal et al. 2021). Because of its lower cost, ease of operation, and low disposal of wastes, adsorption is one of the most effective methods among them (Sawood & Gupta 2020a). Despite several shortcomings such as hydrophilic nature and blocking of pore, carbon based adsorbents have been considered a suitable strong material owing to their relatively low cost.

Due to the obvious constraints of the existing adsorbents, it was suggested that plastic generated chars can be used for contemporaneous arsenic adsorption. Amongst all the other solid wastes produced, plastics have proven to be a devastating concern to control and eradicate from the ecosystem (Foglia 2021). The expanding consumption of plastics has resulted in an annual increase in waste generation. Considering the fact that a large portion of plastic trash is repurposed and energy is recovered, some of it will undoubtedly be buried in landfills, polluting aquifers and emitting ozone-depleting chemicals into the air. As a result, it is not an adequate method for dealing with these contaminants (Hopewell et al. 2009). Furthermore, the scarcity of available land for dumping plastic garbage, as well as its harmful repercussions, necessitates the development of an additional strategy for managing waste material. As a result, the current study proposed an efficient approach for controlling and redirecting them from landfills for use as superior arsenic sorption. An approach for recycling polyethylene terephthalate (PET) trash by processing it into cross-linked hydrogels by PET aminolysis has been suggested. When evaluated as sorbent materials for an anionic dye like Congo Red, PET-derived hydrogels rich in amino groups and aromatic compounds were reported to have a 500 mg/g dye uptake capacity (Chan & Zinchenko 2021). The extraction of EBT dye from secondary effluent has been made possible by the development of metal-organic framework adsorbent from recycled PET bottles. The developed PET-derived MOF is characterized as a credible, added-value substance that serves as a long-lasting adsorbent for organic dyes observed in wastewater (Bool et al. 2022). In terms of uptake rate and capacity, the PET-based activated carbon display superior adsorption behaviour toward p-nitrophenol compared to commercial activated carbon (Mendoza-Carrasco et al. 2016).

The majority of the published research on arsenic removal by adsorption focuses on arsenate with only a few investigations on arsenite mitigation in fixed bed columns (Maia et al. 2021; Razzak et al. 2021). Researchers developed a variety of adsorbents for the removal of arsenite from aqueous system, with varying degrees of effectiveness. Arsenite mitigation by coconut husk char (Yeo et al. 2021), manganese oxide/sand matrix (Bajpai & Chaudhuri 1999), basic C₃O₉Y₂ (Wasay et al. 1996), and granules of TiO₂ (Bang et al. 2005). Metal oxides, such as ferric ox-hydroxide, Al₂O₃, and MnO_x, have proven to have excellent arsenic uptake capacities on their respective surfaces. Because of their higher affinity for inorganic arsenic compounds, ferric ox-hydroxide is extensively used as adsorbent materials. Adsorption of arsenate and arsenite by ferric ox-hydroxide has been reported (Goldberg 2002). They observed that these materials are more effective in mitigating arsenite then adsorbing arsenate in natural aquatic system. Manganese oxides have gained wide acceptance as oxidizers for the adsorption of arsenite (Islam et al. 2018). Mn (4+) is converted to Mn (3+), which is then reduced to Mn (2+) in the entire oxidation-reduction reaction process. Natural Fe₂O₃ as well as Fe-abundunt soil (lateritic) have been found to be effective in adsorbing As (III) from aqueous solution. These minerals are available in large quantities and considerably cheaper (Aredes et al. 2013). The uptake of aqueous arsenic was enhanced in Fe-loaded biochar (q_max = 2,160 μg/g), which was prepared by stirring a solution of Fe salt with biochar. The resulting matrix, on the other hand, has very little magnetization, making it difficult to collect after sorption for regeneration (He et al. 2018). Nevertheless, the majority of As (III) removal investigations have been carried in batch mode, but every adsorbent has advantages and disadvantages with respect of reuse and regeneration, material strength, uptake capacity, pressure drop through fixed bed runs, and so on. Batch reactors are simple to use in a lab setup, but they are impractical in the field operations. Furthermore, because precise scale-up results for column arrangements cannot be acquired from batch findings, the adsorbent's real time applicability should be determined in fixed bed column experiments. There are distinct advantages of adsorption on columns operations. It's easy to use, produces large yields, and can simply be scaled up from a lab setting.

The current research focused on the utilisation of plastic carbon based, Fe loaded matrix as a cost-effective material for As (III) removal, as determined by mild carbonization of plastic trash. Even in extremely small concentration, it is reported to have devastating effects on the human health and environment. Although a variety of adsorbents have been employed to reduce As (III) from polluted areas, the application of chars derived from such waste plastics (PET) is a novel subject of concern. Because of As (III) affinity for Fe, a complex material (Fe₂O₃-imprignated PET char (PETC-FE)) was employed as an adsorbent to investigate its column effectiveness for As(III) removal under up flow circumstances in a column mode after encouraging findings in batch experiments. Breakthrough experiments were conducted to determine the influence of process parameters (influent flow rate, bed height, and inlet concentration) on the breakthrough curve. The adsorption tests were quantitatively modelled employing central composite design (CCD) for sensitivity studies and optimization capacities. Individuals and the cumulative influence of various variables influencing the adsorption mechanism have been studied using the model. Various kinetic models, such as BDST model, Thomas model, and Adam-Bohrat model, were used to simulate the dynamics characteristics of the adsorption. Furthermore, this would be a major boon because it would address both the challenges of waste disposal and heavy metal adsorption utilising a previously existent resource. These can be efficiently used on practical application for the treatment of other heavy metals based on the conclusions gained from examining the prevalent processes and sorption performance of the PET char.

A comprehensive study has been conducted to find an affordable, simple, and environmentally sustainable resolution to disastrous As (III) contamination that may be adopted in poor countries. The findings of this research are presented and discussed.

All the used chemicals were of reagent grade. The sample containers and glassware were cleansed with a soap solution followed by washing using tap water. They were soaked for a minimum 24 h in 15% HNO₃, and then rinsed thrice with deionised water. The aqueous solutions used were prepared with distilled water. As (III) stock solutions were prepared with As₂O₃ (arsenic trioxide) and NaOH, both procured from Merck. 1.3 g of arsenic trioxide As₂O₃ was dissolved in a minimum amount of 20% sodium hydroxide to make As (III) standard solution. It was then diluted to 1 L after being neutralised with HNO₃ (Giri 2019).

Waste PET (polyethylene terephthalate) has been used as a precursor to synthesise plastic chars. Pyrolysis has been done to transform the materials into plastic char after they were prepared with feedstock. Pyrolysis was conducted in a lab scale arrangement consisting of specimen holder, condenser, fluid collectors, muffle furnace, flow meter. In every test run, sample (100 g) was loaded in the reaction zone of muffle furnace and heated to obtain the char. Pyrolysis was carried out under inert conditions at temperatures of 500, 600, and 700 °C, with an 10 °C/min heating rate. Subsequently, the synthesised chars were sieved to get material having particle size in 400–800 μm range. In an Erlenmeyer flask, 10 g PETC was stirred with 1 L 0.1 M FeCl₂ procured from Merck India, with adjusted pH between 3 and 5 (Asghar et al. 2015). The flask was then stirred for a day at 25 °C temperature in a shaker at 70 rpm. The extract was then filtered and the remnant was rinsed numerous times with double distilled water to remove any metal salts and residual precipitates attached to the exterior surface of the PETC-Fe material.

The characterization of synthesized PETC-Fe was done for analysing decomposition characteristics, ultimate and proximate analysis, etc. A thermo-gravimetric analyser (STA 8000 Perkin Elmer) was used to perform thermogravimetric analysis (TGA) on the prepared adsorbent. The CHN/O elemental analyser was being used to examine the elemental compositions. The structural properties of PETC-Fe were carried out using D8 Focus X-Ray diffraction (XRD) equipment. Energy dispersive X-ray diffraction (EDX) coupled scanning electron microscopy (SEM) (Carl Zeiss model-EVO-50) was used to examine the morphology PETC-Fe. A Stenner-85 peristaltic pump was employed to provide a constant supply of As (III) solution to the fixed bed column. An inductively coupled plasma mass spectrometry (ICPMS) (Agilent-7900) was used to measure As (III) from the test solution, and a pH meter was used to measure the pH of the solution (model Hanna-HI2020). MEXA-6000FT model Fourier transform infrared (FTIR) were used to investigate the functional groups present in the adsorbent.

Fixed-bed column experiments were performed utilizing 2 cm internal diameter and 60 cm long borosilicate glass columns. For the purpose of produce varying bed heights, the column was loaded with various amounts of PETC-Fe within two fixed glass wool (0.5 cm) layers. The supporting layers of glass wool were provided to avoid the floatage of PETC-Fe. Using central composite design in response surface methodology (RSM), a column analysis was carried to investigate the impacts of operational variables (influent As (III) concentration, bed height, and influent flow rate on the response function (breakthrough time) of As (III) adsorption. As (III) solutions with the required influent concentration were regulated to the appropriate pH for maximum As (III) uptake and injected into the column in down-flow mode at the specified flow rate through the bed. The flow rates were selected to provide enough runoff per 45 min to allow for realistic analysis of arsenic levels. When the effluent As (III) concentration surpassed 99% of the influent concentration, the column's operation was halted. All tests were carried out in triplicate at room temperature with a 3% experimental error limit and mean results reported.

BDST, Thomas, and Adams–Bohart are mathematical models used to examine the breakthrough curves, in the present study.

RSM is a technique which can be used to model experimental procedures. This process relies on quantitative regression model, which includes finding an optimum model to minimize remaining variances. RSM was originally designed to identify the optimal process variables in the process industry, but it is today employed in a wide range of domains and applications, including not just physical sciences and engineering, but also physiological, medical, and sociology fields (Dixit & Yadav 2019). Presently, a quantitative optimization model has been designed using the RSM based on the (Design-Expert 6.0.8 software) for investigating the combined impact of the process variables (influent As (III) concentration, bed height and flow rate) on the response function (breakthrough time). The statistical model is generated using the CCD programme in terms of uptake of As (III), as it uses the smallest number of samples necessary to optimise the observed process parameters while obtaining the highest breakthrough time.

TGA was used to examine the decomposition characteristics of the raw PET. The ultimate, proximate, and Brunauer-Emmett-Teller (BET) analyses of the synthesised PET chars were also used for detailed characterization (Table 1). Furthermore, the dominant processes and the adsorption effectiveness of the investigated PETC-Fe were investigated. In the following sections, the outcomes of all of these have been reported and discussed.

The moisture content of the PET sample was reported as 1.98 in proximate analysis. Pyrolysis of PET yielded 85.6% volatile matter, suggesting that solid compounds such as ash content and fixed are formed less frequently. The specimen had no N but high C content, according to elemental analyses. PETC had fewer amounts of fixed carbon, 9.31%, but the high amount of C, 71.4%, indicating that while the degree of char produced will be smaller, but the quality will be superior due to the high C content.

RSM has become extremely prevalent for designing, enhancing, and optimising complicated processes, as well as determining the significance of different operating variables (Mondal et al. 2019). For a set of three independent variables, namely, influent As (III) concentration, inlet flow rate, and bed depth, the most efficient CCD in RSM was employed to study their effects on the breakthrough time for fixed-bed continuous of As (III) by PETC-Fe. Table 2 lists the variables' testing limits, as well as their notation and unit in central composite design.

With the help of the Design Expert (6.0.8), a 2³ complete factorial central composite design was obtained. A total of 20 tests in duplicate were used to the central composite design matrix according to this approach, as shown in Table 3.

Analysis of variance (ANOVA) can be used to further examine the model's feasibility and validity (Tables 4 & 5). F-value (Fisher variation ratio), lack of fit, p-value (probability value), adequate precision (AP), R²_d (coefficient of determination), R²_Adj (adjusted coefficient of determination), R²_Pred, were some of the evidences. AP corresponds to signal to noise ratio, which predicts contrasts, the range of expected values at nodes to the prediction error. Model selectivity is adequate when the ratios are higher than four (Zhou et al. 2011; Mishra et al. 2021; Sawood et al. 2021b).

The contour plot of Figure 7 shows the synergetic impact of inlet As (III) concentration and influent flow rate on response (breakthrough time). Within the range of observations, the breakthrough time reduces as the inlet As (III) concentration and influent flow rate rise. This can be elucidated by the fact that PETC-Fe have a finite number of active sites that will be occupied by the adsorbate at a particular concentration (Roy et al. 2017; Zhu et al. 2018). The active sites on the surface of the adsorbent are now more promptly saturated as the influent As (III) concentration rises, reducing breakthrough time. Uptake capacity was also decreased at increased flow rates due to secant As (III) residence time in the fixed bed and solute diffusion into the adsorbent's pores. As a result of leaving the bed before equilibrium has been established, a lesser breakthrough time is obtained (Mohan et al. 2017; Sawood & Gupta 2020b).

The interaction impact of influent As (III) concentration and bed height on breakthrough time is displayed in Figure 8. The breakthrough time reduces with increasing influent As (III) concentration and escalates with increasing bed height within the given range of experiment. The obtained pattern could be explained by the fact that enhancement in bed height generates a higher number of available active adsorption sites resulted from enhanced surface area of adsorbent. As a result, the adsorbate has sufficient time to interact with the adsorbent, giving in a prolonged breakthrough (Patel 2020; Yassin et al. 2021).

The synergetic impact of bed height and influent flow rate on the response function for fixed bed column studies of As (III) uptake by Fe–SCC is shown in Figure 9. The breakthrough time is greatly influenced by the interaction between bed height and influent flow rate. The breakthrough time reduces as the influent flow rate increases, but it increases as the bed height increases. As previously stated, such a breakthrough trend can be justified by inadequate residence time of adsorbate in the fixed bed column as well as the presence of active sites.

Thus, contour plots demonstrate that the response function (breakthrough time), increases when concentration of adsorbate reduces, bed height increases, and influent flow rate reduces. At 9.0 cm bed height, 3 mL/min influent flow rate, and 300.0 μg/L adsorbate concentration, the absorption of 1,892 μg/g for arsenic was recorded at the maximum breakthrough time.

According to a survey of the literature, only little works on fixed bed column analysis have been documented; otherwise, the majority of As (III) mitigation research has been undertaken in batch mode. The results collected under batch operations, on the other hand, are often not suitable in real treatment operations, because the residence duration is insufficient to achieve equilibrium. As a result, equilibrium investigations utilising columns are required. Column reactors have a higher equilibrium absorption capacity of As (III), making the adsorbent appealing for As (III) mitigation filter system. Hence, the adsorption capacity of PETC-Fe under fixed bed column operation is contrasted to that of earlier reported different adsorbents (Table 6). Even though it is hard to accurately correlate the PETC-Fe to other adsorbents due to the diverse test conditions used, still the As (III) absorption capacity of PETC-Fe is found to be relatively considerable and comparable to other adsorbents used for As (III) removal in column operations. The findings suggest that PETC-Fe may be efficiently utilised in a continuous fixed bed operations to mitigate As (III) from aquatic environment.

When the independent variables were kept at 10.20 cm bed height, 1,500.0 μg/L of influent As (III) concentrations, and 3.0 mL/min of influent flow rate at a highest desirability point of 0.998, the top maximum response factor (breakthrough time) was found to be 480.58 min.

Finally, identical validation column experiments were carried out under ideal conditions for their confirmation. The experimental test data, which is consistent with the optimised RSM-central composite design result, implies that PETC-Fe could be an effective and cost-effective adsorbent for As (III) mitigation in the aqueous environment.

The optimal parameters for PETC-Fe to adsorb the arsenic were determined and are shown in Table 7.

Adams–Bohart and Thomas models were utilised to develop a kinetic model in the column and determine the breakthrough curves to explain the fixed-bed column behaviour.

The Adams–Bohart model for adsorption of As (III) was used to describe the initial section of the breakthrough curve using experimental. The respective values of k_AB (kinetic constant) and N_o (maximum uptake capacity) were computed using linear regression analysis on all breakthrough curves and are reported in Table 8. At any flow rates, there is no significant difference in the values of the k_AB and the N_o. As the bed depth deepens, the values of k_AB and N_o reduce. Furthermore, as the initial As (III) concentration rises, the value of k_AB falls and N_o rises. The model is described well by theory of surface reaction and posits that equilibrium attainment is not quick; as a result, the rate of uptake is in good accordance with both, the adsorbent's residual capacity and the concentration. Furthermore, the Adams–Bohart model is used in lesser concentration areas and where mass transfer limits the rate of uptake (López-Cervantes et al. 2018). According to Table 8, the actual breakthrough curves are not near to those expected by Adams–Bohart model, and the coefficient of regression (R²) values are low. As a result, in the range of utilised operating conditions and with present adsorbent, the model cannot be used to predict the experimental findings.

The two unknown variables of the Thomas equation, constant of Thomas model and equilibrium uptake capacity, were determined using the plot ln (Co/Ci 1) against t (linear system form of the Thomas model). Table 3 shows the calculated values of the various parameters of the model, as well as the R². According to Table 9, the values of uptake capacity grow as the inlet As (III) concentration and bed height rise, while the values of constant of Thomas model fall, whereas the values of q_o and k_Th grew when the flow rate increased. Despite the fact that the Thomas model shows certain reasonable modifications for removal conditions, there is no clear connection in the breaking curve prediction. This can be seen in the disparities among experimental results and model-calculated uptake capacity estimates. Although the Thomas model is among the most frequently used to define the behaviour of biosorption process in column operations, its primary constraint is that it is rooted on second-order kinetics and assumes that biosorption is controlled by interfacial mass transfer rather than the chemical reaction. When this approach is employed to represent biosorption systems under specific circumstances, this mismatch can lead to inaccuracies (López-Cervantes et al. 2018).

Plotting the service time t against bed height at Ci/Co equal to 0.15, 0.30, and 0.45 yielded the uptake capacity and rate constant from the BDST model. The tested and expected service times for different bed heights (15, 10, and 6 cm) were estimated using a influent flow rate of 3 mL/min and an influent concentration of 500 μg/L. Table 10 presents the results. The BDST model's projected service time values are consistent with the test service time values. As a result, the BDST model may be suitable for this configuration; also, the significant R² values derived from data support the BDST model's applicability for the current fixed-bed column operations.

Fixed bed column adsorption investigations have been used to evaluate the performance of Fe impregnated chars made from pyrolysis of waste PET for As (III) removal. The impact of independent process variables (bed height, flow rate and concentration) on the breakthrough were designed by CCD. ANOVA confirmed the quadratic model proposed by RSM-CCD. The experimental results were observed to be quite close to the model's predicted values. Contour and perturbation charts were used to predict the impact of the independent variables and their interactions on response function (breakthrough time). The breakthrough time was shown to be highly influenced by the process variables. With a raise in inlet As (III) concentration, the total amount of As (III) adsorbed rose, while the removal percentage reduced. When lesser flow rates and larger bed heights were used, the PETC-Fe column's performance enhanced. Owing to the complicated adsorption mechanism, the BDST approach of breakthrough data estimations slightly differed from the test findings over breakpoint. The results from the column studies did not have a satisfactory correlation with the results from the mass transfer model. According to the optimised CCD result, the PETC-Fe adsorbent has proven to be a sustainable and highly effective adsorbent. Consequently, it can be claimed that use of statistical model is an efficient approach for optimising and design of As (III) removal process. The information reported in this research can be used to design and implement an effective As (III) removal approach even while treating any potable water contamination. For three consecutive adsorption-desorption cycles, the PETC-Fe's regeneration efficiency was observed to be 99.14, 98.71, and 99.50 percent, respectively. This means that the PETC-Fe is capable of being used repeatedly for at least three cycles. The current investigation shows that PET char based adsorbent functions well, indicating that they should be used on a bigger field ranges. As a result, PET char based adsorbent can be regarded as sustainable and novel option for As (III) removal and management of plastic waste.

The authors would like to thank PGRL, IIT Kanpur, for providing assistance with SEM, XRD, XPS, VSM, BET and FTIR analysis.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.