This study focuses on the probable use of carbonized char prepared from PET plastic bottles for heavy metals (HMs) adsorption (Cd2+, Pb2+, Cu2+, and Zn2+). The prepared adsorbent is characterized by field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared spectroscopy (FTIR). Batch adsorption experiments were conducted with the influencing of different operational conditions: contact time (1–180 min), adsorbate concentration (25–300 mg/L), adsorbent dose (0.5–6 g/L), pH (3–7), and temperature (25–60 ºC). High coefficient value [Cd2+ (R2 = 0.99), Pb2+ (R2 = 0.97), Cu2+ (R2 = 0.94), and Zn2+ (R2 = 0.98)] of process optimization model suggest that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 86.68, 73.66, 67.10, and 57.04% for Cd2+, Pb2+, Cu2+, and Zn2+ at pH (7), respectively. Furthermore, ANN and BB-RSM revealed a good association between the tested and projected values. The maximum monolayer adsorption capacity of Cd2+, Pb2+, Cu2+, and Zn2+ was 263.157, 78.740, 196.078, and 84.745 mg/g, respectively. Pseudo-second-order was the well-suited kinetics, where Langmuir and Freundlich isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows HMs adsorption is favorable, exothermic, and spontaneous.

  • Carbonized adsorbent was prepared from PET bottle waste by a simple thermal dissociation method.

  • Response surface methodology and artificial neural network express a strong correlation between the experimental and projected heavy metal removal efficiency.

  • Carbonized adsorbents reveal satisfactory results for experimental (Cd = 86.68%, Pb = 73.66%, Cu = 67.10%, and Zn = 57.04%) and real wastewater applications (Cd = 99.2%, Pb = 62.20%, Cu = 97.63%, and Zn = 64.47%).

Rapid industrialization and urbanization development (e.g. diverse domestic and industrial activities) release an enormous amount of heavy metals (HMs)–containing wastewater, which finally discharges to our aquatic and terrestrial environment (Chakraborty et al. 2021a, 2023a; Chanda et al. 2021). Environmental pollution by metals and metalloids is considered a matter of global concern among researchers due to their persistent, non-biodegradability, potentially toxic character and is a severe disease creator to all living organisms via bioaccumulation and biomagnification (Al-Malack & Basaleh 2016; Ghosh et al. 2020a; Chakraborty et al. 2022a, 2022b, 2023b). Therefore, wastewater must be free from HMs before being released into the receiving environment to maintain environmental quality and develop an eco-friendly industry (Boulaiche et al. 2019; Chakraborty et al. 2021b, 2023c). Numerous treatment methods including membrane filtration, sedimentation, oxidation, nanofiltration, photo-catalytic degradation, ozonation, chemical precipitation, biological operations, electrochemical technologies, coagulation/flocculation, solid-phase extraction, ion exchange, and adsorption have been applied for elimination of diverse pollutants (e.g. organic and inorganic) from wastewater (Ghosh et al. 2018, 2020b; Bhattacharjee et al. 2020; Zaman et al. 2022). Furthermore, several treatment methods are either costly or have several drawbacks (Zaman et al. 2022). Barakat (2011) has extensively explored the benefits and limitations of various treatment methods. Adsorption is mostly using effluent treatment techniques with aspects such as easy to operate and design, cost-effectiveness, greater performance, source availability, lower sludge production, flexibility, and insensitivity to hazardous elements (Chakraborty et al. 2020, 2021b). The usability of marketable activated carbon is reducing due to high cost (Zaman et al. 2022). In this favor, many wastes and biomaterials were used as adsorbents for both inorganic and organic pollutants, which are broadly reviewed by Bhattacharjee et al. (2020). Nowadays numerous researchers are trying to develop novel, low-cost, and ecologically viable adsorbents with high performance from waste products for the elimination of hazardous substances from wastewater. In the modern age, plastic products are extensively used in our daily activities. Polyethylene terephthalate (PET) is a widely used polymer around the world for its particular properties such as being lightweight, cost-effective, clearness, a good insulator, exceptional flexibility, and easy to handle (Djahed et al. 2016; Rahmawati et al. 2019), so a huge amount of plastic waste is generated yearly that is a pressing concern for the environment (El Essawy et al. 2017). PET is widely produced from public and industrial discarded materials that have no potential beneficiary application. Therefore, PET waste management is considered a global burden, especially in developing countries (Mallakpour & Behranvand 2016), where burning and landfilling are the commonly used disposal methods, which are highly responsible for environmental pollution by emitting toxic gaseous pollutants and polluting landfilling surrounding the ecosystem (e.g. aquatic and terrestrial) (Saha & Ghoshal 2005). Though recycling is another method for PET waste management instead of burning and landfilling, this method is not effective due to a lack of technical difficulties and lower economic return; in this viewpoint, activated carbon/char preparation (e.g. thermal and chemical activation) from PET waste would be a substitute technique as compared with other techniques (e.g. incineration, pyrolysis) (Djahed et al. 2016). Providentially, PET discarded products contain high carbon and lower impurities, which act as a new window for developing activated carbon/char and pollutants adsorbing agents (Cuerda-Correa et al. 2016). In experimental studies, process modeling and optimization are very important to improve the system performance, but conventional methods could optimize a single parameter at a time, which increases experimental time and cost (Gadekar & Ahammed 2019). Correct optimum conditions could not be attained because of the incompetence of experimental results to reflect relations among factors affecting the process (Musa et al. 2023). Recently, many researchers have paid attention to applying artificial neural networks (ANN) and response surface methodology (RSM) for optimizing and modeling the diverse experimental parameters at a time that enhances the system performance (Khan et al. 2020). Those machine learning approaches are a reliable and powerful tool that help overwhelm the system limitations and assess actual results using experimental data. Consequently, the combination of a smaller number of experiments and the hidden performances of post-process parameters could attain a more actual solution. So, modeling using the RSM and ANN approaches could assess the real associations between the input parameters and the response parameters of a process by applying experimental data (Gadekar & Ahammed 2019; Musa et al. 2023). It is a soft computing technique where required results can be achieved via alternating network weights (Gadekar & Ahammed 2019). So, it does not need any particular understanding of the physical/chemical procedure that moves the system. Nowadays, ANN- and RSM-based approaches have been used in diverse areas of environmental engineering, especially for water and wastewater treatment processes (Nair & Ahammed 2014; Agarwal et al. 2016; Karri & Sahu 2018; Musa et al. 2023), but very few studies have been conducted for HM adsorption. So, the objectives of this study were (i) to investigate the removal performance of carbonized PET plastic bottles for HM (Cd2+, Pb2+, Cu2+, and Zn2+) removal from simulated wastewater using diverse operational conditions such as pH, time of contact, HM concentration, and adsorbent dose applying machine learning statistical analysis; and (ii) to explore the adsorption mechanism using diverse models, such as isotherm, kinetic, and thermodynamic models.

Materials and reagents

In the entire experiment, every chemical and reagent was laboratory-grade, purchased from Sigma-Aldrich (Germany), namely, CuSO4.5H2O (≥98%), ZnCl2 (≥98%), PbCl2 (98%), and CdCl2 (98%). The required amount of metal salts is used to prepare the stock solution (1,000 ppm), and the stock solution's pH value was less than 2.0 using HNO3.

Adsorbent preparation from PET plastic bottle and characterization

Portable drinking water bottles were selected as PET plastic waste, collected from the local area, Jashore, Bangladesh. First, the waste bottle was washed with deionized water to remove visible impurities, cut into small sizes, and dried at 80 °C in an electric oven (Oven DSO-500D, Taiwan) until it was moisture free and cooled at ambient conditions. After that, char was produced by burning in an electric furnace at 600 °C for a 100 min retention time. Then, carbonized products were crushed, and preferred size portions (0.5–1.0 mm) were collected through a conventional sieve. Finally, they were stored in a sealed glass bottle for next experimental uses. The synthesized carbonized products were characterized using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The surface morphology of the prepared carbonized char was investigated with field emission scanning electron microscopy (FE-SEM) (Zeiss Sigma 300, Carl Zeiss, Germany) at 10 kV voltages. Before analysis, the carbonized powder was coated with gold for better imaging and to prevent native electrical charges. The surface chemistry was investigated by FTIR (Nicolet™ iS20, Thermo Scientific, USA), where the recorded spectra range varied from 400 to 4,000 cm−1 with 50 scans. The yield (%) of produced carbonized char was calculated using Equation (1):
formula
(1)

Adsorption experiments

The required quantity of metal salts was dissolved in distilled water to prepare 1,000 ppm stock solution and the retained stock solution's pH was less than 2.0 using HNO3, and then successive dilution approaches were used to prepare the preferred working solution from the stock solution. The adsorption of HMs (Cd2+, Pb2+, Cu2+, and Zn2+) onto a carbonized adsorbent was carried out in a batch mode at 200 rpm using the following operational conditions: pH (3–7), time of contact (1–180 min), temperature (25–60 °C), adsorbent dose (0.5–6 g/L), and HMs (Cd2+, Pb2+, Cu2+, and Zn2+) concentration (25–300 mg/L). For pH adjustment, 0.1 N acid (HNO3) and base (NaOH) solution were used. After a certain time, samples were taken, filtered, and acidified for metal analysis. Atomic absorption spectroscopy (AAS-7000, Shimadzu, Japan) was used to determine the concentration of HMs in raw and treated samples. A duplicate test was conducted to gather accurate results. The total HM adsorption rate and removal efficiency were estimated using Equations (2) and (3), respectively:
formula
(2)
formula
(3)
where Co is the initial HM concentration (mg/L), Ce is the equilibrium HM concentrations (mg/L), qe is the amount of HMs adsorbed (mg/g), V is the volume of liquid solution (L), and ms is the adsorbent mass (g).

Adsorption isotherm and kinetic experiments

Adsorption isotherm studies were carried out into 250 mL HM solutions of varied HM concentrations (25–300 mg/L), at pH 7, where 1 g/L carbonized char dose was added, and the solution was stirred at 200 rpm at 25 °C temperature for 90 min. While kinetic experiments were run in 300 mL HM solution at a fixed concentration (100 mg/L) and the other condition kept constant, then samples were taken out after the following time intervals 1, 5, 7, 10, 15, 20, 30, 60, 90, 120, 150, and 180 min; filtered; acidified; and analyzed. This study applied Langmuir and Freundlich isotherms for equilibrium data modeling, while pseudo-first-order and pseudo-second-order models were used for kinetic modeling (detailed in Table S1).

Error analysis

Error analysis is applied for every used model to assess the level of error as compared with obtained and experimental results. In this study, residual sum square (RSS), chi-square (χ2) tests, and root mean square error (RMSE) error analysis test (Equations (4)–(6)) were calculated to determine which adsorption isotherm and kinetic models fitted to experimental data (Chakraborty et al. 2023a). A smaller error value denotes the model that fits the data the best. Equations (4)–(6) describe the formula for determining the best-fit model:
formula
(4)
formula
(5)
formula
(6)
where qexp is the observed experimental adsorption data (mg/g) from the kinetic models, qcal is the calculated adsorption data (mg/g) from models, and n represents the number of datasets.

Desorption study

For the desorption study, metal-loaded carbonized char was attained from adsorption isotherm experiments, filtered, and dried. Finally, the experiment was conducted in distilled water with diverse pH and stirring the solution at 200 rpm for 90 min. Equation (7) was applied to calculate the outcome:
formula
(7)

Adsorption thermodynamics

Thermodynamics is a vital parameter for adsorption study, where temperature variation is needed for conducting this study. In this study, Gibbs free energy change (ΔG), enthalpy (ΔH), and entropy (ΔS) are calculated by applying Equations (8)–(10):
formula
(8)
formula
(9)
formula
(10)
where T is the temperature (K), Kd is the equilibrium constant, and R is the universal gas constant (J mol−1 K−1).

Process optimization

The Box–Behnken design (BBD) approach is an appropriate statistical tool widely used for process optimization, where the least number of experiments were applied to explore the probable association between the experimental parameters and their influences on the adsorbate adsorption (Gadekar & Ahammed 2019). This study uses a three-level three factorial BBD where three factors are defined as X1 = pH, X2 = metal concentration, and X3 = adsorbent dose, and three levels are stated as upper (1), central (0), and lower (−1) (detailed in Table S2). This model runs 17 experiments using Stat-Ease software (Design-Expert 13.0 trial version, Stat-Ease, Inc.). The following polynomial equation (Equation (11)) is used for BBD modeling:
formula
(11)
where Z is the projected response (HM adsorption (%); qe (Cd), qe (Pb), qe (Cu), qe (Zn), β0 = Constant, βi = linear coefficient. βij = interface coefficients, βii = quadratic coefficients, and Ai and Aj = process variables.

Characterization of adsorbent

The yield of carbonized char was 18%, and Djahed et al. (2016) also recovered about 19% yield of carbonized PET plastic bottles. The point of zero charge (pzc) is the pH for which the net surface charge of the adsorbent is equal to zero (Lemessa et al. 2023). This study uses salt addition methods for pzc adopted from Mahmood et al. (2011). As illustrated in Figure 1(b), the plot of pH difference ((initial pHi − final pHf) vs initial (pHi)) shows that pzc of carbonized char was obtained at pH = 2.99. According to the pzc concept, the adsorbent shows a positive attitude at solution pH < pzc value; conversely, a negative attitude is expressed at solution pH > pzc value. FTIR analysis represents the surface chemistry of the adsorbent (before and after adsorption), where diverse functional groups influence the linkage between the adsorbate and the adsorbent; results obtained are presented in Figure 1(a).
Figure 1

(a) FTIR spectra of adsorbent and (b) pH of point zero charge.

Figure 1

(a) FTIR spectra of adsorbent and (b) pH of point zero charge.

Close modal

The adsorption peak near 1,600 cm−1 matching the in-plane C = C vibration indicates the graphite properties, which are the inborn properties of sp2 graphitic ingredients (Hu et al. 2017). The raw adsorbent shows four major peaks at 3,446, 2,930, 2,363, and 1,583 cm−1 (Figure S1a), indicating the stretching vibration of OH, C–H, C–N, and C = C, respectively (El Essawy et al. 2017). Slight alterations were observed after HM adsorption (Cd2+, Pb2+, Cu2+, and Zn2+) (Figure S1a); the above-mentioned adsorption peaks can link with the HM ions, while, more specifically, the hydroxyl group (OH) highly regulates the linkage between the adsorbent and the adsorbate. A similar result was reported by Chanda et al. (2021) for Ni adsorption using chemically treated mahogany sawdust. The surface feature of carbonized char (before and after adsorption) was estimated through SEM analysis; the results attained are presented in Figure S1. The EDX spectroscopy indicates that after the adsorption process, each HM is adsorbed onto the carbonized char surface (Figure S2b–e).

Adsorption behavior

Effect of contact time

To assess the adsorption behavior and equilibrium adsorption capabilities of carbonized adsorbent for HMs (Cd2+, Pb2+, Cu2+, and Zn2+), adsorption was evaluated at diverse contact times (presented in Figure 2(a)). These experiments were evaluated using a fixed metal concentration of 100 mg/L, temperature of 25 °C, pH equal to 7, and carbonized adsorbent mass of 1 g/L. Generally, three steps were involved during the adsorption process, as illustrated in Figure 2(a). Three stages controlled the whole adsorption procedure: (1) quick adsorption was achieved at an early stage (1–10 min) due to bulk concentration of HM ions and huge vacant space on the adsorbent surface; (2) after 10–90 min, the adsorption efficiency becomes slow due to the decline of existing binding sites with time; and (3) finally, the adsorption efficiency becomes comparatively very low within 90–180 min due to blocking of almost all vacant space (outer and inner site) on the adsorbent surface (Chakraborty et al. 2023b). So, adsorption reached equilibrium in 90 min, from all metals, which was chosen as the equilibrium contact time for further experiments. Futalan et al. (2019) provide a similar explanation for the adsorption of HMs using spent coffee grounds.
Figure 2

Adsorption of HMs using carbonized char at diverse operational factors: (a) contact time, (b) pH, (c) initial HM concentration ((carbonized char dose = 1 g/L; HM concentration = 100 mg/L; optimum pH = 7), temperature = 25 °C; equilibrium contact time = 90 min)), and (d) adsorbent dose.

Figure 2

Adsorption of HMs using carbonized char at diverse operational factors: (a) contact time, (b) pH, (c) initial HM concentration ((carbonized char dose = 1 g/L; HM concentration = 100 mg/L; optimum pH = 7), temperature = 25 °C; equilibrium contact time = 90 min)), and (d) adsorbent dose.

Close modal

Effect of pH

For adsorption science, pH is considered a vital factor because it influences the metal salt ionization rate, the modification of adsorbent inner and outer binding sites, and shifting of the adsorbent surface charge (Chakraborty et al. 2023a). Figure 2(b) shows the adsorption performance of HMs using carbonized adsorbent at varied solution pH of 3–7. The removal of HM increase (Cd2+ = 64–72%; Pb2+ = 7–35%; Cu2+ = 37–64% and Zn2+ = 22–32%) with increasing solution pH (3–7) (Figure 2(b)), because the surfaces of adsorbents were deprotonating, so electrostatic attraction between the absorbate and the adsorbent also increased at an increasing pH. The adsorbent surface becomes positive in a highly acidic environment due to the influence of oxygen-linked functional groups, where high proton clouds enrich electrostatic repulsion between adsorbate and adsorbent, resulting in lower HM removal. Though high pH increases the adsorption capacity, it also assists to precipitate the HMs by influencing the –OH group present in the solution confirmed by Wang & Qin (2005), which makes the adsorption study more complex. Therefore, a pH of 7 was selected as the optimum pH to avoid the precipitation of HMs. Similar results for the adsorption of HMs onto Carnauba straw powder were reported by Pereira et al. (2020).

Effect of initial HM concentration

To investigate the performance of carbonized adsorbents for HM adsorption with diverse concentrations (25–300 mg/L), an experiment was conducted where other factors were kept constant (t = 25 °C, contact time = 90 min, pH = 7, and dose = 1 g/L). Figure 2(c) shows that with increasing HM concentration (25–300 mg/L), the elimination performance of HMs (Cd2+ = 87–63%, Pb2+ = 77–25%, Cu2+ = 72–45%, and Zn2+ = 57–23%) (Figure 2(c)) decreases due to saturating carbonized adsorbent external surface at a fixed dose, where HM ions cover the vacant space. However, the adsorption capacity of the carbonized adsorbent increased (Cd2+ = 23–190 mg/g, Pb2+ = 19–74 mg/g, Cu2+ = 18–136 mg/g, and Zn2+ = 14–69 mg/g) due to a higher interface between HM ions and adsorbent active sites with increasing HM concentration, where high driving forces led to overcome the mass transfer between liquid and solid phases (Zaman et al. 2021).

Effect of adsorbent dose

Pollutant elimination from wastewater is significantly influenced by adsorbent dosage (Chakraborty et al. 2020). To assess the suitable adsorbent dose, batch adsorption experiments of HMs onto carbonized adsorbent were conducted at 25 ± 2 °C, with different adsorbent doses (1–6 g/L) and keeping the other conditions constant (pH = 7, HM concentration = 100 mg/L, CT = 90 min). Figure 2(d) reveals that the HM removal percentage improved with rising carbonized adsorbent doses (0.5–6 g/L) for HMs (Cd2+ = 64–89%, Pb2+ = 24–67%, Cu2+ = 49–78%, and Zn2+ = 24–43%), respectively, due to lots of active exchangeable sites on the carbonized adsorbent surface, resulting in greater adsorption (Chakraborty et al. 2023a). While the adsorption capacity gradually reduces (Cd2+ = 129–15 mg/g, Pb2+ = 47–11 mg/g, Cu2+ = 49–15 mg/g, and Zn2+ = 48–7 mg/g) with increasing adsorption dose (0.5–6 g/L) might be the aggregation of HM ions onto the carbonized adsorbent. A related elucidation was given by Al-Malack & Basaleh (2016).

Kinetic models and adsorption mechanism

Adsorption kinetics is needed to design an effluent treatment plant unit, where the adsorbent could remove pollutants at a certain rate. In the present study, two commonly used kinetics models such as Lagergren's pseudo-first-order and Ho's pseudo-second-order, were applied for HM (Cd2+, Pb2+, Cu2+, and Zn2+) adsorption onto carbonized adsorbents. The model accuracy depends on the high correlation coefficient (R2) and lower error values (RSS, chi-square (χ2), and RMSE) of the model, and the applied model parameters are shown in Table 1. The pseudo-second-order kinetic model shows higher R2 and lower RSS, χ2, and RMSE values for HM adsorption as compared with pseudo-first-order kinetic; in addition, the calculated (qe,cal) value from the pseudo-second-order kinetic model also corresponds to the experimental value (qe,exp), indicating that pseudo-second-order was the best-fitted kinetic model for adsorption data. Therefore, chemical adsorption controlled the total adsorption process (i.e. electrostatic interactions between the absorbate and adsorbent), where rapid adsorption occurred at the first phase and gradually slowed at time progress due to the lack of adsorbent site that is supported by Figure 2(a); previous studies also reported a similar outcome (Chakraborty et al. 2023d; Lemessa et al. 2023).

Table 1

Kinetic parameters for HMs adsorption onto carbonized char

ModelsParametersCd2+Cu2+Pb2+Zn2+
 qe,exp (mg/g) 70.470 64.120 35.040 31.930 
Pseudo-first-order qe,cal (mg/g) 3.291 28.635 29.362 4.348 
K1 (min–10.041 0.078 0.038 0.0317 
R2 0.993 0.774 0.911 0.631 
RSS 54,845.950 13,976.130 403.754 9,076.489 
χ2 2,239.826 55.764 1.885 301.835 
RMSE 67.605 34.127 5.800 27.502 
Pseudo-second-order qe,cal (mg/g) 71.942 68.493 39.215 32.894 
K2 (g/mg/min) 0.021 0.003 0.001 0.017 
H (mg/g/min) 112.35 15.673 2.423 19.267 
R2 1.000 0.998 0.996 0.999 
RSS 81.569 227.062 27.279 154.603 
χ2 0.099 0.343 0.099 0.449 
RMSE 2.607 4.349 1.507 3.589 
IP diffusion Kdiff (mg/g/min0.50.386 2.690 2.596 0.536 
C (mg/g) 67.159 37.302 6.641 26.548 
R2 0.966 0.678 0.735 0.833 
ModelsParametersCd2+Cu2+Pb2+Zn2+
 qe,exp (mg/g) 70.470 64.120 35.040 31.930 
Pseudo-first-order qe,cal (mg/g) 3.291 28.635 29.362 4.348 
K1 (min–10.041 0.078 0.038 0.0317 
R2 0.993 0.774 0.911 0.631 
RSS 54,845.950 13,976.130 403.754 9,076.489 
χ2 2,239.826 55.764 1.885 301.835 
RMSE 67.605 34.127 5.800 27.502 
Pseudo-second-order qe,cal (mg/g) 71.942 68.493 39.215 32.894 
K2 (g/mg/min) 0.021 0.003 0.001 0.017 
H (mg/g/min) 112.35 15.673 2.423 19.267 
R2 1.000 0.998 0.996 0.999 
RSS 81.569 227.062 27.279 154.603 
χ2 0.099 0.343 0.099 0.449 
RMSE 2.607 4.349 1.507 3.589 
IP diffusion Kdiff (mg/g/min0.50.386 2.690 2.596 0.536 
C (mg/g) 67.159 37.302 6.641 26.548 
R2 0.966 0.678 0.735 0.833 

The experimental data were also evaluated by intraparticle (IP) diffusion to determine the diffusion mechanisms. The IP plot did not pass through the origin (Figure S3) and high intercept values (Table 2), representing that exterior diffusion was the rate-limiting step as interior diffusion for HM adsorption using carbonized adsorbent, which may happen at the same time. Futalan et al. (2019), Chanda et al. (2021), and Chen et al. (2022) observed related findings in their adsorption study.

Table 2

Isotherm parameters for adsorption of HMs onto carbonized char

Isotherm modelsParametersCd2+Cu2+Pb2+Zn2+
Langmuir qmax (mg/g) 263.157 196.078 78.740 84.745 
b (L/mg) 0.019 0.0147 0.025 0.014 
RL 0.148–0.677 0.184–0.730 0.115–0.610 0.187–0.734 
R2 0.884 0.991 0.927 0.923 
RSS 728.385 76.321 265.284 385.644 
χ2 1.556 0.207 1.235 1.711 
RMSE 12.069 3.906 7.284 8.782 
Freundlich KF (mg/g) (L/mg)1/n 10.480 5.722 9.819 5.033 
n 1.639 1.535 2.805 2.104 
R2 0.992 0.9773 0.952 0.953 
RSS 150.350 732.773 124.494 241.169 
χ2 0.328 1.963 0.577 1.080 
RMSE 5.483 12.105 4.989 6.945 
Isotherm modelsParametersCd2+Cu2+Pb2+Zn2+
Langmuir qmax (mg/g) 263.157 196.078 78.740 84.745 
b (L/mg) 0.019 0.0147 0.025 0.014 
RL 0.148–0.677 0.184–0.730 0.115–0.610 0.187–0.734 
R2 0.884 0.991 0.927 0.923 
RSS 728.385 76.321 265.284 385.644 
χ2 1.556 0.207 1.235 1.711 
RMSE 12.069 3.906 7.284 8.782 
Freundlich KF (mg/g) (L/mg)1/n 10.480 5.722 9.819 5.033 
n 1.639 1.535 2.805 2.104 
R2 0.992 0.9773 0.952 0.953 
RSS 150.350 732.773 124.494 241.169 
χ2 0.328 1.963 0.577 1.080 
RMSE 5.483 12.105 4.989 6.945 

Adsorption isotherm modeling

The link between an adsorbate and an adsorbent in an adsorption study is well explained by adsorption isotherms. In this present study, Langmuir and Freundlich isotherms were used, which are usually beneficial in the solid/liquid system, presented in Table 2. Langmuir isotherm was the best-fitted isotherm for Cu2+ adsorption onto a carbonized adsorbent due to its higher correlation coefficient value (R2 = 0.972), lower error (RSS = 0.689), chi-square value (χ2 = 0.044), and RMSE (0.262) as compared to the Freundlich isotherm (R2 = 0.937, RSS = 4.610; RSME = 0.679, and χ2 = 0.313) (Table 1), revealing that Cu2+ molecules produce a single continuous layer with similar dispersal to carbonized char, while Cd2+, Pb2+, and Zn2+ follow the Freundlich isotherm than Langmuir concerning correlation coefficients and error values, showing multi-layer adsorption onto carbonized adsorbents.

The monolayer maximum adsorption capacity of HMs onto carbonized adsorbent was 263.157, 78.740, 196.078, and 84.745 mg/g for Cd2+, Pb2+, Cu2+, and Zn2+, respectively (Table 2). The RL values of carbonized char were between 0 and 1, representing that HM adsorption onto carbonized adsorbents was appropriate under the studied experimental conditions. Conversely, the value of adsorption intensity (n) was higher than 1, and higher KF demonstrated that the adsorption process was promising for HM adsorption from aqueous solutions using carbonized adsorbents (Table 2). This study outcome is also in correspondence with the findings of El Essawy et al. (2017). In addition, the performance of HM adsorption using carbonized adsorbents is comparable with other adsorbents, as presented in Table S3.

Adsorption thermodynamics studies

A thermodynamic study represents the role of temperature in adsorption, the nature of the linkage between the adsorbate and the adsorbent, and the direction and mechanism of reaction with changing the experimental temperature (Chakraborty et al. 2023d). These study results are presented in Table S4 and Figure S5a. A Van't Hoff plot of ln Kd vs 1/T made a straight line (not shown in the figure) with R2 values of 0.798, 0.996, 0.931, and 0.954 for Cd2+, Pb2+, Cu2+, and Zn2+, respectively. Table S4 shows that negative values of ΔG0 demonstrate that HM adsorption processes are thermodynamically spontaneous and useful (El Essawy et al. 2017). The exothermic type of adsorption is confirmed by the negative result of ΔH0, HM ion adsorption decreases with rising temperature, and no remarkable deviations were found after 298 K (250 °C). Therefore, the next experiments were conducted at this temperature. The positive ΔS0 also suggests the decreasing chance between the carbonized adsorbent and metal ion interface, which mainly occurred through the chemical adsorption mechanism through the ion exchange process. Sahmoune (2019) detected the exothermic reaction for HM adsorption using green adsorbents.

BBD and regression model

For process optimization, the three factors including pH, initial HMs concentration, and the adsorbent dose were applied, and the studied responses are presented in Table S5 and Figures 3 and 4, respectively. The elimination of HMs ranges from 63.4 to 86.68%, 13.78 to 67.1%, 37 to 73.66%, and 2.22 to 57.04% for Cd2+, Pb2+, Cu2+, and Zn2+, respectively (Table S5). The BBD produces 3D surface plots to understand the relation between the tested variables. This design also helps identify the ideal experimental settings (Kousha et al. 2015). Figures 3 and 4 show the effect of solution pH, initial HM concentration, and adsorbent dose on HM elimination efficiency using prepared carbonized adsorbent. Due to increasing solution pH, the interaction between positive charge adsorbate and negatively charged adsorbent increased; consequently, the maximum removal was achieved at a pH of 7. A high carbonized adsorbent dose provides greater surface areas and huge exchangeable sites, resulting in greater adsorption performance achieved at the maximum adsorbent dose. The HM removal efficiency decreased with increasing HM concentration from 25 to 300 mg/L, and the highest removal was found at 25 mg/L for all HMs.
Figure 3

BBD for Cd2+ ((a) actual vs predicted plot, (b) pH, (c) adsorbent dose, and (d) initial concentration) and Pb2+ ((e) actual vs predicted plot, (f) pH, (g) adsorbent dose, and (h) initial HM concentration); color spectra show highest (red) to lowest (blue) removal efficiency.

Figure 3

BBD for Cd2+ ((a) actual vs predicted plot, (b) pH, (c) adsorbent dose, and (d) initial concentration) and Pb2+ ((e) actual vs predicted plot, (f) pH, (g) adsorbent dose, and (h) initial HM concentration); color spectra show highest (red) to lowest (blue) removal efficiency.

Close modal
Figure 4

BBD for Cu2+ ((a) actual vs predicted plot, (b) pH, (c) adsorbent dose, and (d) initial concentration) and Zn2+ ((e) actual vs predicted plot, (f) pH, (g) adsorbent dose, and (h) initial HM concentration); color spectra shows highest (red) to lowest (blue) removal efficiency.

Figure 4

BBD for Cu2+ ((a) actual vs predicted plot, (b) pH, (c) adsorbent dose, and (d) initial concentration) and Zn2+ ((e) actual vs predicted plot, (f) pH, (g) adsorbent dose, and (h) initial HM concentration); color spectra shows highest (red) to lowest (blue) removal efficiency.

Close modal
The statistical association between the nominated experimental factors and the response was explained by a quadratic model with corresponding coded factors and are best fitted using the following equations:
formula
(12)
formula
(13)
formula
(14)
formula
(15)

Analysis of variance (ANOVA) is an analytical technique that is applied to detect the validity and suitability of Fisher's, F-test, and Student's t-test models. The input effective variables (A = pH, B = HMs concentration, and C = adsorbent dose) were considered for statistical evaluation of the empirical models, presented in Tables S6–S9, respectively, where pH (A) and adsorbent dose (C) show a significant effect for HM removal from aqueous solution. The p-values of all quadratic models (Cd2+ = 0.0001, Pb2+ = 0.0001, Cu2+ = 0.0020, and Zn2+ = 0.0002) were less than 0.05, suggesting that the quadratic model fits the HM (Cd2+, Pb2+, Cu2+, and Zn2+) adsorption data on the carbonized adsorbent. Lack of fit is more significant than the pure error in terms of the p-value (p < 0.0001), revealing that the design model precisely explains the data performance for the experiments and the factors examined have very significant effects on removal efficiency (Chakraborty et al. 2023d). In addition, A (pH), C (adsorbent dose), AC (pH–adsorbent dose), BC (HM concentration–adsorbent dose), A2 (pH), and B2 (HM concentration) are significant model (p < 0.05) terms for HM adsorption using carbonized adsorbents. Moreover, the R2 value of the HM adsorption quadratic model (Cd2+ = 0.99, Pb2+ = 0.97, Cu2+ = 0.94, and Zn2+ = 0.98) showed that 99, 97, 94, and 98% of the whole variability of the result was explained by this model. A good agreement was attained between experimental (R2) and projected (R2adj) results for all HMs (Figures 3 and 4), showing that the model is a good fit, where most of the data point near the straight line. On the other hand, the F-values of Cd2+, Pb2+, Cu2+, and Zn2+ were 118.05, 39.47, 11.42, and, 25.50, respectively, suggesting that the models are significant. There is only a 0.01, 0.01, 0.20, and 0.02% chance of creating noise for Cd2+, Pb2+, Cu2+, and Zn2+ adsorption onto carbonized adsorbent due to higher F-value, respectively. The signal-to-noise ratio for Cd2+ (34.99), Pb2+ (21.81), Cu2+ (12.01), and Zn2+ (17.26) was larger than 4 representing a satisfactory signal. So, these quadratic models are suitable for explaining the adsorption of HMs onto CPETPW. Renu et al. (2018) and Rouniasi et al. (2018) used this model for HM adsorption from wastewater using activated carbon oxide nanosheets and modified wheat bran, respectively.

ANN modeling

ANNs are widely used for recording the non-linear relation between independent and dependent variables and are suitable to apply to any condition (Gadekar & Ahammed 2019). This study applies a multi-level feed-forward neural network, which is directed in the following order: input–hidden–output. The applied ANN has 60% training, 20% validation, and 20% testing networks. The input parameters (pH, adsorbent dose, and initial HM concentration) were selected for ANN, while the percentage of HM removal was selected as the output layer. Trial and error techniques were applied to achieve model accuracy, and validation and testing were carried out using MATLAB (R2020a).

Figure 5 represents the topology for HMs adsorption including 3:5:1, 3:4:1, 3:4:1 and 3:5:1 for Cd2+, Pb2+, Cu2+, and Zn2+, respectively (Table S10). The high and low frequencies of hidden neurons directly affect the ANN presentation and the appraisal of accuracy. So, the ideal quantities of hidden neuron selection assist in escaping over- and under-estimation (Gadekar & Ahammed 2019). ANN performance is improved with rising neuron numbers, but the coefficients of the determinant (R2) did not represent the same outcome in the training phase. In the case of all HM (Cd2+, Pb2+, Cu2+, and Zn2+) adsorption, all training, validation, and testing phases of tan-sigmoidal and topology were selected according to a high R-value and its associated lower MSE value (Table S10). Good associations between experimental and ANN-predicated results (Figures 6 and 7) indicate that the ANN model was suitable for describing HM adsorption using carbonized char. Khajeh et al. (2012) and Khan et al. (2020) stated the same relevant result for HM adsorption modeling using ANN.
Figure 5

ANN network with topology: (a) Cd2+, (b) Pb2+, (c) Cu2+, and (d) Zn2+.

Figure 5

ANN network with topology: (a) Cd2+, (b) Pb2+, (c) Cu2+, and (d) Zn2+.

Close modal
Figure 6

Linear fit for experimental and predicted concentration removal using ANN for (a) Cd2+ and (b) Pb2+.

Figure 6

Linear fit for experimental and predicted concentration removal using ANN for (a) Cd2+ and (b) Pb2+.

Close modal
Figure 7

Linear fit for experimental and predicted concentration removal using ANN for (c) Cu2+ and (d) Zn2+.

Figure 7

Linear fit for experimental and predicted concentration removal using ANN for (c) Cu2+ and (d) Zn2+.

Close modal

Real wastewater experiment

This study utilizes carbonized adsorbents for exploring the performance of removing HMs from real wastewater (RWW) experiments, where wastewater collected from an industrial area and optimum experiment conditions (adsorbent dose = 1 g/L, optimum pH = 7, temperature = 25 °C, and equilibrium contact time = 90 min) were applied for capitalizing the performance of the adsorbent in RWW. The properties of the collected wastewater were as follows: pH = 5.2, conductivity = 8,840 μS/cm, and total dissolved solids = 4,231 mg/L. The initial concentration of Cd2+, Pb2+, Cu2+, and Zn2+ in RWW was 0.501, 19.260, 0.683, and 0.355 mg/L, respectively. After the treatment at pH 7, the concentration was 0.004, 7.280, 0.012, and 0.126 mg/L, respectively. The removal rates of HMs (Cd2+, Pb2+, Cu2+, and Zn2+) were 99.2, 62.2, 97.6, and 64.4%, respectively (Figure S4), suggesting that a carbonized adsorbent will be suitable for eliminating HMs (Cd2+, Pb2+, Cu2+, and Zn2+) from industrial wastewater.

Desorption study

This process is applied to assess the possibility of further contamination when the treated adsorbent comes into the environment. The desorption rate of the adsorbent is significantly influenced by the nature of bonding (ionic bonds, Van der Waals forces, or covalent) between adsorbate and adsorbent (Chakraborty et al. 2023a). Here, a desorption study was conducted with diverse pH values (pH 5–11), where carbonized chars were taken from isotherm studies, dried, and the required amount was used for experiments. Figure S5b shows that the desorption percentage of HMs (Cd2+, Pb2+, Cu2+, and Zn2+) was not increasing significantly instead of increasing pH, which might be the possibility of the existing strong chemical bonding between HM ions and carbonized adsorbent (Figure S7), confirming the eco-friendly properties. Mouni et al. (2018) and Chakraborty et al. (2021b) provide a similar explanation in their study.

The presence of HMs in the environment from industrial sources is a matter of great concern. So, this study tried to develop a new, inexpensive, and environmentally friendly wastewater treatment method where carbonized char is produced from PET bottle waste for removing HMs from wastewater. The study finding shows that carbonized adsorbent shows high performance in removing HMs from wastewater. The elimination efficiency of HMs decreases with increasing HMs concentration and temperature while enhancing with increasing adsorbent dose. The equilibrium contact time of all HMs was 90 min. BBD and ANN models showed a good agreement between the predicted and experimental results. Pseudo-second-order was the best-matching kinetic model for HM adsorption data. The equilibrium data were well explained by the Langmuir isotherm for Cu2+, while the Freundlich isotherm was used for Cd2+, Pb2+, and Zn2+. Thermodynamic study shows that the adsorption is exothermic and spontaneous for HM removal. According to the study performance, carbonized char could be a suitable and effective adsorbent for removing HMs (Cd2+, Pb2+, Cu2+, and Zn2+) from wastewater, where a centralized wastewater treatment system is not accessible.

The authors would like to thank the Department of Environmental Science and Technology, Jashore University of Science and Technology, Jashore 7408, Bangladesh, for providing access to laboratory facilities.

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

The authors declare there is no conflict.

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H2Open Journal
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),
656
669
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https://doi.org/10.2166/h2oj.2022.027
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