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This study focuses on the simultaneous uptake of Pb(II) and Cr(VI) from industrial wastewater by walnut shell (WS), almond shell (AS), peanut shell (PS), and coconut shell (CS) adsorbents. Among the used adsorbents, the CS adsorbent exhibited the greatest BET surface area of 18.97 m2/g and porosity of 63.17% and the WS adsorbent also had the highest pore volume of 0.3536 m3/g. Lead and chromium removal were optimized using response surface methodology via a central composite design (CCD) approach. The efficiency of lead and chromium uptake from the wastewater was enhanced by increasing the concentration of WS, AS, PS, and CS adsorbents (Cads.) and decreasing the flow rate (Q) of the wastewater. Under the optimal conditions (Cads. = 0.85 g/L and Q = 2.5 mL/min), the maximum lead and chromium uptake from steel company wastewater was achieved using CS (92%) and WS (97.2%) adsorbents, respectively. The actual lead and chromium removal values were well-fitted based on a high Rpred2, confirming the validity of the CCD model. The acceptable performance of these green adsorbents in the simultaneous removal of chromium and lead from the wastewater introduces the WS, AS, PS, and CS adsorbents as inexpensive and available candidates for industrial wastewater treatment containing heavy metals.

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  • The WS, AS, PS, and CS adsorbents were prepared and applied as eco-friendly low-cost materials for lead and chromium uptake.

  • The optimum conditions for heavy metal removal were successfully assessed using the RSM based on the CCD model.

  • The highest lead and chromium removal efficiency from wastewater was attained using CS and WS green sorbents.

cheap sorbents, central composite design, lead and chromium removal, wastewater treatment
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The mitigation of pollution in water and wastewater has become an imperative task to safeguard ecosystems and human health (Azimi et al. 2019; Samimi & Moghadam 2024b). In recent years, a spectrum of techniques has been developed and employed for the removal of contaminants, encompassing physical, chemical, and biological processes, such as precipitation (Song et al. 2022), ion exchange (Swanckaert et al. 2022), adsorption (Samimi & Amiri 2024; Samimi & Safari 2022), membrane filtration (Cevallos-Mendoza et al. 2022), electrochemical treatment (Hassani et al. 2022), biological processes (Samimi & Shahriari Moghadam 2020; Saneha et al. 2023), oxidation–reduction reactions (Esfahani et al. 2023), and advanced oxidation processes (AOPs) (Hidayah et al. 2022). Wastewater pollution, arising from industrial, agricultural, and domestic sources, encompasses a wide spectrum of contaminants, including heavy metals (Al-Azzawi & Saleh 2023), organic compounds (Naderi et al. 2023), dyes (Cheraghipoor et al. 2024; Kumar et al. 2023), and emerging pollutants (Mohadesi et al. 2024). Selecting an appropriate method or a combination thereof depends on multiple factors, including the nature of the heavy metals present, treatment site requirements, cost-effectiveness, and operational feasibility. Among the diverse array of methods available for pollution remediation, adsorption processes utilizing various adsorbents have garnered significant attention due to their efficiency, versatility, and eco-friendly nature (Jia et al. 2023). The various adsorbents such as activated carbon (Hazbehiean et al. 2022), graphene-based materials (Saravanan et al. 2022), zeolites (Turyasingura et al. 2023), biochar (Piri & Sepehr 2022), agricultural waste-derived adsorbents (Hamad & Idrus 2022), and nanomaterials (Ehzari et al. 2022; Samimi et al. 2024) exhibit unique surface properties, porosity, and chemical compositions that render them capable of selectively adsorbing a wide range of pollutants from wastewater.

The contamination of water and wastewater by heavy metals originating from industrial, agricultural, and domestic sources has emerged as a pressing environmental concern globally (Sulistyowati et al. 2023). The persistent nature and potential toxicity of heavy metals necessitate effective wastewater treatment strategies to mitigate their adverse effects on ecosystems and human health (Samimi et al. 2023b). The advancement of diverse methodologies for heavy metal removal from wastewater is paramount in safeguarding water quality and environmental sustainability.

The selection of an appropriate adsorbent for heavy metal removal depends on multiple factors, including the type and concentration of pollutants, surface characteristics of the adsorbent, cost-effectiveness, and operational feasibility. The quest for sustainable and cost-effective methods for heavy metal removal from wastewater has led to the application of alternative materials, specifically waste products, as potential remediation agents (Topare & Wadgaonkar 2023). The waste materials, often considered burdensome byproducts, are increasingly recognized for their inherent adsorption, ion exchange, and precipitation capabilities, offering promising prospects for heavy metal removal. Furthermore, adsorbents derived from waste materials or sustainable sources with the principles of green chemistry and circular economy offer dual benefits of pollution remediation and resource utilization. The selection of waste materials for heavy metal removal is influenced by factors such as their availability, cost-effectiveness, abundance, surface characteristics, and affinity for heavy metal ions (Thakur et al. 2022).

Lead (Pb) and chromium (Cr), prevalent in industrial effluents, pose significant environmental risks even at low concentrations. Low-cost plant-based biomass (Paranjape & Sadgir 2023) and agricultural wastes such as banana peels (Afolabi et al. 2021), rice husks (Shi et al. 2019), and biochar derived from poplar sawdust (Cheng et al. 2021) have demonstrated remarkable potential in Pb(II) removal from wastewater. These materials possess abundant functional groups (such as carboxyl, hydroxyl, and amine) capable of binding lead ions through surface complexation or ion exchange mechanisms (Samimi & Shahriari-Moghadam 2021). Studies highlight their high adsorption capacities and selectivity for Pb(II) ions, making them promising candidates for wastewater remediation (Rostamzadeh Mansour et al. 2022). Adsorbents derived from waste materials, such as activated carbon from waste products (Jimenez-Paz et al. 2023), biosorbents from agricultural residues (Gupta et al. 2024), and modified clays (El-Kordy et al. 2023), exhibit notable efficiency in Cr(VI) removal. Their surface properties facilitate strong interactions with Cr(VI) ions, involving adsorption, reduction, or precipitation mechanisms. These adsorbents have shown substantial potential in reducing Cr(VI) levels in wastewater, offering sustainable solutions for its removal.

Utilizing waste materials as adsorbents for Pb(II) and Cr(VI) removal from wastewater presents an environmentally friendly and economically feasible approach. Ongoing research aimed at investigating shells derived from walnut, almond, peanut, and coconut in simultaneous adsorptive removal of Pb(II) and Cr(VI) from steel company wastewater. Furthermore, to maximize Pb and Cr uptake by adsorbents from industrial wastewater, factors affecting operational conditions were statistically optimized by response surface methodology (RSM) and the central composite design (CCD) approach.

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Material and adsorbent preparation

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In the present study, the wastewater entering the wastewater treatment unit of Mobarakeh Steel Company (Isfahan province, Iran) was investigated. The analyzed wastewater is related to the galvanized unit and the tin-plated unit. Walnut shell (WS), almond shell (AS), peanut shell (PS), and coconut shell (CS) with particle sizes less than 125 μm were prepared and used as adsorbents. This work used the AG Floor Electric Mill A60 to crush the shells. Double distilled water was used in all experiments.

Preparation of the used adsorbents

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First, waste materials, such as WS, AS, PS, and CS, were collected. Each shell was washed with distilled water and dried for 24 h at 80 °C. After that, the shells were crushed and separated using a sieve of less than 125 μm particles. No chemical or other physical treatment was carried out on the adsorbents.

Experimental process

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All experiments were performed in an adsorbent bed (330 mL). The wastewater flow entered the adsorbent bed at ambient temperature by a peristaltic pump (BT100-1F model). Finally, after passing through the adsorbent, it exited the bed as an overflow of treated wastewater. The output was collected as a sample of treated wastewater to perform the desired analysis. A schematic of the setup used in this work is shown in Figure 1. The effect of the effluent flow rate entering the adsorbent bed and the amount of each of the four types of adsorbents on the removal of lead and chromium was investigated. The concentration of lead and chromium (before and after the heavy metal uptake process) was determined using OES-ICP analysis. The removal percentages of lead and chromium were calculated using the following equations, respectively:
(1)
(2)
where [Pb]0 and [Cr]0 are the amounts of lead (4.555 mg/L) and chromium (0.390 mg/L) in wastewater as well as [Pb] and [Cr] are lead and chromium concentrations after the adsorption process.
Figure 1
Schematic of the setup used in this study to remove heavy metals from wastewater through the adsorption process.
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Schematic of the setup used in this study to remove heavy metals from wastewater through the adsorption process.

Figure 1
Schematic of the setup used in this study to remove heavy metals from wastewater through the adsorption process.
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Schematic of the setup used in this study to remove heavy metals from wastewater through the adsorption process.

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Characterization of green absorbents

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In the current work, BET analysis (BELSORP MINI II, N2 gas absorption at 77 K) was applied to investigate the specific surface area of WS, AS, PS, and CS adsorbents. Surface morphology and particle size in the samples were examined by SEM (MIRA3 TESCAN). FTIR spectroscopy of the samples was also performed to determine the functional groups in the samples (Thermo Nicolet AVATAR 360 FTIR).

Design of experiments and data analysis

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In the current work, the effect of independent variables on heavy metal removal percentage and the optimal operating conditions were evaluated and predicted by the RSM based on the CCD via Design Expert software version 7.0.0. Table 1 shows the operational factors (adsorption dose and wastewater flow rate) at three levels with two distances from the axial point to the central point (α = 1.5).

Table 1

Operational parameters for the removal of heavy metals (Pb(II) and Cr(VI))

FactorUnitSymbolLevels
− 1.5− 10+ 1+ 1.5
Adsorption dose g/L Cads. 0.10 0.25 0.55 0.85 1.00 
Wastewater flow rate mL/min Q 1.0 2.5 5.5 8.5 10.0 
FactorUnitSymbolLevels
− 1.5− 10+ 1+ 1.5
Adsorption dose g/L Cads. 0.10 0.25 0.55 0.85 1.00 
Wastewater flow rate mL/min Q 1.0 2.5 5.5 8.5 10.0 
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Designing experiments by the CCD method and responses (heavy metal removal) are presented in Table 2. ANOVA, used in this work, is a dependable approach for examining and determining the level of confidence in experimental data (Aviantara et al. 2023; Samimi & Nouri 2023).

Table 2

Designing experiments using the CCD method and responses

No.Cads., g/LQ, mL/minPb(II) removal, %
Cr(VI) removal, %
WSASPSCSWSASPSCS
1 0.55 5.50 61.1 67.3 64.7 70.3 82.2 73.5 78.1 70.3 
2 0.85 8.50 57.8 59.8 57.2 67.2 76.5 65.4 70.3 60.4 
3 0.25 8.50 38.5 38.9 40.1 40.8 61.2 55.3 59.7 51.5 
4 1.00 5.50 75.2 83.7 72.2 86.3 88.8 82.1 86.7 75.7 
5 0.85 2.50 83.9 90.8 80.5 92.0 97.2 91.5 96.2 83.4 
6 0.55 1.00 59.9 76.2 62.3 80.1 75.7 68.4 71.2 67.7 
7 0.55 5.50 59.3 66.1 64.1 72.3 85.4 74.3 79.5 69.4 
8 0.25 2.50 39.2 46.9 39.8 49.8 58.3 50.8 52.4 50.3 
9 0.55 10.00 47.4 52.0 46.5 53.7 72.1 62.7 64.9 59.8 
10 0.55 5.50 58.2 66.8 63.4 73.5 80.3 75.5 76.3 70.9 
11 0.10 5.50 30.3 31.8 27.2 33.3 53.4 48.2 52.2 43.1 
No.Cads., g/LQ, mL/minPb(II) removal, %
Cr(VI) removal, %
WSASPSCSWSASPSCS
1 0.55 5.50 61.1 67.3 64.7 70.3 82.2 73.5 78.1 70.3 
2 0.85 8.50 57.8 59.8 57.2 67.2 76.5 65.4 70.3 60.4 
3 0.25 8.50 38.5 38.9 40.1 40.8 61.2 55.3 59.7 51.5 
4 1.00 5.50 75.2 83.7 72.2 86.3 88.8 82.1 86.7 75.7 
5 0.85 2.50 83.9 90.8 80.5 92.0 97.2 91.5 96.2 83.4 
6 0.55 1.00 59.9 76.2 62.3 80.1 75.7 68.4 71.2 67.7 
7 0.55 5.50 59.3 66.1 64.1 72.3 85.4 74.3 79.5 69.4 
8 0.25 2.50 39.2 46.9 39.8 49.8 58.3 50.8 52.4 50.3 
9 0.55 10.00 47.4 52.0 46.5 53.7 72.1 62.7 64.9 59.8 
10 0.55 5.50 58.2 66.8 63.4 73.5 80.3 75.5 76.3 70.9 
11 0.10 5.50 30.3 31.8 27.2 33.3 53.4 48.2 52.2 43.1 
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The p-value (<0.05) and F-value were determined for the statistical significance analysis of the quadratic model data (Moghadam & Samimi 2022; Samimi & Moghadam 2024a). The definition of the calculated F-value involves averaging the mean-square regression and the mean-square residual reported in previous studies (Samimi & Moghadam 2018; Samimi et al. 2023a).

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Sorbent characterization

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According to the BET analysis presented in Table 3, the CS adsorbent had the highest BET surface area (18.97 m2/g) and porosity percentage (63.17%). However, the WS adsorbent had the highest pore volume (0.3536 m3/g). Surface morphologies of the WS, AS, PS, and CS adsorbents examined by SEM are shown in Figure 2. Various adsorbents presented in the resolution of 5 μm had different shapes. The prepared WS, AS, PS, and CS adsorbents had porous structures that can be considered suitable for adsorbing and trapping metal ions (Samimi & Mansouri 2024). However, as seen in Figure 2(a)–(d), porosity and non-uniformity of WS and CS surfaces are evident compared to other images.
Table 3

Results of BET analysis for all adsorbents used in this study

SampleBET surface area (m2/g)Pore volume (cm3/g) Average pore diameter (nm)Porosity (%)
WS 15.83 0.3536 22.63 60.19 
AS 8.63 0.1235 18.91 50.12 
PS 11.39 0.0506 29.21 43.87 
CS 18.97 0.1963 24.39 63.17 
SampleBET surface area (m2/g)Pore volume (cm3/g) Average pore diameter (nm)Porosity (%)
WS 15.83 0.3536 22.63 60.19 
AS 8.63 0.1235 18.91 50.12 
PS 11.39 0.0506 29.21 43.87 
CS 18.97 0.1963 24.39 63.17 
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Figure 2
SEM images of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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SEM images of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

Figure 2
SEM images of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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SEM images of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

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The FTIR analyses of the WS, AS, PS, and CS adsorbents are shown in Figure 3. The adsorption bonds at 3,427, 3,410, 3,421, and 3,414 cm−1 are related to the –OH group in WS, AS, PS, and CS adsorbents, respectively. The peaks at 2,926, 2,925, 2,926, and 2,923 cm−1 attributed to the stretching vibration peak at the C–H group in WS, AS, PS, and CS adsorbents, respectively. The stretching vibration peaks at 1,623, 1,621, 1,633, and 1,609 cm−1 corresponded to the C = O group (Samimi 2024) in WS, AS, PS, and CS adsorbents, respectively. The adsorption bonds at 1,053, 1,057, 1,035, and 1,050 cm−1 are attributed to the C–O group (Samimi & Shahriari-Moghadam 2023) in WS, AS, PS, and CS adsorbents, respectively. The active sites on the surface of the adsorbent, mentioned above could affect lead and chromium adsorption.
Figure 3
FTIR spectrum of different shell powders: (a) WS, (b) AS, (c) PS, and (d) CS.
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FTIR spectrum of different shell powders: (a) WS, (b) AS, (c) PS, and (d) CS.

Figure 3
FTIR spectrum of different shell powders: (a) WS, (b) AS, (c) PS, and (d) CS.
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FTIR spectrum of different shell powders: (a) WS, (b) AS, (c) PS, and (d) CS.

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Analysis of the RSM with CCD approach

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Equation (3), as a full quadratic mathematical model, was operated to demonstrate the relationship between the response variable and lead/chromium removal percentage (Samimi & Moeini 2020; Oliver Paul Nayagam & Prasanna 2023):
(3)
where β0 is the constant; β1 and β2 are linear coefficients; β11 and β22 are quadratic coefficients; β12 is the interaction coefficient between Cads. and Q; response refers to the Pb or Cr removal.

The quadratic model coefficients of Equation (3), coefficient of determination (R2), adjusted coefficient (⁠⁠), and predicted coefficient (⁠⁠) are summarized in Table 4. As shown in Table 4, all coefficients of determination were noticeable. However, these values were higher in used adsorbents for lead removal from wastewater.

Table 4

Quadratic model coefficients for lead and chromium removal from wastewater and the accuracy of the presented models

ModelQuadratic model coefficients
Coefficient of determination
β0β1β2β12β11β22R2
Pbrem.,WS 1.999 125.055 5.079 −7.056 −31.582 −0.271 0.9912 0.9825 0.9454 
Pbrem.,AS 12.298 145.381 2.542 −6.389 −49.364 −0.180 0.9932 0.9864 0.9524 
Pbrem.,PS −7.292 160.974 6.799 −6.556 −68.892 −0.457 0.9983 0.9966 0.9896 
Pbrem.,CS 14.265 152.823 2.739 −4.389 −64.187 −0.291 0.9961 0.9922 0.9794 
Crrem.,WS 13.962 141.510 7.512 −6.556 −57.615 −0.438 0.9767 0.9533 0.8680 
Crrem.,AS 5.365 138.398 8.454 −8.500 −47.077 −0.451 0.9813 0.9626 0.8708 
Crrem.,PS 5.526 138.889 9.389 −9.222 −42.309 −0.492 0.9851 0.9901 0.9057 
Crrem.,CS 10.267 133.810 6.134 −6.722 −55.628 −0.341 0.9855 0.9710 0.8995 
ModelQuadratic model coefficients
Coefficient of determination
β0β1β2β12β11β22R2
Pbrem.,WS 1.999 125.055 5.079 −7.056 −31.582 −0.271 0.9912 0.9825 0.9454 
Pbrem.,AS 12.298 145.381 2.542 −6.389 −49.364 −0.180 0.9932 0.9864 0.9524 
Pbrem.,PS −7.292 160.974 6.799 −6.556 −68.892 −0.457 0.9983 0.9966 0.9896 
Pbrem.,CS 14.265 152.823 2.739 −4.389 −64.187 −0.291 0.9961 0.9922 0.9794 
Crrem.,WS 13.962 141.510 7.512 −6.556 −57.615 −0.438 0.9767 0.9533 0.8680 
Crrem.,AS 5.365 138.398 8.454 −8.500 −47.077 −0.451 0.9813 0.9626 0.8708 
Crrem.,PS 5.526 138.889 9.389 −9.222 −42.309 −0.492 0.9851 0.9901 0.9057 
Crrem.,CS 10.267 133.810 6.134 −6.722 −55.628 −0.341 0.9855 0.9710 0.8995 
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The ANOVA of the second-order model for Pb(II) and Cr(VI) removal using WS, AS, PS, and CS adsorbents are summarized in Supplementary material, Tables S1–S8. Factors are considered to have a higher level of significance when the F-value is higher and the p-values are less than 0.05. According to the results in Supplementary material, Tables S2 and S5, two-way interaction for wastewater flow rate (Q2) had p-values greater than 0.05, which indicated their non-significance. All the other parameters, which had p-value and F-value in the range, were significant. According to the CCD results, the lack-of-fit term's p-value for all quadratic models (except the quadratic model for Pb(II) removal by AS), as indicated in Supplementary material, Tables S1–S8, did not demonstrate significance, thereby confirming the validity of the model (Aravind et al. 2016).

Comparison of the actual and predicted results for Pb and Cr from wastewater in the presence of WS, AS, PS, and CS adsorbents is demonstrated in Figures 4 and 5. As can be seen in Figures 4 and 5, the actual Pb/Cr removal amounts are well-fitted on the lines, which shows the validity of the CCD model. The high values of ⁠, presented in Table 4, also confirm these results.
Figure 4
Comparison of the actual and predicted results for lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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Comparison of the actual and predicted results for lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

Figure 4
Comparison of the actual and predicted results for lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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Comparison of the actual and predicted results for lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

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Figure 5
Comparison of the actual and predicted results for Cr removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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Comparison of the actual and predicted results for Cr removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

Figure 5
Comparison of the actual and predicted results for Cr removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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Comparison of the actual and predicted results for Cr removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

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Three-dimensional (3D) response surface plots were created using the model equation to visualize the combined effects on the predicted response value through the exploration of different parameter interactions (Figures 6 and 7) (Samimi & Shahriari-Moghadam 2024). As it is clear from Figures 6 and 7, increasing the concentration of WS, AS, PS, and CS adsorbents along with decreasing the wastewater flow rate led to an increase in the lead and chromium uptake efficiency from the wastewater.
Figure 6
3D plots of lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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3D plots of lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

Figure 6
3D plots of lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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3D plots of lead removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

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Figure 7
3D plots of chromium removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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3D plots of chromium removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

Figure 7
3D plots of chromium removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.
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3D plots of chromium removal from wastewater in the presence of different adsorbents: (a) WS, (b) AS, (c) PS, and (d) CS.

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The removal lead percentage increased from 30.3 to 83.9 using the WS adsorbent (Figure 6(a)), from 27.2 to 80.5 using the AS adsorbent (Figure 6(b)), from 31.8 to 90.8 using the PS adsorbent (Figure 6(c)), and from 33.3 to 92 using the CS adsorbent (Figure 6(d)), respectively. The removal chromium percentage increased from 53.4 to 97.2 using the WS adsorbent (Figure 7(a)), from 48.2 to 91.5 using the AS adsorbent (Figure 7(b)), from 52.2 to 96.2 using the PS adsorbent (Figure 7(c)), and from 43.1 to 83.4 using the CS adsorbent (Figure 7(d)), respectively.

The ability of WS (Figures 6(a) and 7(a)), AS (Figures 6(b) and 7(b)), and PS (Figures 6(c) and 7(c)) adsorbents to chromium removal was more than lead, while this trend was the opposite for the CS (Figures 6(d) and 7(d)) adsorbent. Maximum lead removal from steel company wastewater was achieved using the CS adsorbent (92%) and the maximum chromium uptake was obtained by the WS adsorbent (97.2%) at Cads. = 0.85 g/L and Q = 2.5 mL/min. A brief graphical comparison of Pb(II) and Cr(VI) removal using various adsorbents is also presented in Figure 8.
Figure 8
Graphical comparison of Pb(II) and Cr(VI) removal using various adsorbents. (a) (Akiode et al. 2023), (b) (Zou et al. 2023), (c) (Rahman et al. 2023), (d) (Lin et al. 2013), (e) (Khan et al. 2016), (f) (Sharma et al. 2016), (g) (El Kassimi et al. 2023), (h) (Karri et al. 2020), (i) (Islam et al. 2022), (j) (Ghorbani et al. 2020), (k) (Ghasemi et al. 2014a), (l) (Ghasemi et al. 2014b), (m) (Janyasuthiwong et al. 2015), (n) (Jalali & Aboulghazi 2013), and (o) (Ezeonuegbu et al. 2021).
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Graphical comparison of Pb(II) and Cr(VI) removal using various adsorbents. (a) (Akiode et al. 2023), (b) (Zou et al. 2023), (c) (Rahman et al. 2023), (d) (Lin et al. 2013), (e) (Khan et al. 2016), (f) (Sharma et al. 2016), (g) (El Kassimi et al. 2023), (h) (Karri et al. 2020), (i) (Islam et al. 2022), (j) (Ghorbani et al. 2020), (k) (Ghasemi et al. 2014a), (l) (Ghasemi et al. 2014b), (m) (Janyasuthiwong et al. 2015), (n) (Jalali & Aboulghazi 2013), and (o) (Ezeonuegbu et al. 2021).

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Developing cost-effective and straightforward techniques to eliminate heavy metals from wastewater is crucial for ensuring environmental sustainability. The current research was performed with the aim of the simultaneous optimal adsorption of Pb(II) and Cr(VI) from industrial wastewater using WS, AS, PS, and CS adsorbents. Based on the results, the CS adsorbent demonstrated the highest BET surface area and porosity, while the WS adsorbent possessed the greatest pore volume. The highest Pb and Cr removal from steel company wastewater was attained by utilizing CS (92%) and WS (97.2%) adsorbents, under the optimal conditions of Cads. = 0.85 g/L and Q = 2.5 mL/min. According to the results derived from the design of experiments, the lack-of-fit term's p-value for the CCD model and high values confirmed the validity of the quadratic model. The WS, AS, PS, and CS adsorbents have proven to be effective in removing both chromium and lead from the wastewater of a steel company. Their satisfactory performance makes them suitable and affordable options for treating industrial wastewater that contains heavy metals. These green adsorbents are readily accessible and can be utilized for the treatment of such wastewater.

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

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The authors declare there is no conflict.

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