Abstract

This research study encompasses the utilization of new adsorbents fabricated from pine sawdust for the adsorption of heavy metals and phenol from simulated industrial wastewater. Batch trials are conducted to evaluate the activity of these adsorbents for a possible substitution of the costly commercial adsorbents. The maximum adsorption capacities are evaluated and linked to the physicochemical characteristics of the adsorbents. The maximum monolayer adsorption capacity (qmax) of the adsorbents corresponds to the specific surface area of the adsorbents. The adsorbents with the larger specific surface area have shown higher qmax estimates (phenol adsorption is an exception). The highest amount of the phenol pollutant adsorbed by steam-activated sawdust (SAS) is 10.0 mg/g. The performance of SAS is found to be of the same order as the commercial activated carbon for the removal of Pb and Zn. Equilibrium data for the metal removal are in concordance with the Freundlich adsorption isotherm, whereas the phenol elimination has satisfied the Langmuir adsorption isotherm model. Kinetic data are fitted to Lagergren pseudo-first-order, pseudo-second-order, and the intraparticle diffusion models. Thus, kinetic parameters, rate constants, equilibrium adsorption capacities, and related correlation coefficients for each kinetic model are determined and discussed. The results suggest that the adsorption of Cr follows pseudo-second-order kinetics, indicating chemisorption for the tested adsorbents such that the intraparticle diffusion is not the only step that controls the overall process for Cr adsorption. At the end of this study, the production cost of the SAS adsorbent is estimated ($52 per kg) and compared to the cost of the commercial AC adsorbent in the industrial sector which has a great variation ($80–300 per kg) based on size and location plant. The results of this study can be used for the design of a suitable ecological control procedure to mitigate the harmful effects of industrial wastewater.

HIGHLIGHTS

  • The effects of several parameters, including the pH, temperature, initial phenol concentration, and contact time, should be investigated, and the desirable adsorption conditions, the adsorption kinetics model, and the equilibrium model for heavy metals and phenol will be selected.

  • The steam-activated sawdust adsorbent could be a promising solution to the removal of heavy metals and phenol from industrial wastewater.

NOMENCLATURE

     
  • Symbol

    Descriptionand(Units)

  •  
  • Ce

    Equilibrium liquid-phase solute concentration (mg/L)

  •  
  • Co

    Initial liquid-phase solute concentration (mg/L)

  •  
  • Ct

    Liquid-phase solute concentration at time, t (mg/L)

  •  
  • k

    Number of independent variables (/)

  •  
  • kad

    Pseudo-first-order rate constant (min−1)

  •  
  • KF

    Freundlich constant related with adsorption capacity (mg/g)

  •  
  • kid

    Intraparticle diffusion rate constant (mg/g min1/2)

  •  
  • KL

    Langmuir affinity constant (l/mg)

  •  
  • k2

    Pseudo-second-order rate constant (g mg−1 min−1)

  •  
  • m

    Mass of dry adsorbent (g)

  •  
  • n

    Adsorption intensity (/)

  •  
  • Pr

    Percentage removal (%)

  •  
  • qe

    Equilibrium adsorption capacity (mg/g)

  •  
  • qmax

    Maximum monolayer adsorption capacity (mg/g)

  •  
  • qt

    Amount of solute adsorbed at time, t (mg/g)

  •  
  • R2

    Correlation coefficient (/)

  •  
  • SBET

    BET surface area (m2/g)

  •  
  • Smi

    Micropore-specific area (m2/g)

  •  
  • t

    Time min

  •  
  • V

    Volume of solution (L)

  •  
  • Vme

    Mesopore volume (cm3/g)

  •  
  • Vmi

    Micropore volume (cm3/g)

  •  
  • Vt

    Total pore volume (cm3/g)

  •  
  • Wf

    Final weight of the product (g)

  •  
  • Wo

    Initial weight of the raw material (g)

  •  
  • λmax

    Maximum wavelength (nm)

  •  
  • SAS

    Steam-activated sawdust

  •  
  • CAC

    Commercial activated carbon

  •  
  • ICP-MS

    Inductively coupled plasma mass spectrometry

  •  
  • FT-IR

    Fourier transform infrared spectroscopy

  •  
  • SEM

    Scanning electron microscope

  •  
  • XRD

    X-ray diffractometer

  •  
  • BET

    Brunaeur–Emmett–Teller

INTRODUCTION

The growth and diversity of industrial activities have contributed massively to the development and economic progress of the countries. Unfortunately, this development is almost always accompanied by an immense degradation of the ecosystems via the generation and discharge of different types of pollutants (McKay 1984; Woo-seok 2017). Pollutants, such as heavy metals (e.g., cadmium (Cd), lead (Pb), chromium (Cr), and mercury (Hg)) and phenol, are widely discharged in the wastewater from industries. Some of these heavy metals are carcinogenic. It is, therefore, necessary to remove heavy metals and phenol from wastewater to comply with the regulations pertaining to the environment and human health (McKay 1984; Carvalho et al. 2007). Several technologies are developed for the treatment and purification of wastewater including chemical precipitation, oxidation, reduction, solvent extraction, electrolytic extraction, reverse osmosis, ion-exchange, dilution, and adsorption. Among such treatment methods, the adsorption process is found to be the most suitable technique to remove pollutants from wastewater (McKay 1984; Carvalho et al. 2007; Qiu & Zheng 2007). It is preferred mostly due to its simplicity, high-efficiency characteristics, design flexibility, technological maturity, and ability to regenerate the exhausted adsorbents. The efficiency of adsorption depends on the type of the adsorbent used, the nature of the solution to be treated, the molecular size of the solute, and the polarity of the contaminant (McKay 1984; Carvalho et al. 2007). The wastewater treatment using adsorbents prepared from agricultural waste is technically feasible. Mckay (1984) and Carvalho et al. (2007) utilized activated carbon (AC) to adsorb contaminants from wastewater. Some other studies have reported the use of zeolites in wastewater treatment (Elboughdiri 2020). In addition, synthetic polymeric and silica-based materials have also been reported as adsorbents (Mojoudi et al. 2019). AC is the most widely used adsorbent for the removal of various types of metals. Nevertheless, the application of AC is not feasible due to its high price and the cost associated with the regeneration of the spent adsorbent that is significantly lost during the purification process (Ngah & Hanafiah 2008; Caetano et al. 2009; Lütke et al. 2019).

Several natural products have shown an ability to selectively bind molecules from aqueous solutions. Among these products, sawdust is of great interest in terms of the heavy metal and phenol removal efficiency. It is considered as a promising tool for the treatment of contaminated effluents due to its abundant availability and the requirement of little processing (Ajmal et al. 1996; Srivastava et al. 2006; Ngah & Hanafiah 2008).

Most research studies reported in literature encompass the batch adsorption of single-component aqueous solutions that is not valid for a multicomponent system (Ajmal et al. 1996; Srivastava et al. 2006; Omprakash et al. 2017). The sorption capacities calculated from the batch mode are useful in providing information about the effectiveness of the adsorbate–adsorbent system only (Liyana-Pathirana & Shahidi 2005; Witek-Krowiak 2013). The present work focuses on the use of adsorbents prepared from sawdust to study the co-adsorption of Cr3+, Zn2+, Pb2+, and phenol from industrial wastewater. The kinetic model and equilibrium parameters are determined to understand the mechanism of adsorption, and the cost of production of the steam-activated sawdust (SAS) adsorbent is estimated and compared to the commercial AC (CAC).

MATERIALS AND METHODS

Materials

Stock solutions of Cr, Zn, Pb, and phenol were prepared by using chemical products supplied by Karl Fischer®. Nitrogen (N2) gas, supplied by AIR LIQUIDE® (Tunisia), was employed during the preparation of AC from sawdust. Dilute HCl and NaOH were utilized for pH adjustment. All synthetic solutions were prepared using distilled water. The adsorbents were derived from pine sawdust, thanks to their availability and desirable physical characteristics.

Equipment

The heavy metal ion concentrations were determined using the ICP-MS (Agilent, 7500). For all the samples, the phenol levels were quantified using the UV–VIS (Shimadzu, 1800) at a fixed wavelength (λmax = 269.0 nm) utilizing cuvettes of quartz.

An oven (Memmert, UN30) was utilized for heating the samples (until 250 °C) and drying purposes. Nevertheless, if higher temperatures were needed, a muffle furnace (FALC, FM 8.2) was used. An incubator with a shaking platform (Thermo Scientific, 222000) was utilized to mix the adsorbate–adsorbent suspensions throughout batch adsorption tests. The pH level was determined utilizing a pH-meter (Hanna Instruments, HI-991003).

The AC from sawdust was prepared in a fluidized-bed reactor (FBR). A regulated peristaltic pump was employed to adjust the water flow into the device and to furnish the used water feed into the adsorption column. Furthermore, the X-ray diffractometer (XRD; Thermo Scientific, ARL™ X'TRA), Fourier transform infrared spectroscopy (FT-IR) (Bruker, ALPHA II), and scanning electron microscope (SEM; Keysight Techologie, U9320A 8500) were used to characterize the adsorbents used.

Preparation and characterization of adsorbents

The adsorbent efficiency is influenced by the particle size. For the sake of precision and consistency, the particle sizes of the adsorbents need to be identical. A size range of 53–180 μm is used for the adsorbents studied. The identification of the adsorbents is made by the evaluation of functional groups, phases, surface morphology, specific surface area, and pore structural indicators.

The X-ray diffraction spectrum is obtained using an XRD with radiation at 40 kV and 50 mA to determine the presence of phases. Fourier transform infrared (FT-IR) spectra of the adsorbents are recorded in the range of 500–4,000 cm−1 to explore the number and positions of the functional groups available for the binding of the adsorbate. The surface morphology of the adsorbents is visualized in a SEM operated at the accelerating voltage of 20 keV. The Brunaeur–Emmett–Teller (BET) surface area (SBET) and pore structural indicators of the adsorbents are evaluated from the adsorption–desorption isotherm of N2 at 77 K employing the Micrometrics Surface Area and the Porosity Analyzer.

Steam-activated sawdust

The sawdust was washed three times with distilled water. This was followed by drying the sawdust in an oven at 120 °C for 24 h to remove the moisture content and organic impurities. It was pulverized and sieved to a particle size range of 53–180 μm. For the carbonization step, 150 g of sawdust was fed into the FBR that was preheated to 600 °C for 1 h and purged with N2 gas. The resulting product was removed from the reactor and allowed to cool in a desiccator. The carbonized sawdust was fed into the reactor under N2 flow and activated at 800 °C for 1 h. The AC thus obtained was designated as SAS that was kept in a desiccator for adsorption tests.

Commercial AC

Extruded AC pellet (C270-186121) is used extensively in the chemical and pharmaceutical industries. For the comparison of adsorption efficiency, the extruded AC pallet was purchased from Thermo Fisher Scientific®. This CAC adsorbent (particle size = 53–180 μm) results in a very small hydrodynamic pressure drop in liquid-phase applications.

Simulated wastewater preparation

The wastewater solutions of Cr, Zn, Pb, and phenol (1,000 mg/L) were prepared and diluted with distilled water to achieve the pollutant concentrations in synchrony with the concentrations of the usual industrial wastewater reported in the literature. These concentrations range from 20 to 200 mg/L for phenol and 0.1 to 100 mg/L for heavy metals (Cr, Zn, and Pb) (Iglesias-Carres et al. 2019). The pH of the solutions was regulated by HCl or NaOH (0.2 M).

Adsorption experiments

Batch adsorption experiments were conducted to determine the optimum adsorbent weight, equilibrium time, adsorption kinetics, and adsorption isotherm. For the adsorption experiments, the initial concentrations of the adsorbate were determined, and the blank solutions were utilized to check if there was any adsorption on the surface of the conical flask. The data acquired from the kinetic adsorption trials were employed to estimate the adsorption capacity, qe (mg/g), using the following equation:
formula
(1)
where Co and Ce are the preliminary and equilibrium adsorbate concentrations (mg/L), respectively, V is the volume of industrial wastewater (L), and m is the weight of adsorbent (g).

Equilibrium and kinetic studies were conducted in a fixed-bed column (Pyrex glass, I.D. = 2.5 cm, L = 45 cm) at ambient temperature. The tests were performed in a downflow mode that guarantees that the bed stays well packed throughout the experiment and a maximum contact between the adsorbent and the adsorbate prevails. A constant volumetric flow rate, pH, and the initial concentration of the simulated wastewater feed were maintained throughout the experiment.

Optimum adsorbent weight

The optimum weights for treating the industrial wastewater utilizing SAS and CAC were evaluated by changing the adsorbent mass from 0.2 to 4.0 g under the chosen residence time (48 h) at a pH value of 4.0 by employing contaminated water constituted of 50 mg/L of the heavy metal ions and 110 mg/L of phenol. The chosen agitation velocity and temperature for the trial runs were 250 rpm and 25 °C, respectively. The percentage reduction of metal ions from the aqueous solution was evaluated by utilizing the following equation:
formula
(2)

Equilibrium and kinetic studies

The equilibrium adsorption isotherm data were generated by making a fixed predetermined optimum weight of adsorbent (SAS or CAC) contact with 100 mL of industrial wastewater with the concentration of metal ions and phenol ranging from 20 to 90 mg/L at a pH value of 4.0 in 250 mL Erlenmeyer flasks. The flasks were agitated at 250 rpm at 25 °C for a predetermined contact time. At the end of the experiment, the residual concentrations of the metal ions and phenol were measured. The equilibrium adsorption capacity (qe) values were calculated using Equation (1), and the experimental results were fitted to the adsorption isotherms.

For kinetic studies, a predetermined optimum weight of the adsorbent was made to contact with 100 mL of simulated wastewater consisting of 50 mg/L of the heavy metal ions and 110 mg/L of phenol at a pH value of 4.0 and agitated at 250 rpm at 25 °C. At different intervals of time, 0.5 mL of solutions were withdrawn and analyzed. The adsorption capacity at time t (qt, mg/g) was calculated using the following equation:
formula
(3)
where Ct (mg/L) is the residual concentration at time t.

RESULTS AND DISCUSSION

The efficiency of the adsorbents is juxtaposed, and an attempt is made to correlate the physicochemical properties of adsorbents with their efficiency. Moreover, the adsorption findings for eliminating heavy metals and phenol utilizing SAS and CAC are presented and interpreted. Since only limited data are available on the kinetics of Cr reduction by sawdust-based adsorbents, estimates of the maximum adsorption capacity are made from the Langmuir isotherm model to show the adsorption kinetic modeling of Cr for the two adsorbents. This decision was taken because there are restricted references on the kinetics of Cr reduction employing adsorbents extracted from sawdust.

Characterization of the SAS

Figure 1 illustrates the X-ray diffraction of the SAS. It shows that SAS is amorphous and may contain carbon and graphite.

Figure 1

The X-ray diffraction profile of SAS.

Figure 1

The X-ray diffraction profile of SAS.

The FT-IR spectrum of SAS before and after adsorption is presented in Figure 2. The peak positions show major absorption bands at 2,656, 2,089, 1,997, 1,841, 1,561, 1,215, 947, and 669 cm−1. The band at 2,669 cm−1 is likely to represent the O–H stretching in carboxylic acids. The peak at 2,089 cm−1 represents the C–C stretching in alkynes, and the peak at 1,561 cm−1 is the characteristic peak for C=O in the Quinone structure (Elboughdiri 2018a). The band at 1,215 cm−1 is due to C–H deformation vibration. The peak at 669.3 cm−1 can be assigned to the out-of-plane C–H bending mode of an aromatic compound (Al-Qodah & Shawabkah 2009). Following the adsorption of heavy metals and phenol, the bands at 2,656, 2,363, 1,842, 1,561, 947, and 669 shifts to 2,899, 2,604, 1,749, 1,522, 1,016, and 629 cm−1, respectively. All such results propose chemical fixation of heavy metals and phenol onto SAS.

Figure 2

FT-IR spectra of SAS before and after the adsorption of heavy metals and phenol.

Figure 2

FT-IR spectra of SAS before and after the adsorption of heavy metals and phenol.

The changes seen in the intensity and positions of peaks (Figure 2) indicate that heavy metals (Cr, Zn, and Pb) and phenol create functional groups that are available on SAS complexes. Such modifications confirm that the adsorption process has occurred. Also, the shift in wave numbers corresponds to changes in the energy of the functional groups, suggesting the involvement of carboxyl, hydroxyl in the binding of heavy metals, and the formation of complexes. Similar observations were observed in the heavy metal removal using SAS and other adsorbents (Al-Omair & El-Sharkawy 2007; Wiwid et al. 2014; Banerjee et al. 2016; Solgi et al. 2017).

Figure 3 represents SEM images at different magnifications. It shows that the external surface of SAS is filled with cavities that emerge due to the evolution of volatile compounds throughout the carbonization step. Identical results are reported by Mohanty et al. (2005) for the morphological properties of AC fabricated from sawdust.

Figure 3

SEM micrographs of SAS before adsorption at 140× (a) and 1,200× (b) magnification.

Figure 3

SEM micrographs of SAS before adsorption at 140× (a) and 1,200× (b) magnification.

Characterization of the CAC

Figure 4 represents the XRD spectrum of CAC. It shows that AC can be defined as an amorphous form of graphite with a random structure of graphite plate showing a highly porous structure with a set of cracks and crevices on the molecular level. Juxtaposing the XRD sample of CAC with different adsorbents indicates that the intensity of the peak about 42.3° that represents graphite follows the order: CAC > SAS. In addition, the intensity of the peak about 24.4° because of carbon in the adsorbents follows the order: SAS > CAC.

Figure 4

The X-ray diffraction profile of CAC.

Figure 4

The X-ray diffraction profile of CAC.

Figure 5 illustrates the FT-IR spectra of CAC before and after the adsorption. Juxtaposing the FT-IR spectra of CAC and SAS before adsorption shows that the intensity of the peaks on the spectra of CAC is smaller than SAS. Moreover, the adsorbents possess frequent functional groups that are C–H in aromatic and aliphatic compounds, C=O in the Quinone structure, carbonyl groups in aldehydes and ketones, C–O groups, alkyl groups, and the O–H groups in the carboxylic acids.

Figure 5

FT-IR spectra of CAC before and after the adsorption of heavy metals and phenol.

Figure 5

FT-IR spectra of CAC before and after the adsorption of heavy metals and phenol.

The SEM micrograph in Figure 6 is very much comparable to that of SAS that shows a heterogeneous phase material. The CAC possesses a honey comb-like structure that is highly porous (Figure 6).

Figure 6

SEM images of CAC before adsorption at 140× (a) and 600× (b) magnification.

Figure 6

SEM images of CAC before adsorption at 140× (a) and 600× (b) magnification.

Surface area and porosity of the adsorbents

The surface features of the adsorbents (Table 1) show that the surface area of SAS is within the range of CAC surface area (500–1,500 m2/g). Consequently, their efficiency is anticipated to be identical to that of CAC (ElHajam et al. 2020).

Table 1

Surface features and pore structure of the SAS and CAC

AdsorbentSBET (m2/g)Smi (m2/g)Vt (cm3/g)Vmi (cm3/g)Vme (cm3/g)Vmi/Vt
SAS 795.68 588.80 0.386 0.258 0.128 0.668 
CAC 1,057.04 785.68 0.584 0.387 0.197 0.663 
AdsorbentSBET (m2/g)Smi (m2/g)Vt (cm3/g)Vmi (cm3/g)Vme (cm3/g)Vmi/Vt
SAS 795.68 588.80 0.386 0.258 0.128 0.668 
CAC 1,057.04 785.68 0.584 0.387 0.197 0.663 

SBET, Brunaeur–Emmett–Teller (BET) surface area (m2/g); Smi, micropore-specific area (m2/g); Vt, total pore volume (cm3/g) ; Vmi, micropore volume (cm3/g); Vme, mesopore volume (cm3/g); Vmi/Vt, micropore volume and total pore volume ratio.

Optimum adsorbent mass

Figure 7 indicates that the percentage elimination of Cr, Zn, Pb, and phenol augments with an increasing adsorbent weight. Increasing the amount of adsorbent results in an elevation in the number of active surface sites for adsorption (Goel et al. 2005). The highest efficiency of the heavy metal and phenol removal is observed at an optimum weight of 2.0 and 1.8 g for SAS and CAC, respectively.

Figure 7

Removal of heavy metals and phenol using SAS (a) and CAC (b) at pH = 4.0, temperature = 25 °C, shaking speed = 250 rpm, contact time = 48 h, initial metal concentration = 50 mg/L, initial phenol concentration = 110 mg/L.

Figure 7

Removal of heavy metals and phenol using SAS (a) and CAC (b) at pH = 4.0, temperature = 25 °C, shaking speed = 250 rpm, contact time = 48 h, initial metal concentration = 50 mg/L, initial phenol concentration = 110 mg/L.

Kinetic studies

The observed order of residence time can be related to the specific surface area of the adsorbents, and the rapid kinetics has significant practical importance ensuring efficiency (Elboughdiri 2018b). The experimental results of the effect of contact time on the batch adsorption of heavy metals and phenol at an initial pH value of 4.0 at 25 °C are presented in Figure 8. The initial rapid adsorption paves the way to a very slow approach to equilibrium. The contact time selected was 4 h for the removal of heavy metals and phenol using SAS and CAC.

Figure 8

Data for determining contact time using SAS (a) and CAC (b) for the treatment of industrial wastewater.

Figure 8

Data for determining contact time using SAS (a) and CAC (b) for the treatment of industrial wastewater.

Adsorption kinetic modeling

Several models are applied to evaluate the experimental information related to the adsorption mechanism. Pseudo-first-order, pseudo-second-order, and the Weber–Morris kinetic models were tried to determine the kinetic indicators and the adsorption route (Bingxin et al. 2020). To define the equilibrium isotherms, the experimental information of the removal of Cr, Zn, Pb, and phenol is discussed using the Langmuir and Freundlich isotherm models. The findings are shown in Figure 9(a)–9(c).

Figure 9

(a) Lagergren pseudo-first-order plots for Cr adsorption on SAS and CAC. (b) Pseudo-second-order plots for Cr adsorption on SAS and CAC. (c) Weber and Morris intraparticle diffusion plots for Cr adsorption on SAS and CAC.

Figure 9

(a) Lagergren pseudo-first-order plots for Cr adsorption on SAS and CAC. (b) Pseudo-second-order plots for Cr adsorption on SAS and CAC. (c) Weber and Morris intraparticle diffusion plots for Cr adsorption on SAS and CAC.

The evaluated equilibrium adsorption capacity (qe) estimates and coefficients linked to the pseudo-first-order and pseudo-second-order kinetic plots are given in Table 2. The correlation coefficients (R2) are 0.817 and 0.834 for the pseudo-first-order and pseudo-second-order, respectively. Furthermore, the qe values anticipated by the pseudo-first-order model are not in agreement with the practical estimates that suggest that Cr adsorption did not satisfy the pseudo-first-order model. Moreover, the R2 value for the pseudo-second-order model is 0.9999, and the anticipated qe estimates agreed well with the practical findings.

Table 2

Kinetic parameters for Cr adsorption

ModelParameterSASCAC
Pseudo-first-order kad (min−10.0507 0.0392 
qe (mg/g) 1.0544 1.6673 
R2 0.8170 0.8340 
Pseudo-second-order k2 (g·mg−1 min−10.3126 0.1934 
qe (mg/g) 2.2883 3.1746 
R2 0.9999 0.9999 
Experimental qe (mg/g) 2.1650 3.0256 
ModelParameterSASCAC
Pseudo-first-order kad (min−10.0507 0.0392 
qe (mg/g) 1.0544 1.6673 
R2 0.8170 0.8340 
Pseudo-second-order k2 (g·mg−1 min−10.3126 0.1934 
qe (mg/g) 2.2883 3.1746 
R2 0.9999 0.9999 
Experimental qe (mg/g) 2.1650 3.0256 

The results indicate that the pseudo-second-order kinetic model fits well with the kinetics adsorption process for SAS and CAC that makes it better than the pseudo-first-order kinetic model.

Figure 9(c) presents the multilinear plots of the adsorbed chromium ion mass per unit mass of adsorbent versus t1/2 for SAS and CAC. The initial time period is attributed to the diffusion of Cr through the solution to the external surface of the adsorbent. This is followed by the first linear portion that describes the gradual adsorption stage, where the intraparticle diffusion is rate-limiting. The second linear portion is attributed to the final equilibrium stage, where intraparticle diffusion starts to slow down due to the lower concentration of Cr in the solution (Alzaydien 2009; RuiYan et al. 2019; Sumon et al. 2020). The slope of the first linear portion characterizes the rate parameter corresponding to the intraparticle diffusion.

The data presented in Table 3 show that the adsorption rate is higher in the first stage (kid1) as compared to the second stage (kid2). In the beginning, Cr is adsorbed by the external surface of the adsorbent particles. Therefore, the sorption rate is significantly elevated. When the external surface saturates, Cr3+ starts to diffuse deep inside the pores of the adsorbent particles. At this stage, the diffusion resistance increases, and therefore the diffusion rate decreases. Due to the decrease of the chromium ion concentration in the solution, the diffusion rate gets lower. Therefore, the changes in kid1 and kid2 may be attributed to the adsorption stages of the external and internal surfaces and the equilibrium approach. Similar phenomena are reported by Sun & Yang (2003).

Table 3

Intraparticle diffusion constants for Cr adsorption

AdsorbentParameter
kid1 (mg/g min)R12kid2 (mg/g min)R22
SAS 0.575 0.995 0.008 0.815 
CAC 0.886 0.998 0.020 0.842 
AdsorbentParameter
kid1 (mg/g min)R12kid2 (mg/g min)R22
SAS 0.575 0.995 0.008 0.815 
CAC 0.886 0.998 0.020 0.842 

Equilibrium modeling

The numerical values of the results of industrial wastewater treatment utilizing SAS and CAC were fitted to the Langmuir and Freundlich model. The findings are presented in Figures 10 and 11, respectively.

Figure 10

Fitting curves of adsorption for Cr (a), Zn (b), Pb (c), and phenol (d) removal using SAS and CAC through the Freundlich model.

Figure 10

Fitting curves of adsorption for Cr (a), Zn (b), Pb (c), and phenol (d) removal using SAS and CAC through the Freundlich model.

Figure 11

Fitting curves of adsorption for Cr (a), Zn (b), Pb (c), and phenol (d) removal using SAS and CAC through the Langmuir model.

Figure 11

Fitting curves of adsorption for Cr (a), Zn (b), Pb (c), and phenol (d) removal using SAS and CAC through the Langmuir model.

If juxtaposed to the Langmuir isotherm, it is observed that the Freundlich isotherm resulted in an excellent fit with the experimental data for all heavy metals and phenol.

The empirical parameters for both isotherms are presented in Table 4. The sequence of sorption affinity for both SAS and CAC is such that: Cr > Zn > Pb. This similarity in the sequence of sorption affinity can be explained by the fact that both CAC and SAS have almost similar physicochemical properties. The qmax values show that the order for Cr-binding affinity is such that: CAC > SAS, which agrees with the qe values calculated from the pseudo-second-order model.

Table 4

Freundlich and Langmuir isotherm constants for industrial wastewater using SAS and CAC

AdsorbateAdsorbentLangmuir
Freundlich
qmaxKLR2nKFR2
(mg/g)(L/mg)(mg/g)
Chromium SAS 9.524 0.096 0.9589 1.244 0.520 0.9702 
CAC 15.39 0.028 0.9441 1.603 1.062 0.9453 
Zinc SAS 4.016 10.826 0.9757 2.439 3.639 0.9904 
CAC 5.435 0.240 0.9627 3.378 3.606 0.9823 
Lead SAS 0.464 0.107 0.9276 0.249 0.0006 0.9862 
CAC 0.591 0.129 0.8482 0.193 0.0003 0.9186 
Phenol SAS 10.000 0.003 0.9906 0.438 1.622 0.991 
CAC 19.600 0.317 0.9958 1.214 4.645 0.9749 
AdsorbateAdsorbentLangmuir
Freundlich
qmaxKLR2nKFR2
(mg/g)(L/mg)(mg/g)
Chromium SAS 9.524 0.096 0.9589 1.244 0.520 0.9702 
CAC 15.39 0.028 0.9441 1.603 1.062 0.9453 
Zinc SAS 4.016 10.826 0.9757 2.439 3.639 0.9904 
CAC 5.435 0.240 0.9627 3.378 3.606 0.9823 
Lead SAS 0.464 0.107 0.9276 0.249 0.0006 0.9862 
CAC 0.591 0.129 0.8482 0.193 0.0003 0.9186 
Phenol SAS 10.000 0.003 0.9906 0.438 1.622 0.991 
CAC 19.600 0.317 0.9958 1.214 4.645 0.9749 

The removal of phenol by CAC and SAS is better described by the Langmuir isotherm. The experimental data deviate more in the case of the Freundlich model than the Langmuir model (Figures 10(d) and 11(d)) for the studied adsorbents. This is indicated by the regression coefficient (R2) values of the linear plots. The sequence of sorption affinity for phenol is in accordance with the increase in the surface area of the adsorbents with CAC having the highest and SAS having the lowest surface area.

Cost analysis

Costs were developed assuming a 79% yield of SAS (158 g output), 320 days/year of production, for 24 h/day at $10 per h (Tables 5 and 6). The production of SAS requires a fixed capital investment of $1.575 (Table 5) and an operating cost of $6.678 (Table 6). The estimation of the equipment purchase cost was determined following McMaster-CARR (McMaster-CARR) and McGraw Hill (McGraw Hill) websites.

Table 5

Estimation of the purchased equipment cost for SAS production and the cost of their usage for 3 h

EquipmentCapacityCost ($)Working cost for 3 h ($)
N2 cylinder 8 L/min 200 0.008 
Rotameter 50 L/min 300 0.012 
Beakers 2 × 250 mL, 2 × 500 mL 40 0.016 
Peristaltic pump 5.41 g/min 150 0.006 
Temperature controller 100–1,000 °C 200 0.008 
Electric furnace 600 °C 2,000 0.078 
FBR 2 L 15,000 0.586 
Thermocouple 100–1,000 °C 50 0.002 
Acetone 2.5 L 90 0.800 
Sieve 20–200 μm 150 0.006 
Balance 2.5 kg 700 0.027 
Muffle furnace 20–180 °C 600 0.023 
Desiccator 0.5 L 100 0.004 
Total 1.575 
EquipmentCapacityCost ($)Working cost for 3 h ($)
N2 cylinder 8 L/min 200 0.008 
Rotameter 50 L/min 300 0.012 
Beakers 2 × 250 mL, 2 × 500 mL 40 0.016 
Peristaltic pump 5.41 g/min 150 0.006 
Temperature controller 100–1,000 °C 200 0.008 
Electric furnace 600 °C 2,000 0.078 
FBR 2 L 15,000 0.586 
Thermocouple 100–1,000 °C 50 0.002 
Acetone 2.5 L 90 0.800 
Sieve 20–200 μm 150 0.006 
Balance 2.5 kg 700 0.027 
Muffle furnace 20–180 °C 600 0.023 
Desiccator 0.5 L 100 0.004 
Total 1.575 
Table 6

The operating costs for SAS

ItemCapacity/descriptionCost ($)
Raw materials (Sawdust) 200 g 0.003 
Utilities (Steam, water, electricity) 1 kg of steam, 1 L of water, 0.3 MWh of electricity 2.500 
Labor (Operating labor, maintenance labor) One technician for 15 min 2.500 
Supplies (Operating supplies, maintenance supplies) One technician of maintenance for 1 h/month 0.014 
General works (General and administrative) One administrator for 10 min 1.670 
Total 6.687 
ItemCapacity/descriptionCost ($)
Raw materials (Sawdust) 200 g 0.003 
Utilities (Steam, water, electricity) 1 kg of steam, 1 L of water, 0.3 MWh of electricity 2.500 
Labor (Operating labor, maintenance labor) One technician for 15 min 2.500 
Supplies (Operating supplies, maintenance supplies) One technician of maintenance for 1 h/month 0.014 
General works (General and administrative) One administrator for 10 min 1.670 
Total 6.687 

The operating costs for labor, supplies, and general works are based on percentages given in Peters and Timmerhaus (cited in Ng et al. 2003; Lima et al. 2008).

The total fixed capital investment was estimated and presented in Table 7 by assuming the economic life of 10 years for all equipment and 1 year for the laboratory glassware. Capital costs were estimated on percentages of the total equipment cost according to Peters and Timmerhaus (cited in Ng et al. 2003; Lima et al. 2008).

Table 7

Summary of costs for SAS

SourceCost ($)
Purchased equipment cost From Table 5  1.575 
Capital cost From Table 5  0.001 
Total fixed capital investment Purchased equipment cost plus capital cost 1.576 
Total operating cost From Table 6  6.687 
Estimated production of SAS 158 g produced in 3 h – 
Estimated cost for steam-activated sawdust production per kg Total fixed capital investment plus total operating cost) dived by the estimated production of SAS 52.297 
SourceCost ($)
Purchased equipment cost From Table 5  1.575 
Capital cost From Table 5  0.001 
Total fixed capital investment Purchased equipment cost plus capital cost 1.576 
Total operating cost From Table 6  6.687 
Estimated production of SAS 158 g produced in 3 h – 
Estimated cost for steam-activated sawdust production per kg Total fixed capital investment plus total operating cost) dived by the estimated production of SAS 52.297 

Table 7 shows that the estimated cost for SAS is $52 per kg. The cost of CAC is $80–300 per kg (Indiamart). This production cost concerns the industrial sector and has a great variation based on plant's size and location plant, which affects the energy consumption and other production utilities. Therefore, it is still higher than the estimated laboratory production cost.

CONCLUSION

In this study, the SAS adsorbent is successfully prepared, and its efficiency for the removal of recalcitrant compounds from industrial wastewater is juxtaposed to the CAC adsorbent.

From the data of the present investigation, the following can be concluded:

  • The discharge of industrial wastewater from numerous industries is almost inevitable. Our findings can assist for conceiving and implementing a convenient ecological control plan to reduce the harmful effects of the industrial wastewater.

  • The SAS adsorbent has proved to be a promising adsorbent media for the removal of heavy metals and phenol from industrial wastewater.

  • The physicochemical properties of SAS and CAC are mostly identical except for the specific surface area.

  • The adsorbents’ surfaces are complicated because of the existence of various functional groups. Certain functional groups, such as –OH and –COOH that are responsible for cation binding, are detected on the adsorbents’ surfaces.

  • The maximum monolayer adsorption capacity of the adsorbents corresponds to the specific surface area of the adsorbents. The adsorbents with an elevated specific surface area possess higher maximum monolayer adsorption capacities with a few exceptions. The largest amount (10.0 mg/g) adsorbed by SAS belongs to phenol with Cr on the second largest position (9.5 mg/g).

  • Equilibrium data for the metals’ removal data satisfy well with the Freundlich isotherm; however, phenol removal data satisfy well with the Langmuir adsorption isotherm model. Additionally, the removal of Cr followed pseudo-second-order kinetics proposing chemisorption because of the sharing of electrons among the adsorbent surface and the orbitals of Cr3+ for the tried adsorbents. Intraparticle diffusion is not the only stage dominating the global adsorption method of Cr in the studied adsorbents.

  • The effect of several variables, such as pH, temperature, preliminary phenol concentration, and residence time, should be examined. Furthermore, the wanted adsorption circumstances, adsorption kinetics model, and equilibrium model for heavy metals and phenol will be selected.

  • The production cost of the CAC adsorbent at the industrial level has a large variation ($80–300 per kg) based on plant's size and location. It stills higher than the estimated laboratory production cost of the SAS adsorbent ($52 per kg).

Additional pretreatment trials would be performed to ameliorate the physicochemical features such as pore volume and adsorption active sites. To enlarge the practicability of the SAS adsorbent, tests need to be accomplished using actual industrial wastewater instead of a synthesized one.

FUNDING

This research has been funded by the Research Deanship of the University of Ha'il, Saudi Arabia, through the project RG-20 101.

CONFLICTS OF INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

DATA AVAILABILITY STATEMENT

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

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