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Environmental Technology & Innovation
journal homepage:www.elsevier.com/locate/eti
Recycling biochar derived from tannery liming sludge for chromium adsorption in static and dynamic conditions
Sofia Payel, Md. Abul Hashem
∗, Md. Anik Hasan
Department of Leather Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
a r t i c l e i n f o
Article history:
Received 26 July 2021
Received in revised form 30 September 2021 Accepted 3 October 2021
Available online 6 October 2021 Keywords:
Breakthrough analysis Batch and column modeling Chromium
Adsorption and desorption Tannery solid waste Liming operation
a b s t r a c t
In tannery industry, unhairing and liming (termed as liming) are indispensable but it is a very polluting part of process when producing quality leather. Expelled wastewater from liming generates significant amounts of liming sludge. In this study, efficacies of liming sludge biochar derived for adsorption of chromium from tannery wastewater are described. The biochar was characterized by FTIR, SEM, EDX, and BET technolo- gies, and pHzpc. Surface area and responsible functional groups of biochar were as follows: 9.2 m2/g and C-H, C-N, C-O, C=C, N-H, S=O, and O-H. EDX analysis revealed chromium adsorption and SEM images led to changes in surface morphology. pHzpcfor dry liming sludge and biochar were 4.1 and 6.1, respectively. Batch adsorption was verified by assorted parameters including biochar dose, shaking speed, contact time, and dilution factor. Column study results confirm the highest chromium adsorption efficiencies of 408.12 and 533.41 mg/g for 3 cm and 5 cm bed height, respectively.
Regression coefficient suggested that adsorption obeys the Freundlich isotherm and pseudo-second order kinetics in batch mode, while it follows the Yoon–Nelson model in column mode. The physicochemical parameters of tannery wastewater prior and post- adsorption revealed good removal efficiencies for chloride content (Cl), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) were, respectively, 50.5%, 80.1%, and 85.5%. Desorption process recovers 69.37 mg/g Cr from adsorbed biochar which could be reused in the tanning process. Consequently, an innovative state-of-the-art tannery liming sludge biochar can satisfactorily use in tannery wastewater treatment by following wealth-from-waste principle.
©2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
The huge amounts of wastewater generated from thousands of small and large-scale industries, especially leather tanning, chemical manufacturing, battery manufacturing, mining sites drainage, metallurgy, and electroplating are dis- charged to natural surface waters with poor or simply no remediation treatment at all (Yusuf and Song,2020). Discharged wastewater poses serious chronic and toxic effects on all living beings because they bio-accumulate, are not biodegradable, are environmentally persistent, and dangerously toxic (Zhang et al.,2020). The tanning industry is one of the world’s most polluting industries and frowned upon due to its higher solid waste and effluent generation. Leather production requires a series of batch operations where a huge amount of water is consumed and incorporates various chemical treatments to achieve the desired final leather quality. In Bangladesh annually 2.2×104m3of wastewater is generated from the tannery
∗ Corresponding author.
E-mail addresses: [email protected],[email protected](M.A. Hashem).
https://doi.org/10.1016/j.eti.2021.102010
2352-1864/©2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.
org/licenses/by-nc-nd/4.0/).
Nomenclature
AE extent of surface coverage and activation energy (g mg−1) AT Temkin isotherm equilibrium binding constant (Lg−1) BE the initial adsorption rate (mg(g min)−1)
bT constant related to the heat of sorption (J/mol) Ce equilibrium Cr concentration (mg/L)
Co initial concentration (mg/L)
CR concentration of metal removed (mg/L) Ct concentration at time t (mg/L)
K1 pseudo first-order kinetic constants (min−1)
K2 pseudo second-order kinetic constants (g mg-1min−1) KBA kinetic constant (L/mg min)
Kdif intra-particle diffusion rate constants (mgg−1min−1/2) KF Freundlich constant ((mg/g) (L/mg)1/n)
KL Langmuir equilibrium adsorption constant (L/mg) KT Temkin isotherm constant (L/g)
KTh Thomas rate constant (L/(min mg)) KYN Yoon and Nelson rate constant (min−1)
M mass of adsorbent (g)
md mass of adsorbate removed after desorption (g) mo mass of adsorbate adsorbed before desorption (g)
mtotal total mass of metal discharged to the column (mg)
n adsorption intensity
No saturation concentration (mg/L) Q volumetric flow rate (mLmin−1)
qb the bed capacity at breakthrough point (mg/g)
qe adsorption capacity of the adsorbent at equilibrium (mg/g) qm maximum adsorbed amount of Cr per mass of adsorbent (mg/g) qt adsorption capacity of the adsorbent at time t (mg/g)
qtotal mass of metal adsorbed (mg)
R ideal gas constant (J (mol K)−1)
RL separation factor
t sampling time (min)
T temperature (K)
ttotal total process time (min)
U superficial velocity (cm/min)
V adsorbate volume (L)
Vb volume processed at breakthrough point (L)
Vff effluent volume (mL)
z bed depth of the fixed bed column (cm)
θ volumetric flow rate (L/min)
τ time needed for 50% adsorbate breakthrough (min)
(Whitehead et al.,2019). The combined effluent from the tanning industry contains a mixture of various toxic chemicals, organic matter, lime, and heavy metals like chromium, Cr (Bhuiyan et al.,2010).
In tanneries, chrome tanning is carried out with trivalent chromium salt, basic chromium sulfate, Cr(OH)SO4where only a fraction of the salt is coordinated by the pelt while the rest is discharged as effluent. The remaining chromium in the wastewater can be converted to hexavalent chromium through the influence of oxidizing agents, heat, pH, pressure, etc. (Bashir et al.,2020). However, these can trigger mutagenic, carcinogenic, teratogenic effects on human, damage plants and kill flora and fauna (USEPA (U.S. Environmental Protection Agency),2017). Although trivalent chromium is essential for living organisms in trace amounts, at higher concentrations it is potentially lethal (Masindi et al.,2021).
In the recent years, popular technologies have been devised to remove Cr from wastewater including ion exchange, coagulation, membrane process, chemical precipitation, and flotation (Yusuf and Song,2020). However, these techniques are challenging to apply in developing countries. Recently, as a cost-effective, applicable, enticing remedy, adsorption
Fig. 1. FT-IR analysis of pure biochar and chromium loaded biochar.
Table 1
Physiochemical parameters of liming and chrome tanning wastewater.
Parameters Liming wastewater Chrome tanning wastewater (ECR, 1997) Unit
Before Coagulation After Coagulation Before Adsorption After Adsorption
pH 11.5±0.3 8.7±0.2 3.9±0.1 5.2±0.3 6–9 –
EC 39.7±1.7 17.2±1.5 68.5±1.4 73.8±1.7 1.2 mS
Cl− 5.78±1.4 2.86±1.7 12.86±0.9 5.93±1.1 0.6 g/L
TDS 23.17±1.2 12.49±1.3 35.92±1.2 42.43±.1.1 2.1 g/L
TSS 27.83±1.1 13.18±1.2 – – 0.15 g/L
BOD 13.37±2.9 1.95±2.4 35.92±1.9 0.23±02.4 0.25 g/L
COD 19.38±1.8 3.85±1.6 49.91±1.7 0.36±1.3 0.4 g/L
is being widely studied for its decontamination of pollutants from the environment (Arabkhani and Asfaram,2020). In adsorption of Cr, utilizing caffeic acid functionalized corn starch,Lysinibacillussp. has shown promising results although these are based on hexavalent Cr (Liu et al.,2021;Zhu et al.,2021). Adsorbents derived from natural products, agricultural and industrial by-products as well as waste are intensively investigated for removing chromium from wastewater (Ma et al.,2019). Previously, different types of industrial sludge were modified to increase the adsorption properties such as that from paper mills (Yoon et al.,2017), sewage plants (Chen et al.,2017), tanneries (Zhai et al.,2021) and food producers (Mahapatra et al.,2012). They can provide a better source of adsorbent due to low ash content but contains much organic matter (Guo et al.,2021).
In tannery beamhouse, liming is the primary chemical operation where unwanted hide/skin substances (keratin, non-structural proteins) are removed from the raw hide/skin. Resultant wastewater from the liming operation contains significant amounts of sulfide, lime, dissolved proteins, fat, suspended solids, and dissolved solids which cause the noticeable brown color and malodor to appear in the liming effluent (Tamersit and Bouhidel,2020). Liming is considered as the highest pollution yielding stage of leather processing which makes up 60%–70% of the total pollution load of a tannery (Xu et al.,2009). From liming every year in Bangladesh, approximately 4.08×104tons of liming sludge is generated which is disposed of indiscriminately without any treatment (Paul et al.,2013). This huge volume of sludge has the potential of reutilization by converting it into a value-added product. After modification, biochar could be produced from the liming sludge which could be used for in-house tannery wastewater treatment. In the recent past, various biochar derived from bagasse,Gliricidia, rice husk, pineal shell as well as biochar based catalyst was used for industrial wastewater treatment (Liang et al.,2021;Qiu et al.,2021;Xiang et al.,2020). Biochar is also derived from sludge of various wastewater i.e. piggery (Cai et al.,2020), urban (Li et al.,2019), textile (Wang et al.,2019), paper mill (Mohammadi et al.,2019), and municipal (Fang et al.,2021). However, depending on the wastewater most of the sludge-based biochar contain toxic substances which might be responsible for secondary pollution (Jellali et al.,2021). On the other hand, liming wastewater from tannery contains organic carbonaceous residue which is the main constituent of the liming sludge having the insignificant amount of toxic elements (Khambhaty et al.,2017). Compared to other biochar, the biochar derived from tannery liming sludge could resolve two leading problems (i) it will reveal a way of solid waste management and (ii) recycling of waste from wastewater treatment.
This study aims to derive biochar from the tannery liming sludge for in-house chromium adsorption from tan- nery wastewater. Biochar was characterized by the Energy Dispersive X-ray (EDX), Fourier transform infrared (FTIR)
Fig. 2. EDX study of pure biochar (a) and chromium loaded biochar (b).
Table 2
BET surface analysis of liming sludge and biochar.
Parameter Liming sludge Biochar Unit
Specific surface area 1.58 9.20 m2/g
Pore volume 0.00059 0.00072 cm3/g
Pore size 21.27 5.30 nm
Average particle size 265 652 nm
spectroscopy, Scanning Electron Microscope (SEM), and Brunauer-Emmett-Teller (BET). Batch and column studies were explored considering the parameters-biochar dose, contact time, shaking speed, dilution factor, column bed height and verified the process in a laboratory. The mechanism and performance was evaluated by various isotherm, kinetics, and column breakthrough model. A desorption study was performed to recover the adsorbed chromium from the biochar.
2. Materials and methods 2.1. Liming sludge preparation
Liming wastewater was transferred into a plastic barrel sourced from a local tannery in Khulna, Bangladesh. Slowly, aluminum sulfate, Al2(SO4)3coagulant was mixed with liming wastewater and subjected to continuous stirring. 8.5–9.0 pH was ensured according to the method employed byDixit et al.(2015). The sludge took the form of sediment after 6 h at the bottom of the barrel. After decantation, the liming sludge was kept in a burlap bag. After straining the excess water, the sludge was collected and brought to the laboratory to create a biochar.
2.2. Biochar derived from liming sludge
The collected liming sludge was sun-dried; oven dried at 103±2◦C for 24 h, ground using an automatic laboratory rotor mill (Pulverisette, Germany) and then homogenously mixed. The sludge was thermally amended in a limited oxygen state at 600 ◦C in a muffle furnace (Thermo Scientific, Germany) for 3 h. The biochar was then stored prior to the experimental studies.
Fig. 3. SEM image of liming sludge (scale bar 20µm) (a) pure biochar (scale bar 20µm) (b) and Cr loaded biochar (scale bar 20µm) (c).
Table 3
Evaluation of column performance, breakthrough and exhaustion points at 3 cm and 5 cm bed height.
Parameters Column bed height (cm) Unit
3 5
Vff 15.84 24 L
qtotal 2707.14 4101.72 mg
mtotal 5414.28 8203.45 mg
qe 408.12 533.41 mg/g
ttotal 6000 7080 min
Co 3418.103 3418.103 mg/L
Breakthrough time,tb 3600 5400 min
Saturation time,ts 3780 5580 min
Exhaustion time,te 3960 6000 min
qb 5206.09 4866.78 mg/g
Volume Processed at breakthrough,Vb 7.92 14.4 L
Volume of Adsorbent Bed 0.942 1.885 mL
BV 840.76 763.93 –
AER 0.64 0.698 g/L
EBCT 2.34 3.93 min
Fig. 4. Effect of bed height on fixed bed column efficiency.
2.3. Characterization
The liming and chrome tanning wastewater, liming sludge, and derived biochar were characterized for physicochemical and surface texture. The liming sludge was characterized in terms of pH (Brunner,1978), moisture content (%) (BIS,1971), and dry matter (%). The wastewater was analyzed by pH, electrical conductivity (EC), total dissolved solids (TDS) and total suspended solids (TSS) (APHA,2012), biochemical oxygen demand (BOD) (APHA-5210 B,2001), chemical oxygen demand (COD) (APHA-5220 C,1997), and chloride (Cl−) (APHA-4500 B,1989). Each parameter was evaluated three times to establish the mean value and standard deviation. The prepared biochar was characterized before and after adsorption of Cr in terms of Fourier Transform Infrared (FTIR) Spectroscopy (Spectrum 100, Perkin Elmer, USA), Scanning Electron Microscope (SEM) (JEOL JSM-6490, USA), and Energy Dispersive X-ray (EDX) (Sigma HV, Carl Zeiss Microscopy Ltd.). The surface area, volume and diameter of pores, and mean particle size of the liming sludge and biochar were measured following Brunauer–Emmett–Teller (BET) analysis on the basis of nitrogen by Particle Analytical (Micromeritics Gemini 2375 and Gemini V). The details of the characterization are added in the supplementary information (SI 1.).
2.4. pH point of zero charge (pHzpc)
pH point of zero charge was evaluated to measure the charge of the raw liming sludge and prepared biochar by following the pH drift method originally formulated byMular and Roberts(1966). This method is explained byKosmulski and Mączka(2019). The detailed method of this work is described in supplementary information (SI 2.).
2.5. Batch adsorption
Batch-wise assessment was carried out where the adsorption behavior of chromium on the biochar was studied at fixed conditions. The one factor at a time (OFAAT) approach was implemented to satisfy this objective. The influencing parameters studied for batch adsorption including biochar dose, contact time, shaking speed and dilution factor. Initial pH change was avoided to keep pH below 4.0 (4.0<pH) and thereby prevent chromium precipitation and to maintain the solubility of chromium ion (Wang et al.,2020). The effect of biochar intake was observed by changing the biochar mass at 0.05, 0.1, 0.2, 0.3, 0.45, 0.6, 0.7, 0.95, 1.1, 1.2, 1.4, and 1.7 g for 50 mL of wastewater with 4 h shaking at 150 rpm in an orbital shaker (GFL-Orbital Shaker, Model 3017, Germany), and 44 h settling. The contact times were changed to 30, 60, 90, 120, 150, 180, 210, and 240 min while other parameters were unchanged (50 mL wastewater, dose 0.7 g, 150 rpm and settling 44 h). The shaking speeds were 100, 150, 200 and 250 rpm for 50 mL wastewater, biochar dose, shaking and settling time 0.7 g, 3 h and 44 h, respectively. The impact of dilution factor was observed by varying it at 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 while the other conditions remained unchanged. The residual chromium content was measured using atomic absorption spectroscopy (AAS) (SpectrAA-240FS, Agilent, USA). All experiments were performed in triplicate and the mean value and standard deviation were utilized for the graph.
The biochar uptake and chromium removal percentage were calculated using Eqs.(1)and(2)(Oliveira et al.,2020):
Adsorbent uptake,qt = (C0−Ct)×V
m (1)
Chromium removal (%) = (C0−Ct)
C0 ×100 (2)
Fig. 5. Breakthrough behavior analysis by Thomas (a) Yoon and Nelson (b) and Bohart–Adams model (c).
2.6. Fixed-bed column adsorption
A fixed bed glass column was established for chromium adsorption with 2 cm internal diameter (i.d.) and 30 cm length. Two columns were prepared with 3 cm and 5 cm bed height for enclosing the biochar in layering glass wool
Fig. 6. Effect of biochar dose (a) contact time (b) shaking speed (c) and dilution factor (d) on chromium adsorption capacity.
and glass beads on both sides, which helped to support the biochar medium (Mthombeni et al.,2018). The fixed-bed
column adsorption is depicted in the supplementary figure (Fig. S1). The wastewater was delivered into the column using a gravitational flow at a controlled flow rate 4 mL/min throughout the experiment. The chromium wastewater was passed through the column and the samples at the column outlet were measured for their chromium content after a fixed time period using AAS. Each sample was analyzed in triplicate.
2.7. Investigation of column performance and breakthrough point
In fixed bed column adsorption, the biochar situated close to the inlet point satiates first. Slowly the adsorption approaches the outlet point and after completing saturation of the biochar adsorption sites, the inlet and outlet concentrations are virtually the same. For column performance evaluation, breakthrough curves were analyzed as a plot of time vs. C/C0. Breakthrough curves are the ratio of concentration of the solute at a specific time t to its initial concentration in the column inlet. The effluent volume was determined by Eq.(3)(Oliveira et al.,2020):
Vff=q×ttotal (3)
The cumulative mass of adsorbed heavy metal,qtotal(mg) is located beneath the breakthrough at a fixed flow rate and feed concentration. It is calculated based on the following Eq.(4)(Oliveira et al.,2020;Yahya and Odigure,2015):
qtotal= q 1000
∫ t=total
t=0
CRdt (4)
The metals transmitted to the column,mtotal(mg) were calculated using Eq.(5):
mtotal= C0qttotal
1000 (5)
The metal removal (%) is given by Eq.(6)(Yahya and Odigure,2015):
% Removal= qtotal
mtotal×100 (6)
.
The equilibrium uptake,qeis the portion of the metal that is adsorbed (qtotal) in the column by one unit of dry biochar (m). It is demonstrated using the following Eq.(7)(Nguyen et al.,2015):
qe= qtotal
m (7)
The following equation, Eq.(8)served to determine the bed capacity after reaching the breakthrough point (Bhaumik et al.,2013):
qb= C0 m
∫ Vb
0
(1− Ct
C0) dV (8)
The bed volume (BV) and column size can help explain the total cost of the column (Mthombeni et al.,2018;Onyango et al.,2009). The number of required BV to reach the breakthrough point greatly affects how well the column performs (Onyango et al.,2009), while BV is given by the following expression, Eq.(9)(Bhaumik et al.,2013):
BV= Volume of treated water at breakthrough point (L)
Volume of adsorbent bed (L) (9)
The adsorbent exhaustion rate (AER) dictates the biochar that has to be supplanted. It is described as the amount of biochar exhausted for every unit of treated water volume at the point of breakthrough (Mthombeni et al.,2018) and is measured by Eq.(10):
AER= Mass of adsorbent (g)
Volume of water treated (L) (10)
When the value is low, AER represents better output results of the column (Onyango et al.,2009).
Empty bed contact time (EBCT) is the required time to fill the biochar bed by the influent which is directly related to the flow rate and biochar volume. The EBCT, Eq.(11)finds the required adsorption column size and evaluates the best usage of biochar during adsorption in a fixed bed column. It does this by determining the capital and operating costs (Mthombeni et al.,2018):
EBCT= Adsorbent bed volume (mL)
Flowrate (mL/min) (11)
2.8. Mathematical modeling of breakthrough behavior
The successful column adsorption method is based on accurate estimation of the effluent concentration breakthrough or curve concentration time profile. Several experimental designs have been devised for fixed bed adsorption column (Yahya et al.,2020a). In this study the Thomas, Yoon–Nelson, and Bohart–Adams models were applied to anticipate the efficiency of the adsorption breakthrough curve and column performance (Amirnia et al.,2016). The model hypothesis and equations are stated in the supplementary information (SI 3.).
2.9. Adsorption isotherms
The adsorption was investigated by Langmuir, Freundlich, and Temkin isotherm. Langmuir theory (Langmuir,1916, 1918) represents the partition mechanism of the adsorbate on the adsorbent (solid) and liquid phase following the linear isotherm model. The interactions of the adsorbate and biochar are due to electrostatic bonding, the Van der Waals and hydrophobic interactions (Guo and Wang,2019). The Langmuir isotherm model is linearly expressed by following Eq.(12).
1 qe = 1
qmKL × 1 Ce+ 1
qm (12)
The non-dimensional separation factor (RL) was measured from Langmuir isotherm (supplementary information SI 4.).
To represent the nonlinear adsorption theory, one of the most popular isotherms is the Freundlich model (Freundlich, 1906). The Freundlich model’s linear form is stated by the Eq.(13).
log qe=log KF+1
nlog Ce (13)
The Temkin model implies a multi-layer adsorption process primarily focuses on a linear decline in adsorption energy instead of an exponential decrease as presumed by the Freundlich isotherm. The Temkin isotherm model specifically considers the relationship between adsorbent and adsorbate as well as the amount of biochar saturated during the process (Temkin and Pyzhev,1940). The Temkin model is expressed as follows, according to the Eq.(14).
qe =BTlnAT+BTlnCe (14)
BT = RT bT
2.10. Adsorption reaction kinetics
The reaction rate’s dependence on preliminary metal ion concentrate was explored by employing Lagergren’s solu- tion of pseudo-first-order (PFO) reaction, Ho and McKay’s solutions pseudo-second-order (PSO) reaction, intra-particle diffusion models, and Elovich kinetic model., They are respectively expressed in the following Eqs.(15),(16),(17),(18) (Lagergren,1898;Ho et al.,2000;Priastomo et al.,2020;Chien and Clayton,1980).
ln(qe−qt)=lnqe−K1t;h0=K1qe (15)
t qt = 1
K2q2e + t
qe (16)
qt=Kdift1/2+C (17)
qt= 1
AEln(AEBE)+ 1
AEln(t) (18)
To evaluate the kinetics parameters, the value of log (qe–qt) vs. time (t), t/qt vs. t,qtvs. t1/2, andqtvs. ln(t) were plotted linearly for the PFO, PSO, intra-particle diffusion, and Elovich kinetic model, respectively. The rate constants of PFO kinetic, PSO kinetic, and intra-particle diffusion were estimated from the linear plotting of Eqs.(16)–(18), respectively. Then the correlation coefficients of the kinetic models were compared to find the best model fit for adsorption.
2.11. Desorption
15 g of biochar was saturated at the equilibrium condition and the biochar material was recovered after filtration. The biochar was dehydrated at 105◦C using an oven for 24 h. The eluent, H2SO4(0.5 and 1.0 M) and HNO3(0.5 and 1.0 M) were used for desorption studies. 1.5 g of chromium adsorbed biochar was mixed with 100 mL of the desorbing solutions, stirred for 12 h at 150 rpm and then settled for 6 h. It was carried out to replace the adsorbed chromium with the acid’s H+ions (Gonçalves et al.,2017). The chromium desorption (%) was determined from the following expression, Eq.(19) and is written as follows:
Desorption (%)= md
m0 ×100 (19)
3. Results and discussion
3.1. Wastewater and sludge characterization
Table 1depicts the physicochemical parameters of liming wastewater before and after coagulation; chrome tanning wastewater before and after treatment with biochar. The liming wastewater was exploited to produce liming sludge and deployed to remedy chrome tanning wastewater. The pH, EC, Cl−, TDS, TSS, BOD, and COD of the liming wastewater were 11.5, 39.7 mS, 5.78, 23.17, 27.83, 13.37, and 19.38 g/L, respectively.Villalobos-Lara et al.(2021) studied combined tannery wastewater and found the level of BOD, COD, TDS, and TSS to be 1.8, 14, 50, 6 g/L, respectively. It is comprehensible that after coagulation the parameters were reduced from the liming wastewater, especially EC, Cl−, BOD, and COD; they were 56.7%, 50.5%, 85.5%, and 80.1%, respectively. Chrome tanning wastewater was also characterized before and after adsorption via liming sludge biochar. For chrome tanning wastewater treatment with liming sludge biochar, it can be stated that the pH, and BOD were within the standard (ECR, 1997). The amounts of Cl−, BOD, and COD depleted were 53.9%, 99.4%, and 99.3%, respectively. Hashem et al.(2020) revealed the mitigation of the former parameters by 56%
(Cl−), 93.4% (BOD), and 92.6% (COD), respectively. The liming sludge was characterized for moisture, dry matter and pH.
It emerged that the pH of the liming sludge was closer to 8.5, while the percentages of dry matter and moisture content in the liming sludge were 60.84% and 39.2%, respectively.
After coagulation, treated liming wastewater parameters were significantly reduced. This will remarkably lessen the treatment load during further treatment of the liming wastewater in the Central Effluent Treatment Plant (CETP). In case of chrome tanning wastewater, the adsorption with biochar provided less Cl−, BOD, and COD. The BOD and COD were within the standard limit (ECR (Environment Conservation Rules),1997). This indicates promising results of the treated wastewater.
3.2. FT-IR analysis
Fig. 1represents the FTIR graph for pure and Cr loaded biochar. This figure shows that after adsorption the intensity changes for the chromium loaded biochar. The characteristic peak at 3430 cm−1 expresses the stretching of free intermolecular O-H groups which were found in pure biochar (Gogoi et al.,2018). The peaks at 1628 and 1502 cm−1 indicate the bending and stretching of N-H, and C=C groups from amine and cyclic alkene compound, and strong N-O stretching of nitro compound, respectively (Wang et al.,2020). The peaks shifting at 1110 and 1085 cm−1relate to the strong C-O stretching of aliphatic ether, primary and secondary alcohol compounds. Moreover, there are distinct shifts at 869 and 884 cm−1wavelengths and these highlight the presence of strong C-H and C=C bending vibrations of 1,2,4- tri substituted and alkene groups, respectively. The thermal treatment created intermolecular hydrogen bonding which produced –OH groups in biochar. This tended to elevate the pH of the biochar after the thermal action (Li et al.,2017).
The band characteristics are listed in Table S1.
The FTIR data indicates the disappearance of functional groups, especially –OH, N-H, –CO after the adsorption process.
These functional groups in the biochar are significant for adsorption. The –OH, N-H, –CO groups might help in bonding with the Cr and might be responsible for the increase of wastewater pH during the adsorption (Li et al.,2017).
3.3. EDX analysis
Fig. 2represents the EDX analysis of pure biochar and chromium loaded biochar. The pure biochar reveals the presence of C, O, Al, Si, and Ca in significant amounts. The C and O might originate from organic materials present in the liming sludge and the Al indicates the aggregate formation during sludge preparation (Villalobos-Lara et al.,2021). The Ca ion might derive from the lime (calcium oxide, CaO) that is used in the liming process. After chromium adsorption, the biochar displays clear changes in the peak. The presence of chromium in the loaded biochar verifies the adsorption process satisfactorily. As well, Na, Cl, S, P, Mg, Ca, and K are present. The presence of Na and Cl might be due to the salt used during the hide and skin preservation operations. The sources of S could be the basic chromium sulfate used in chrome tanning operations (Villalobos-Lara et al.,2021). The shift in peaks for Si, O, Na, and Al after adsorption clearly confirms that chemical components were part of the adsorption process (Rambabu et al.,2020).
In the EDX analysis, the presence of chromium is visible only after the adsorption which indicates the adsorption of chromium onto the biochar. Thus, the elemental analysis demonstrates: firstly, that the chromium is adsorbed on the biochar surface by this method; and secondly, the presence of Cr is confirmed after adsorption.
3.4. SEM analysis
Fig. 3presents the SEM images of liming sludge, pure biochar and chromium loaded biochar, respectively, to understand the surface morphology, and change in structure due to adsorption process. The surface of the liming sludge looks rough and coarser than the biochar inFig. 3(a), whereas the biochar has a smooth surface and cloud-like material inFig. 3(b).
During the thermal activation, the organic compounds and some other materials present in the liming sludge break down.
This could be due to the disintegration of the cellulose, hemicellulose and organic molecules during heat treatment (Zhao
Table 4
Thomas, Yoon–Nelson and Bohart–Adams models parameters.
Model Parameters Unit 3 cm 5 cm
Thomas KTh mL/mg min 4.39×10−4 6.44×10−4
qo mg/g 12554.51 6092.09
R2 – 0.9589 0.9703
Yoon Nelson Model KYN min−1 0.0021 0.0021
T min 4042.143 4720.524
R2 – 0.9708 0.9803
Bohart Adams Model KBA L/mg min 2.93×10−7 4.39×10−7
No mg/L 2.315×107 1.71×107
R2 – 0.8619 0.7996
et al.,2021). However, after adsorption the change in surface texture is clearly observed where the chromium is adsorbed on the biochar. The difference in consistency of the pore size is also visible. The changes in the surface morphology of the biochar with a more porous surface provide a larger specific surface area to the biochar. This indicates that the biochar will impart good adsorption capacity. The adsorption of Cr is also observed after the adsorption process through the changes in texture. Therefore, the adsorption of chromium ions on the biochar was confirmed by the SEM images.
3.5. BET surface area
The measurements involved in the BET analysis are presented inTable 2. The specific surface areas for liming sludge and biochar were calculated through BET amounted to 1.58 and 9.20 m2/g, respectively. The surface area of the derived biochar was increased due to the thermal activation of liming sludge.Xu et al.(2021) found the specific surface areas for carbon sphere and activated carbon sphere to be 19.1 and 1491.2 m2/g, respectively.
At a higher surface area the adsorption capacity increases. However, such an increase in area is carried out through chemical activation (Zeng et al.,2021) which raises the biochar production costs. For liming sludge and the biochar the total pore volume was 0.00059 and 0.00072 cm3/g, respectively.
Increased pore density enhances the surface area which in turn facilitates the adsorption process (Xu et al.,2019) as observed in this study. The pore size and average particle size of the liming sludge and biochar respectively decreased from 21.27 nm to 5.30 nm for pore size and increased from 25 nm to 652 nm for average particle size. The pore size and average particle size of the biochar was within the mesoporous category according toIUPAC(1972). The increase in the specific surface area, as well as particle size due to the derivation of liming sludge to biochar, facilitates the adsorption process. Therefore, the biochar could be beneficial in the adsorption of Cr from tannery wastewater.
3.6. Fixed-bed column behavior
The fixed-bed column investigation was carried out with chrome tanning wastewater of 3418.1 mg/L chromium at 4 mL/min flow rate with two bed heights of 3 cm and 5 cm, respectively. The breakthrough curve for varying bed heights is expressed inFig. 4. It is noticed that with the increase of bed height from 3 to 5 cm this causes an increase in breakthrough as well as exhaustion time. The reason might be the increase in the bed height and biochar mass, and the available adsorption sites also increases. Moreover, the retention time for the effluent rises as the effluent takes more time to reach the outlet (Singh et al.,2017; Singha et al.,2012). As a result, the volume of total treated effluent expands. The reason might be that when the bed height is low, the mass transfer process is affected by axial dispersion. Consequently, the adsorbate diffusion falls. These results indicate that higher bed height has the potential to treat larger volumes of wastewater over a longer period of time. A similar outcome was observed where due to the increase in bed height, the exhaustion time, as well as the total volume of treated effluent, also increased (Nithya et al.,2020;Ajmani et al.,2020;
Yahya et al.,2020a;Mthombeni et al.,2018).
3.7. Column performance breakthrough point of column bed
The breakthrough point of a fixed bed column is the point where the inlet and outlet concentration ratio rises from 0.05 to 0.9. Adsorption capacity is calculated at the breakthrough time which is reached when the ratio increases to 0.5.
However, the column can still operate till the ratio is 0.9 and the exhaustion time is found when the outlet concentration is virtually the same as the inlet concentration, i.e. the ratio becomes≈1 (Chowdhury et al.,2014).
Table 3includes different parameters of the fixed bed column at the breakthrough and exhaustion points as well as the column performance. It emerged that when the bed height increases, the total effluent volume increases from 15.84 L to 24 L. Moreover, with the elevation of column bed height, removal efficacy improved from 67.39% to 72.62%. The initial chromium content of the wastewater was 3418.1 mg/L. At 3 cm bed height, the cumulative mass of adsorbed and transmitted metal to the column were 2707.14 and 5414.28 mg, and at 5 cm bed height 4101.72 and 8203.45 mg,
Fig. 7. pH point of zero charge of biochar and liming sludge (a) and change of pH with biochar dose (b).
respectively. The explanation could be that the enlarged bed height, adsorption sites as well and retention time increases all make it possible to treat a large volume of water and enhance the adsorption process (Singh et al.,2017). The lower height bed gets saturated faster due to less active site. This results in a rapid breakthrough point and consequently, removal efficiency also increases.Nithya et al.(2020) andAjmani et al. (2020) also documented that at the lower bed height; the saturation occurs faster, which provides higher removal efficiency.Table 3shows that the breakthrough time, saturation time, and exhaustion time increased with bed height rising from 3600 to 5400 min, 3780 to 5580 min, 3960 to 6000 min, respectively. Lower bed height contains less bed mass which might reduce the mass transfer zone and metal ion diffusion rate (Mthombeni et al.,2018). After comparing the two bed heights, the higher bed accommodates an enlarged mass transfer zone with the availability of sufficient active sites (Singha et al.,2012).Table 3indicates that when the bed height is larger, the mass transfer zone broadens and it enhances the breakthrough as well as exhaustion time. This results in efficient treatment of a larger amount of wastewater.
In this work, the treated volume of wastewater and number of bed volume processes shifted from 7.92 L to 14.4 L and 840.76 to 916.61 with respect to bed height. The breakthrough capacities were calculated to be 5206.09 and 4866.78 mg/g, respectively. The AER and EBCT were measured for two different bed heights as 0.64 and 0.698 g/L, and 2.34 and 3.93 min, respectively. The BV and AER are deemed to be performance indicators for fixed bed column adsorption. In this investigation, the larger bed height provided better data, possibly explained by that in the larger bed, the surface area and biochar load are higher. This creates great potential for more chromium adsorption. Consequently, in industrial- scale application, larger bed height should be chosen for overall column bed performance. It should be noted that when designing of the column, the bed depth and particle diameter ratio should be larger than 20 to avoid wall as well as axial dispersion impacts (Nithya et al.,2020).
Fig. 8. Adsorption isotherm: Langmuir isotherm (a) Freundlich isotherm (b) and Temkin isotherm (c).
3.8. Mathematical modeling of breakthrough behavior 3.8.1. Thomas model
According to the Thomas model, the column adsorption process obeys the Langmuir isotherm with second order kinetics. Moreover, it describes that the mass transfer during adsorption is not restricted by only chemical reactions and is applied with insignificant diffusion resistance (Aksu and Gönen,2004).Fig. 5(a) represents the Thomas model plotted by ln[(C0/Ct)-1] vs.t to calculateKThandq0. The Thomas parameters are listed inTable 4. The regression coefficient,R2 of the study suggests that the experimental data fits well when the bed height is higher. The R2value for the 5 cm bed height was 0.9703. It means that at a higher bed height the value ofKTH decreases andqo increases which echoes the findings ofAjmani et al.(2020). However, the adsorption capacity measured by the Thomas model did not align with the experimental capacity. This behavior was also observed byMthombeni et al.(2018).
3.8.2. Yoon-Nelson model
Fig. 5(b) andTable 4express the Yoon Nelson graph and data representation. The value ofKYNandτ for bed height 3 cm and 5 cm denotes that the 50% breakthrough time (τ) escalates with bed height although the rate constant,KYN, remains the same. The increase in 50% breakthrough time demonstrates that the adsorption will last longer due to the availability of greater retention space. This was also established by other research (Yahya et al.,2020a). The regression coefficient,R2shows that the data set are well fitted (R2≥0.97) in this model. However, it is more suitable when the bed height is larger.
3.8.3. Bohart–Adams model
The Bohart–Adams model established a relationship between Ct/C0 and t in an uninterrupted packed bed column adsorption established on surface interaction theory (Bohart and Adams,1920). The model speculated that adsorption equilibrium does not occur spontaneously and the adsorption rate proportionately depends on the residuary adsorbent capacity and adsorbent concentration (Karimi et al., 2012). From Fig. 5(c) and Table 4it is understood that with the increase of bed depth, the value ofKABincreases andNodecreases. This is consistent withNguyen et al.(2015) andWang et al.(2015). However, the lower regression value of the model for both bed heights means that the stated model does not support the proposed adsorption practice.
3.9. Dose optimization
Fig. 6(a) shows the effect of biochar doses of 0.05, 0.10, 0.20, 0.30, 0.45, 0.60, 0.70, 0.95, 1.10, 1.20, 1.45, and 1.70 g/50 mL wastewater on adsorption capacity. With the increase in adsorption dose, the adsorption capacity at first rises and then falls as shown inFig. 6. This behavior relates to the available adsorption sites at lower dose which diminished even though the doses rose. At a lower dose, the amount of Cr is higher than the available surface area. As a result, the adsorbate (Cr) gets easily saturated on the biochar surface. With increasing the biochar dose, adsorbable surface area for Cr adsorption was increased. But due to the change in the ratio of biochar surface area to Cr concentration, the adsorption capacity was decreased when the biochar dose was increased (Shoukat et al.,2017;Tahir et al.,2016). The aggregation of the particles might also be responsible for the Cr adsorption capacity.Manzoor et al.(2019) stated this is a common phenomenon during adsorption. The highest adsorption capacity was found at a dose of 0.70 g/50 mL which was considered to be the best possible dose.
3.10. Contact time optimization
Fig. 6(b) represents the adsorption potential of the system. It seems that the adsorption capacity increases up to 180 min and then decreases. The cause of the increase could be linked to the initially large number of pores available for adsorption as observed by other researchers (Shobier et al.,2020;Yahya et al.,2020b;Manzoor et al.,2019). However, after reaching equilibrium (180 min), the adsorption rate falls which might be unconventional. However, the associated reasoning could be the repulsive forces of the adsorbed chromium and the free chromium present in the solution (Khan et al.,2017). Moreover, the increasing contact time after equilibrium could free some of the adsorbed chromium back into the solution. Therefore, for this experiment 180 min was selected as the best contact time for a 50 mL sample volume.
3.11. Shaking speed optimization
The removal and adsorption of Cr was measured at differing shaking speeds of 50, 100, 150, 200, 250 rpm with a dose of 0.7 g and 180 min contact time for 50 mL wastewater.Fig. 6(c) expresses the positive change in adsorption ability when the speed rose and reached the maximum at 150 rpm. However, after that the adsorption capacity fell at 200 and 250 rpm.Swathy et al.(2019) explained that higher shaking speed could aggravate the adsorbed Cr ions by raising their kinetic energy. This could promote possible desorption of the Cr ions, subsequently worsening the removal efficiency.
3.12. Dilution factor optimization
Fig. 6(d) depicts the effect of dilution factor on adsorption capacity, and the latter increases when there is an increase in the dilution factor by up to 2.5, i.e., the decrease in initial concentration. This might be because as dilution increases, the ratio of available chromium ion adsorbing sites on biochar to total chromium ions present in the bulk solution also expands (Chen et al.,2019). However, after that the adsorption capacity did fall, the lower dilution, i.e., higher chromium concentration created a larger concentration gradient as the driving force. This helped to override the resistance of chromium and biochar surface mass transfer. Consequently, this phenomenon enhanced the amount of chromium being driven to the binding sites of the biochar (Chen et al.,2019).
3.13. pHzpc
The isoelectric point or pH of zero point of charge (pHzpc) represents the pH value at which the surface anionic and cationic charges of the biochar are equal. The pHzpcestablishes the working pattern for the metal adsorption (Rambabu et al.,2020). When the pH<pHzpc, the biochar surface turns cationic due to protonation. Conversely, when the solution pH>pHzpc, the surface is negative which favors cationic ion adsorption (Gonçalves et al.,2017). In this experiment, the pHzpcof both dried liming sludge and liming biochar was determined.Fig. 7(a, b) represents the pHzpcof the liming sludge biochar and sludge, respectively, which proved to be 6.1 and 4.1, respectively. It was perceived that the pHzpcdid change when liming sludge was thermally treated to produce the biochar. The change in pHzpc strongly suggests a chemical change in the sludge that alters the availability of the active groups, thus shifting the pHzpc(Covington,2011). The data indicates that when the solution pH is greater than 6.1, the chromium ions will be adsorbed on the negative charges of the biochar surface. This result is consistent with the changes of pH during dose estimation, as shown inFig. 7(c). The increase in the dose escalates the solution pH and the percentage removed. The best removal efficiency emerged when the solution pH was 6.4, which was greater than the pHzpc. This pH point of dose optimization test is consistent with the pHzpcvalue.
3.14. Isotherms
The adsorption isotherm represents the adsorption process through different mathematical modeling to provide the necessary information for an optimized equilibrium adsorption process (Khosravi et al.,2018). Moreover, the isotherm analysis describes the adsorption mechanism with the adsorbate characteristics on the biochar surface (Martins et al., 2015). This investigation seeks to describe Cr adsorption onto the surface of lime sludge biochar through three adsorption models: Langmuir isotherm, Freundlich isotherm, and Temkin isotherm. These are explained in more detail below.
3.14.1. Langmuir isotherm
Langmuir isotherm presumes monolayer adsorption on biochar surface and explores the binding capacity onto the active sites of biochar (Liu et al.,2020). According to this model, the adsorption process is in proportion to the active biochar surface fraction whereas the desorption system is proportionate to the covered biochar surface fraction (Ayawei et al.,2017).
Fig. 8(a) shows the plotted 1/qe vs. 1/Ce presenting the Langmuir isotherm and Table 5 tabulates the isotherm parameters. The value of the Langmuir separation factor,RL is 0.2241 which is within 0 to 1 and this indicates that the adsorption process is favorable (Rambabu et al.,2020;Ayawei et al.,2017). The regression coefficient of the isotherm was 0.9527.
3.14.2. Freundlich isotherm
Freundlich isotherm explains the behavior of adsorbate molecules on the heterogeneous biochar surface (Boulaiche et al.,2019). The isotherm defines the heterogenic surface with the exponential dissemination of the biochar active sites’
energies (Ayawei et al.,2015). In this theory, the heterogeneity factor, n, indicates the deviation from linear adsorption. If the adsorption process is linear, physical, or chemical then the value of n is 1, >1, or <1, respectively (Khosravi et al.,2018).
Table 5shows that the value of n in this study is 1.42 which is >1 which implies a physical adsorption process occurring.
Moreover, the regression coefficient was found to be 0.9905, as depicted inFig. 8(b). In comparison with the Langmuir isotherm, it discloses that chromium adsorption on the biochar fits the Freundlich isotherm better than the Langmuir isotherm. The isotherm parameters stipulate that the current study follows heterogeneous adsorption by physical bonding aligning with the Freundlich model.
3.14.3. Temkin isotherm
The Temkin model exclusively considers the biochar–adsorbate interaction by expressing with a special factor. This model presumes that with coverage the adsorption heat of all the molecules decrease linearly instead of logarithmically at medial concentration. The adsorption process could be characterized by the binding energies at an even distribution, up to the maximum level (Martins et al.,2015;Temkin and Pyzhev,1940).Fig. 8(c) shows the Temkin model as a plotted qe vs. lnCe and the parameters are revealed in Table 5. The value of the bonding factor, bT is 10.68 which indicates physical interactions between the adsorbate and biochar (Shobier et al.,2020). However, the correlation coefficient value, R2(0.9003) indicates that this model is ill-suited to explain the adsorption.
Table 5
Adsorption isotherm parameters for Temkin, Freundlich and Langmuir model.
Temkin isotherm Freundlich isotherm Langmuir isotherm
Parameter Value Parameter Value Parameter Value
bT(KJ/mol) 10.68 n 1.42 qm(mg/g) 714.29
lnAT −4.78 logKF 0.509 KL(L/mg) 0.0013
AT(L/g) 0.0084 KF((mg/g)(L/mg)1/n) 3.23 RL 0.2241
R2 0.9003 R2 0.9905 R2 0.9527
Table 6
Adsorption kinetics parameters for 1st order, 2nd order, Intra Particle Diffusion and Elovich.
Pseudo First Order Pseudo Second Order Intra Particle Diffusion Elovich
Parameter Value Parameter Value Parameter Value Parameter Value
k1
(min−1)
0.0029 k2
(g mg−1 min−1)
0.0002 Kdiff
(mgg−1min−1/2)
4.4497 AE
(g mg−1)
0.045
qe,cal(mg/g) 99.27 qe,cal(mg/g) 99.01 C 13.222 BE
(mg(g min)−1)
3.422
R2 0.9394 R2 0.9922 R2 0.9657 R2 0.9842
3.15. Kinetics
The adsorption kinetics helps to design the adsorption system at the optimum operating conditions in the batch process. The adsorption kinetics are dependent on several steps, including the solute transfer to the adsorbent surface, from adsorbent surface towards the intra-particle adsorbent active sites and the retention time on these active sites (Tahir et al.,2016). The kinetics analysis helps to predict information on the adsorbate–biochar interaction, i.e., whether it is a form of physisorption or chemisorption (Taha et al.,2018). The adsorption kinetics were analyzed to explain the mechanism, rate and chemical reaction of the adsorption process through PFO, PSO, intraparticle diffusion, and the Elovich model. The kinetic models and parameters are written inFig. 9andTable 6. The sequence of the regression coefficient (R2) of the models is: PSO (0.9922) > Elovich (0.9842) > Intra Particle Diffusion (0.9657) > PFO (0.9394). The correlation coefficient indicates how well the data set fit a model. The lower correlation coefficient of SFO indicates that the adsorption does not occur exclusively as one active site per every ion (Zou et al.,2011). The Elovich model highlights an elevated correlation value higher than the first order and close to the second order. Also, the Elovich model is often applied for heterogeneous adsorbate surface (Yahya et al.,2020b). This basically agrees with the isotherm data which designates the adsorption occurring on a heterogeneous surface.
The R2 value indicates that the PSO model fits better the adsorption process. This model presumes that adsorption occurs over two stages, in which the first one reaches the equilibrium very quickly and the second requires more time (Khambhaty et al.,2009). The highest experimental adsorption capacity was found for the PFO model as 99.27 mg g−1 which is very much closer to the PSO model as 99.01 mg g−1. Although the PFO showed slightly a higher adsorption capacity, according to the correlation coefficient, the PSO model fits better for this adsorption process. The adsorption rate found for this model was 1.96 mg g−1 min−1. This implies the practical usefulness of the study. This model can convincingly explain the adsorption process through physicochemical interactions between adsorbate and biochar (Robati, 2013). Several adsorption studies of chromium onto C-phenylcalix[4]pyrogallolarene, cobalt ferrite-supported activated carbon, nanocomposite also followed the PSO model (Priastomo et al.,2020;Yahya et al.,2020b;Khosravi et al.,2018).
The analysis and comparison among the four adsorption kinetics model showed that the PSO model is better to explain the dynamics of the adsorption reaction.
3.16. Desorption
Fig. 10and Table S2 illustrate the chromium desorption study of the chromium adsorbed biochar with H2SO4(0.5M and 1.0 M) and HNO3(0.5M and 1.0 M). Results indicate that when the molarity of the H2SO4increases, desorption percentage diminishes from 80.51% to 60.91%. (Sahu et al.,2009) also noted that the desorption percentage depend on the H2SO4 molarity. It was observed that the percentage of desorption and H2SO4molarity are inversely proportional.
This might be due to the formation of basic chromium sulfate, Cr(OH)SO4 (Rengaraj et al.,2001). However, unlike H2SO4, desorption efficiency slightly increases from 52.23% to 56.30% with the increment of HNO3 concentration.
Following this process, 0.5 and 1.0 M of H2SO4 could desorb 52.48, 69.37 mg/g Cr and 0.5 and 1.0 M of HNO3 could desorb 48.51, 45.00 mg/g Cr, respectively. This recovered chromium could then be reused after required treatment and purification and not go to waste.
Fig. 9. Adsorption kinetics: Pseudo First Order (a) Pseudo Second Order (b) Intra Particle Diffusion (c) and Elovich model (d).
Fig. 10. Desorption study with H2SO4and HNO3.
3.17. Adsorption mechanism
The probable mechanism for Cr adsorption onto liming sludge biochar could be due to electrostatic attraction, hydrogen bonding and dipole interaction (Tran et al.,2017). The electrostatic interaction of biochar and Cr mechanism could be explained from the pHzpcand pH of the solution. According to explanation in Section3.13, at pH> pHzpc, the negatively charged surface of the biochar binds with the cationic Cr. This was also observed byTran et al.(2017). From FTIR analysis (Section3.2) the presence of –OH functional group at 3430 cm−1shift after adsorption. This could be due to the hydrogen bonding between the hydroxyl and Cr. Moreover, the carbonyl and amino group of the biochar could be responsible for dipole–dipole interaction. The pair of electrons available in the oxygen and nitrogen might act as electron donor whereas the free d-orbital of Cr could act as electron acceptors (Dinh et al.,2019). Therefore, the adsorption of Cr onto the liming sludge biochar could possibly be due to electrostatic attraction, hydrogen bonding or dipole interaction.
4. Conclusion
This study explored the utilization of liming sludge biochar for dynamic and static chromium adsorption from tannery wastewater. The one factor at a time batch study and column performance report maximum chromium adsorption capacities of 152.12 mg/g and 533.41 mg/g, respectively. The pHzpc of the biochar specifies that at pH>6.1, cationic ion adsorption is possible. The isotherm and kinetics study discloses chemisorption of chromium on the heterogeneous surface. The mathematical model of the column breakthrough curve shows a highest linearity in the Yoon–Nelson model, thus confirming the dependence of adsorption on chromium breakthrough on biochar surface. The characterization of biochar surface morphology, functional group, and surface area points to the possibility of creating a biochar based on liming sludge. Till now, no research has been done on using liming sludge as a biochar for treating tannery wastewater.
This study not only acknowledges the trailblazing ‘waste control by waste’ approach but also shows how to make good use of abandoned tannery liming sludge waste.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
CRediT authorship contribution statement
Sofia Payel:Conceptualization, Methodology, Formal analysis, Writing – original draft.Md. Abul Hashem:Resources, Validation, Writing – review & editing, supervision.Md. Anik Hasan:Methodology, Data curation, Writing – review &
editing.
