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Heavy metal sorption in biosorbents – Using spent grain from the brewing industry
Journal of Cleaner Production 225 (2019) 112e120
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Heavy metal sorption in biosorbents e Using spent grain from the brewing industry Sławomir Wierzba*, Andrzej Kłos University of Opole, Institute of Biotechnology, Kardynała Kominka 6a, 40-035, Opole, Poland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 July 2018 Received in revised form 23 March 2019 Accepted 26 March 2019 Available online 30 March 2019
The sorption properties of brewer's spent grain (BSG) were assessed in terms of its usefulness in removing heavy metals from solutions. The specific objectives of the investigation involved studying sorption equilibrium and kinetics, assessing the influence of other cations on sorption efficiency, and examining the possibility of bed regeneration. The studies were carried out in a static system, with a constant ratio of solution volume to biosorbent mass, and using a flow through system. The affinity of metal cations to BSG functional groups was established and increased in the series: Mn2þ z Zn2þ < Ni2þ < Cd2þ < Cu2þ < Pb2þ. The BSG's sorption capacities (mmol g1), determined using the Langmuir model, ranged from 0.020 (Mn) to 0.041 (Pb), with measurements of uncertainty not exceeding 20%. It was determined that a relatively high concentration of calcium cations (7.5 mmol L1) versus copper cation concentration (0.03 mmol L1) in solution, restricted the sorption efficiency by approximately 80%. The optimum pH of the solution used to carry out the sorption was determined to be within the range of 4.5e5.5. It was found that the functional groups of the BSG lose their efficacy over consecutive sorption and desorption cycles due to being blocked, or due to their chemical decomposition. This indicated that the proper interpretation of the results requires a statistical assessment of the measurements of uncertainty and of the uncertainty of determination of the sorption parameters. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Biosorption Heavy metals Sorption kinetics and equilibrium Measurements uncertainty Bed regeneration
1. Introduction The increase in surface water pollution has been one of the main problems in the protection of the natural environment from anthropo-pressure. Apart from the numerous organic compounds discharged into surface waters from communal and industrial effluent, and the leachate from fields; heavy metals cause particular concern (Ahmaruzzaman, 2011). Cadmium, mercury, and lead are among the metals with defined, strong toxic characteristics (Souza et al., 2017). Lead poisoning may cause disorders of the gastrointestinal system, kidneys, and central nervous system (Palin et al., 2016); and cadmium poisoning causes hypertension, anaemia, and emphysema (Jain et al., 2015). Many industries discharge considerable amounts of heavy metals in their waste water and effluent, for example, mining, metal processing, electroplating, tanning, dyes, and battery manufacturing (Lu and Gibb, 2008). In recent years considerable efforts have been made to design and implement technologies to remove heavy metals from waste water,
* Corresponding author. E-mail address: [email protected] (S. Wierzba). https://doi.org/10.1016/j.jclepro.2019.03.286 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
preventing their transfer to surface waters. The present study is in line with contemporary trends in bioeconomy in terms of the use of bio-waste for the treatment and possible recovery of heavy metals from waste water. In its research programme ‘Horizon (2020),’ the European Commission has attached particular importance to bioeconomy, and included it in the investment programme. There is no universal process for removing heavy metals from waste water and effluent. A combination of methods such as chemical precipitation, coagulation, ultrafiltration, electrolytic processes, reverse osmosis, oxidation, membrane filtration, ion exchange, and adsorption are used in practice (Ahmaruzzaman, 2011). Literature sources indicate that liquid phase sorption is one of the most popular methods for removing pollution from water solutions (El-Shafey, 2010). The process is an attractive alternative for treating polluted water, in particular when the sorbent used is not expensive and does not require conditioning prior to use. The need for easily accessible, cheap and efficient materials, with a well-developed surface, and that has a large sorption capacity, drives research on the use of industrial waste materials, both raw and modified (Ahmaruzzaman, 2011). Mining and power industries, agriculture, forestry, and food processing industries are
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sources of such waste materials. Thermal power plant ash (Tofan et al., 2008), rice husk ash (Srivastava et al., 2009), lignin isolated from black liquor, paper industry waste products (Guo et al., 2008), xanthated nano banana cellulose (Pillai et al., 2013), meranti sawdust (Rafatullah et al., 2009), chemically modified sawdust from white pine (Salazar-Rabago and Leyva-Ramos, 2016), beech sawdust (Witek-Krowiak, 2013), eucalyptus bark saw dust, mango bark saw dust, pineapple fruit peel (Mishra et al., 2010), Acacia leucocephala bark powder straw (Munagapati et al., 2010), hazelnut (Corylus avellana) and almond shells (Prunus dulcis) (Pehlivan et al., 2009), waste rice straw (Rocha et al., 2009), barley straw (Thevannan et al., 2010), peanut shells (Tas¸ar et al., 2014), chemically modified rice bran (Fatima et al., 2013), and liquor distillers' grain (Zhang et al., 2016) have been considered for use in removing heavy metals from water solutions. The brewing industry generates large volumes of by-products, including spent grain (BSG), hops, and yeast. BSG represents approximately 85% of all the by-products generated by the industry; it is produced all year round in all types of breweries and is relatively cheap (Mussatto, 2014). BSG is used, among other things, as animal fodder (Aliyu and Bala, 2013). Due to its high fibre content and antioxidants, BSG can be used as an additive for diet foodstuffs, or as a source of ingredients for the pharmaceutical and cosmetics industries (Moreira et al., 2013); and because of its chemical composition it can also be used to remove heavy metal cations from waste water. This chemical composition consists of the following elements (% of dry mass): glucan (21.7%), xylan (13.6%), arabinan (5.6%), lignin (19.4%), protein (24.7%), ash (4.2%) and extracts (10.8%) (Meneses et al., 2013); as well as numerous related functional groups like hydroxyl, amine, and carboxyl. In the literature, sources describe a small number of studies that focus on the bonding of heavy metal ions, such as lead (Li et al., 2010, 2009), chromium (Ferraz et al., 2015) and copper (Lu and Gibb, 2008) by BSG. The main objective of these studies was to describe the mechanisms involved in the process, and determine the influence of environmental conditions on sorption efficiency. The main problem with describing the sorption characteristics of various biosorbents is the lack of statistical analysis of the results. When describing the process kinetics and equilibrium, the authors usually provide correlation coefficients but do not analyse the uncertainty in the determination of the sorption parameters. It has been demonstrated that the uncertainty of parameters determined from the Langmuir model may be close to or higher than the set value, depending on the experimental method used (Rajfur et al., 2012). The objective of these studies was to determine and compare the sorption parameters for selected heavy metals when using BSG to remove them from solutions. The influence of other cations in the solution, and pH levels, was considered. Certain important parameters, including statistical ones, which characterise the usefulness of biosorbents in the process, were calculated and discussed. A hypothesis was put forward that BSG, as a by-product, can be used as a biosorbent for the removal of heavy metals from waste water, and the main metal sorption mechanism is a reversible heterophasic ion exchange reaction that enables the regeneration and multiple use of the BSG bed.
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(Wierzba et al., 2018). After conditioning, the BSG was rinsed a few times with demineralised water until the water in which the BSG was immersed achieved a stable pH. The material was then dried for 24 h at temperature of 323 K until a constant mass was obtained. 2.1. The research method The sorption of heavy metals (Mn, Ni, Cu, Zn, Cd and Pb) from solution was carried out in static and flow-through systems. In the static system, the BSG sample (0.5 g) was immersed in 200 mL of metal solution. The solution was then thoroughly mixed using a magnetic agitator. The process was carried out for 60 min. Changes in metal concentrations were determined in the solution before (cM(0)) and after (cM(1)) sorption process. Metal concentrations in the BSG after sorption (qM(1)) was determined from Equation (1):
qMð1Þ ¼
cMð0Þ cMð1Þ ,V m
(1)
Where V is the volume of the sorption solution, and m is the sorbent mass. In the flow-through system, biosorbent samples (approximately 1 g) were placed in a perforated container through which the metal solution was directed at the rate of approximately 150 mL min1, ensuring a constant concentration of metal in the solution in which the biosorbent was immersed (Fig. 1). The process was carried out for 60 min. The amount of metal was determined for the initial solution and the BSG before and after the sorption process. The hydraulic retention time of the solution in the chamber in which the container with the sample was placed, amounted to approximately 40 s. In the static system the parameters of the kinetics of the sorption were determined by taking regular measurements of metal concentrations in the solution throughout the process. An assessment of the possibility of bed regeneration was then carried out. The studies of the process kinetics were carried out using solutions of metals with initial concentrations of 0.03 ± 0.002 mmol L1; while the initial concentrations used to determine the Langmuir isotherm were varied within the range of 0.015e0.50 mmol L1. To optimise the sorption process, the initial pH of the metal solutions was adjusted using hydrochloric acid to between 4.4 and 4.5. To assess the possibilities for the regeneration and multiple use of BSG, copper sorption and desorption were repeated several times
2. Materials and methods BSG with a grain size of 1.25e1.50 mm was used in these studies. The BSG (60 g) was rinsed with demineralised water that had a conductivity of k ¼ 0.5 mS cm1. Next, to saturate the functional groups with hydrogen cations, the material was conditioned for 60 min in an HCl solution with a concentration of 0.03 mol L1
Fig. 1. Diagrams showing the sorption assemblies for a static system and a flowthrough system (numerical values are in mm).
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in a static set up. Copper was selected due to its high affinity for the active centres of the BSG, and the low uncertainty of the determination of this metal. The copper sorption was carried out using 200 mL of copper sulphate solution with a concentration of 0.02 mmol L1; desorption was carried out in an HCl solution of 100 mL volume. The sorption and desorption cycles lasted for 60 min. The optimum acid concentration was determined by comparing the copper desorption efficiency to 1 g of BSG previously saturated with copper cations that were immersed for 60 min in HCl solutions with concentrations ranging from 0.01 to 0.5 mol L1. 2.2. Equipment and reagents An absorbing atomic spectroscope, model iCE 3500 made by Thermo Electron Corporation, USA, was used to determine heavy metal concentrations. A pH meter, model CP551, made by Elmetron Sp.j. from Zabrze (PL), was used to determine the pH of the solutions in which the biosorbents were immersed; its absolute reading error was: DpH ¼ 0.02. The BSG (0.4 g) was mineralised in a mix of nitric acid and hydrogen peroxide in a Speedwave Four (Berghof, DE) microwave mineraliser. MERCK reagents were used to prepare the solutions. 2.3. Quality and quality assurance The experiments were repeated 10 times, using the same initial solutions. The standard deviation from the mean value did not exceed 5% and, in the case of the solutions diluted below the upper limit of the determination characteristics for the iCE 3500, the standard deviation was not higher than 7%. Table 1 presents the detection limits (MDL) and minimum quantity limits (MQL) of the device, calculated where the mass of organic material to be mineralised was m ¼ 0.4 g dry mass; and the solution volume post mineralisation was V ¼ 25 mL. The result was converted to 1 g of organic material. Detailed data about the equipment, together with an evaluation and a quality assurance, have been published in (Kłos et al., 2015). 2.4. Results, interpretation, and method The pseudo-second-order reaction model (Ho and McKay, 1999) that was used to interpret the results (Equation (2)), is most frequently applied to describe the parameters of heavy metals sorption kinetics in biomasses. The Langmuir isotherm model was used to describe the equilibrium parameters (Equation (3)).
t qMðtÞ
¼
completion of the sorption process; qM(max) is the sorbent sorption capacity, determined for t ¼ 60 min; and KL is the Langmuir equilibrium constant. The independent variables in Equations (2) and (3) are the time (t) and the reciprocal of the concentration of the metal in the solution: cM(1); and the dependent variables are t/qM(t) and the reciprocal of the concentration of the metal in the sorbent: qM(1). In the case of the heterophasic ion exchange reaction, the Langmuir constant describes the reaction equilibrium state: Mþ þ R $ MR
(4)
Such an equilibrium is established in the flow-through set up (Fig. 1) in which the cations desorbed from the BSG are washed out of the reaction chamber. In the static set up in which the BSG is immersed in a defined volume of the solution, the following equilibrium was established: Mþ þ CtR $ MR þ Ctþ
(5)
Where Ctþ is the cation desorbed from the grain during the ions exchange process. To assess the influence of alkalinisation on the creation of nonactive forms of metal hydroxides, the following well known formula was used: pHprec. ¼ z1 ∙ (14 ∙ z e log cþz M e pKsp)
(6)
Where pHprec. is the pH value above which hydroxides precipitate; z is the cation valence; cþz M is the cation concentration in solution (mol L1); and Ksp is the solubility product constants (pKsp ¼ log Ksp). The Excel software package was used to assess the uncertainty of determining the linear parameters of the equations. 3. Results and discussion To assess the sorption characteristics of the BSG and the possibilities for using this material to remove heavy metals from waste water; the sorption kinetics and equilibrium, the influence of other cations on the efficiency of heavy metal sorption, and the possibility of bed regeneration, were analysed. Changes in the pH values of the solutions were monitored during the experiments carried out on sorption kinetics and equilibrium. The measured pH values did not exceed pHprec.(Equation (6)). 3.1. The kinetics and sorption equilibrium of heavy metals in BSG
1 1 ,t 2 þ qMðeqÞ k”, qMðeqÞ
(2)
1 1 1 1 ¼ þ , qMð1Þ qMðmaxÞ qMðmaxÞ ,KL cMð1Þ
(3)
Where k” is the constant speed of the pseudo-second-order reaction; qM(t) is the metal concentration in the sorbent over time t, and qM(eq) is the metal concentration at equilibrium; qM(1) is the metal concentration in the sorbent and in the solution: cM(1), after Table 1 MDL and MQL values determined for 1 d.m. of mass (mg g1 d.m.). Metal:
Mn
Ni
Cu
Zn
Cd
Pb
MDL MQL
0.0010 0.0030
0.0010 0.0031
0.00088 0.00264
0.00030 0.00090
0.00026 0.00079
0.0014 0.0041
The kinetic parameters of the sorption were determined in order to compare BSG sorption characteristics against the studied metals. Initial concentrations of the metal in solution were 0.030 ± 0.002 mmol L1. The sorption was carried out in the static set up for 60 min, after which the recorded changes in concentration were at the limit of the quantification and resolution of the measurement method (of the order of 102 to 101 mmol L1). Samples of the solution were taken every 5 min for AAS testing. Equation (1) was used to calculate the momentary concentrations of the metals that had accumulated in the BSG. The results are presented in Fig. 2. Table 2 presents data on the parameters for the straight lines shown in Fig. 2, and the kinetic parameters of the process, which were determined on the basis of the pseudo-second-order reaction model. The values of qM(eq) show an affinity series of metal cations and the BSG functional groups. Considering the uncertainty of the
S. Wierzba, A. Kłos / Journal of Cleaner Production 225 (2019) 112e120
Fig. 2. The kinetics of heavy metal sorption in BSG; (cM(0) ¼ 0.030 ± 0.002 mmol L1).
Table 2 The parameters for straight lines y ¼ a∙x þ b, and sorption kinetic parameters determined using the pseudo-second-order reaction model; s e standard uncertainty of directional coefficient (sa) and free term (sb), R2 e correlation coefficient, qM(eq) e metal concentration in the BSG in equilibrium (mmol g1), k” e constant speed of the pseudo-second-order reaction (g mmol1 min1), u e composite uncertainty. Metal
a
sa
b
sb
R2
qM(eq) ± s(%)
k” ± u(%)
Mn Ni Cu Zn Cd Pb
280.9 245.3 199.3 272.7 222.6 167.7
2.5 3.5 1.6 4.2 5.4 2.5
5.560 3.471 1.944 4.828 2.718 1.310
193 131 61 155 199 78
0.997 0.998 0.999 0.998 0.994 0.998
0.0035 ± 0.8 0.0041 ± 1.4 0.0050 ± 0.8 0.0037 ± 1.5 0.0045 ± 2.4 0.0060 ± 1.5
14.7 ± 5.8 17.1 ± 4.7 20.6 ± 3.5 15.1 ± 4.4 19.9 ± 6.5 21.2 ± 6.5
measurements, one may state that the affinity of the cations increases in the series: Mn2þ z Zn2þ < Ni2þ < Cd2þ < Cu2þ < Pb2þ; whereas, the constant speed of the k” reaction, which is proportional to the qM(eq) value, due to the uncertainty of determination; defines the affinity series in a less precise way: Mn2þ z Zn2þ < Ni2þ < Cd2þ z Cu2þ z Pb2þ. Based on Equation (2) one can also define the efficiency of the process. The values, qM(60)/qM(eq), determined for the analysed metals indicate that after 60 min of contact with the solution and the BSG, the sorption process advances by approximately 80%. By using the parameters for the straight lines presented in Table 2, it is possible to determine the contact time required to achieve a set level of the advancement of the process. For example, a 95% advancement of the sorption process for copper is achieved after approximately 190 min of contact between the solution and the BSG, and an equilibrium close to qM(eq) is achieved after approximately two days. The Langmuir isotherm model (Equation (3)) was used to describe the parameters of the heavy metal sorption's equilibrium in the BSG. Sorption was carried out in a flow-through set up, which better reflects the sorption processes occurring in flow-through devices that are used in practice. The results are presented in Fig. 3. Parameters for the straight lines presented in Fig. 3 and sorption parameters determined from Langmuir model in a flow-through set up are collected in Table 3. Note that qM(max) < qM(eq). The data shown in Table 3 indicate that, despite the large values of the correlation coefficients, determining the affinity series for metal cations and the BSG functional groups is ambiguous; this results from the uncertainty of the measurements. Based on the value qM(max) it can be stated that the metal
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Fig. 3. Langmuir isotherms describing the heavy metal sorption's equilibrium parameters in a flow-through set up.
Table 3 The parameters for straight lines y ¼ a∙x þ b and sorption parameters determined from the Langmuir model in a flow-through set up; s e standard uncertainty: sa e directional coefficient, sb e free term, R2 e correlation coefficient, qM(max) e sorption capacity (mmol g1), KL e Langmuir constant, u e composite uncertainty. Metal
a
sa
b
sb
R2
qM(max) ± s(%)
KL ± u(%)
Mn Ni Cu Zn Cd Pb
2.99 2.537 1.95 2.740 2.249 0.901
0.16 0.088 0.11 0.044 0.024 0.043
48.9 35.2 30.4 45.1 33.5 24.2
4.2 2.3 3.6 1.5 1.3 4.3
0.982 0.993 0.980 0.998 0.999 0.986
0.020 ± 9 0.028 ± 7 0.033 ± 12 0.022 ± 3 0.030 ± 4 0.041 ± 18
16.3 ± 11 13.9 ± 8 15.6 ± 14 16.5 ± 4 14.9 ± 5 26.8 ± 19
cations affinity increases in the series: Mn2þ z Zn2þ < Ni2þ z Cd2þ z Cu2þ < Pb2þ (comparison in mol). Based on the Langmuir equilibrium constant, which describes the equilibrium of the reaction presented in Equation (4), one can determine only a good affinity for lead in comparison to other metals: Mn2þ z Zn2þ z Ni2þ z Cd2þ z Cu2þ < Pb2þ (mol). The sequence of metals creating the affinity series is the same as the sequence determined by the kinetic data (Table 2). A method using electroanalytical techniques, such as pH and conductivity measurements, was proposed in determining the metal cations affinity for the active centres of biosorbents. The metal cations affinity for the active centres of the BSG, determined on the basis of pH measurements, increases in the series: Naþ < Kþ < Mg2þ < Mn2þ z Zn2þ < Ni2þ z Ca2þ < Cd2þ < Cu2þ < Pb2þ (val, gram-equivalent, mol concentration converted to cation electric charge) (Wierzba et al., 2018). Table 4 compares the values of the sorption capacities of various biosorbents determined using the Langmuir isotherm model. Sorption parameters are interpreted by most authors as: mg g1, mg L1. These results require conversion. Reactions given in mols relation, and only interpretation of concentrations expressed in mols enables a correct assessment of the affinity of cations for the functional groups of biosorbents. For the purpose of this article, Table 4 uses concentrations converted to mmol g1. The table also presents the results of metal sorption in BSG that had been conditioned in demineralised water for 24 h. The sorption characteristics of the swollen BSG increased by over 100%. The results presented in the table are approximations, with uncertainty resulting from the repeatability of the results with the BSG prepared in this way amounts to 40%. The comparison of the BSG sorption capacities versus other
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Table 4 Comparison of sorption capacities of various biosorbents (mmol g1). Sorbent
Mn
Ni
Cu
Zn
Cd
Pb
Literature
Coconut shell Coffee wastes Peanut shell Almond shell Jatoba fruit shell Mango bark saw dust Rice husk Rice straw Rice bran Rice husk ash Rice straw Sugarcane bagasse Sugarcane bagasse spent grain Liquor distillers' grains, raw Wheat straw Beech sawdust Meranti sawdust Lignin Fly ash Hazelnut shell powder Lichens Hypogymnia physodes BSG Conditioned BSG
e e e e e e e e e e e 0.012 e e e e e e e e e e 0.020 0.044
e 0.123 e e e e e e e 0.083 e e e e e e e 0.613 0.102 e e e 0.028 0.052
0.067 0.183 e e e e e 0.043 e e e e e 0.164 e e 0.476 0.504 0.360 0.074 0.333 0.071 0.033 0.080
e e e e e 0.016 0.290 0.040 e e e e e e e e e e 0.172 e e e 0.022 0.051
e e e e 0.242 e e e e 0.027 0.123 e e e e 0.022 e e e 0.051 e e 0.030 0.065
e e 0.188 0.039 0.095 e e e 0.387 e e e 0.148 e 0.058 0.070 e 0.165 0.432 e 0.158 e 0.041 0.092
(Acheampong et al., 2011) (Escudero et al., 2008) Tas¸ar et al. (2014) Pehlivan et al. (2009) Souza et al. (2017) Mishra et al. (2010) El-Shafey (2010) Rocha et al. (2009) Fatima et al. (2013) Srivastava et al. (2009) (Ding et al., 2012) (Esfandiar et al., 2014) Palin et al. (2016) Lu and Gibb (2008) Zhang et al. (2016) (Mahmood-ul-Hassan et al., 2015) Witek-Krowiak (2013) Rafatullah et al. (2009) Guo et al. (2008) Tofan et al. (2008) (Lü et al., 2017) (Kłos et al. 2005) This work This work
sorbents is not favourable. Only in one of the quoted articles (Souza et al., 2017) was an attempt made to assess the uncertainty of the measurements of sorption capacity: qM(max). The assessment was carried out by determining the standard deviation for the experimental values from the assumed value. It was demonstrated that the standard deviation of the values qM(max), determined from the Langmuir model, ranges from 14% to 22%. Other studies have shown that even with relatively high correlation coefficients (R2 > 0.9), the uncertainty of the sorption capacity as determined from Langmuir's model may be close to or higher than the calculated value, qM(max) (Rajfur et al., 2012). As mentioned above, the articles reduce the assessment of the uncertainty to quoting the correlation coefficient R2. In this article, the uncertainty of the qM(max) measurements versus the analysed metals does not exceed 20% (Table 3). The results of authors’ own studies presented in Table 4 also indicate that BSG conditioned in demineralised water immediately before the sorption process significantly improves sorption efficiency (by approximately 100% in reference to mean values); please note that the uncertainty of the results for repeated experiments using BSG that had not been conditioned in demineralised water, did not exceed 7%, whereas, the uncertainty of results for conditioned BSG, was close to 40%. This is due to the swelling capacity of BSG, which may trigger other sorption mechanisms that were not observed in unconditioned BSG, for example, physical adsorption. Under experimental conditions, the uncertainty for determining the sorption parameters reached 100% and were, therefore, not interpreted. However, in practical applications of BSG for the removal of heavy metals from waste water, this phenomenon may
have positive ramifications. The comparison of qM(eq) values, determined on the basis of kinetic measurements carried out in the static set up (Table 2), with qM(1) values, determined from the Langmuir isotherms in the flowthrough set up, for cM(eq) concentrations, deserves special attention. The values of cM(eq) for the studies carried out in the static set up can be determined by transforming Equation (1) as follows: cM(eq) ¼ cM(eq) e qM(eq) ∙ m/V; whereas the values for qM(1), for the assumed concentrations cM(eq), may be determined on the basis of the parameters of the straight lines describing the Langmuir equilibrium (Table 3). The sorption was carried out for 60 min in both the static and flow-through set ups, and the results are presented in Table 5. The interpretation of the results presented in Table 5 is, in many ways, speculation due to the uncertainty of the measurements; however, it is justified by the physics and chemistry of the processes. For the same conditions of relative equilibrium achieved after the process had run for a duration of 60 min, the values of qM(1) determined in the flow-through set up from the Langmuir model are higher (by 19% on average) in comparison to the static set up, as interpreted using the pseudo-second-order reaction model, and close to the assumed values of qM(eq). This results from the fact that, in a static set up, with a set volume of solution and mass of sorbent, a double exchange heterophasic equilibrium is obtained, as described by Equation (5). Whereas in a flow-through set up, the equilibrium can be described by Equation (4), which, in turn, reflects the Langmuir isotherm model. As a result, the difference in the determined values of the parameters may achieve 20% due to
Table 5 Comparison of sorption parameters determined from the pseudo-second-order reaction model (sorption in a static set up) and Langmuir model (sorption in a flow-through set up). Metal
Mn Ni Cu Zn Cd Pb
Pseudo-second-order reaction model
Langmuir isotherm
cM(eq) (mmol L1)
qM(1) (mmol g1)
qM(eq) (mmol g1)
qM(1) (mmol g1)
0.0120 0.0117 0.0109 0.0118 0.0118 0.0060
0.0026 0.0033 0.0043 0.0028 0.0037 0.0053
0.0034 0.0041 0.0050 0.0037 0.0045 0.0060
0.0032 0.0040 0.0048 0.0036 0.0045 0.0057
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Fig. 4. The influence of Na, Ca, and Mn on copper sorption in BSG.
the methodology of the studies. 3.2. Influence of other cations on heavy metal sorption Cations of other metals are also usually present in waste water that contains heavy metal cations. The pH reaction is also a
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characteristic parameter of waste water. Fig. 4 shows the influence of sodium, calcium, and manganese cations on the sorption of copper from solutions. The experiment was carried out in a flowthrough set up (Fig. 1). Copper was selected due to its high affinity for the active centres of the BSG and the low uncertainty in the determination of this metal (Kłos and Rajfur, 2013). The copper concentration in the BSG samples was determined before and after the sorption process. The results indicate a considerable reduction in the sorption efficiency of copper cations in the BSG while in the presence of other cations in the solution. The presence of sodium cations with concentrations of 15 mmol L1 reduces copper sorption by nearly 60%, whereas the presence of calcium and manganese cations with concentrations of 7.5 mmol L1, reduces it by nearly 80%. The conductivity of solutions with such cation concentrations is approximately 1.5 mS cm1. The data indicate that waste water salinity is one of the most important criteria in using BSG to remove heavy metals. It should be noted that the concentration of copper cations in the solution was 0.030 ± 0.02 mmol L1, and was two orders of magnitude lower than the highest concentration of calcium cations. In Fig. 5, the influence of the pH of the solution on copper sorption in BSG is presented. The sorption was carried out in a static set up. The pH reaction was stabilised with a solution of H2SO4 or NaOH. The amount of copper was determined in the BSG and in the solutions before and after the process, and in the solution after the sorption process, acidified to pH ¼ 1. The initial copper
Fig. 5. The influence of the pH of the solution on copper sorption in BSG; a) measurements taken from the BSG before and after sorption, b) measurements taken from the solution before sorption, and in an acidified solution after sorption, c) measurements taken from the solution before sorption, and in non-acidified solution after sorption.
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to correctly interpret the phenomenon, it is necessary to measure the changes in the pH of the solutions during the sorption process. 3.3. BSG regeneration
Fig. 6. Efficiency of copper removal from BSG, for various concentration of hydrochloric acid.
concentration in the solution was 0.026 ± 0.02 mmol L1. In Fig. 5, the value of pHprec. is marked, above which the hydroxides will precipitate, at the set copper concentration (Equation (6)). The influence of the pH of the solutions on the processes of heavy metal sorption in various biosorbents has often been considered in studies that are aimed at assessing the potential of using biomass in water and sewage remediation processes (Monteiro et al., 2009). Such studies have also been carried out using natural and modified mineral sorbents (Wu and Zhou, 2009). Most study results show that the maximum sorption of metals is obtained within the pH range of 4e6, depending on the metal being adsorbed, its initial concentration, and the applied sorbent. This is confirmed by the results shown in Fig. 5. The optimum pH level of the sorption of the BSG was within the range of 4.5e5.5. The reduction in heavy metal sorption in acidic solutions is explained by the competitive sorption of hydrogen cations; whereas in alkaline solutions, it is explained by the creation of hydro-complexes, insoluble metal compounds (mainly hydroxides) (Tuzen et al., 2009), the blocking of active centres, and a change in the ionic form of the metal (Uluozlu et al., 2008). The results presented in Fig. 5 indicate that within the pH values that are higher than pHprec., copper cations create insoluble hydroxides, indeterminable in a solution with AAS method. To assess the actual efficiency of the metal cations sorption, in the case of Equation (1) it is necessary to acidify the solution after sorption, or directly measure metal concentrations in the sorbent after sorption. The pH increase occurs frequently as a consequence of the parallel sorption of metal and hydrogen cations from the solution (Rajfur et al., 2012). In this case,
The graph in Fig. 6 presents the influence of the concentration of HCl on the efficiency of copper desorption using BSG. The process was carried out for 60 min. Further studies were carried out using a hydrochloric acid solution at a concentration of 0.1 mol L1 in a desorption process (copper removal efficiency >95%). In Fig. 7, eight consecutive cycles of copper sorption and desorption in BSG are interpreted. For comparison, desorption was carried out in two ways: 1) using 100 mL of the same acid for all eight cycles, 2) using a new acid solution for each consecutive cycle. Sorption and desorption cycles are presented in Fig. 7a (the reduction of copper in the BSG is shown as “”). The results indicate that the sorption efficiency of the BSG decreases after each cycle of regeneration in acid, which is comparable in option 1 and option 2, without stating the cause of this effect. The data shown in Fig. 7b provides more information. The efficiency of the sorption process (S), expressed as a percentage, was calculated by referencing the efficiencies of consecutive cycles to the first one, whereas the efficiency of desorption (D), also expressed as a percentage and shown as “”, was calculated by referencing the quantity of desorbed copper to the quantity of sorbed copper in a given cycle. This interpretation results in two important conclusions. The increase in copper concentration in the acid used in consecutive cycles in option 1 causes a reduction in the desorption efficiency, which can be interpreted as the sorption equilibrium of the double exchange heterophasic reaction; but it has no major influence on the reduction of copper sorption in the BSG in comparison to option 2. This indicates that the main cause of sorption reduction is a blockage, or the chemical disintegration of functional groups, under the influence of the acid. An analysis of the tendencies of the changes in sorption efficiency by means of a logarithmic function (option 2), y ¼ 36∙lnx þ 100 (R2 ¼ 0.97), demonstrates that BSG loses its sorption characteristics by the sixteenth cycle. In comparison with beech sawdust, a decrease in copper sorption capacity of 5e10% was recorded after each sorption and desorption cycle. Similarly, as in this research, sorption reduction was explained by the irreversible masking of functional groups on the surface of beech sawdust, as a consequence of metal ions and the influence of used eluent (Witek-Krowiak, 2013). Under the experimental conditions in option 1, the concentration of cations in the copper desorbed in the HCl solution was approximately five times greater than the solution from which sorption was carried out.
Fig. 7. Efficiency of consecutive cycles of the sorption (S) and desorption (D) of copper from BSG; C e desorption in option 1, x e desorption in option 2.
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4. Conclusions A description of the sorption characteristics of the biosorbents should involve determining the kinetics and the equilibrium of the heavy metal sorption process; defining of the contact time between the biosorbent and the solution, after which a near equilibrium status is achieved; and determining the sorption capacity and the affinity series of metals to functional groups. An assessment of the uncertainty in determining these parameters is an essential element of such studies. The study method is also important (static or flow-through). The application assessment of the studies should also include the influence of other cations on the efficiency of the sorption and discuss the possibility of bed regeneration. The present study showed, among other things, that the actual equilibrium between metal cations and BSG in the solution is achieved after approximately two days, while under experimental conditions (60 min), the efficiency is, on average, 80% and increases to 95% only after another 130 min. It was determined that the affinity of metal cations for the BSG's functional groups (expressed in mmol g1) increases in the series: Mn2þ z Zn2þ < Ni2þ < Cd2þ < Cu2þ < Pb2þ. It was also demonstrated that the presence of other cations in the solution is a major obstacle to heavy metals sorption; additionally it was found that the functional groups of the BSG lose their activity over consecutive sorption and desorption cycles due to being blocked, or their chemical decomposition. The multiple desorption of copper cations in 0.1 mol HCl solution enabled this metal to be concentrated by a factor of approximately 5 times compared to the solution of copper cations from which sorption was carried out. The study results also indicate the appropriateness of the pseudo-second-order equation model and the Langmuir isotherm model in describing the heavy metal cation sorption processes. These studies also demonstrate the possibility for practical applications using BSG waste in removing heavy metals from waste water. Despite many biosorbents exhibiting better sorption properties, an important advantage of BSG is its availability, as it is produced in virtually all breweries. However, its use as a heavy metal biosorbent requires a number of studies, including a focus on changes in structure and sorption properties under the influence of long-term contact between BSG and the solution. Application studies require the optimisation of the contact time between the BSG and the solution, as well as the adjustment of optimum pH parameters, and depending on the composition of the treated waste water. The presented sorption parameters e among others: the determined series of affinity of the heavy metals, and the interpreted sorption restriction related to the presence of other cations in the solution e demonstrate that the appropriate selection of parameters in respect to treated waste water may lead to relatively selective metal sorption. References Acheampong, M.A., Pereira, J.P.C., Meulepas, R.J.W., Lens, P.N.L., 2011. Biosorption of Cu(II) onto agricultural materials from tropical regions. J. Chem. Technol. Biotechnol. 86, 1184e1194. https://doi.org/10.1002/jctb.2630. Ahmaruzzaman, M., 2011. Industrial wastes as low-cost potential adsorbents for the treatment of wastewater laden with heavy metals. Adv. Colloid Interface Sci. 166, 36e59. https://doi.org/10.1016/j.cis.2011.04.005. Aliyu, S., Bala, M., 2013. Brewer's spent grain: a review of its potentials and applications. Afr. J. Biotechnol. 10, 324e331. https://doi.org/10.4314/ajb.v10i3. Ding, Y., Jing, D., Gong, H., Zhou, L., Yang, X., 2012. Biosorption of aquatic cadmium(II) by unmodified rice straw. Bioresour. Technol. 114, 20e25. https://doi.org/ 10.1016/j.biortech.2012.01.110. El-Shafey, E.I., 2010. Removal of Zn(II) and Hg(II) from aqueous solution on a carbonaceous sorbent chemically prepared from rice husk. J. Hazard Mater. 175, 319e327. https://doi.org/10.1016/j.jhazmat.2009.10.006. n, C., Marzal, P., Villaescusa, I., 2008. Effect of EDTA on divalent Escudero, C., Gabaldo metal adsorption onto grape stalk and exhausted coffee wastes. J. Hazard. Mater. 152, 476e485. https://doi.org/10.1016/j.jhazmat.2007.07.013.
119
Esfandiar, N., Nasernejad, B., Ebadi, T., 2014. Removal of Mn(II) from groundwater by sugarcane bagasse and activated carbon (a comparative study): application of response surface methodology (RSM). J. Ind. Eng. Chem. 20, 3726e3736. https:// doi.org/10.1016/j.jiec.2013.12.072. Fatima, T., Nadeem, R., Masood, A., Saeed, R., Ashraf, M., 2013. Sorption of lead by chemically modified rice bran. Int. J. Environ. Sci. Technol. 10, 1255e1264. https://doi.org/10.1007/s13762-013-0228-x. Ferraz, A.I., Amorim, C., Tavares, T., Teixeira, J.A., 2015. Chromium(III) biosorption onto spent grains residual from brewing industry: equilibrium, kinetics and column studies. Int. J. Environ. Sci. Technol. 12, 1591e1602. https://doi.org/ 10.1007/s13762-014-0539-6. Guo, X., Zhang, S., Shan, X., 2008. Adsorption of metal ions on lignin. J. Hazard Mater. 151, 134e142. https://doi.org/10.1016/j.jhazmat.2007.05.065. Ho, Y.S., McKay, G., 1999. Pseudo-second order model for sorption processes. Process Biochem. 34, 451e465. https://doi.org/10.1016/S0032-9592(98)00112-5. Jain, N., Johnson, T.A., Kumar, A., Mishra, S.V., Gupta, N., 2015. Biosorption of Cd(II) on jatropha fruit coat and seed coat. Environ. Monit. Assess. 187 https://doi.org/ 10.1007/s10661-015-4658-4. łkowski, D., Ziembik, Z., Kłos, A., Bochenek, Z., Bjerke, J.W., Zagajewski, B., Zio o czuk-Sr dka, A., Tommervik, H., Krems, P., Jerz, D., Rajfur, M., Dołhan ska, M., 2015. The use of mosses in biomonitoring of selected areas in Zielin Poland and Spitsbergen in the years from 1975 to 2014. Ecol. Chem. Eng. S 22, 201e218. https://doi.org/10.1515/eces-2015-0011. Kłos, A., Rajfur, M., 2013. Influence of hydrogen cations on kinetics and equilibria of heavy-metal sorption by algae-sorption of copper cations by the alga Palmaria palmata (Linnaeus) Weber & Mohr (Rhodophyta). J. Appl. Phycol. 25, 1387e1394. https://doi.org/10.1007/s10811-012-9970-6. Kłos, A., Rajfur, M., Wacławek, M., Wacławek, W., 2005. Ion equilibrium in lichen surrounding. Bioelectrochemistry 66, 95e103. https://doi.org/10.1016/ j.bioelechem.2004.04.006. Li, Q., Chai, L., Wang, Q., Yang, Z., Yan, H., Wang, Y., 2010. Fast esterification of spent grain for enhanced heavy metal ions adsorption. Bioresour. Technol. 101, 3796e3799. https://doi.org/10.1016/j.biortech.2010.01.003. Li, Q., Chai, L., Yang, Z., Wang, Q., 2009. Kinetics and thermodynamics of Pb(II) adsorption onto modified spent grain from aqueous solutions. Appl. Surf. Sci. 255, 4298e4303. https://doi.org/10.1016/j.apsusc.2008.11.024. Lü, L., Jiang, X., Jia, L., Ai, T., Wu, H., 2017. Kinetic and thermodynamic studies on adsorption of Cu2þ, Pb2þ, methylene blue and malachite green from aqueous solution using AMPS-modified hazelnut shell powder. Chem. Res. Chinese Univ. 33, 112e118. https://doi.org/10.1007/s40242-017-6243-6. Lu, S., Gibb, S.W., 2008. Copper removal from wastewater using spent-grain as biosorbent. Bioresour. Technol. 99, 1509e1517. https://doi.org/10.1016/ j.biortech.2007.04.024. Mahmood-ul-Hassan, M., Suthar, V., Rafique, E., Ahmad, R., Yasin, M., 2015. Kinetics of cadmium, chromium, and lead sorption onto chemically modified sugarcane bagasse and wheat straw. Environ. Monit. Assess. 187 https://doi.org/10.1007/ s10661-015-4692-2. Meneses, N.G.T., Martins, S., Teixeira, J.A., Mussatto, S.I., 2013. Influence of extraction solvents on the recovery of antioxidant phenolic compounds from brewer's spent grains. Separ. Purif. Technol. 108, 152e158. https://doi.org/10.1016/ j.seppur.2013.02.015. Mishra, V., Balomajumder, C., Agarwal, V.K., 2010. Biosorption of Zn (II) onto the surface of non-living biomasses: a comparative study of adsorbent particle size and removal capacity of three different biomasses. Water, Air, Soil Pollut. 211, 489e500. https://doi.org/10.1007/s11270-009-0317-0. Monteiro, C.M., Castro, P.M.L., Malcata, F.X., 2009. Use of the microalga Scenedesmus obliquus to remove cadmium cations from aqueous solutions. World J. Microbiol. Biotechnol. 25, 1573e1578. https://doi.org/10.1007/s11274-0090046-y. Moreira, M.M., Morais, S., Carvalho, D.O., Barros, A.A., Delerue-Matos, C., Guido, L.F., 2013. Brewer's spent grain from different types of malt: evaluation of the antioxidant activity and identification of the major phenolic compounds. Food Res. Int. 54, 382e388. https://doi.org/10.1016/j.foodres.2013.07.023. Munagapati, V.S., Yarramuthi, V., Nadavala, S.K., Alla, S.R., Abburi, K., 2010. Biosorption of Cu(II), Cd(II) and Pb(II) by Acacia leucocephala bark powder: kinetics, equilibrium and thermodynamics. Chem. Eng. J. 157, 357e365. https:// doi.org/10.1016/j.cej.2009.11.015. Mussatto, S.I., 2014. Brewer's spent grain: a valuable feedstock for industrial applications. J. Sci. Food Agric. 94, 1264e1275. https://doi.org/10.1002/jsfa.6486. Palin, D., Rufato, K.B., Linde, G.A., Colauto, N.B., Caetano, J., Alberton, O., Jesus, D.A., Dragunski, D.C., 2016. Evaluation of Pb (II) biosorption utilizing sugarcane bagasse colonized by Basidiomycetes. Environ. Monit. Assess. 188 https:// doi.org/10.1007/s10661-016-5257-8. Pehlivan, E., Altun, T., Cetin, S., Iqbal Bhanger, M., 2009. Lead sorption by waste biomass of hazelnut and almond shell. J. Hazard Mater. 167, 1203e1208. https:// doi.org/10.1016/j.jhazmat.2009.01.126. Pillai, S.S., Deepa, B., Abraham, E., Girija, N., Geetha, P., Jacob, L., Koshy, M., 2013. Biosorption of Cd(II) from aqueous solution using xanthated nano banana cellulose: equilibrium and kinetic studies. Ecotoxicol. Environ. Saf. 98, 352e360. https://doi.org/10.1016/j.ecoenv.2013.09.003. Rafatullah, M., Sulaiman, O., Hashim, R., Ahmad, A., 2009. Adsorption of copper (II), chromium (III), nickel (II) and lead (II) ions from aqueous solutions by meranti sawdust. J. Hazard Mater. 170, 969e977. https://doi.org/10.1016/ j.jhazmat.2009.05.066. Rajfur, M., Kłos, A., Wacławek, M., 2012. Sorption of copper(II) ions in the biomass of
120
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alga Spirogyra sp. Bioelectrochemistry 87, 65e70. https://doi.org/10.1016/ j.bioelechem.2011.12.007. Rocha, C.G., Zaia, D.A.M., Alfaya, R.V. da S., Alfaya, A.A. da S., 2009. Use of rice straw as biosorbent for removal of Cu(II), Zn(II), Cd(II) and Hg(II) ions in industrial effluents. J. Hazard Mater. 166, 383e388. https://doi.org/10.1016/j.jhazmat. 2008.11.074. Salazar-Rabago, J.J., Leyva-Ramos, R., 2016. Novel biosorbent with high adsorption capacity prepared by chemical modification of white pine (Pinus durangensis) sawdust. Adsorption of Pb(II) from aqueous solutions. J. Environ. Manag. 169, 303e312. https://doi.org/10.1016/j.jenvman.2015.12.040. Souza, I.P.A.F., Cazetta, A.L., Pezoti, O., Almeida, V.C., 2017. Preparation of biosorbents from the Jatoba (Hymenaea courbaril) fruit shell for removal of Pb(II) and Cd(II) from aqueous solution. Environ. Monit. Assess. 189 https://doi.org/ 10.1007/s10661-017-6330-7. Srivastava, V.C., Mall, I.D., Mishra, I.M., 2009. Competitive adsorption of cadmium(II) and nickel(II) metal ions from aqueous solution onto rice husk ash. Chem. Eng. Process. Process Intensif. 48, 370e379. https://doi.org/10.1016/j.cep.2008.05. 001. € Tas¸ar, S¸., Kaya, F., Ozer, A., 2014. Biosorption of lead(II) ions from aqueous solution by peanut shells: equilibrium, thermodynamic and kinetic studies. J. Environ. Chem. Eng. 2, 1018e1026. https://doi.org/10.1016/j.jece.2014.03.015. Thevannan, A., Mungroo, R., Niu, H.C., 2010. Biosorption of nickel with barley straw. Bioresour. Technol. 101, 1776e1780. https://doi.org/10.1016/j.biortech.2009.10. 035.
Tofan, L., Paduraru, C., Bilba, D., Rotariu, M., 2008. Thermal power plants ash as sorbent for the removal of Cu(II) and Zn(II) ions from wastewaters. J. Hazard Mater. 156, 1e8. https://doi.org/10.1016/j.jhazmat.2007.11.098. Tuzen, M., Sari, A., Mendil, D., Soylak, M., 2009. Biosorptive removal of mercury(II) from aqueous solution using lichen (Xanthoparmelia conspersa) biomass: kinetic and equilibrium studies. J. Hazard Mater. 169, 263e270. https://doi.org/ 10.1016/j.jhazmat.2009.03.096. Uluozlu, O.D., Sari, A., Tuzen, M., Soylak, M., 2008. Biosorption of Pb(II) and Cr(III) from aqueous solution by lichen (Parmelina tiliaceae) biomass. Bioresour. Technol. 99, 2972e2980. https://doi.org/10.1016/j.biortech.2007.06.052. Wierzba, S., Rajfur, M., Nabrdalik, M., Kłos, A., 2018. The application of electroanalytical methods to determine affinity series of metal cations for functional biosorbent groups. J. Electroanal. Chem. 809, 8e13. https://doi.org/10.1016/ j.jelechem.2017.12.037. Witek-Krowiak, A., 2013. Application of beech sawdust for removal of heavy metals from water: biosorption and desorption studies. Eur. J. Wood Wood Prod. 71, 227e236. https://doi.org/10.1007/s00107-013-0673-8. Wu, P., Zhou, Y.-S., 2009. Simultaneous removal of coexistent heavy metals from simulated urban stormwater using four sorbents: a porous iron sorbent and its mixtures with zeolite and crystal gravel. J. Hazard Mater. 168, 674e680. https:// doi.org/10.1016/j.jhazmat.2009.02.093. Zhang, Y., Luo, X., Lin, X., Huang, S., 2016. A sorbent based on liquor distillers' grains for the removal of Pb(II) and Cr(III) from aqueous solution. Procedia Environ. Sci. 31, 785e794. https://doi.org/10.1016/j.proenv.2016.02.074.
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Heavy metal sorption in biosorbents e Using spent grain from the brewing industry Sławomir Wierzba*, Andrzej Kłos University of Opole, Institute of Biotechnology, Kardynała Kominka 6a, 40-035, Opole, Poland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 July 2018 Received in revised form 23 March 2019 Accepted 26 March 2019 Available online 30 March 2019
The sorption properties of brewer's spent grain (BSG) were assessed in terms of its usefulness in removing heavy metals from solutions. The specific objectives of the investigation involved studying sorption equilibrium and kinetics, assessing the influence of other cations on sorption efficiency, and examining the possibility of bed regeneration. The studies were carried out in a static system, with a constant ratio of solution volume to biosorbent mass, and using a flow through system. The affinity of metal cations to BSG functional groups was established and increased in the series: Mn2þ z Zn2þ < Ni2þ < Cd2þ < Cu2þ < Pb2þ. The BSG's sorption capacities (mmol g1), determined using the Langmuir model, ranged from 0.020 (Mn) to 0.041 (Pb), with measurements of uncertainty not exceeding 20%. It was determined that a relatively high concentration of calcium cations (7.5 mmol L1) versus copper cation concentration (0.03 mmol L1) in solution, restricted the sorption efficiency by approximately 80%. The optimum pH of the solution used to carry out the sorption was determined to be within the range of 4.5e5.5. It was found that the functional groups of the BSG lose their efficacy over consecutive sorption and desorption cycles due to being blocked, or due to their chemical decomposition. This indicated that the proper interpretation of the results requires a statistical assessment of the measurements of uncertainty and of the uncertainty of determination of the sorption parameters. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Biosorption Heavy metals Sorption kinetics and equilibrium Measurements uncertainty Bed regeneration
1. Introduction The increase in surface water pollution has been one of the main problems in the protection of the natural environment from anthropo-pressure. Apart from the numerous organic compounds discharged into surface waters from communal and industrial effluent, and the leachate from fields; heavy metals cause particular concern (Ahmaruzzaman, 2011). Cadmium, mercury, and lead are among the metals with defined, strong toxic characteristics (Souza et al., 2017). Lead poisoning may cause disorders of the gastrointestinal system, kidneys, and central nervous system (Palin et al., 2016); and cadmium poisoning causes hypertension, anaemia, and emphysema (Jain et al., 2015). Many industries discharge considerable amounts of heavy metals in their waste water and effluent, for example, mining, metal processing, electroplating, tanning, dyes, and battery manufacturing (Lu and Gibb, 2008). In recent years considerable efforts have been made to design and implement technologies to remove heavy metals from waste water,
* Corresponding author. E-mail address: [email protected] (S. Wierzba). https://doi.org/10.1016/j.jclepro.2019.03.286 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
preventing their transfer to surface waters. The present study is in line with contemporary trends in bioeconomy in terms of the use of bio-waste for the treatment and possible recovery of heavy metals from waste water. In its research programme ‘Horizon (2020),’ the European Commission has attached particular importance to bioeconomy, and included it in the investment programme. There is no universal process for removing heavy metals from waste water and effluent. A combination of methods such as chemical precipitation, coagulation, ultrafiltration, electrolytic processes, reverse osmosis, oxidation, membrane filtration, ion exchange, and adsorption are used in practice (Ahmaruzzaman, 2011). Literature sources indicate that liquid phase sorption is one of the most popular methods for removing pollution from water solutions (El-Shafey, 2010). The process is an attractive alternative for treating polluted water, in particular when the sorbent used is not expensive and does not require conditioning prior to use. The need for easily accessible, cheap and efficient materials, with a well-developed surface, and that has a large sorption capacity, drives research on the use of industrial waste materials, both raw and modified (Ahmaruzzaman, 2011). Mining and power industries, agriculture, forestry, and food processing industries are
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sources of such waste materials. Thermal power plant ash (Tofan et al., 2008), rice husk ash (Srivastava et al., 2009), lignin isolated from black liquor, paper industry waste products (Guo et al., 2008), xanthated nano banana cellulose (Pillai et al., 2013), meranti sawdust (Rafatullah et al., 2009), chemically modified sawdust from white pine (Salazar-Rabago and Leyva-Ramos, 2016), beech sawdust (Witek-Krowiak, 2013), eucalyptus bark saw dust, mango bark saw dust, pineapple fruit peel (Mishra et al., 2010), Acacia leucocephala bark powder straw (Munagapati et al., 2010), hazelnut (Corylus avellana) and almond shells (Prunus dulcis) (Pehlivan et al., 2009), waste rice straw (Rocha et al., 2009), barley straw (Thevannan et al., 2010), peanut shells (Tas¸ar et al., 2014), chemically modified rice bran (Fatima et al., 2013), and liquor distillers' grain (Zhang et al., 2016) have been considered for use in removing heavy metals from water solutions. The brewing industry generates large volumes of by-products, including spent grain (BSG), hops, and yeast. BSG represents approximately 85% of all the by-products generated by the industry; it is produced all year round in all types of breweries and is relatively cheap (Mussatto, 2014). BSG is used, among other things, as animal fodder (Aliyu and Bala, 2013). Due to its high fibre content and antioxidants, BSG can be used as an additive for diet foodstuffs, or as a source of ingredients for the pharmaceutical and cosmetics industries (Moreira et al., 2013); and because of its chemical composition it can also be used to remove heavy metal cations from waste water. This chemical composition consists of the following elements (% of dry mass): glucan (21.7%), xylan (13.6%), arabinan (5.6%), lignin (19.4%), protein (24.7%), ash (4.2%) and extracts (10.8%) (Meneses et al., 2013); as well as numerous related functional groups like hydroxyl, amine, and carboxyl. In the literature, sources describe a small number of studies that focus on the bonding of heavy metal ions, such as lead (Li et al., 2010, 2009), chromium (Ferraz et al., 2015) and copper (Lu and Gibb, 2008) by BSG. The main objective of these studies was to describe the mechanisms involved in the process, and determine the influence of environmental conditions on sorption efficiency. The main problem with describing the sorption characteristics of various biosorbents is the lack of statistical analysis of the results. When describing the process kinetics and equilibrium, the authors usually provide correlation coefficients but do not analyse the uncertainty in the determination of the sorption parameters. It has been demonstrated that the uncertainty of parameters determined from the Langmuir model may be close to or higher than the set value, depending on the experimental method used (Rajfur et al., 2012). The objective of these studies was to determine and compare the sorption parameters for selected heavy metals when using BSG to remove them from solutions. The influence of other cations in the solution, and pH levels, was considered. Certain important parameters, including statistical ones, which characterise the usefulness of biosorbents in the process, were calculated and discussed. A hypothesis was put forward that BSG, as a by-product, can be used as a biosorbent for the removal of heavy metals from waste water, and the main metal sorption mechanism is a reversible heterophasic ion exchange reaction that enables the regeneration and multiple use of the BSG bed.
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(Wierzba et al., 2018). After conditioning, the BSG was rinsed a few times with demineralised water until the water in which the BSG was immersed achieved a stable pH. The material was then dried for 24 h at temperature of 323 K until a constant mass was obtained. 2.1. The research method The sorption of heavy metals (Mn, Ni, Cu, Zn, Cd and Pb) from solution was carried out in static and flow-through systems. In the static system, the BSG sample (0.5 g) was immersed in 200 mL of metal solution. The solution was then thoroughly mixed using a magnetic agitator. The process was carried out for 60 min. Changes in metal concentrations were determined in the solution before (cM(0)) and after (cM(1)) sorption process. Metal concentrations in the BSG after sorption (qM(1)) was determined from Equation (1):
qMð1Þ ¼
cMð0Þ cMð1Þ ,V m
(1)
Where V is the volume of the sorption solution, and m is the sorbent mass. In the flow-through system, biosorbent samples (approximately 1 g) were placed in a perforated container through which the metal solution was directed at the rate of approximately 150 mL min1, ensuring a constant concentration of metal in the solution in which the biosorbent was immersed (Fig. 1). The process was carried out for 60 min. The amount of metal was determined for the initial solution and the BSG before and after the sorption process. The hydraulic retention time of the solution in the chamber in which the container with the sample was placed, amounted to approximately 40 s. In the static system the parameters of the kinetics of the sorption were determined by taking regular measurements of metal concentrations in the solution throughout the process. An assessment of the possibility of bed regeneration was then carried out. The studies of the process kinetics were carried out using solutions of metals with initial concentrations of 0.03 ± 0.002 mmol L1; while the initial concentrations used to determine the Langmuir isotherm were varied within the range of 0.015e0.50 mmol L1. To optimise the sorption process, the initial pH of the metal solutions was adjusted using hydrochloric acid to between 4.4 and 4.5. To assess the possibilities for the regeneration and multiple use of BSG, copper sorption and desorption were repeated several times
2. Materials and methods BSG with a grain size of 1.25e1.50 mm was used in these studies. The BSG (60 g) was rinsed with demineralised water that had a conductivity of k ¼ 0.5 mS cm1. Next, to saturate the functional groups with hydrogen cations, the material was conditioned for 60 min in an HCl solution with a concentration of 0.03 mol L1
Fig. 1. Diagrams showing the sorption assemblies for a static system and a flowthrough system (numerical values are in mm).
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in a static set up. Copper was selected due to its high affinity for the active centres of the BSG, and the low uncertainty of the determination of this metal. The copper sorption was carried out using 200 mL of copper sulphate solution with a concentration of 0.02 mmol L1; desorption was carried out in an HCl solution of 100 mL volume. The sorption and desorption cycles lasted for 60 min. The optimum acid concentration was determined by comparing the copper desorption efficiency to 1 g of BSG previously saturated with copper cations that were immersed for 60 min in HCl solutions with concentrations ranging from 0.01 to 0.5 mol L1. 2.2. Equipment and reagents An absorbing atomic spectroscope, model iCE 3500 made by Thermo Electron Corporation, USA, was used to determine heavy metal concentrations. A pH meter, model CP551, made by Elmetron Sp.j. from Zabrze (PL), was used to determine the pH of the solutions in which the biosorbents were immersed; its absolute reading error was: DpH ¼ 0.02. The BSG (0.4 g) was mineralised in a mix of nitric acid and hydrogen peroxide in a Speedwave Four (Berghof, DE) microwave mineraliser. MERCK reagents were used to prepare the solutions. 2.3. Quality and quality assurance The experiments were repeated 10 times, using the same initial solutions. The standard deviation from the mean value did not exceed 5% and, in the case of the solutions diluted below the upper limit of the determination characteristics for the iCE 3500, the standard deviation was not higher than 7%. Table 1 presents the detection limits (MDL) and minimum quantity limits (MQL) of the device, calculated where the mass of organic material to be mineralised was m ¼ 0.4 g dry mass; and the solution volume post mineralisation was V ¼ 25 mL. The result was converted to 1 g of organic material. Detailed data about the equipment, together with an evaluation and a quality assurance, have been published in (Kłos et al., 2015). 2.4. Results, interpretation, and method The pseudo-second-order reaction model (Ho and McKay, 1999) that was used to interpret the results (Equation (2)), is most frequently applied to describe the parameters of heavy metals sorption kinetics in biomasses. The Langmuir isotherm model was used to describe the equilibrium parameters (Equation (3)).
t qMðtÞ
¼
completion of the sorption process; qM(max) is the sorbent sorption capacity, determined for t ¼ 60 min; and KL is the Langmuir equilibrium constant. The independent variables in Equations (2) and (3) are the time (t) and the reciprocal of the concentration of the metal in the solution: cM(1); and the dependent variables are t/qM(t) and the reciprocal of the concentration of the metal in the sorbent: qM(1). In the case of the heterophasic ion exchange reaction, the Langmuir constant describes the reaction equilibrium state: Mþ þ R $ MR
(4)
Such an equilibrium is established in the flow-through set up (Fig. 1) in which the cations desorbed from the BSG are washed out of the reaction chamber. In the static set up in which the BSG is immersed in a defined volume of the solution, the following equilibrium was established: Mþ þ CtR $ MR þ Ctþ
(5)
Where Ctþ is the cation desorbed from the grain during the ions exchange process. To assess the influence of alkalinisation on the creation of nonactive forms of metal hydroxides, the following well known formula was used: pHprec. ¼ z1 ∙ (14 ∙ z e log cþz M e pKsp)
(6)
Where pHprec. is the pH value above which hydroxides precipitate; z is the cation valence; cþz M is the cation concentration in solution (mol L1); and Ksp is the solubility product constants (pKsp ¼ log Ksp). The Excel software package was used to assess the uncertainty of determining the linear parameters of the equations. 3. Results and discussion To assess the sorption characteristics of the BSG and the possibilities for using this material to remove heavy metals from waste water; the sorption kinetics and equilibrium, the influence of other cations on the efficiency of heavy metal sorption, and the possibility of bed regeneration, were analysed. Changes in the pH values of the solutions were monitored during the experiments carried out on sorption kinetics and equilibrium. The measured pH values did not exceed pHprec.(Equation (6)). 3.1. The kinetics and sorption equilibrium of heavy metals in BSG
1 1 ,t 2 þ qMðeqÞ k”, qMðeqÞ
(2)
1 1 1 1 ¼ þ , qMð1Þ qMðmaxÞ qMðmaxÞ ,KL cMð1Þ
(3)
Where k” is the constant speed of the pseudo-second-order reaction; qM(t) is the metal concentration in the sorbent over time t, and qM(eq) is the metal concentration at equilibrium; qM(1) is the metal concentration in the sorbent and in the solution: cM(1), after Table 1 MDL and MQL values determined for 1 d.m. of mass (mg g1 d.m.). Metal:
Mn
Ni
Cu
Zn
Cd
Pb
MDL MQL
0.0010 0.0030
0.0010 0.0031
0.00088 0.00264
0.00030 0.00090
0.00026 0.00079
0.0014 0.0041
The kinetic parameters of the sorption were determined in order to compare BSG sorption characteristics against the studied metals. Initial concentrations of the metal in solution were 0.030 ± 0.002 mmol L1. The sorption was carried out in the static set up for 60 min, after which the recorded changes in concentration were at the limit of the quantification and resolution of the measurement method (of the order of 102 to 101 mmol L1). Samples of the solution were taken every 5 min for AAS testing. Equation (1) was used to calculate the momentary concentrations of the metals that had accumulated in the BSG. The results are presented in Fig. 2. Table 2 presents data on the parameters for the straight lines shown in Fig. 2, and the kinetic parameters of the process, which were determined on the basis of the pseudo-second-order reaction model. The values of qM(eq) show an affinity series of metal cations and the BSG functional groups. Considering the uncertainty of the
S. Wierzba, A. Kłos / Journal of Cleaner Production 225 (2019) 112e120
Fig. 2. The kinetics of heavy metal sorption in BSG; (cM(0) ¼ 0.030 ± 0.002 mmol L1).
Table 2 The parameters for straight lines y ¼ a∙x þ b, and sorption kinetic parameters determined using the pseudo-second-order reaction model; s e standard uncertainty of directional coefficient (sa) and free term (sb), R2 e correlation coefficient, qM(eq) e metal concentration in the BSG in equilibrium (mmol g1), k” e constant speed of the pseudo-second-order reaction (g mmol1 min1), u e composite uncertainty. Metal
a
sa
b
sb
R2
qM(eq) ± s(%)
k” ± u(%)
Mn Ni Cu Zn Cd Pb
280.9 245.3 199.3 272.7 222.6 167.7
2.5 3.5 1.6 4.2 5.4 2.5
5.560 3.471 1.944 4.828 2.718 1.310
193 131 61 155 199 78
0.997 0.998 0.999 0.998 0.994 0.998
0.0035 ± 0.8 0.0041 ± 1.4 0.0050 ± 0.8 0.0037 ± 1.5 0.0045 ± 2.4 0.0060 ± 1.5
14.7 ± 5.8 17.1 ± 4.7 20.6 ± 3.5 15.1 ± 4.4 19.9 ± 6.5 21.2 ± 6.5
measurements, one may state that the affinity of the cations increases in the series: Mn2þ z Zn2þ < Ni2þ < Cd2þ < Cu2þ < Pb2þ; whereas, the constant speed of the k” reaction, which is proportional to the qM(eq) value, due to the uncertainty of determination; defines the affinity series in a less precise way: Mn2þ z Zn2þ < Ni2þ < Cd2þ z Cu2þ z Pb2þ. Based on Equation (2) one can also define the efficiency of the process. The values, qM(60)/qM(eq), determined for the analysed metals indicate that after 60 min of contact with the solution and the BSG, the sorption process advances by approximately 80%. By using the parameters for the straight lines presented in Table 2, it is possible to determine the contact time required to achieve a set level of the advancement of the process. For example, a 95% advancement of the sorption process for copper is achieved after approximately 190 min of contact between the solution and the BSG, and an equilibrium close to qM(eq) is achieved after approximately two days. The Langmuir isotherm model (Equation (3)) was used to describe the parameters of the heavy metal sorption's equilibrium in the BSG. Sorption was carried out in a flow-through set up, which better reflects the sorption processes occurring in flow-through devices that are used in practice. The results are presented in Fig. 3. Parameters for the straight lines presented in Fig. 3 and sorption parameters determined from Langmuir model in a flow-through set up are collected in Table 3. Note that qM(max) < qM(eq). The data shown in Table 3 indicate that, despite the large values of the correlation coefficients, determining the affinity series for metal cations and the BSG functional groups is ambiguous; this results from the uncertainty of the measurements. Based on the value qM(max) it can be stated that the metal
115
Fig. 3. Langmuir isotherms describing the heavy metal sorption's equilibrium parameters in a flow-through set up.
Table 3 The parameters for straight lines y ¼ a∙x þ b and sorption parameters determined from the Langmuir model in a flow-through set up; s e standard uncertainty: sa e directional coefficient, sb e free term, R2 e correlation coefficient, qM(max) e sorption capacity (mmol g1), KL e Langmuir constant, u e composite uncertainty. Metal
a
sa
b
sb
R2
qM(max) ± s(%)
KL ± u(%)
Mn Ni Cu Zn Cd Pb
2.99 2.537 1.95 2.740 2.249 0.901
0.16 0.088 0.11 0.044 0.024 0.043
48.9 35.2 30.4 45.1 33.5 24.2
4.2 2.3 3.6 1.5 1.3 4.3
0.982 0.993 0.980 0.998 0.999 0.986
0.020 ± 9 0.028 ± 7 0.033 ± 12 0.022 ± 3 0.030 ± 4 0.041 ± 18
16.3 ± 11 13.9 ± 8 15.6 ± 14 16.5 ± 4 14.9 ± 5 26.8 ± 19
cations affinity increases in the series: Mn2þ z Zn2þ < Ni2þ z Cd2þ z Cu2þ < Pb2þ (comparison in mol). Based on the Langmuir equilibrium constant, which describes the equilibrium of the reaction presented in Equation (4), one can determine only a good affinity for lead in comparison to other metals: Mn2þ z Zn2þ z Ni2þ z Cd2þ z Cu2þ < Pb2þ (mol). The sequence of metals creating the affinity series is the same as the sequence determined by the kinetic data (Table 2). A method using electroanalytical techniques, such as pH and conductivity measurements, was proposed in determining the metal cations affinity for the active centres of biosorbents. The metal cations affinity for the active centres of the BSG, determined on the basis of pH measurements, increases in the series: Naþ < Kþ < Mg2þ < Mn2þ z Zn2þ < Ni2þ z Ca2þ < Cd2þ < Cu2þ < Pb2þ (val, gram-equivalent, mol concentration converted to cation electric charge) (Wierzba et al., 2018). Table 4 compares the values of the sorption capacities of various biosorbents determined using the Langmuir isotherm model. Sorption parameters are interpreted by most authors as: mg g1, mg L1. These results require conversion. Reactions given in mols relation, and only interpretation of concentrations expressed in mols enables a correct assessment of the affinity of cations for the functional groups of biosorbents. For the purpose of this article, Table 4 uses concentrations converted to mmol g1. The table also presents the results of metal sorption in BSG that had been conditioned in demineralised water for 24 h. The sorption characteristics of the swollen BSG increased by over 100%. The results presented in the table are approximations, with uncertainty resulting from the repeatability of the results with the BSG prepared in this way amounts to 40%. The comparison of the BSG sorption capacities versus other
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Table 4 Comparison of sorption capacities of various biosorbents (mmol g1). Sorbent
Mn
Ni
Cu
Zn
Cd
Pb
Literature
Coconut shell Coffee wastes Peanut shell Almond shell Jatoba fruit shell Mango bark saw dust Rice husk Rice straw Rice bran Rice husk ash Rice straw Sugarcane bagasse Sugarcane bagasse spent grain Liquor distillers' grains, raw Wheat straw Beech sawdust Meranti sawdust Lignin Fly ash Hazelnut shell powder Lichens Hypogymnia physodes BSG Conditioned BSG
e e e e e e e e e e e 0.012 e e e e e e e e e e 0.020 0.044
e 0.123 e e e e e e e 0.083 e e e e e e e 0.613 0.102 e e e 0.028 0.052
0.067 0.183 e e e e e 0.043 e e e e e 0.164 e e 0.476 0.504 0.360 0.074 0.333 0.071 0.033 0.080
e e e e e 0.016 0.290 0.040 e e e e e e e e e e 0.172 e e e 0.022 0.051
e e e e 0.242 e e e e 0.027 0.123 e e e e 0.022 e e e 0.051 e e 0.030 0.065
e e 0.188 0.039 0.095 e e e 0.387 e e e 0.148 e 0.058 0.070 e 0.165 0.432 e 0.158 e 0.041 0.092
(Acheampong et al., 2011) (Escudero et al., 2008) Tas¸ar et al. (2014) Pehlivan et al. (2009) Souza et al. (2017) Mishra et al. (2010) El-Shafey (2010) Rocha et al. (2009) Fatima et al. (2013) Srivastava et al. (2009) (Ding et al., 2012) (Esfandiar et al., 2014) Palin et al. (2016) Lu and Gibb (2008) Zhang et al. (2016) (Mahmood-ul-Hassan et al., 2015) Witek-Krowiak (2013) Rafatullah et al. (2009) Guo et al. (2008) Tofan et al. (2008) (Lü et al., 2017) (Kłos et al. 2005) This work This work
sorbents is not favourable. Only in one of the quoted articles (Souza et al., 2017) was an attempt made to assess the uncertainty of the measurements of sorption capacity: qM(max). The assessment was carried out by determining the standard deviation for the experimental values from the assumed value. It was demonstrated that the standard deviation of the values qM(max), determined from the Langmuir model, ranges from 14% to 22%. Other studies have shown that even with relatively high correlation coefficients (R2 > 0.9), the uncertainty of the sorption capacity as determined from Langmuir's model may be close to or higher than the calculated value, qM(max) (Rajfur et al., 2012). As mentioned above, the articles reduce the assessment of the uncertainty to quoting the correlation coefficient R2. In this article, the uncertainty of the qM(max) measurements versus the analysed metals does not exceed 20% (Table 3). The results of authors’ own studies presented in Table 4 also indicate that BSG conditioned in demineralised water immediately before the sorption process significantly improves sorption efficiency (by approximately 100% in reference to mean values); please note that the uncertainty of the results for repeated experiments using BSG that had not been conditioned in demineralised water, did not exceed 7%, whereas, the uncertainty of results for conditioned BSG, was close to 40%. This is due to the swelling capacity of BSG, which may trigger other sorption mechanisms that were not observed in unconditioned BSG, for example, physical adsorption. Under experimental conditions, the uncertainty for determining the sorption parameters reached 100% and were, therefore, not interpreted. However, in practical applications of BSG for the removal of heavy metals from waste water, this phenomenon may
have positive ramifications. The comparison of qM(eq) values, determined on the basis of kinetic measurements carried out in the static set up (Table 2), with qM(1) values, determined from the Langmuir isotherms in the flowthrough set up, for cM(eq) concentrations, deserves special attention. The values of cM(eq) for the studies carried out in the static set up can be determined by transforming Equation (1) as follows: cM(eq) ¼ cM(eq) e qM(eq) ∙ m/V; whereas the values for qM(1), for the assumed concentrations cM(eq), may be determined on the basis of the parameters of the straight lines describing the Langmuir equilibrium (Table 3). The sorption was carried out for 60 min in both the static and flow-through set ups, and the results are presented in Table 5. The interpretation of the results presented in Table 5 is, in many ways, speculation due to the uncertainty of the measurements; however, it is justified by the physics and chemistry of the processes. For the same conditions of relative equilibrium achieved after the process had run for a duration of 60 min, the values of qM(1) determined in the flow-through set up from the Langmuir model are higher (by 19% on average) in comparison to the static set up, as interpreted using the pseudo-second-order reaction model, and close to the assumed values of qM(eq). This results from the fact that, in a static set up, with a set volume of solution and mass of sorbent, a double exchange heterophasic equilibrium is obtained, as described by Equation (5). Whereas in a flow-through set up, the equilibrium can be described by Equation (4), which, in turn, reflects the Langmuir isotherm model. As a result, the difference in the determined values of the parameters may achieve 20% due to
Table 5 Comparison of sorption parameters determined from the pseudo-second-order reaction model (sorption in a static set up) and Langmuir model (sorption in a flow-through set up). Metal
Mn Ni Cu Zn Cd Pb
Pseudo-second-order reaction model
Langmuir isotherm
cM(eq) (mmol L1)
qM(1) (mmol g1)
qM(eq) (mmol g1)
qM(1) (mmol g1)
0.0120 0.0117 0.0109 0.0118 0.0118 0.0060
0.0026 0.0033 0.0043 0.0028 0.0037 0.0053
0.0034 0.0041 0.0050 0.0037 0.0045 0.0060
0.0032 0.0040 0.0048 0.0036 0.0045 0.0057
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Fig. 4. The influence of Na, Ca, and Mn on copper sorption in BSG.
the methodology of the studies. 3.2. Influence of other cations on heavy metal sorption Cations of other metals are also usually present in waste water that contains heavy metal cations. The pH reaction is also a
117
characteristic parameter of waste water. Fig. 4 shows the influence of sodium, calcium, and manganese cations on the sorption of copper from solutions. The experiment was carried out in a flowthrough set up (Fig. 1). Copper was selected due to its high affinity for the active centres of the BSG and the low uncertainty in the determination of this metal (Kłos and Rajfur, 2013). The copper concentration in the BSG samples was determined before and after the sorption process. The results indicate a considerable reduction in the sorption efficiency of copper cations in the BSG while in the presence of other cations in the solution. The presence of sodium cations with concentrations of 15 mmol L1 reduces copper sorption by nearly 60%, whereas the presence of calcium and manganese cations with concentrations of 7.5 mmol L1, reduces it by nearly 80%. The conductivity of solutions with such cation concentrations is approximately 1.5 mS cm1. The data indicate that waste water salinity is one of the most important criteria in using BSG to remove heavy metals. It should be noted that the concentration of copper cations in the solution was 0.030 ± 0.02 mmol L1, and was two orders of magnitude lower than the highest concentration of calcium cations. In Fig. 5, the influence of the pH of the solution on copper sorption in BSG is presented. The sorption was carried out in a static set up. The pH reaction was stabilised with a solution of H2SO4 or NaOH. The amount of copper was determined in the BSG and in the solutions before and after the process, and in the solution after the sorption process, acidified to pH ¼ 1. The initial copper
Fig. 5. The influence of the pH of the solution on copper sorption in BSG; a) measurements taken from the BSG before and after sorption, b) measurements taken from the solution before sorption, and in an acidified solution after sorption, c) measurements taken from the solution before sorption, and in non-acidified solution after sorption.
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to correctly interpret the phenomenon, it is necessary to measure the changes in the pH of the solutions during the sorption process. 3.3. BSG regeneration
Fig. 6. Efficiency of copper removal from BSG, for various concentration of hydrochloric acid.
concentration in the solution was 0.026 ± 0.02 mmol L1. In Fig. 5, the value of pHprec. is marked, above which the hydroxides will precipitate, at the set copper concentration (Equation (6)). The influence of the pH of the solutions on the processes of heavy metal sorption in various biosorbents has often been considered in studies that are aimed at assessing the potential of using biomass in water and sewage remediation processes (Monteiro et al., 2009). Such studies have also been carried out using natural and modified mineral sorbents (Wu and Zhou, 2009). Most study results show that the maximum sorption of metals is obtained within the pH range of 4e6, depending on the metal being adsorbed, its initial concentration, and the applied sorbent. This is confirmed by the results shown in Fig. 5. The optimum pH level of the sorption of the BSG was within the range of 4.5e5.5. The reduction in heavy metal sorption in acidic solutions is explained by the competitive sorption of hydrogen cations; whereas in alkaline solutions, it is explained by the creation of hydro-complexes, insoluble metal compounds (mainly hydroxides) (Tuzen et al., 2009), the blocking of active centres, and a change in the ionic form of the metal (Uluozlu et al., 2008). The results presented in Fig. 5 indicate that within the pH values that are higher than pHprec., copper cations create insoluble hydroxides, indeterminable in a solution with AAS method. To assess the actual efficiency of the metal cations sorption, in the case of Equation (1) it is necessary to acidify the solution after sorption, or directly measure metal concentrations in the sorbent after sorption. The pH increase occurs frequently as a consequence of the parallel sorption of metal and hydrogen cations from the solution (Rajfur et al., 2012). In this case,
The graph in Fig. 6 presents the influence of the concentration of HCl on the efficiency of copper desorption using BSG. The process was carried out for 60 min. Further studies were carried out using a hydrochloric acid solution at a concentration of 0.1 mol L1 in a desorption process (copper removal efficiency >95%). In Fig. 7, eight consecutive cycles of copper sorption and desorption in BSG are interpreted. For comparison, desorption was carried out in two ways: 1) using 100 mL of the same acid for all eight cycles, 2) using a new acid solution for each consecutive cycle. Sorption and desorption cycles are presented in Fig. 7a (the reduction of copper in the BSG is shown as “”). The results indicate that the sorption efficiency of the BSG decreases after each cycle of regeneration in acid, which is comparable in option 1 and option 2, without stating the cause of this effect. The data shown in Fig. 7b provides more information. The efficiency of the sorption process (S), expressed as a percentage, was calculated by referencing the efficiencies of consecutive cycles to the first one, whereas the efficiency of desorption (D), also expressed as a percentage and shown as “”, was calculated by referencing the quantity of desorbed copper to the quantity of sorbed copper in a given cycle. This interpretation results in two important conclusions. The increase in copper concentration in the acid used in consecutive cycles in option 1 causes a reduction in the desorption efficiency, which can be interpreted as the sorption equilibrium of the double exchange heterophasic reaction; but it has no major influence on the reduction of copper sorption in the BSG in comparison to option 2. This indicates that the main cause of sorption reduction is a blockage, or the chemical disintegration of functional groups, under the influence of the acid. An analysis of the tendencies of the changes in sorption efficiency by means of a logarithmic function (option 2), y ¼ 36∙lnx þ 100 (R2 ¼ 0.97), demonstrates that BSG loses its sorption characteristics by the sixteenth cycle. In comparison with beech sawdust, a decrease in copper sorption capacity of 5e10% was recorded after each sorption and desorption cycle. Similarly, as in this research, sorption reduction was explained by the irreversible masking of functional groups on the surface of beech sawdust, as a consequence of metal ions and the influence of used eluent (Witek-Krowiak, 2013). Under the experimental conditions in option 1, the concentration of cations in the copper desorbed in the HCl solution was approximately five times greater than the solution from which sorption was carried out.
Fig. 7. Efficiency of consecutive cycles of the sorption (S) and desorption (D) of copper from BSG; C e desorption in option 1, x e desorption in option 2.
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4. Conclusions A description of the sorption characteristics of the biosorbents should involve determining the kinetics and the equilibrium of the heavy metal sorption process; defining of the contact time between the biosorbent and the solution, after which a near equilibrium status is achieved; and determining the sorption capacity and the affinity series of metals to functional groups. An assessment of the uncertainty in determining these parameters is an essential element of such studies. The study method is also important (static or flow-through). The application assessment of the studies should also include the influence of other cations on the efficiency of the sorption and discuss the possibility of bed regeneration. The present study showed, among other things, that the actual equilibrium between metal cations and BSG in the solution is achieved after approximately two days, while under experimental conditions (60 min), the efficiency is, on average, 80% and increases to 95% only after another 130 min. It was determined that the affinity of metal cations for the BSG's functional groups (expressed in mmol g1) increases in the series: Mn2þ z Zn2þ < Ni2þ < Cd2þ < Cu2þ < Pb2þ. It was also demonstrated that the presence of other cations in the solution is a major obstacle to heavy metals sorption; additionally it was found that the functional groups of the BSG lose their activity over consecutive sorption and desorption cycles due to being blocked, or their chemical decomposition. The multiple desorption of copper cations in 0.1 mol HCl solution enabled this metal to be concentrated by a factor of approximately 5 times compared to the solution of copper cations from which sorption was carried out. The study results also indicate the appropriateness of the pseudo-second-order equation model and the Langmuir isotherm model in describing the heavy metal cation sorption processes. These studies also demonstrate the possibility for practical applications using BSG waste in removing heavy metals from waste water. Despite many biosorbents exhibiting better sorption properties, an important advantage of BSG is its availability, as it is produced in virtually all breweries. However, its use as a heavy metal biosorbent requires a number of studies, including a focus on changes in structure and sorption properties under the influence of long-term contact between BSG and the solution. Application studies require the optimisation of the contact time between the BSG and the solution, as well as the adjustment of optimum pH parameters, and depending on the composition of the treated waste water. The presented sorption parameters e among others: the determined series of affinity of the heavy metals, and the interpreted sorption restriction related to the presence of other cations in the solution e demonstrate that the appropriate selection of parameters in respect to treated waste water may lead to relatively selective metal sorption. References Acheampong, M.A., Pereira, J.P.C., Meulepas, R.J.W., Lens, P.N.L., 2011. Biosorption of Cu(II) onto agricultural materials from tropical regions. J. Chem. Technol. Biotechnol. 86, 1184e1194. https://doi.org/10.1002/jctb.2630. Ahmaruzzaman, M., 2011. Industrial wastes as low-cost potential adsorbents for the treatment of wastewater laden with heavy metals. Adv. Colloid Interface Sci. 166, 36e59. https://doi.org/10.1016/j.cis.2011.04.005. Aliyu, S., Bala, M., 2013. Brewer's spent grain: a review of its potentials and applications. Afr. J. Biotechnol. 10, 324e331. https://doi.org/10.4314/ajb.v10i3. Ding, Y., Jing, D., Gong, H., Zhou, L., Yang, X., 2012. Biosorption of aquatic cadmium(II) by unmodified rice straw. Bioresour. Technol. 114, 20e25. https://doi.org/ 10.1016/j.biortech.2012.01.110. El-Shafey, E.I., 2010. Removal of Zn(II) and Hg(II) from aqueous solution on a carbonaceous sorbent chemically prepared from rice husk. J. Hazard Mater. 175, 319e327. https://doi.org/10.1016/j.jhazmat.2009.10.006. n, C., Marzal, P., Villaescusa, I., 2008. Effect of EDTA on divalent Escudero, C., Gabaldo metal adsorption onto grape stalk and exhausted coffee wastes. J. Hazard. Mater. 152, 476e485. https://doi.org/10.1016/j.jhazmat.2007.07.013.
119
Esfandiar, N., Nasernejad, B., Ebadi, T., 2014. Removal of Mn(II) from groundwater by sugarcane bagasse and activated carbon (a comparative study): application of response surface methodology (RSM). J. Ind. Eng. Chem. 20, 3726e3736. https:// doi.org/10.1016/j.jiec.2013.12.072. Fatima, T., Nadeem, R., Masood, A., Saeed, R., Ashraf, M., 2013. Sorption of lead by chemically modified rice bran. Int. J. Environ. Sci. Technol. 10, 1255e1264. https://doi.org/10.1007/s13762-013-0228-x. Ferraz, A.I., Amorim, C., Tavares, T., Teixeira, J.A., 2015. Chromium(III) biosorption onto spent grains residual from brewing industry: equilibrium, kinetics and column studies. Int. J. Environ. Sci. Technol. 12, 1591e1602. https://doi.org/ 10.1007/s13762-014-0539-6. Guo, X., Zhang, S., Shan, X., 2008. Adsorption of metal ions on lignin. J. Hazard Mater. 151, 134e142. https://doi.org/10.1016/j.jhazmat.2007.05.065. Ho, Y.S., McKay, G., 1999. Pseudo-second order model for sorption processes. Process Biochem. 34, 451e465. https://doi.org/10.1016/S0032-9592(98)00112-5. Jain, N., Johnson, T.A., Kumar, A., Mishra, S.V., Gupta, N., 2015. Biosorption of Cd(II) on jatropha fruit coat and seed coat. Environ. Monit. Assess. 187 https://doi.org/ 10.1007/s10661-015-4658-4. łkowski, D., Ziembik, Z., Kłos, A., Bochenek, Z., Bjerke, J.W., Zagajewski, B., Zio o czuk-Sr dka, A., Tommervik, H., Krems, P., Jerz, D., Rajfur, M., Dołhan ska, M., 2015. The use of mosses in biomonitoring of selected areas in Zielin Poland and Spitsbergen in the years from 1975 to 2014. Ecol. Chem. Eng. S 22, 201e218. https://doi.org/10.1515/eces-2015-0011. Kłos, A., Rajfur, M., 2013. Influence of hydrogen cations on kinetics and equilibria of heavy-metal sorption by algae-sorption of copper cations by the alga Palmaria palmata (Linnaeus) Weber & Mohr (Rhodophyta). J. Appl. Phycol. 25, 1387e1394. https://doi.org/10.1007/s10811-012-9970-6. Kłos, A., Rajfur, M., Wacławek, M., Wacławek, W., 2005. Ion equilibrium in lichen surrounding. Bioelectrochemistry 66, 95e103. https://doi.org/10.1016/ j.bioelechem.2004.04.006. Li, Q., Chai, L., Wang, Q., Yang, Z., Yan, H., Wang, Y., 2010. Fast esterification of spent grain for enhanced heavy metal ions adsorption. Bioresour. Technol. 101, 3796e3799. https://doi.org/10.1016/j.biortech.2010.01.003. Li, Q., Chai, L., Yang, Z., Wang, Q., 2009. Kinetics and thermodynamics of Pb(II) adsorption onto modified spent grain from aqueous solutions. Appl. Surf. Sci. 255, 4298e4303. https://doi.org/10.1016/j.apsusc.2008.11.024. Lü, L., Jiang, X., Jia, L., Ai, T., Wu, H., 2017. Kinetic and thermodynamic studies on adsorption of Cu2þ, Pb2þ, methylene blue and malachite green from aqueous solution using AMPS-modified hazelnut shell powder. Chem. Res. Chinese Univ. 33, 112e118. https://doi.org/10.1007/s40242-017-6243-6. Lu, S., Gibb, S.W., 2008. Copper removal from wastewater using spent-grain as biosorbent. Bioresour. Technol. 99, 1509e1517. https://doi.org/10.1016/ j.biortech.2007.04.024. Mahmood-ul-Hassan, M., Suthar, V., Rafique, E., Ahmad, R., Yasin, M., 2015. Kinetics of cadmium, chromium, and lead sorption onto chemically modified sugarcane bagasse and wheat straw. Environ. Monit. Assess. 187 https://doi.org/10.1007/ s10661-015-4692-2. Meneses, N.G.T., Martins, S., Teixeira, J.A., Mussatto, S.I., 2013. Influence of extraction solvents on the recovery of antioxidant phenolic compounds from brewer's spent grains. Separ. Purif. Technol. 108, 152e158. https://doi.org/10.1016/ j.seppur.2013.02.015. Mishra, V., Balomajumder, C., Agarwal, V.K., 2010. Biosorption of Zn (II) onto the surface of non-living biomasses: a comparative study of adsorbent particle size and removal capacity of three different biomasses. Water, Air, Soil Pollut. 211, 489e500. https://doi.org/10.1007/s11270-009-0317-0. Monteiro, C.M., Castro, P.M.L., Malcata, F.X., 2009. Use of the microalga Scenedesmus obliquus to remove cadmium cations from aqueous solutions. World J. Microbiol. Biotechnol. 25, 1573e1578. https://doi.org/10.1007/s11274-0090046-y. Moreira, M.M., Morais, S., Carvalho, D.O., Barros, A.A., Delerue-Matos, C., Guido, L.F., 2013. Brewer's spent grain from different types of malt: evaluation of the antioxidant activity and identification of the major phenolic compounds. Food Res. Int. 54, 382e388. https://doi.org/10.1016/j.foodres.2013.07.023. Munagapati, V.S., Yarramuthi, V., Nadavala, S.K., Alla, S.R., Abburi, K., 2010. Biosorption of Cu(II), Cd(II) and Pb(II) by Acacia leucocephala bark powder: kinetics, equilibrium and thermodynamics. Chem. Eng. J. 157, 357e365. https:// doi.org/10.1016/j.cej.2009.11.015. Mussatto, S.I., 2014. Brewer's spent grain: a valuable feedstock for industrial applications. J. Sci. Food Agric. 94, 1264e1275. https://doi.org/10.1002/jsfa.6486. Palin, D., Rufato, K.B., Linde, G.A., Colauto, N.B., Caetano, J., Alberton, O., Jesus, D.A., Dragunski, D.C., 2016. Evaluation of Pb (II) biosorption utilizing sugarcane bagasse colonized by Basidiomycetes. Environ. Monit. Assess. 188 https:// doi.org/10.1007/s10661-016-5257-8. Pehlivan, E., Altun, T., Cetin, S., Iqbal Bhanger, M., 2009. Lead sorption by waste biomass of hazelnut and almond shell. J. Hazard Mater. 167, 1203e1208. https:// doi.org/10.1016/j.jhazmat.2009.01.126. Pillai, S.S., Deepa, B., Abraham, E., Girija, N., Geetha, P., Jacob, L., Koshy, M., 2013. Biosorption of Cd(II) from aqueous solution using xanthated nano banana cellulose: equilibrium and kinetic studies. Ecotoxicol. Environ. Saf. 98, 352e360. https://doi.org/10.1016/j.ecoenv.2013.09.003. Rafatullah, M., Sulaiman, O., Hashim, R., Ahmad, A., 2009. Adsorption of copper (II), chromium (III), nickel (II) and lead (II) ions from aqueous solutions by meranti sawdust. J. Hazard Mater. 170, 969e977. https://doi.org/10.1016/ j.jhazmat.2009.05.066. Rajfur, M., Kłos, A., Wacławek, M., 2012. Sorption of copper(II) ions in the biomass of
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S. Wierzba, A. Kłos / Journal of Cleaner Production 225 (2019) 112e120
alga Spirogyra sp. Bioelectrochemistry 87, 65e70. https://doi.org/10.1016/ j.bioelechem.2011.12.007. Rocha, C.G., Zaia, D.A.M., Alfaya, R.V. da S., Alfaya, A.A. da S., 2009. Use of rice straw as biosorbent for removal of Cu(II), Zn(II), Cd(II) and Hg(II) ions in industrial effluents. J. Hazard Mater. 166, 383e388. https://doi.org/10.1016/j.jhazmat. 2008.11.074. Salazar-Rabago, J.J., Leyva-Ramos, R., 2016. Novel biosorbent with high adsorption capacity prepared by chemical modification of white pine (Pinus durangensis) sawdust. Adsorption of Pb(II) from aqueous solutions. J. Environ. Manag. 169, 303e312. https://doi.org/10.1016/j.jenvman.2015.12.040. Souza, I.P.A.F., Cazetta, A.L., Pezoti, O., Almeida, V.C., 2017. Preparation of biosorbents from the Jatoba (Hymenaea courbaril) fruit shell for removal of Pb(II) and Cd(II) from aqueous solution. Environ. Monit. Assess. 189 https://doi.org/ 10.1007/s10661-017-6330-7. Srivastava, V.C., Mall, I.D., Mishra, I.M., 2009. Competitive adsorption of cadmium(II) and nickel(II) metal ions from aqueous solution onto rice husk ash. Chem. Eng. Process. Process Intensif. 48, 370e379. https://doi.org/10.1016/j.cep.2008.05. 001. € Tas¸ar, S¸., Kaya, F., Ozer, A., 2014. Biosorption of lead(II) ions from aqueous solution by peanut shells: equilibrium, thermodynamic and kinetic studies. J. Environ. Chem. Eng. 2, 1018e1026. https://doi.org/10.1016/j.jece.2014.03.015. Thevannan, A., Mungroo, R., Niu, H.C., 2010. Biosorption of nickel with barley straw. Bioresour. Technol. 101, 1776e1780. https://doi.org/10.1016/j.biortech.2009.10. 035.
Tofan, L., Paduraru, C., Bilba, D., Rotariu, M., 2008. Thermal power plants ash as sorbent for the removal of Cu(II) and Zn(II) ions from wastewaters. J. Hazard Mater. 156, 1e8. https://doi.org/10.1016/j.jhazmat.2007.11.098. Tuzen, M., Sari, A., Mendil, D., Soylak, M., 2009. Biosorptive removal of mercury(II) from aqueous solution using lichen (Xanthoparmelia conspersa) biomass: kinetic and equilibrium studies. J. Hazard Mater. 169, 263e270. https://doi.org/ 10.1016/j.jhazmat.2009.03.096. Uluozlu, O.D., Sari, A., Tuzen, M., Soylak, M., 2008. Biosorption of Pb(II) and Cr(III) from aqueous solution by lichen (Parmelina tiliaceae) biomass. Bioresour. Technol. 99, 2972e2980. https://doi.org/10.1016/j.biortech.2007.06.052. Wierzba, S., Rajfur, M., Nabrdalik, M., Kłos, A., 2018. The application of electroanalytical methods to determine affinity series of metal cations for functional biosorbent groups. J. Electroanal. Chem. 809, 8e13. https://doi.org/10.1016/ j.jelechem.2017.12.037. Witek-Krowiak, A., 2013. Application of beech sawdust for removal of heavy metals from water: biosorption and desorption studies. Eur. J. Wood Wood Prod. 71, 227e236. https://doi.org/10.1007/s00107-013-0673-8. Wu, P., Zhou, Y.-S., 2009. Simultaneous removal of coexistent heavy metals from simulated urban stormwater using four sorbents: a porous iron sorbent and its mixtures with zeolite and crystal gravel. J. Hazard Mater. 168, 674e680. https:// doi.org/10.1016/j.jhazmat.2009.02.093. Zhang, Y., Luo, X., Lin, X., Huang, S., 2016. A sorbent based on liquor distillers' grains for the removal of Pb(II) and Cr(III) from aqueous solution. Procedia Environ. Sci. 31, 785e794. https://doi.org/10.1016/j.proenv.2016.02.074.