• binary system;
  • biosorption;
  • copper;
  • lead;
  • olive stone;
  • pine bark


  1. Top of page
  2. Abstract

In the present work, biosorption of copper(II) in the presence of lead(II) ions by olive stone and pine bark was investigated in batch and continuous mode. Particularly, it pretends provide new information regarding binary biosorption of metals in continuous flow. The behavior of competitive Cu(II) and Pb(II) biosorption in batch was successfully described by the multicomponent Sips model, obtaining maximum capacities for Cu(II) and Pb(II) of 1.34 and 2.12 mg/g, respectively for olive stone, and 6.05 and 10.04 mg/g, respectively, for pine bark. Olive stone and pine bark removed the target metal ions in the selectivity order of Pb(II)>Cu(II). In continuous mode the biosorption capacity for Pb(II) (6.59 and 26.01 mg/g for olive stone and pine bark, respectively) were also higher than copper one (2.19 and 12.66 mg/g for olive stone and pine bark, respectively), indicating the higher affinity of lead for the two biosorbents in continuous system too. Finally, the results of this study demonstrated that pine bark could be a better biosorbent than olive stone. © 2013 American Institute of Chemical Engineers Environ Prog, 33: 192–204, 2014


  1. Top of page
  2. Abstract

Heavy metals can enter a water supply from either industrial activities such as microelectronics, electroplating, battery manufacture, metallurgical and fertilizer industries or acid rain breaking down soils and releasing heavy metals into streams, lakes, rivers and groundwater. Most of them, such as Pb, Cd or Hg, do not take part in biological processes and tend to accumulate in terrestrial and aquatic ecosystems and they can get into the body via inhalation, ingestion and skin adsorption. They are extremely harmful to humans, animals and plants mainly because of their accumulation in the body [1-3].

Copper and lead are particularly hazardous heavy metals because they accumulate into living tissues via food chain. The Cu2+ ion is the most environmentally relevant species to aquatic systems and is generally considered the most toxic form of Cu to aquatic life. A large body of literature indicates that bioavailability or toxicity of trace metals in aquatics systems is directly correlated to concentrations of free metals ions rather than to total or complexes metal concentrations. The Pb2+ ion is other important toxic species to environment, besides it causes effects on children and adults, even at low concentrations [4]. Furthermore, in real wastewaters containing heavy metals is very improbable to have just one metal species in solution. Thus, research studies have been extended to systems concerning two or more metals in an attempt to approach biosorption tests to actual conditions of water pollution [5-7].

There are several methods available to achieve the reduction of heavy metals in wastewater [8]. Some of them are chemical precipitation, ion exchange, reverse osmosis, adsorption on activated carbon, and solvent extraction. However, the applicability of all these methods is often limited because of several disadvantages including incomplete metal removal, high capital and operational costs, high reagent and energy requirements, and generation of toxic sludge or other waste products that are difficult to remove [9-11].

Recently, biosorption has attracted growing interest, as a more efficient and low-cost process. The major advantages of biosorption over conventional treatment methods include: low cost, high efficiency of metal removal from dilute solutions, minimization of chemical and/or biological sludge; possibility of regeneration of biosorbent and of metal recovery, between other [12].

Biosorbents can be divided into two categories: living or nonliving microorganisms such as algae, fungi and bacteria, and agricultural or forestry byproducts such as peanut shells, soybean hulls, and tree bark [13]. The advantages of the agricultural and forestry byproducts as biosorbents are the facts that they do not have to be specially produced for this purpose, as they are byproducts or wastes from agricultural or forestry processes, and they are already available in large quantities [14]. In recent years, a number of agricultural and forestry byproducts such as rice husk [15], agave bagasse [16], orange peel [17], soybean meal waste [18], and cork biomass [19] have been used for heavy metal removal from waters and wastewaters. In particular, olive stone and pine bark may constitute promising low-cost biosorbent among biomaterials, since these materials are produced in great quantities in the Mediterranean area, and has no market value. Olive stone and pine bark were previously investigated as biosorbent (Table 1). These research activities have shown that olive stone and pine bark are effective biosorbents for the removal of heavy metals in aqueous solutions. However, they lack of applicability for real wastewater treatment systems since these biosorption studies are frequently restricted to experiments in batch mode and are focused on single systems. Therefore, the present article pretends provide new information regarding binary biosorption of metals in continuous flow. In particular, the effect of the presence of lead on the sorption uptake of copper by olive stone and pine bark and the relative affinity of the biomass for each metal were analyzed.

Table 1. Review of published literature on the biosorption of heavy metals using olive stone and pine bark
BiosorbentMetalMode of operationType of systemReference
Acid-treated olive stonePb(II)ContinuousSingle[20]
Acid-treated olive stonePb(II)ContinuousSingle[21]
Olive stoneCr(III)Batch, ContinuousSingle[22]
Olive stonePb(II)BatchSingle[23]
Olive stoneCr(VI)ContinuousSingle[24]
Olive stonePb(II)BatchSingle[25]
Olive stonePb(II), Cr(III)BatchBinary[26]
Olive stoneCr(III), Cr(VI)BatchSingle, Binary[27]
Olive stoneCd(II), Cr(III), Pb(II)BatchSingle[28]
Olive stoneCd(II)BatchSingle[29]
Olive stoneCd(II), Cu(II), Ni(II), Pb(II)BatchSingle, Binary[30]
Olive stoneCd(II)BatchSingle[31]
Olive stone, Pine bark Cu(II)Batch, ContinuousSingle[32]
Olive stone, Pine bark Cu(II)BatchSingle[33]
Pine barkCu(II), Zn(II), Cd(II), Ni(II), Pb(II), Co(II), Ca(II)BatchMultimetal[34]
Pine barkPb(II)BatchSingle[35]
Pine barkHg(II)BatchSingle[36]
Succinylated olive stoneCd(II)BatchSingle[37]


  1. Top of page
  2. Abstract

Biosorbent Materials

  • Olive stone (OS) was provided by an oil extraction plant “Cooperativa Nuestra Señora del Castillo” located in Vilches, province of Jaen (Spain). The stones were obtained from the separation process of the olive cake with an industrial pitting machine.
  • Pine bark (PB) was provided by Carsan Biocombustibles S.L. factory from Granada (Spain).

The two solids were milled with an analytical mill (IKA MF-10) and <1.000 mm fraction was chosen for the characterization and biosorption tests without any pretreatment.

Characterization of OS and PB

Elemental analysis was performed with an EA 1108 CHNS elemental analyzer (Fison's Intruments). The oxygen content was obtained indirectly by difference.

Surface area and pore size distribution were determined by mercury intrusion porosimetry (MIP) generated using a mercury porosimeter (Quantachrome, model Poremaster 60).

The particle size range of the biosorbents was determined by milling 100 g of OS and PB (from an initial size ranging between 4 mm and 8 mm), passing them through different sieves and shaken for 10 min with a bottom pan placed to collect samples finer than the meshes.

Potentiometric titrations were used to quantify total acidic/basic functional groups present on the biosorbents surface. Potentiometric titration measurements were carried out using an automated titrator (Metrohm 794 Basic Titrino). Biosorbent suspensions of OS and PB (2 g of solid in 50 mL of deionized water) were fluxed by N2 to remove CO2 and titrated by standard solutions of 0.1 M NaOH (basic branch) and 0.1 N HCl (acid branch). After each addition of titrant (NaOH or HCl) the suspension was allowed to reach equilibrium under magnetic stirring and then the pH was measured by a pH meter.

An IR analysis was performed with a Fourier Transform Infrared Spectrometer (PerkinElmer, Spectrum 65) to identify the chemical groups present in the biosorbent and complete the study of the functional groups.

Preparation of Cu(II) and Pb(II) Solutions

Stock solutions of Cu2+ and Pb2+ were prepared by dissolving the necessary amount of Cu(NO3)2·H2O and Pb(NO3)2·3H2O in distilled water. They were further diluted to obtain desired concentrations of the metals. The pH of the solution was adjusted using 0.1 M HCl and 0.1 M NaOH solutions.

Batch Biosorption Experiments

Biosorption experiments were made by mixing 1 g of biosorbent with 100 mL of the synthetic metal solutions in a 150 mL jacketed reactor connected to a thermostat-controlled bath in order to keep temperature at 25°C. The pH was kept constant at a value of 5, since it is the optimal value of pH for copper and lead biosorption in monometallic systems and since the precipitation of these metals do not occur at this value. Subsequently, when metal ions and biosorbent have been in contact during 120 min, the liquid phase is taken out from the reactor and centrifuged for 10 min. Finally, the supernatant solution is filtered and analyzed for determining metal concentrations by a flame atomic absorption (AA) spectrophotometer (Perkin Elmer, Model AAnalyst 200). All experiments were performed in duplicate.

Metal removal efficiencies were calculated using the following equation:

  • display math(1)

And the amount of metal uptake as follows:

  • display math(2)

where qe (mg/g) is the amount biosorbed at equilibrium, C0 (mg/L) and Ce (mg/L) are the initial and the equilibrium metal concentrations, respectively, m (g) is the mass of biosorbent, and V (L) is the volume of solution.

Continuous Biosorption Experiments

For continuous biosorption experiments, a jacket glass column with an internal diameter of 15 mm and a length of 230 mm was packed with a known mass of biosorbent. To enable a uniform inlet flow of the solution into the column, glass beads of 5-mm diameter were placed to attain a desired height. The glass bead layer at the bottom of the column helped in even distribution of the metal solution through the top of the column and also prevented the removing of the sorbent at the bottom. The metal solution at desired initial concentration was pumped in up-flow mode at a constant flow rate using a peristaltic pump. Previously, the best biosorption conditions were obtained by performing a previous factorial design varying inlet metal concentration, bed depth and flow rate. Binary biosorption tests were performed under these conditions. Column effluent samples were collected at frequent time intervals and analyzed for effluent metal concentration. The Cu(II) and Pb(II) in the remaining solution were analyzed in an Atomic Absorption Spectrometer (Perkin-Elmer, model AAnalyst 200). All experiments were performed in duplicate.


  1. Top of page
  2. Abstract

Characterization of Biosorbents

Results of physical–chemical characterization of OS and PB are presented in Table 2. Elemental analysis results showed that OS and PB have a similar elemental composition, being composed mainly of carbon and oxygen. The moisture content was lower than 6% for both biosorbents. This low moisture content facilitates the preparation of these solids as biosorbents, since they require no drying before its use.

Table 2. Physical and chemical properties of OS and PB
Property OSPB
Moisture, % 5.433.54
Elemental analysisC, %52.3448.15
 H, %7.115.51
 N, %0.030.38
 S, %<0.1<0.1
 O, %40.4745.91
BET surface area, m2/g 0.4180.560
Pore volume, cm3/g 0.8852.670
Pore diameter, Å 136641
Particle size >1.00017.325.58
 distribution, %1.000–0.71042.1328.21
 0.710–0.500 18.0420.43
Total titratable sites, mol/g 7.88 × 10−55.22 × 10−4
Acid titratable sites, mol/g 4.18 × 10−59.07 × 10−4
Point of charge zero (pHPZC) 5.174.84

Pores of OS are smaller than PB ones. Pores of OS have an average pore width of 136 Å while those of PB have an average pore width of 641 Å On the other hand, although BET surface areas are very similar; total pore volumes differ significantly, 0.885 and 2.670 cm3/g for OS and PB, respectively. Both effects involve that ions have more facility to access to the PB surface than to the OS one.

For both biosorbents the particle size distribution was similar, the 0.500–0.710 mm and 0.710–1.00 mm fractions represent almost 50% of the total mass. This indicates that, after milling, most of the biosorbent particles have a diameter in a range between 0.500 and 1.00 mm.

Moreover, OS and PB are characterized by a certain degree of surface chemical heterogeneity, which is related to the presence of different functional groups. The chemical characterization of biosorbent is also very important, mainly to determine the active groups of biosorbent, as depending on the functional groups present in the biosorbent, the interaction mechanism can be different. Results show clearly, PB has higher site concentrations than OS in the whole pH range studied. It could explain the higher biosorption capacity of PB. Finally, data of pHPZC illustrate that their surfaces exhibited a slightly acidic character.

The comparisons of the FT-IR spectra of unloaded and metal-loaded biomass were given in Figure 1. Characteristic peaks of lignocelullosic materials were found [38-40]. The band at 3347–3335 cm−1 is O[BOND]H stretching of polymeric compounds. The doublet peaks at wave number of 2928–2918 cm−1 and 2853 cm−1 (for PB) are due to the asymmetric and symmetric stretch of aliphatic chains ([BOND]–CH) and their bending vibrations are 1372–1323 cm−1. On the other hand, 1732 cm−1 is stretching vibration of COO, C[DOUBLE BOND]O, and the peak at 1606–1594 cm−1 is due to stretching vibration of C[DOUBLE BOND]O. Respect to 1460–1422 cm−1 can be of phenolic O[BOND]H and C[DOUBLE BOND]O stretching of carboxylates, band at 1266–1232 cm−1 vibration of carboxylic acids, and peak at 1028 cm−1 vibration of C[BOND]O and O[BOND]H of polysaccharides. Finally, the peak at <900 cm−1 can be assigned to C[BOND]Hn aliphatics or aromatics, although this could be attributed to an interaction between metal ions and N containing bioligands.


Figure 1. FT-IR spectra of unloaded and metal-loaded biomass using (a) OS and (b) PB as biosorbents.

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FTIR spectra of biosorbents after metal biosorption indicated no significant changes in wavenumbers present in unloaded biomass. However, some changes can be found in the peaks at 3347, 2928, 1732, 1460, and 1323 cm−1 for OS and 3335, 1732, 1442 and less than 900 cm−1 for PB. These results implied that not only involvement of the related functional groups in biosorption of copper and lead but also the possibility that biosorption could be taken place through ion exchange mechanism.

The absorbance of the peaks in metal loaded sample was subsequently lower than in unloaded sample, following the order: unloaded > Cu-loaded > Pb-loaded > Pb,Cu-loaded, which agrees with the results obtained in biosorption tests (presented in following sections).

Biosorption of Binary System Cu(II)–Pb(II) in Batch Mode

Preliminar Study of Biosorption of Binary system Cu(II)–Pb(II)

In this work, the effect of the presence of Pb(II) in biosorption of Cu(II) by OS and PB has been analyzed. According to results obtained in previous works about biosorption of both metals in monometallic systems [32, 41], experiments with different initial total concentrations of Cu(II)/Pb(II) (5/5, 20/20, 50/50 and 100/100 mg/L) have been performed.

Figure 2 shows the removal efficiency for each metal and the total, for each mixture experimented using OS (Figure 2a) and PB (Figure 2b) as biosorbents.


Figure 2. Removal efficiencies of copper and lead by biosorption onto (a) OS and (b) PB at different initial concentrations of both metals.

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Results show that an increase of initial concentrations of metals in aqueous solution decreases the removal efficiency of each metal and the total, being more significant when OS is used as biosorbent. It is also observed that lead ions have more affinity for both biosorbents than copper ones, since the percentage removal of lead is always higher in all bimetallic mixtures tested. Otherwise, comparing the two biosorbents, OS has less capacity to remove the metals than PB, especially when the initial metal concentration is greater.

To show more clearly these results, Figure 3 shows the copper and lead biosorption capacities of OS (Figure 3a) and PB (Figure 3b) versus total initial metal concentration. Biosorption capacity was increased as the initial metal concentration increased till the binding sites are not saturated. These results provide the evidence that the initial concentration generates an important driving force to overcome all mass transfer resistance of copper and lead between the aqueous and solid phases. In addition, if the initial metal concentration continues to rise the biosorption capacity remains practically constant. It indicates that biosorption capacity is highly dependent upon the available binding sites on the surface of the biosorbents and surface saturation of biosorbents is dependent on the initial metal ion concentrations. Furthermore, copper biosorption capacities are lower than those of lead for both biosorbents. OS provides maximum values of biosorption capacity (qe) of 1.34 and 2.12 mg/g for Cu(II) and Pb(II), respectively and PB biosorption capacities of 6.05 and 10.04 mg/g for Cu(II) and Pb(II), respectively. This confirms the higher biosorption capacity of lead by the two solids, and the different biosorption capacities among them. Finding for example that, the copper biosorption capacity of PB is almost five times higher than of OS.


Figure 3. Biosorption capacity of copper and lead by (a) OS and (b) PB at different initial concentrations of both metals.

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Most of biosorption studies in multimetallic systems have revealed that functional groups presented more affinity to some ions than to others. It can be related, between other factors, with certain ionic characteristics of metal, as ionic radius, the electronegativity or covalent index that relates both parameters. In general, biosorbents usually have a higher affinity for ions with larger covalent index, producing more competitiveness between ions with similar characteristics [42-44]. Table 3 shows values of ionic radius, electronegativity and covalent index of copper and lead. Pb(II) is classified as a “class b” ion, while Cu(II) is classified as borderline ions. On the basis of this argument, it is possible to explain the competition effects observed in the present study. In general, the greater the covalent index, the greater is the “class b” character, and consequently its potential to form covalent bonds with functional groups of the biosorbent. The higher covalent index of lead respect to copper could explain the above results. Similar results for cation biosorption have been reported by Han et al. [43] or Iqbal and Edyvean [45] that also found a reduction in the sorption of Cu(II) in the presence of Pb(II) by manganese oxide coated sand and fungal biomass immobilized within a loofa sponge, respectively.

Table 3. Electronegativity, ionic radius (Å) and covalent index of copper and lead ions
Electronegativity, Xm1.902.33
Ionic radius, r0.691.20
Covalent index, CI = Xm2(r + 0.85)5.5611.1

Binary Competitive Cu(II)–Pb(II) Isotherms

Biosorption isotherm studies are of fundamental importance in determining the biosorption capacity of binary Cu(II)–Pb(II) system onto the biomass. When dealing with multimetal systems, the traditional models for representing isotherms of monometallic system can be modified to take into consideration the metals involved and interactions between them. Isotherms based on competitive sorption are the most commonly used to describe bimetallic systems and can be classified in two groups according to relation with isotherms for a monometallic system [46-48]:

  1. Competitive isotherms related only with parameters of single isotherms: competitive Langmuir model (often called Extended Langmuir model), competitive Freundlich model (often called Extended Freundlich model), and so forth.
  2. Competitive isotherms related with parameters of single isotherms and correction factors: competitive modified Langmuir model, competitive modified Freundlich model, and so forth.

In this work, the Extended Langmuir model and the Extended Sips model, two of the most common multicomponent biosorption models, have been tested.

Extended Langmuir Isotherm

For the Langmuir multicomponent biosorption model the major assumption is that the surface sites are uniform, so that the sorbates Cu(II) and Pb(II) compete for the same surface sites. The extended Langmuir isotherm can be represented by the following equation [49]:

  • display math(3)

where qei is the biosorption capacity at equilibrium for metal “i” and Cei and Cej are the equilibrium concentrations of metals “i” and “j.” The theoretical maximum biosorption capacity, qmax, and the Langmuir equilibrium constants for metal “i” and “j,” bi and bj, are obtained by fitting of equation with experimental data for a multimetallic system. For a binary system the above equation is broken down into the following two:

  • display math(4)
  • display math(5)

Those equations would be resolved together to obtain model parameters. In this model the qmax value is exclusively for metals and due to assumption discussed above about surface uniformity and competition by the same binding site of sorbates. When maximum capacities change for different multimetallic systems, one possible explanation is that the active sites are not homogeneous, if not that some places are specific to certain heavy metals. However, this fact is not consistent with Langmuir model considerations [49].

Extended Sips Isotherm

One of model most used to study the equilibrium of multicomponent systems is the combination between Langmuir and Freundlich models, developed by Sips (1948). This model shows a better reproduction of experimental data using additional parameters. The equation can be given by the following expression [50]:

  • display math(6)

where qmi is the theoretical maximum biosorption capacity of metal “i” and ni and nj the Sips constants for metals “i” and “j” obtained by fitting of equation with experimental data for a multimetallic system.

For a binary system, Eq. (6) can be decomposed into the following two expressions:

  • display math(7)
  • display math(8)

Both models have been adjusted using Excel for Windows by minimizing of Marquardt's percent standard deviation (MPSD), which responds to the following expression [51]:

  • display math(9)

where d is the number of experimental data and p is the number of parameters in the isotherm equation.

Besides this parameter, it is also estimated the residual sum of squares (RSS), represented by the following expression:

  • display math(10)

The uptake of Cu(II) by biosorption onto OS and PB in the binary Cu(II)-Pb(II) system at pH 5 as a function of equilibrium copper concentration for different initial lead concentrations (0, 20, 50, and 100 mg/L) is shown in Figure 4. An increase in the initial copper concentration at a given level of the competing metal (lead) led to a generalized increase in the equilibrium sorption capacity of copper. However, the capacities of OS and PB for Cu(II) in the binary system do not vary when initial lead concentration increases. It seems to imply that lead exhibit a slight antagonistic effect on copper biosorption behavior at experimental conditions tested. In addition, experimental results were analyzed using the multicomponent Extended Langmuir and Extended Sips models and optimal parameters estimated for these models are listed in Table 4. The Extended Sips model reproduces better experimental results than the Langmuir one, although the difference between the fitting of the two models is not significant.


Figure 4. Values of copper biosorption capacity versus copper equilibrium concentration in solution for each of initial lead concentrations tested using (a) OS and (b) PB as biosorbents.

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Table 4. Values of constants and fitting parameters of Extended Langmuir and Extended Sips isotherms
Extended Langmuir
Extended Sips

Biosorption capacities of lead obtained with the Extended Sips model are higher than those of copper, showing again the higher affinity of lead for both biosorbents. Furthermore, a larger biosorption capacity of PB is observed, obtaining a value of qm of 12.66 and 26.01 mg/g for Cu(II) and Pb(II), respectively, versus values of 2.19 and 6.59 mg/g for Cu(II) and Pb(II), respectively, when OS is used as biosorbent. Such different biosorption power of biosorbents was likely due to the difference in the composition of OS and PB cell walls and primarily to the amount of functional groups present on the surface of their cells, but also to the difference on physical properties as surface area or pore volume.

Also, in Table 5 values of biosorption capacities of Cu(II) and Pb(II) obtained in single metal system, fitting with Sips model and values obtained with Extended Sips model for binary system are shown.

Table 5. Maximum biosorption capacities (qm) of copper and lead in single and binary systems
 Isotherm of single system qm, mg/gIsotherm of binary system qm, mg/gQm/Qs

A mixture of metals can show three possible behaviors: antagonism, synergism, and no-interaction. If Qm is the biosorption capacity of one metal ion in the presence of the other metal ion and Qs the biosorption capacity for the same metal ion when it is present alone in the solution, these possible cases are [52]:

  • display math
  • display math
  • display math

In Table 5, values obtained of Qm/Qs for both metals, are approximately the unity, suggesting that lead does not produce an important suppression of copper biosorption when both metal ions are present in solution for tested conditions.

Finally, to evaluate the fitting of Extended Sips isotherm, the equilibrium biosorption capacity of copper was also calculated by this model and the estimated values were represented on plots of three-dimensional surfaces. Figure 5 shows experimental data of copper biosorption capacity and biosorption surfaces obtained by Extended Sips model for both biosorbents. As shown in Figure 5, the Sips equation exhibited an excellent reproduction of the experimental data at the whole concentration range.


Figure 5. Copper biosorption capacity versus copper and lead equilibrium concentrations in the Cu(II)–Pb(II) binary biosorption system using (a) OS and (b) PB as biosorbents. Experimental results are shown by discrete points and the model prediction by the Extended Sips equation as the mesh surface.

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These results are in agreement with those found in literature about the study of multimetallic systems biosorption using different biosorbents, particularly to binary systems Pb–Cu. Table 6 presents a review of maximum biosorption capacities in binary systems by different biosorbents. Most of authors agree that multicomponent systems study is complex due to interaction and competition processes between different metals present in solution, besides of the complex procedure to obtain experimental isotherms. Conversely, some of proposed models are, sometimes, too simple to describe the complexity of multimetallic systems, and other, too complex to be used in practice [42, 48, 56].

Table 6. Maximum biosorption capacities in binary systems by different biosorbents
Biosorbentqm, mg/g (mmol/g)Secondary metalModelReference
Peat biomass(0.29)Cd(II)
  • Negligible uptake of protons.
  • Metal valence is not considered to influence uptake
  • Sorption system is in equilibrium
  • Competitive model
Sphaerotilus natans(0.68)Cd(II)
  • Langmuir competitive
Ascophyllum nodosum(0.98)Cd(II)
  • Langmuir competitive
Activated sludge(0.52)Pb(II)
  • Negligible uptake of protons.
  • Metal valence is not considered to influence uptake
  • Sorption system is in equilibrium
  • Competitive model
Caulerpa lentillifera(0.09)Cd(II)
  • Negligible uptake of protons.
  • Metal valence is not considered to influence uptake
  • Sorption system is in equilibrium
  • Competitive model
Olive stone waste(0.04)Cd(II)
  • Langmuir competitive
  • Langmuir competitive
Ca-alginate beads(0.01)Pb(II)
  • Langmuir competitive
Chicory pulp(0.29)Zn(II)
  • Langmuir competitive
Olive stone3.10 (0.03)Pb(II)
  • Langmuir competitive
In this work
Pine bark19.94 (0.22)Pb(II)
  • Langmuir competitive

Biosorption of Binary System Cu(II)-Pb(II) in a Packed Bed Column

The effect of competition of Cu(II) and Pb(II) ions for binding sites of OS and PB was also evaluated in a packed bed column. According to data obtained in previous tests, different combinations of the experimental conditions were investigated: (i) flow rate = 2 mL/min, inlet Cu(II) concentration = 20 mg/L, inlet Pb(II) concentration = 20 mg/L and bed height =13.4 cm (equivalent to 15 g of OS and 7.5 g of PB) and (ii) flow rate = 6 mL/min, inlet Cu(II) concentration = 50 mg/L, inlet Pb(II) concentration = 50 mg/L and bed height = 4.4 cm (equivalent to 5 g of OS and 2.5 g of PB). Comparison of individual and binary breakthrough curves of Cu(II) and Pb(II) ions are shown in Figures 6 and 7. For both mixtures the breakthrough curves demonstrated that the biosorption capacity of the column is different for each component and the data reflect the higher affinity of Pb(II) for OS and PB than Cu(II). For the mixture containing lower inlet concentrations of both components [20 mg/L of Cu(II) and 20 mg/L of Ni(II)], lower flow rate and higher bed height, breakthrough curves were more dispersed and breakthrough occurred considerably later than those of other mixture containing higher inlet concentrations of both components, higher flow rate and higher lower bed height. The most significant parameters for Cu(II) and Pb(II) biosorption of breakthrough curves are given in Table 7 in terms of the biosorption (equilibrium) capacity of the column, the amount of metal loading on biosorbent surface, the total biosorption yield, breakthrough and exhausted time according to Eqs. (11)-(13).


Figure 6. Experimental breakthrough curves of (a, d) Cu(II) and (b, e) Pb(II) in single system and (c, f) in binary system using OS and PB as biosorbent. Flow rate of 2 mL/min; total inlet metal concentration of 40 mg/L; Bed height of 14.3 cm.

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Figure 7. Experimental breakthrough curves of (a, d) Cu(II) and (b, e) Pb(II) in single system and (c, f) in binary system using OS and PB as biosorbent. Flow rate of 6 mL/min; total inlet metal concentration of 100 mg/L; Bed height of 4.4 cm.

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Table 7. Characteristic parameters of breakthrough curves corresponding to Figures 6 and 7
 MetalBiosorbentVeff, mLqtotal, mgmtotal, mgR, %qe, mg/gCe, mg/Ltb, mintex, min
Figure 5aCuOS52013.5218.7272.220.90110.0045
Figure 5bPbOS52028.6528.65100.01.9100.00
Figure 5cPbOS52015.1015.1899.471.0060.168
Figure 5dCuPB52028.6528.65100.03.8200.00
Figure 5ePbPB52028.6528.65100.03.8200.00
Figure 5fPbPB52015.1215.1899.602.0160.127
Figure 6aCuOS156025.31179.7714.085.06099.02150
Figure 6bPbOS1560125.34197.6463.4225.07046.35110260
Figure 6cPbOS156034.7792.0437.786.95436.710170
Figure 6dCuPB156043.64179.7724.2717.46087.26
Figure 6ePbPB1560146.87197.6474.3258.75032.5495 
Figure 6fPbPB156068.4292.0474.3427.36815.14160

Volume of the effluent, Vef (mL):

  • display math(11)

where ttotal is the total flow time (min) and Q is the volumetric flow rate (mL/min).

Total mass of metal biosorbed (the area under the breakthrough curve) for a defined feed concentration:

  • display math(12)

where qtotal is the total mass of metal biosorbed (mg) and CR is the concentration of metal removal (mg/L).

Total amount of metal ions sent to the column:

  • display math(13)

Total metal removal (% R):

  • display math(14)

The amount of metal biosorbed at equilibrium or biosorption capacity, qe (mg of metal sorbed/g of sorbent), and the equilibrium metal concentration, Ce (mg/L):

  • display math(15)
  • display math(16)

where w is the mass of the biosorbent (g).

Exhausted Time

When the volume of the fluid begins to flow through the column, the mass-transfer zone varies from 0% of the inlet concentration (corresponding to the solute-free sorbent) to 100% of the inlet concentration (corresponding to the total saturation). From a practical point of view, the exhausted time, tex, is established when the concentration in the effluent is higher than 90–95% of the inlet concentration.

Service or Breakthrough Time

The breakthrough time, tb, is established when the metal concentration in the effluent reaches a determined value, generally related to the permitted disposal limit for each metal.

Results reported in Table 7 indicate that the amount of metal biosorbed at equilibrium or the biosorption capacity of each biosorbent was influenced and significantly increased or reduced by the presence of other metal. The most logical reason for this friendly or antagonistic action could be the higher affinity of biosorption sites on the cells of the biosorbents for Pb(II) ions. For example, in Table 7, copper biosorption capacity is 0.901 mg/g when the packed bed column is fed with a solution with a copper concentration equal to 40 mg/L, lead biosorption capacity is 1.910 mg/g when the packed bed column is fed with a solution with a lead concentration equal to 40 mg/L and total biosorption capacity is 1.697 mg/g if a binary solution with an inlet copper concentration of 20 mg/L and an inlet lead concentration of 20 mg/L is fed.

Conversely, for the first type of experiments (Figure 6), copper and lead were removed almost completely in the operation time for both biosorbents, with values of removal efficiency nearly to 100%. However, the values of biosorption capacities for both metals were generally low. It is reasonable, due to the column practically operates only until service or breakthrough time.

In the second type of experiments (Figure 7), differences between the behavior of each biosorbent were obtained. For OS, practically complete breakthrough curves for two metals were obtained, and the biosorption capacity of copper and lead were similar. For PB, practically complete breakthrough curve was obtained for the copper while for lead was not reached the exhaustion of the column. These results are similar to obtain by Vilar et al. [59], that studied the continuous biosorption of Pb/Cu in fixed-bed column using algae Gelidium and granulated agar extraction algal wastes, and obtained that Cu breaks through the column faster than Pb due to its lower affinity to the binding groups at the biosorbent surface. Also, Hawari and Mulligan [[60]] reported than Cu broke through the column faster than Pb when biosorption tests in a flow-through column were carried out with an equimolar mixture of Pb, Cd, Cu, and Ni.

Percentages of metal removal (copper or lead) are lower than those obtained in the first type of experiments, while biosorption capacities are higher (values nearly to 5 mg/g for copper and 25 mg/g for lead using OS and 17 mg/g for copper and 59 mg/g for lead using PB in single systems and 12 and 37 mg/g for OS and PB, respectively, in binary system).

Also, it is remarkable that OS has lower metal biosorption capacities than PB at the same conditions. The biosorption capacity of Pb(II) was also consistently greater than biosorption capacity of Cu(II) for both single-ion biosorption and co-biosorption with the bimetal solution. It confirmed the higher affinity of lead by both biosorbents, as batch experiments had shown previously.

Finally, if behavior in batch reactors is compared to performance in a fixed bed column at the same operating conditions, biosorption capacities of copper and lead are higher when biosorption process is carried out in a fixed bed column. For example, to a mixture of copper and lead (50 mg/L of copper + 50 mg/L of lead), copper biosorption capacities of OS were 1.19 and 5.09 mg/g and lead biosorption capacities of OS 1.66 and 6.95 mg/g in batch and continuous systems, respectively. Similarly, copper biosorption capacities of PB were 2.85 and 9.39 mg/g and lead biosorption capacities of PB 5.03 and 27.37 mg/g, operating in a batch reactor and a fixed bed column, respectively.

In conclusion, the study of biosorption in multimetallic system in packed bed column is complex and results obtained by different authors using different metals and biosorbents are very different. However, in most of the studies, a competition between the different metals present in solution for the same binding sites is produced. Most authors attribute greater affinity of one metal for biosorbent to characteristic of ions as ionic radius, formation of hydrated species, electronegativeness, etc and the different binding mechanism of ions to solid surface [61-63].


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  2. Abstract

In this study, results demonstrated that both biosorbents studied, OS and PB, can be used effectively for decontaminating aqueous solutions contaminated with copper in single and binary systems. Nevertheless, PB could be a better biosorbent than OS. In batch mode, the values of the parameter Qm/Qs obtained for both metals are approximately the unity, suggesting that lead does not produce an important interference onto copper biosorption when both metal ions are present in solution for tested conditions. Conversely, the behavior of competitive Cu(II) and Pb(II) biosorption in batch was successfully described by the Extended Sips model, obtaining maximum capacities for Cu(II) and Pb(II) of 1.34 and 2.12 mg/g, respectively for olive stone, and 6.05 and 10.04 mg/g, respectively for pine bark. In continuous mode the biosorption capacity for Pb(II) (6.59 and 26.01 mg/g for olive stone and pine bark, respectively) were also higher than copper one (2.19 and 12.66 mg/g for olive stone and pine bark, respectively), indicating the higher affinity of lead for the two biosorbents by in continuous system too.


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  2. Abstract

The authors are grateful to the Spanish Ministry of Science and Innovation for financial support received (Project CTM2009-10294).


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  2. Abstract
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