**4. Influence of parameters in biosorption process**

32% of mass loss was observed in the temperature range of 128–268°C. This stage occurs due to the decomposition of organic matter, probably the protein component, present in seeds; and iii) the third step occurs from 268°C to 541°C with decomposition of the greater part of the seed components, which probably includes fatty acids, for example, oleic acid has a boiling point of 360°C. At 950°C a total residue of around 14.6% was observed, due to the ash content and

probably inorganic oxides.

234 Applied Bioremediation - Active and Passive Approaches

**Figure 8.** X–ray diffractogram for *Moringa oleifera* seeds [36].

**Figure 9.** Thermogravimetric curve for *Moringa oleifera* seeds [36].

The morphological characteristics of the crushed seeds obtained using a scanning electron microscopy (SEM) can be seen in Figure 10. The results reveal that the material exhibits a relatively porous matrix with heterogeneous pore distribution. This feature is attributed to the fact that the whole seed comprises a wide variety of biomass components. The presence of

Many variables can influence metal biosorption and experimental parameters such as tem‐ perature, stirring time, pH, particle size of the biomass, ionic strength and competition between metal ions can have a significant effect on metal binding to biomass. The biomass mass also influences the adsorption process because as the adsorbent dose increases the number of adsorbent particles also increases and there is greater availability of sites for adsorption. Some of the most important factors affecting metal binding are discussed below. In general, adsorp‐ tion experiments are carried out in batch mode.

The pH is one of the most important parameters affecting any adsorption process. This dependence is closely related to the acid-base properties of various functional groups on the adsorbent surfaces [39]. The literature shows that a heterogeneous aqueous mixture of *M. oleifera* seeds contains various functional groups, mainly amino and acids groups. These groups have the ability to interact with metal ions, depending on the pH. An increase in metal adsorption with increasing pH values can be explained on the basis of competition between the proton and metal ions for the same functional groups, and a decrease in the positive surface charge, which results in a higher electrostatic attraction between the biosorbent surface and the metal [40]. Low pH conditions allow hydrogen and hydronium ions to compete with metal binding sites on the biomass, leading to poor uptake. Biosorbent materials primarily contain weak acidic and basic functional groups. It follows from the theory of acid–base equilibrium that, in the pH range of 2.5–5, the binding of heavy metal cations is determined primarily by the dissociation state of the weak acidic groups. Carboxyl groups (–COOH) are important groups for metal uptake by biological materials. At higher solution pH, the solubility of a metal complex decreases sufficiently for its precipitation, leading to a reduced sorption capacity. Therefore, it is recommendable to study biosorption at pH values where precipitation does not occur. Biomasses are materials with an amphoteric character; thus, depending on the pH of the solution, their surfaces can be positively or negatively charged. At pH values greater than the point of zero discharge (*pHpzc)*, the biomass surface becomes negatively charged, favoring the adsorption of cationic species. However, adsorption of anionic species will be favored at pH *< pHpzc*. The *pHpzc* of the *M. oleifera* seeds is between 6.0 and 7.0 [41], indicating that the surface of the biosorbent presents acid characteristics. Figure 11 illustrates the surface charge or the point of zero net proton charge of *Moringa oleifera* seeds. The surface charge of the seeds is positive at pH *<* PZC, is neutral at pH = PZC and is negative at pH *>* PZC. The variation in pH caused by protonation and deprotonation of the adsorbent reflects the presence of functional groups. Table 3 shows the use of components of the *M oleifera* in the pH range of 2.5 to 8.0.

*Moringa oleifera* is capable of directly sorbing metal ionic species from aqueous solutions. An interesting characteristic assigned to these biosorbents is the high abrasive content and the relative chemical resistance, allowing them to be subjected to different chemical treatments to increase their affinity and/or specificity for metal ions. Results previously published show the potential use of untreated seeds, although biosorbent materials are generally derived from plant biomass through different kinds of simple procedures. They may be chemically pre‐ treated for better performance and/or suitability for process applications. However, good results have been obtained when the seeds were treated with NaOH. This treatment can remove organic and inorganic matter from the sorbent surface. Chemical treatments are commonly performed employing alkaline solutions or with phosphoric and citric acids [42]. Recently, however, efforts have been made to remove and subsequently also recover metals. Metal-saturated biosorbent materials can be easily regenerated applying a simple (e.g. acidic) wash which then contains a very high concentration of released metals in a small volume,

Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent

**(°C)**

**pH Contact time (min)**

http://dx.doi.org/10.5772/56157

237

22 3.5 – 8.0 60 [4]

40 5.0 50 [32]

30 6.0 240 [31]

Zn(II) 30 7.0 50 [16]

**Ref.**

making the solution quite amenable to metal recovery.

Seeds Petroleum ether Cd (II)

Leaves NaOH and Citric acid Cd (II)

Wood Activated carbon Cu(II)

NaOH, H2SO4 CTAB HCl Ca(OH)2 Triton X-100 H3PO4 Al(OH)3

Pod Original state

**Moringa Oleifera Modifying agent(s) Heavy metal Temperature**

Cu (II) Co (II) Ni (II) Pb (II)

Cu(II) Ni(II)

Ni(II) Zn(II)

Bark Original state Ni(II) 50 6.0 60 [35]

Leaves NaOH and Citric acid Pb(II) 40 5.0 50 [34] Bark Original state Pb(II) 25 5.0 30 [19]

**Figure 11.** Point of zero net proton charge of *Moringa oleifera* seeds.

It has been noted that the temperature can influence the sorption process. Simple physical sorption processes are generally exothermic, i.e., the equilibrium constant decreases with increasing temperature. According to data reported in the literature (Table 3), the binding of the metal to different parts of the *M. oleifera* plant can be observed when the temperature is raised from 22 to 50 °C.

The contact time (or stirring time) is another important parameter that influences the efficiency of the adsorption process. As can be seen in Table 3, a period of 5 min was chosen for the nickel sorption process and good results were obtained; however, longer times (240 min) are required when using activated carbon.

*Moringa oleifera* is capable of directly sorbing metal ionic species from aqueous solutions. An interesting characteristic assigned to these biosorbents is the high abrasive content and the relative chemical resistance, allowing them to be subjected to different chemical treatments to increase their affinity and/or specificity for metal ions. Results previously published show the potential use of untreated seeds, although biosorbent materials are generally derived from plant biomass through different kinds of simple procedures. They may be chemically pre‐ treated for better performance and/or suitability for process applications. However, good results have been obtained when the seeds were treated with NaOH. This treatment can remove organic and inorganic matter from the sorbent surface. Chemical treatments are commonly performed employing alkaline solutions or with phosphoric and citric acids [42]. Recently, however, efforts have been made to remove and subsequently also recover metals. Metal-saturated biosorbent materials can be easily regenerated applying a simple (e.g. acidic) wash which then contains a very high concentration of released metals in a small volume, making the solution quite amenable to metal recovery.

Therefore, it is recommendable to study biosorption at pH values where precipitation does not occur. Biomasses are materials with an amphoteric character; thus, depending on the pH of the solution, their surfaces can be positively or negatively charged. At pH values greater than the point of zero discharge (*pHpzc)*, the biomass surface becomes negatively charged, favoring the adsorption of cationic species. However, adsorption of anionic species will be favored at pH *< pHpzc*. The *pHpzc* of the *M. oleifera* seeds is between 6.0 and 7.0 [41], indicating that the surface of the biosorbent presents acid characteristics. Figure 11 illustrates the surface charge or the point of zero net proton charge of *Moringa oleifera* seeds. The surface charge of the seeds is positive at pH *<* PZC, is neutral at pH = PZC and is negative at pH *>* PZC. The variation in pH caused by protonation and deprotonation of the adsorbent reflects the presence of functional groups. Table 3 shows the use of components of the *M oleifera* in the pH range of

02468 10 12

It has been noted that the temperature can influence the sorption process. Simple physical sorption processes are generally exothermic, i.e., the equilibrium constant decreases with increasing temperature. According to data reported in the literature (Table 3), the binding of the metal to different parts of the *M. oleifera* plant can be observed when the temperature is

The contact time (or stirring time) is another important parameter that influences the efficiency of the adsorption process. As can be seen in Table 3, a period of 5 min was chosen for the nickel sorption process and good results were obtained; however, longer times (240 min) are required

initial pH

2.5 to 8.0.

0

**Figure 11.** Point of zero net proton charge of *Moringa oleifera* seeds.

2

4

6

final pH

raised from 22 to 50 °C.

when using activated carbon.

8

10

12

236 Applied Bioremediation - Active and Passive Approaches



where *qe* is the amount of metal adsorbed at equilibrium (mg g-1), *Ce* is the concentration of metal in solution at equilibrium (mg L-1), and *qm* (mg g-1) and *KL* (L mg-1) are the Langmuir constants related to the adsorption capacity (amount of adsorbate needed to form a complete monolayer) and adsorption energy, respectively. The constants *qm* and *KL* can be calculated

Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent

The Freundlich model describes adsorption onto an energetically heterogeneous surface not

where *qe* is the amount of metal adsorbed at equilibrium (mg g-1), *Ce* is the concentration of

lich constants related to the multilayer adsorption capacity and adsorption intensity, respec‐ tively. According to the theory, *n* values between 1 and 10 represent favorable adsorption

Log *qe* versus Log *Ce*. Experimental adsorption results with high coefficient correlation (R2

The Dubinin-Radushkevich model has been used to distinguish between physical and chemical adsorption [53]. The Dubinin-Radushkevich is more general than the Langmuir model because it does not assume a homogenous surface or constant sorption. The Dubinin-

where, *qm* (mg g-1) is the theoretical sorption capacity (mol g-1), *ε* is the Polanyi potential which is related to the equilibrium concentration and the constant *β* gives the mean energy of sorption, *E* (KJ mol-1). The constants *qm* and *β* are obtained from the intercept and slope of ln *qe* versus

, respectively. If the magnitude of *E* is between 8 and 16 KJ mol-1 the adsorption process proceeds by ion-exchange or chemisorptions, while for values of E < 8 KJ mol-1 the adsorption process is of a physical nature [54]. In [31] reported that the sorption energy (*E*) values obtained with the Dubinin-Radushkevich model showed that the interaction between metal ions and

The Temkin isotherm model is based on the assumption that the heat of adsorption of all the molecules in the layer decreases linearly with coverage due to adsorbent-adsorbate interac‐ tions, and the adsorption is characterized by a uniform distribution of binding energies, up to

a maximum binding energy [50]. The model is represented by the following equation:

the adsorbent proceeded principally by physical adsorption.

values obtained for Freundlich isotherms have been reported as shown in Table 4.

*<sup>n</sup>* (2)

http://dx.doi.org/10.5772/56157

(mg g-1)(L mg)(1/n) and *n* (g L-1) are the Freund‐

)

239

and *n* can be calculated from the slope and intercept of the plot of

*qe* <sup>=</sup>*qme*(-*β*∈2 ) (3)

*qe* = *B ln* (*ACe*) (4)

limited by the monolayer capacity [48]. It can be presented in the following form:

*qe* = *K <sup>f</sup> Ce*

from the intercepts and the slopes of the linear plots of *Ce/qe* versus *Ce*.

metal in solution at equilibrium (mg L-1), and *Kf*

conditions [52]. Values of *Kf*

Radushkevich equation is given by:

*ε2*

CTAB: Cetyl trimethylammonium bromide, SDS: Sodium dodecyl sulfate

**Table 3.** Study parameters for the removal of metal ions using *Moringa oleifera*.

### **5. Adsorption models**

An important physicochemical aspect in terms of the evaluation of sorption processes is the sorption equilibrium. Adsorption isotherms are a basic requirement in understanding how the adsorbate is distributed between the liquid and solid phases when the adsorption process reaches the equilibrium state [45, 46]. Over the years a wide variety of isotherm models have been introduced. The most commonly used isotherm models include Langmuir [47], Freund‐ lich [48], Dubinin-Radushkevich [49] and Temkin [50].

It can be observed that in most of the cases the Langmuir adsorption model has been success‐ fully used to predict metal adsorption processes. The Langmuir isotherm model assumes monolayer adsorption onto an adsorbent surface containing a finite number of identical sites and without interaction between adsorbed molecules. The Langmuir isotherm model assumes that: each site can accommodate only one molecule or atom; the surface is energetically homogenous; there is no interaction between neighboring adsorbed molecules or atoms; and there are no phase transitions [51]. The Langmuir equation is expressed as follows:

$$q\_c = \frac{q\_m \cdot K\_L \cdot C\_v}{1 + K\_L C\_v} \tag{1}$$

where *qe* is the amount of metal adsorbed at equilibrium (mg g-1), *Ce* is the concentration of metal in solution at equilibrium (mg L-1), and *qm* (mg g-1) and *KL* (L mg-1) are the Langmuir constants related to the adsorption capacity (amount of adsorbate needed to form a complete monolayer) and adsorption energy, respectively. The constants *qm* and *KL* can be calculated from the intercepts and the slopes of the linear plots of *Ce/qe* versus *Ce*.

**Moringa Oleifera Modifying agent(s) Heavy metal Temperature**

Cr(III) Ni(II)

As (V)

Shelled seeds Original state Cd(II) - 6.5 40 [14] Seeds Original state Ag(I) 25 6.5 20 [36] Seeds NaOH Ni(II) 25 4.0-6.0 5 [44]

An important physicochemical aspect in terms of the evaluation of sorption processes is the sorption equilibrium. Adsorption isotherms are a basic requirement in understanding how the adsorbate is distributed between the liquid and solid phases when the adsorption process reaches the equilibrium state [45, 46]. Over the years a wide variety of isotherm models have been introduced. The most commonly used isotherm models include Langmuir [47], Freund‐

It can be observed that in most of the cases the Langmuir adsorption model has been success‐ fully used to predict metal adsorption processes. The Langmuir isotherm model assumes monolayer adsorption onto an adsorbent surface containing a finite number of identical sites and without interaction between adsorbed molecules. The Langmuir isotherm model assumes that: each site can accommodate only one molecule or atom; the surface is energetically homogenous; there is no interaction between neighboring adsorbed molecules or atoms; and

there are no phase transitions [51]. The Langmuir equation is expressed as follows:

*qm KL Ce* 1+ KLCe

*qe*=

SDS Shelled seeds Original state Cd(II)

238 Applied Bioremediation - Active and Passive Approaches

Shells Original state As (III)

CTAB: Cetyl trimethylammonium bromide, SDS: Sodium dodecyl sulfate

lich [48], Dubinin-Radushkevich [49] and Temkin [50].

**Table 3.** Study parameters for the removal of metal ions using *Moringa oleifera*.

CTAB H3PO4 H2SO4 HCl

Husk and pods Unmodified

**5. Adsorption models**

**(°C)**



6.5 7.5

2.5

Pb(II) 30 5.8 120 [3]

**pH Contact time (min)**

**Ref.**

(1)

40 [15]

60 [43]

The Freundlich model describes adsorption onto an energetically heterogeneous surface not limited by the monolayer capacity [48]. It can be presented in the following form:

$$\mathfrak{q}\_e = \mathcal{K}\_f \; \mathsf{C}\_e^n \tag{2}$$

where *qe* is the amount of metal adsorbed at equilibrium (mg g-1), *Ce* is the concentration of metal in solution at equilibrium (mg L-1), and *Kf* (mg g-1)(L mg)(1/n) and *n* (g L-1) are the Freund‐ lich constants related to the multilayer adsorption capacity and adsorption intensity, respec‐ tively. According to the theory, *n* values between 1 and 10 represent favorable adsorption conditions [52]. Values of *Kf* and *n* can be calculated from the slope and intercept of the plot of Log *qe* versus Log *Ce*. Experimental adsorption results with high coefficient correlation (R2 ) values obtained for Freundlich isotherms have been reported as shown in Table 4.

The Dubinin-Radushkevich model has been used to distinguish between physical and chemical adsorption [53]. The Dubinin-Radushkevich is more general than the Langmuir model because it does not assume a homogenous surface or constant sorption. The Dubinin-Radushkevich equation is given by:

$$
\eta\_e = \eta\_m e^{\left(\cdot \beta \in^2\right)} \tag{3}
$$

where, *qm* (mg g-1) is the theoretical sorption capacity (mol g-1), *ε* is the Polanyi potential which is related to the equilibrium concentration and the constant *β* gives the mean energy of sorption, *E* (KJ mol-1). The constants *qm* and *β* are obtained from the intercept and slope of ln *qe* versus *ε2* , respectively. If the magnitude of *E* is between 8 and 16 KJ mol-1 the adsorption process proceeds by ion-exchange or chemisorptions, while for values of E < 8 KJ mol-1 the adsorption process is of a physical nature [54]. In [31] reported that the sorption energy (*E*) values obtained with the Dubinin-Radushkevich model showed that the interaction between metal ions and the adsorbent proceeded principally by physical adsorption.

The Temkin isotherm model is based on the assumption that the heat of adsorption of all the molecules in the layer decreases linearly with coverage due to adsorbent-adsorbate interac‐ tions, and the adsorption is characterized by a uniform distribution of binding energies, up to a maximum binding energy [50]. The model is represented by the following equation:

$$q\_e = B \ln\left(AC\_e\right) \tag{4}$$

where *A (L g-1)* and *B (J mg-1)* are Temkin isotherm constants relating to adsorption potential and heat of adsorption, respectively. A plot of *qe* versus ln *Ce* gives the values of Temkin constants *A* and *B*. In the adsorption of copper, nickel and zinc onto activated carbon produced from *Moringa oleifera* wood [31] the Temkin isotherm showed a higher correlation coefficient, which may be due to the linear dependence of the heat of adsorption on low or medium coverage. The repulsive force probably occurs between the different adsorbate species or for intrinsic surface heterogeneity may be associated with the linearity.

**Heavy metal Langmuir model Freundlich model Ref.**

> 0.99 > 0.99

Ni(II) 30. 38 0.31 0.9994 - - - [35]

0.9979 0.9528 0.9973

Pb(II) 209.54 0.038 > 0.99 - - - [34] Ni(II) 29.6 - 0.9913 - - - [44] Ag(II) 23.13 0.1586 0.9935 - - - [36] Zn(II) 52.08 0.150 0.9994 50.35 - 0.9953 [16]

> 0.94 0.96 0.96

> 0.96 0.98

Pb(II) - - 0.9981 - - - [3] Pb (II) - - - 8.6 2.8 0.9981 [19] Cd(II) - - - 3.04 1.37 - [14]

The pseudo-first order equation, also known as the Lagergren equation, is expressed as follows

at time *t* and equilibrium, respectively; and *k1* (min−1) is the pseudo-first order rate constant of the sorption process and *t* (min) is the mixing time [60]. Table 5 presents the data of calculated

the assumption that the adsorption rate is proportional to the number of free sites available,

In most studies discrepancies occurred between the value of *qe* calculated by the pseudo-first order model and the experimental *qe*, as shown in Table 5, highlighting the inability of this

and *qe* (mg g−1) are the amount of metal ions adsorbed per unit weight of the adsorbent

log (*qe* - *qt*) =log *qe* - *k*<sup>1</sup>

3.8563 3.7708 -

0.037 0.029 0.023 > 0.99

R2 **Kf**

Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent

**(mg g-1) (L mg)(1/n)**

**n (g L-1)**


2.9214 2.2528 -



2.303 *t* (5)

). This kinetics model is based on

**R2**

http://dx.doi.org/10.5772/56157

0.9976 0.9996 -

[31]

241

*q***m (mg g-1)**

171.37 167.90 163.88

11.534 17.668 19.084

1.06 1.01 0.94

1.59 2.16

Cd (II) Cu(II) Ni(II)

Cu(II) Zn(II) Ni(II)

Cd(II) Cr(III) Ni(II)

As (III) As (V)

[16, 61]:

where *qt*

**KL (L mg -1)**

0.2166 0.1430 0.6165

0.51 0.40 0.34

0.04 0.09

**Table 4.** Langmuir and Freundlich isotherm parameters for *Moringa oleifera.*

*qe*, pseudo-first order rate (*k1*) and correlation coefficient (*R2*

occurring exclusively onto one site per ion [34, 62].

Table 4 details some of the results for the biosorption studies using *Moringa oleifera* which have been reported in the literature from 2006 onwards. From this table it is clear that *Moringa oleifera* shows versatility, removing a variety metals under favorable conditions and is among the most promising metal biosorbents.

Comparing the Langmuir and Freundlich models, *M. oleifera* seeds demonstrated a good removal capacity for Co(II), Cu(II), Pb(II), Cd(II) and Ag(I), as compared to reports related to other parts of the plant (Table 3). The variations in the removal percentage for metal ions can be explained by the different ionic radii of chemical species. In general, for the single metal solutions, ions with larger ionic radii are preferentially adsorbed. Among the metals tested, Pb(II) has the largest ionic radius and hence shows the highest adsorption percentage, whereas Co(II) presents the lowest level of adsorption [55].

Kinetics models are important in evaluating the basic qualities of an adsorbent as well as the time required for the removal of particular metals, the effectiveness of the adsorbent and the identification of the types of mechanisms involved in an adsorption system [56-58]. In order to investigate the mechanism of biosorption and its potential rate-controlling steps, which include the mass transfer and chemical reaction processes, kinetics models are exploited to test experimental data obtained in kinetics studies. These usually show an initial period of rapid metal adsorption with a subsequent decreased until reaching equilibrium of the system. This occurs due to the rapid adsorption of metallic ions by the surface of the adsorbent followed by a step of slow diffusion of ions from the surface film to the adsorption sites in the micropores which are less accessible [59].

In practice, the kinetic studies are carried out in batch experiments, typically varying the adsorbate concentration, the adsorbent mass, the agitation time and the temperature, as well as the type of adsorbent and adsorbate. Subsequently, the data are processed and used in the linear regression to determine the kinetics model which provides the best fit. However, for the validity of the order of the adsorption process two criteria should be evaluated, the first based on the regression coefficient *(R2 )* and the second on the calculated *qe* values, which must approach the experimental *qe* [60]. The main models used to evaluate the kinetics model profile are pseudo-first-order and pseudo-second order. However, other models are also are applied, such as Bangham's model and the Weber and Morris sorption kinetic model.

Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent http://dx.doi.org/10.5772/56157 241


**Table 4.** Langmuir and Freundlich isotherm parameters for *Moringa oleifera.*

where *A (L g-1)* and *B (J mg-1)* are Temkin isotherm constants relating to adsorption potential and heat of adsorption, respectively. A plot of *qe* versus ln *Ce* gives the values of Temkin constants *A* and *B*. In the adsorption of copper, nickel and zinc onto activated carbon produced from *Moringa oleifera* wood [31] the Temkin isotherm showed a higher correlation coefficient, which may be due to the linear dependence of the heat of adsorption on low or medium coverage. The repulsive force probably occurs between the different adsorbate species or for

Table 4 details some of the results for the biosorption studies using *Moringa oleifera* which have been reported in the literature from 2006 onwards. From this table it is clear that *Moringa oleifera* shows versatility, removing a variety metals under favorable conditions and is among

Comparing the Langmuir and Freundlich models, *M. oleifera* seeds demonstrated a good removal capacity for Co(II), Cu(II), Pb(II), Cd(II) and Ag(I), as compared to reports related to other parts of the plant (Table 3). The variations in the removal percentage for metal ions can be explained by the different ionic radii of chemical species. In general, for the single metal solutions, ions with larger ionic radii are preferentially adsorbed. Among the metals tested, Pb(II) has the largest ionic radius and hence shows the highest adsorption percentage, whereas

Kinetics models are important in evaluating the basic qualities of an adsorbent as well as the time required for the removal of particular metals, the effectiveness of the adsorbent and the identification of the types of mechanisms involved in an adsorption system [56-58]. In order to investigate the mechanism of biosorption and its potential rate-controlling steps, which include the mass transfer and chemical reaction processes, kinetics models are exploited to test experimental data obtained in kinetics studies. These usually show an initial period of rapid metal adsorption with a subsequent decreased until reaching equilibrium of the system. This occurs due to the rapid adsorption of metallic ions by the surface of the adsorbent followed by a step of slow diffusion of ions from the surface film to the adsorption sites in the micropores

In practice, the kinetic studies are carried out in batch experiments, typically varying the adsorbate concentration, the adsorbent mass, the agitation time and the temperature, as well as the type of adsorbent and adsorbate. Subsequently, the data are processed and used in the linear regression to determine the kinetics model which provides the best fit. However, for the validity of the order of the adsorption process two criteria should be evaluated, the first based

approach the experimental *qe* [60]. The main models used to evaluate the kinetics model profile are pseudo-first-order and pseudo-second order. However, other models are also are applied,

such as Bangham's model and the Weber and Morris sorption kinetic model.

*)* and the second on the calculated *qe* values, which must

intrinsic surface heterogeneity may be associated with the linearity.

the most promising metal biosorbents.

240 Applied Bioremediation - Active and Passive Approaches

which are less accessible [59].

on the regression coefficient *(R2*

Co(II) presents the lowest level of adsorption [55].

The pseudo-first order equation, also known as the Lagergren equation, is expressed as follows [16, 61]:

$$\log\left(q\_{\epsilon} \cdot q\_{t}\right) = \log q\_{\epsilon} \text{ -- } \frac{k\_{1}}{2.303}t \tag{5}$$

where *qt* and *qe* (mg g−1) are the amount of metal ions adsorbed per unit weight of the adsorbent at time *t* and equilibrium, respectively; and *k1* (min−1) is the pseudo-first order rate constant of the sorption process and *t* (min) is the mixing time [60]. Table 5 presents the data of calculated *qe*, pseudo-first order rate (*k1*) and correlation coefficient (*R2* ). This kinetics model is based on the assumption that the adsorption rate is proportional to the number of free sites available, occurring exclusively onto one site per ion [34, 62].

In most studies discrepancies occurred between the value of *qe* calculated by the pseudo-first order model and the experimental *qe*, as shown in Table 5, highlighting the inability of this model to describe the kinetics of the metal ion adsorption processes. In general, calculated *qe* values are smaller than the experimental *qe*, which may occur because of a time lag, probably due to the presence of the boundary layer or external resistance at the beginning of the sorption process [63]. Considering the papers detailed in Table 5, only in [43] and [14] used only the pseudo-first order kinetics model to examine the data obtained, even though in the latter case the correlation values obtained were relatively low. In [43] noted no change in the adsorption rate constant when varying the concentrations of As(III) and As(V) and therefore this model could describe the adsorption process. In [14] used this model to compare the adsorption rate constants of ternary metal ions and single metal ions and noted that these constants were lower for ternary metal ions. Their explanation for this was that metal ions compete for vacant sites and uptake by binding sites within the shortest possible time.

The pseudo-second-order kinetics model is also based on the assumption that the sorption rate is controlled by a chemical sorption mechanism involving electron sharing or electron transfer between the adsorbent and adsorbate [64]. It can be expressed as:

$$\frac{t}{\frac{q\_t}{q\_t}} = \frac{1}{k\_2 q\_e^{-2}} + \frac{t}{q\_e} \tag{6}$$

layer effect will be. When there is a complete fit of the model the value of *cid* should be zero, and the deviation of this constant is due to differences in the mass transfer rate during the initial and final stages of adsorption. This is indicative that there is some degree of boundary layer control and shows that the intra-particle diffusion is not the only rate-limiting step, and

Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent

is controlled by intra-particle diffusion, while if the data exhibit multi-linear plots then two or more steps influence the adsorption process [67]. In two studies performed in [32, 19], multilinear plots were observed with three distinct steps involved in the biosorption, the initial region of the curve relative relating to the adsorption on the external surface. The second region corresponds to the gradual uptake, where the intra-particle diffusion is the rate-limiting step.

In reference [3] compared different types of carbon through the *kid* values and observed that the effect of intra-particle diffusion may be significantly increased by chemically modifying the adsorbents. Although none of the data collected in the studies detailed in Table 5 were well-described by the kinetics model proposed by Weber and Moris, the intraparticle diffusion

Bangham's model evaluates whether pore diffusion is the only rate-controlling step in the

*t*; *co* (mg L-1) is the initial metal ion concentration in liquid phase; *m* (g L‒1) is adsorbent concentration at time *t* (min); *V* (L) solution volume, *t* (min) is the mixing time and *ko* (L g-1) and *σ* (*σ* < 1) are constants of Bangham's model. Of the studies published, only in [3] used Bangham's kinetic model to compare the rate constants for the adsorption of Pb(II) onto different types of functionalized carbon prepared from the seed husks and pods of *M.*

The temperature is reportedly an important parameter for the adsorption of metal ions. An increase or decrease in temperature can cause a change in the amount of metal removed or adsorbed by the adsorbent. A change in temperature causes a change in the thermodynamic parameters of free energy (*∆G°*), enthalpy (*∆H°*) and entropy (*∆S°*). These parameters are important to understand the adsorption mechanism [68]. For a given temperature, a phenom‐ enon is considered to be spontaneous if the *∆G°* has a negative value. Moreover, if *∆H°* is positive the process is endothermic and if it is negative the process is exothermic [69]. Negative values of *∆S°* show a decreased randomness or increased order at the metal-biomass interface. The positive value showed a change in the biomass structure during the sorption process, causing an increase in the disorder of the system [68]. The parameters *∆G°* (kJ mol-1), *∆H°* (kJ

(mg g−1) is the amount of metal ions adsorbed per unit weight of the adsorbent at time

adsorption process [65] and can be represented by the following equation:

*co* - *qtm* ) =log ( *kom*

*oleifera* and thereby assess the efficiency of the functionalization of this material.

mol-1) and *∆S°* (J mol-1 K-1) can be evaluated from the following equations [70].

versus *t1/2* gives a straight line, then the sorption process

http://dx.doi.org/10.5772/56157

243

2.303 *<sup>V</sup>* ) + *σ*log *t* (8)

thus several processes operating simultaneously may control the adsorption [34].

According to this model, if the plot of *qt*

where *qt*

The final plateau region indicates the equilibrium uptake.

may not be the only rate-limiting step in these studies.

log *log* ( *co*

where *qt* and *qe* (mg g−1) are the amount of metal ions adsorbed per unit weight of the adsorbent at time *t* and equilibrium, respectively; and *k2* (g mg-1 min−1) is the pseudo-second order rate constant of the sorption process and *t* (min) is the mixing time.

Table 5 presents the data of calculated *qe*, pseudo-second order rate (*k2*) and correlation coefficient (*R2* ). For most of the pseudo-second order kinetics models the calculated *qe* values approach the experimental *qe* values and the correlation coefficients are close to 1, indicating a good ability of this model to describe the kinetics of the metal ion adsorption process. This observation indicates that the rate-limiting steps in the biosorption of metallic ions are chemisorption involving valence forces through the sharing or exchange of electrons between the sorbent and the sorbate, complexation, coordination and/or chelation, in which mass transfer in the solution was not involved.

Considering that neither the pseudo-first-order nor the pseudo-second-order model can identify the diffusion mechanism, other kinetic models are needed to study this process, such as Bangham's model and the Weber and Morris sorption kinetics model [65]. The latter model is also known as the intra-particle diffusion model, this process in many cases being the ratelimiting step, which can be determined through the following equation:

$$\mathbf{q}\_t = \mathbf{k}\_{id}\mathbf{t}\ ^\ast \mathbf{t} \tag{7}$$

where *qt* (mg g−1) is the amount of metal ions adsorbed per unit weight of the adsorbent at time *t*, *cid* (mg g−1) is a constant of Weber and Morris, and *kid* (mg g−1 min−1/2) is the intra-particle diffusion rate constant and *t* (min) is the mixing time [66]. The value of the intercept gives an idea of the thickness of the boundary layer, i.e., the larger the intercept the greater the boundary layer effect will be. When there is a complete fit of the model the value of *cid* should be zero, and the deviation of this constant is due to differences in the mass transfer rate during the initial and final stages of adsorption. This is indicative that there is some degree of boundary layer control and shows that the intra-particle diffusion is not the only rate-limiting step, and thus several processes operating simultaneously may control the adsorption [34].

model to describe the kinetics of the metal ion adsorption processes. In general, calculated *qe* values are smaller than the experimental *qe*, which may occur because of a time lag, probably due to the presence of the boundary layer or external resistance at the beginning of the sorption process [63]. Considering the papers detailed in Table 5, only in [43] and [14] used only the pseudo-first order kinetics model to examine the data obtained, even though in the latter case the correlation values obtained were relatively low. In [43] noted no change in the adsorption rate constant when varying the concentrations of As(III) and As(V) and therefore this model could describe the adsorption process. In [14] used this model to compare the adsorption rate constants of ternary metal ions and single metal ions and noted that these constants were lower for ternary metal ions. Their explanation for this was that metal ions compete for vacant sites

The pseudo-second-order kinetics model is also based on the assumption that the sorption rate is controlled by a chemical sorption mechanism involving electron sharing or electron transfer

and *qe* (mg g−1) are the amount of metal ions adsorbed per unit weight of the adsorbent

). For most of the pseudo-second order kinetics models the calculated *qe* values

at time *t* and equilibrium, respectively; and *k2* (g mg-1 min−1) is the pseudo-second order rate

Table 5 presents the data of calculated *qe*, pseudo-second order rate (*k2*) and correlation

approach the experimental *qe* values and the correlation coefficients are close to 1, indicating a good ability of this model to describe the kinetics of the metal ion adsorption process. This observation indicates that the rate-limiting steps in the biosorption of metallic ions are chemisorption involving valence forces through the sharing or exchange of electrons between the sorbent and the sorbate, complexation, coordination and/or chelation, in which mass

Considering that neither the pseudo-first-order nor the pseudo-second-order model can identify the diffusion mechanism, other kinetic models are needed to study this process, such as Bangham's model and the Weber and Morris sorption kinetics model [65]. The latter model is also known as the intra-particle diffusion model, this process in many cases being the rate-

limiting step, which can be determined through the following equation:

*qt* =*kid t*

1

(mg g−1) is the amount of metal ions adsorbed per unit weight of the adsorbent at time

*t*, *cid* (mg g−1) is a constant of Weber and Morris, and *kid* (mg g−1 min−1/2) is the intra-particle diffusion rate constant and *t* (min) is the mixing time [66]. The value of the intercept gives an idea of the thickness of the boundary layer, i.e., the larger the intercept the greater the boundary

*qe* (6)

<sup>2</sup> <sup>+</sup> *cid* (7)

and uptake by binding sites within the shortest possible time.

242 Applied Bioremediation - Active and Passive Approaches

between the adsorbent and adsorbate [64]. It can be expressed as:

constant of the sorption process and *t* (min) is the mixing time.

transfer in the solution was not involved.

where *qt*

coefficient (*R2*

where *qt*

*t qt* <sup>=</sup><sup>1</sup> *k*2*qe* <sup>2</sup> + *t* According to this model, if the plot of *qt* versus *t1/2* gives a straight line, then the sorption process is controlled by intra-particle diffusion, while if the data exhibit multi-linear plots then two or more steps influence the adsorption process [67]. In two studies performed in [32, 19], multilinear plots were observed with three distinct steps involved in the biosorption, the initial region of the curve relative relating to the adsorption on the external surface. The second region corresponds to the gradual uptake, where the intra-particle diffusion is the rate-limiting step. The final plateau region indicates the equilibrium uptake.

In reference [3] compared different types of carbon through the *kid* values and observed that the effect of intra-particle diffusion may be significantly increased by chemically modifying the adsorbents. Although none of the data collected in the studies detailed in Table 5 were well-described by the kinetics model proposed by Weber and Moris, the intraparticle diffusion may not be the only rate-limiting step in these studies.

Bangham's model evaluates whether pore diffusion is the only rate-controlling step in the adsorption process [65] and can be represented by the following equation:

$$\log\left[\log\left(\frac{c\_o}{c\_o \cdot q\_i m}\right)\right] = \log\left(\frac{k\_o m}{2.303\,\text{V}}\right) + \sigma\log t \tag{8}$$

where *qt* (mg g−1) is the amount of metal ions adsorbed per unit weight of the adsorbent at time *t*; *co* (mg L-1) is the initial metal ion concentration in liquid phase; *m* (g L‒1) is adsorbent concentration at time *t* (min); *V* (L) solution volume, *t* (min) is the mixing time and *ko* (L g-1) and *σ* (*σ* < 1) are constants of Bangham's model. Of the studies published, only in [3] used Bangham's kinetic model to compare the rate constants for the adsorption of Pb(II) onto different types of functionalized carbon prepared from the seed husks and pods of *M. oleifera* and thereby assess the efficiency of the functionalization of this material.

The temperature is reportedly an important parameter for the adsorption of metal ions. An increase or decrease in temperature can cause a change in the amount of metal removed or adsorbed by the adsorbent. A change in temperature causes a change in the thermodynamic parameters of free energy (*∆G°*), enthalpy (*∆H°*) and entropy (*∆S°*). These parameters are important to understand the adsorption mechanism [68]. For a given temperature, a phenom‐ enon is considered to be spontaneous if the *∆G°* has a negative value. Moreover, if *∆H°* is positive the process is endothermic and if it is negative the process is exothermic [69]. Negative values of *∆S°* show a decreased randomness or increased order at the metal-biomass interface. The positive value showed a change in the biomass structure during the sorption process, causing an increase in the disorder of the system [68]. The parameters *∆G°* (kJ mol-1), *∆H°* (kJ mol-1) and *∆S°* (J mol-1 K-1) can be evaluated from the following equations [70].

$$
\Delta G^{\circ} = -RT\ln K\_{\circ} \tag{9}
$$

**Metal co**

Zn (II)

Pb(II)

As (III) As (V)

Cd(II) Cr(III) Ni(II)

Pb(II)

Pb(II)

Ni(II)

Cu(II) Zn(II) Ni(II)

Cd (II) Cu(II) Ni(II)

**(mg L−1)**

50a 50b 50c

30d 30e 30f 30g 30h

> 25 25 25

> 10 25 40

10.4 30.1 50.4

> 10 25 50

> 30 30 30

**qe, exp (mg g−1)**

> 45.00 45.76 42.80

> > - - - - -

> > - - - -

1.06 1.01 0.94

12.7343 19.8988 23.9233

> 8.7 10.2 12.5

> 9.7 6.74 3.27

8.3406 13.2537 9.5847

> 13.54 13.80 20.86 11.92 13.55 16.01 10.24 12.49 14.07

**qe (mg g-1)**

> - - -

> - - - - -

> - - - -

> - - -

> - - -

> - - -

> - - -

> - - -

> - - - - - - - - -

**Table 5.** Kinetics parameters for metal biosorption using Moringa oleifera.

**k1 (min −1)**

> - - -

> - - - - -

0.047 0.049 0.063 0.065

> 0.51 0.40 0.34

> > - - -

> > - - -

> > - - -

> > - - -

> > - - - - - - - - -










**Pseudo-first-order Pseudo-second-order Ref.**

**min-1)**

4.51 10-4 2.85 10-4 2.04 10-5

> 0.0085 0.0052 0.0060 0.0062 0.0087

> > - - - -

> > - - -

15.35 10.08 9.3

> 27.8 18.2 12.6

1.91 2.70 12.74

0.0848 0.2457 0.0957

> 1.39 1.46 1.22 3.4 1.66 1.56 1.51 1.31 1.52

**h0 (mg g−1 min−1)**

http://dx.doi.org/10.5772/56157


5.131 3.989 4.868 5.243 7.550

> - - - -

> - - -

0.20 0.21 0.23

> 2.5 1.9 1.6

2.03 1.38 1.05

> - - -

2.73 3.09 5.61 5.58 3.37 4.31 1.70 2.29 3.27 **R2**

0.997 0.999 0.997

0.999 0.998 0.998 0.999 0.999

> - - - -

> - - -

0.9974 0.997 0.9995

0.9999 0.9999 0.9998

0.9971 0.9964 0.996

0.9998 1 0.9999

0.9951 0.9969 0.9981 0.9992 0.9958 0.9977 0.9951 0.9952 0.9967 [16]

245

[3]

[43]

[14]

[34]

[19]

[35]

[31]

[32]

**R2 qe (mg g-1) k2 (g mg-1**

46.94 47.16 43.47

Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent

24.57 27.70 28.49 29.08 29.46

> - - - -

> - - -

13.26 20.64 25.01

8.8 10.3 12.53

10.29 7.14 3.43

8.3264 13.2450 9.6154

> 10.99 10.50 15.24 11.03 12.45 12.86 10.24 12.49 14.07

$$
\text{In } K\_c = -\begin{array}{c}
\frac{\Delta H^\circ}{RT} + \frac{\Delta S^\circ}{R}
\end{array}
\tag{10}
$$

where *R*(8.314J mol-1 K-1) is the gas constant, *T*(K) the absolute temperature and *Kc*(mL g-1) the standard thermodynamic equilibrium constant defined by *qe/Ce*. *∆H°* and *∆S°* can be deter‐ mined from the slope and the intercept of the linear plot of Ln *Kc* versus *1/T*.

The studies performed on *Moringa oleifera* using chemically-modified leaves for the adsorption of Pb(II) [34], bark for Ni(II) [35] and leaves for Cd(II), Cu(II) and Ni(II) [32] showed the endothermic nature and spontaneity of the adsorption process. The positive values of ∆S° suggest an increase in randomness at the solid/liquid interface with some structural changes in the sorbate.

### **6. Final considerations**

Although the biosorption of heavy metals from aqueous solutions is a relatively new process that has proven very promising in the removal of contaminants from aqueous effluents, offering significant advantages like the low-cost, availability, profitability, easy of operation and efficiency. Other technologies have also been very attractive ensuring an appropriate process to treat industrial waste effluents [71-77]. However, biosorption is becoming a potential alternative to the existing technologies for the removal and/or recovery of toxic metals from wastewater. The major advantages of biosorption technology are its effectiveness in reducing the concentration of heavy metal ions to very low levels and the use of inexpensive biosorbent materials.

#### **7. Conclusions**

The studies described herein indicate that *Moringa oleifera* seeds are an alternative sorbent for metal ion removal from contaminated waters. This can be found in most papers which report 60 to 90% removal of metals (Cd(II), Cu(II), Ni(II), Pb(II), As(III), As(V), Cr(III) and Zn(II)). In these cases, not only the seeds were used, but also leaves, bark and pods showing the great versatility of this plant. The results show that even with the high heterogeneity of the matrix confirmed through characterization techniques there is a great potential for the application of these seeds in effluent treatment without component separation, which makes the process economically and technically attractive.

Biosorption is the most economical and eco-friendly method for the removal of heavy metals from domestic as well as industrial wastewater and it is particularly important to promote the development of biosorption for industrial processes. Notable advantages are: (a) low cost of Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent http://dx.doi.org/10.5772/56157 245


**Table 5.** Kinetics parameters for metal biosorption using Moringa oleifera.

∆*G* °= - *RT ln Kc* (9)

*<sup>R</sup>* (10)

*ln Kc* = - <sup>∆</sup> *<sup>H</sup>* °

mined from the slope and the intercept of the linear plot of Ln *Kc* versus *1/T*.

in the sorbate.

**6. Final considerations**

244 Applied Bioremediation - Active and Passive Approaches

biosorbent materials.

**7. Conclusions**

economically and technically attractive.

*RT* +

where *R*(8.314J mol-1 K-1) is the gas constant, *T*(K) the absolute temperature and *Kc*(mL g-1) the standard thermodynamic equilibrium constant defined by *qe/Ce*. *∆H°* and *∆S°* can be deter‐

The studies performed on *Moringa oleifera* using chemically-modified leaves for the adsorption of Pb(II) [34], bark for Ni(II) [35] and leaves for Cd(II), Cu(II) and Ni(II) [32] showed the endothermic nature and spontaneity of the adsorption process. The positive values of ∆S° suggest an increase in randomness at the solid/liquid interface with some structural changes

Although the biosorption of heavy metals from aqueous solutions is a relatively new process that has proven very promising in the removal of contaminants from aqueous effluents, offering significant advantages like the low-cost, availability, profitability, easy of operation and efficiency. Other technologies have also been very attractive ensuring an appropriate process to treat industrial waste effluents [71-77]. However, biosorption is becoming a potential alternative to the existing technologies for the removal and/or recovery of toxic metals from wastewater. The major advantages of biosorption technology are its effectiveness in reducing the concentration of heavy metal ions to very low levels and the use of inexpensive

The studies described herein indicate that *Moringa oleifera* seeds are an alternative sorbent for metal ion removal from contaminated waters. This can be found in most papers which report 60 to 90% removal of metals (Cd(II), Cu(II), Ni(II), Pb(II), As(III), As(V), Cr(III) and Zn(II)). In these cases, not only the seeds were used, but also leaves, bark and pods showing the great versatility of this plant. The results show that even with the high heterogeneity of the matrix confirmed through characterization techniques there is a great potential for the application of these seeds in effluent treatment without component separation, which makes the process

Biosorption is the most economical and eco-friendly method for the removal of heavy metals from domestic as well as industrial wastewater and it is particularly important to promote the development of biosorption for industrial processes. Notable advantages are: (a) low cost of

∆ *S* °

the biosorbent, (b) high efficiency for metal removal at low concentration, (c) potential for biosorbent regeneration and metal valorization, (d) high sorption and desorption rates, (e) limited generation of secondary residues, and (f) relatively environmentally-friendly life cycle of the material (easy to eliminate compared to conventional resins, for example).

FT-IR Fourier transform infrared ∆G° free energy, (kJ mol-1) ∆H° enthalpy, (kJ mol-1)

*Kc* standard thermodynamic equilibrium constant defined by *qe/Ce*, (mL g-1)

Bioremediation of Waters Contaminated with Heavy Metals Using *Moringa oleifera* Seeds as Biosorbent

*k2* pseudo-second order rate constant of the sorption process, (g mg-1 min−1)

KL Langmuir constant related to adsorption energy, (L mg-1)

*kid* intra-particle diffusion rate constant, (mg g−1 min−1/2)

*ko* constant of the Bangham's model, (L g-1) σ constant of the Bangham's model

*R* gas constant, (8.314J mol-1 K-1)

SEM scanning electron microscopy

R<sup>2</sup> coefficient correlation ∆S° entropy, (J mol-1 K-1)

*t* mixing time, (min) *T* absolute temperature, (K) TG thermogravimetric

*V* solution volume, (L) XRD X-ray diffraction

**Author details**

Cleide S. T. Araújo1

Luciana M. Coelho3

*m* adsorbent concentration at time *t* (min), (g L‒1)

US EPA United States Environmental Protection Agency

, Dayene C. Carvalho2

\*Address all correspondence to: nmmcoelho@ufu.br

1 State University of Goiás, Anápolis, GO, Brazil

, Helen C. Rezende2

, Nívia M. M. Coelho2\*, Thiago L. Marques2

2 Institute of Chemistry, Federal University of Uberlândia, Uberlândia, MG, Brazil

3 Department of Chemistry, Federal University of Goiás, Catalão, GO, Brazil

, Ione L. S. Almeida2

and Vanessa N. Alves2

,

http://dx.doi.org/10.5772/56157

247

Kf Freundlich constant related to the multilayer adsorption capacity *k1* pseudo-first order rate constant of the sorption process, (min−1)

n Freundlich constant related to the multilayer adsorption intensity

qm Langmuir constant related to the adsorption capacity, (mg g-1)

qe amount of metal adsorbed per unit weight of the adsorbent at equilibrium, (mg g-1)

*qt* amount of metal ions adsorbed per unit weight of the adsorbent at time *t*, (mg g−1)

However, after the metal removal from aqueous solutions by the biomass, the recovery of the metal is an important issue. This can be achieved through a metal desorption process, aimed at weakening the metal-biomass linkage. Thus, studies to evaluate the reversibility of the adsorption reactions involved in the biosorption of heavy metals are of great importance. The problems associated with the disposal of exhausted adsorbent can be solved either by its activation or incineration or its disposal after proper treatment. For biosorption and desorption processes, another important aspect is the biosorbent reuse in successive biosorption-desorp‐ tion cycles, the viability of which is determined by the cost-benefit relationship between the loss in biosorption capacity during the desorption steps and the operational yield in the metal recovery. Thus, further studies need to focus on the development of new clean environmen‐ tally-acceptable technologies.
