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The removal of heavy metals in a packed bed column using immobilized cassava peel waste biomass
Accepted Manuscript Title: The removal of heavy metals in a packed bed column using immobilized cassava peel waste biomass Author: Geoffrey S. Simate Sehliselo Ndlovu PII: DOI: Reference:
S1226-086X(14)00185-3 http://dx.doi.org/doi:10.1016/j.jiec.2014.03.031 JIEC 1977
To appear in: Received date: Revised date: Accepted date:
8-1-2014 6-3-2014 17-3-2014
Please cite this article as: G.S. Simate, S. Ndlovu, The removal of heavy metals in a packed bed column using immobilized cassava peel waste biomass, Journal of Industrial and Engineering Chemistry (2014), http://dx.doi.org/10.1016/j.jiec.2014.03.031 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The removal of heavy metals in a packed bed column using immobilized cassava peel waste
an
us
cr
Geoffrey S. Simate*, Sehliselo Ndlovu
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biomass
School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg,
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P/Bag 3, Wits 2050, South Africa
____________________ * Corresponding author. Tel: +27 11 717 7570; Cell: +27 76 112 6959; Fax: +27 11 717 7599; Email: [email protected] (G. S. Simate)
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Abstract
Several studies on the removal of heavy metals in batch systems using cassava waste biomass have been reported in literature. However, for practical and large scale operations, packed bed columns
ip t
are preferred. This study investigated the biosorption of heavy metals (Cr3+, Co2+ and V3+) onto immobilized cassava peel waste in a packed bed column. Experiments were conducted with 100
cr
mg/L of combined metal ion solutions under different flow rates (0.83 – 1.61 mL/s) and bed depths
us
(5 – 15 cm). The dynamic behaviour of the process was described in terms of the breakthrough curves. The results showed that the removal efficiency was favoured by low flow rate and high bed
an
depth. Biosorption efficiency was found to increase in the order V3+ > Cr3+ > Co2+ for all conditions tested. Amongst the two well-established column models tested, the bed depth service time (BDST)
M
model with biosorption capacities of 99.6, 116.2 and 132.8 mg/L for Co2+, Cr3+ and V3+, respectively, fitted experimental data very well. The column was regenerated and reused
d
consecutively for six cycles without significant loss in biosorbent capacity signifying its
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appropriateness for commercial application.
Keywords: heavy metals; wastewater; biosorption; immobilized cassava peel waste biomass; packed bed; breakthrough curve
1.
Introduction
The presence of heavy metals in water and wastewater effluents is one of the greatest challenges in our time. This is because a variety of physiological and neurological damage to the human body are associated with heavy metal exposure [1]. As a result tough regulatory laws that restrict levels of heavy metals present in water or wastewater have been imposed by several nations in the past decades. Therefore, in order to decrease the levels of heavy metals in the environment, it is 1 Page 2 of 38
necessary to treat wastewaters before discharge [2]. At the moment, a wide range of physical and chemical techniques are available for removal of heavy metals [3]. However, these traditional water and wastewater treatment processes have been shown to be prohibitively expensive and ineffective for very low metal concentrations [4-8]. Therefore, the need to find effective and economical
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alternative techniques for removal of heavy metals is taken as a priority in the water and wastewater treatment industry. One such alternative technique that has attracted a lot of attention in recent years
cr
because of its competitiveness, effectiveness and low cost is biosorption [3,6,8 - 15]. Furthermore,
us
biosorption exhibits several other advantages, such as high selectivity and low energy consumption [16]. In recent past, a wide range of biosorbents such as rice husks [17], neem saw dust [8], beer
an
yeast [18], green algae [19] and fly ash [20] have been investigated. More recently [6], we investigated the removal of some heavy metals from waste effluents using cassava peel waste in a
M
batch system.
d
The biosorption capacity of the cassava peel waste [6] and many other biosorbents obtained from
te
batch equilibrium experiments is useful in providing fundamental information about the
Ac ce p
effectiveness of metal-biosorbent system [21]. However, data obtained from batch systems may not be applied directly to most treatment systems (such as column operations) where contact time is not sufficient for the attainment of the equilibrium [8, 21-23]. Furthermore, batch systems are usually limited to the treatment of small quantities of wastewater [21]. Therefore, for practical and large scale operations, a packed column is preferred to a batch system [8, 19, 22]. This is because a packed column makes the best use of the concentration difference known to be the driving force for heavy metal biosorption. This allows for more efficient utilization of biosorbent capacity and also results in better quality of the effluent [8,22,24]. Hence, a column packed with immobilized cassava peel waste pellets was used in our study. In such systems, the concentration profiles in the liquid and biosorbent phases vary in both space and time [19, 25]. This makes the design and optimisation of a packed bed difficult to perform without a quantitative modelling approach [19, 25]. 2 Page 3 of 38
Models have an important role in technology transfer from laboratory-scale to industrial-scale [24]. Appropriate models can help to analyse and explain experimental data, identify process mechanisms, predict answers to changing operational conditions and optimize processes [24,26].
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From the perspectives of process modelling, the dynamic behaviour of a packed column is described in terms of a breakthrough curve [8, 19, 25]. A breakthrough curve, which is S-shaped, is
cr
a plot of effluent solute concentration versus time. Breakthrough is the point on the S-shaped curve
us
at which the effluent solute concentration reaches its maximum allowable value [8, 22]. On the other hand, the point where the effluent solute concentration reaches 95% of the influent
an
concentration is called the point of column exhaustion [8, 22, 27].
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The shapes of breakthrough curves depend on the nature of the wastewater being treated. If there is only a single adsorbable component in wastewater, the adsorption will be short and the
d
breakthrough curve will be steep [8, 22]. If there is a mixture of components having different
te
adsorption capabilities, the sorption zone will be deep and the breakthrough will be flatter.
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Residence time is the major design parameter for the adsorption systems. The optimum residence time determines the size of the adsorbing column and amount of adsorbent.
As stated earlier, the potential of utilising cassava peel waste as a biosorbent for the removal of some heavy metals from aqueous solutions was investigated in our previous study using a batch process [6]. Although the use of cassava biomass had shown much potential in the development of several bioprocesses in the past, there were still some questions that required answers. Therefore, some of the parameters tested in our previous study [6] include biomass modification, and its reusability. Furthermore, the choice of cassava peel waste was also made from an economic standpoint. Since cassava peel waste has no economic value its conversion into an effective biosorbent is expected to increase its market value and ultimately economically benefit the millions 3 Page 4 of 38
of cassava producers [6]. Therefore, the aim of the current study was to use immobilized cassava peel waste pellets as a biosorbent for the removal of heavy metal ions from water and wastewater in
2.1.
Preparation of heavy metal solutions
cr
Materials and Methods
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2.
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a column mode.
All chemicals used were of analytical grade and were obtained from Merck, South Africa. Synthetic
an
solutions (100 mg/L) with combined metal ions of Cr3+, Co2+ and V3+ were prepared by dissolving required salt quantities of Cr(NO3)3.9H2O, CoSO4.7H2O and VCl3, respectively, in distilled water
M
according to the procedure outlined by American Public Health Association [28]. Relatively low metal ions concentrations of 100 mg/L were used so as to obtain gentle breakthrough curves [29]. It
d
must be noted, however, that industrial effluents usually contain higher values than used in many
te
studies [6]. In the previous study by Ndlovu et al. [6], the average optimum pH for biosorption of
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the heavy metals was 4.0. Therefore, the pH of influent solution was adjusted to 4.0 with a pH meter (827 Metrohm) by using 0.1M H2SO4 and /or 0.1 M NaOH accordingly.
2.2.
Preparation of immobilized cassava peel waste biosorbent
The preliminary preparation of cassava peel waste biomass was described in our earlier study [6]. The thiolation (a process of introducing sulfhydryl group (or thiol group), –SH) procedure of the cassava waste was done as described by Abia et al. [9]. Initially, the cellulose biomass was thoroughly washed with 0.3M HNO3 and was then filtered afterwards. The filtrate was discarded and the residue was washed with distilled water until a pH of 7.0 was obtained. Later, the paste obtained was air-dried. 4 Page 5 of 38
The air-dried biomass was divided into 3 equal portions. The first portion was left untreated. The other two portions were treated with 0.5M and 1.0M thioglycollic acid, respectively, as follows: a 25 g portion was mixed with 250 mL of the required concentration of thioglycollic acid solution in
ip t
the presence of hydroxylamine (NH2OH). The mixture was mechanically stirred for 6 hours at 30°C. This allowed the thiolation of the methylene hydroxyl group of the cellulose pyran ring [9].
cr
In other words, the process led to the exchange of the hydroxyl groups by the sulfhydryl groups in
us
the presence of hydroxylamine (NH2OH) as follows:
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B − CH2OH + HSCH2CO2 H + NH2OH → B − CH2 − SH + CH2CO2 H + HONH+ H 2O
(1)
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where B represents the biomass. The mixture was then allowed to settle overnight and then
d
centrifuged at 2500 x g for 10 minutes. The supernatant was discarded and the paste was air-dried.
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It must be noted, however, that although grinding of dried biomass may yield stable biosorbent
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particles, generally the free biomass has low mechanical strength not suitable for use in column applications [30]. Furthermore, whilst excessive hydrostatic pressures are required to generate suitable flow rates in packed columns, high pressures can disintegrate the free biomass. These problems can be avoided by the use of immobilized biomass systems [31,32]. Immobilized biomass offers many other advantages including better regeneration and reusability, high biomass loading and minimal clogging in continuous flow systems [33-36].
Therefore, in this study, immobilized cassava peel waste biomass was used. Cassava peel waste biomass was immobilized into small sized pellets by, firstly, mixing the free biomass with 250 mL distilled water and left to hydrate for 10 minutes at room temperature. Secondly, the slurry was mixed with equal volume of 3% (w/v) sterile sodium alginate. Finally, the sodium alginate-biomass 5 Page 6 of 38
mixture was added drop wise through a syringe into 0.2 M calcium chloride (CaCl2) solution so as to get even-sized pellets. The sodium alginate-biomass mixture droplets solidified upon contact with CaCl2, forming pellets and thus entrapping biosorbent particles. The pellets were allowed to harden for 30 minutes and were then washed with 0.9% sodium chloride (NaCl) solution to remove
cr
Characterisation tests
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2.3.
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excess calcium ions [37]. The generated pellets had a diameter ranging from 3 - 4 mm.
In order to identify the functional groups responsible for the biosorption in the immobilized cassava
an
peel waste biomass, Fourier transform infrared (FT-IR, Bruker Tensor 27) spectroscopy analysis was carried out. Scanning electron microscopic (SEM, FEI Quanta 400F) studies were also
Packed bed column tests
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2.4.
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conducted to observe the surface morphology of the biosorbent.
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The column experiments were conducted in a glass column with an inner diameter of 5 cm and height of 50 cm packed with immobilized cassava peel waste pellets. The column was packed with immobilized cassava peel waste pellets between two supporting layers of glass wool. The combined metal solution of 100 mg/L concentration at a pH of 4 was pumped upward through the column using a peristaltic pump (Watson Marlow 504S) with variable speed adjustment. To study the effect of bed height and flow rate on biosorption, fixed bed studies were carried out with various bed depths of 5, 10 and 15 cm and at flow rates of 0.83, 1.25 and 1.61 mL/s. Samples were collected at the exit of the column at different time intervals and analyzed for metal ion concentrations using flame atomic absorption spectrophotometer (FAAS) (Varian SpectrAA-55B, Varian Techtron (Pty) Ltd).The columns were run till exhaustion of the biosorbent.
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2.5.
Regeneration and reusability studies
After exhaustion of the immobilized cassava peel waste pellets, it was necessary to regenerate the column for further use. Regeneration was carried out by pumping 0.1M H2SO4 solution through the
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bed in the upward direction for 3 hrs to allow Cr3+, Co2+ and V3+ to be eluted from the biosorbent. Subsequently, the column was washed and rewashed with distilled water and 0.3M HNO3,
cr
respectively. The biomass was rewashed with 0.3M HNO3 to ensure that there were no traces of
us
ions that remained attached (or adsorbed) to the biomass. The column was again fed with influent and sorption studies were carried out. The cycles of sorption followed by desorption, washing and
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rewashing were repeated to evaluate the biomass sorption capacity.
Results and Discussion
3.1.
Characterisations of immobilized cassava peel waste biomass
te
d
M
3.
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The FT-IR and SEM were the two techniques used in characterisation of immobilized cassava peel waste biomass. The FT-IR spectra before and after thiolation of cassava peel waste biomass are shown in Figure 1. It can be seen from Figure 1 (a) and (b) that the major difference between the two biomass samples is the presence of the sulfhydryl group (-SH). The presence of sulfhydryl group confirmed with FT-IR proves that thiolation process adds sulfhydryl functional groups on the immobilized cassava peel waste biomass. Though –COO, C=O and other functional groups present in cassava biomass do adsorb metal ions, our previous study showed that the thiolation process improved the metal ion sorption [6]. It was thus concluded that the metal ions were incorporated within the biosorbent through interaction with the active sulfhydryl functional groups. In other words, the presence of sulfhydryl functional groups enhanced biosorption.
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The FT-IR of immobilized cassava peel waste biomass before biosorption is shown in Figure 2. The figure clearly shows that there were no significant changes in the peaks compared to Figure 1 (b) which indicates the presence of the same kind of functional groups in the biosorbent. Figure 3 shows the FT-IR spectra of immobilized cassava peel waste biomass after biosorption of metal ions.
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The FT-IR spectra of Figure 3 (after biosorption) show shifts of several peaks compared to the FTIR spectra of Figure 2 (before biosorption). In other words, the FT-IR spectra of Co2+, Cr3+ and V3+
cr
sorbed immobilized cassava waste biomass showed that the peaks expected at 3363, 2974, 2563,
us
1653, 1443, 1320, 1029 and 613 cm-1 had shifted to 3394,2981, 2630, 1637, 1438, 1325, 1028 and 615 cm-1. This shift may be attributed to the changes in counter ions associated with carboxylate,
an
hydroxylate and -SH anions suggesting that acidic groups such as carboxyl, hydroxyl and thiol are responsible for metal ion uptake. In other words, the changes observed in the spectrum indicate the
M
possible involvement of the stated functional groups found on the surface of the biomass in biosorption process. The shifts (or disappearance) of some peaks in the FT-IR spectra is in
te
d
agreement with findings of similar studies reported in literature [21, 38, 39].
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Desorption of heavy metals from metal-laden biomass was tested by utilizing various concentrations of H2SO4. The best concentration would be the one that would release bound metal from the surface without altering the surface characteristics of the biomass. The SEM is one of the useful techniques applied in the examination of the surface morphology of biosorbents [21] thus it was employed in this study. Figure 4 depicts the surface morphology of immobilized cassava peel waste biomass before and after desorption with different concentration of H2SO4. The pictures indicate that the biosorbent has a rough, irregular and porous surface thus providing large area for metal-surface interaction and biosorption [22]. It is clearly seen that desorption with H2SO4 has brought some visible changes (flake like structures) on the surface of the biosorbent. From the figure it can be seen that as H2SO4 concentration increases, the pellets of immobilized cassava peel waste biomass cluster together resulting in the overall reduction of the surface area. No significant 8 Page 9 of 38
changes in the biosorbent surface were noted after elution with 0.1M H2SO4 such that the biomass remained suitable for reuse. The 1.5M and 2M H2SO4, however, resulted in significant changes in the structure of immobilized cassava peel waste biomass, thus was not suitable for reuse.
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In addition to the SEM images, the FT-IR spectra of the biomass desorbed with different concentrations of H2SO4 are presented in Figures 5. The biomass that was exposed to 0.1M H2SO4
cr
(Figures 5(a)) was similar to the original immobilised biomass (Figure 2), this means that the
us
desorption with low acid concentration did not change the binding sites of biomass and thus it remained suitable for subsequent cycles. As the concentration of H2SO4 increased from 1M to 2M
an
(Figure 5(a) to 5(c)), the structure of the biomass changed and there was a complete loss of –SH group at 2M H2SO4 concentration. The damage or removal of –SH groups at high concentration of
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H2SO4 is expected to reduce the sorption capacity of cassava. Therefore, all desorption and
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Effect of column bed depth
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3.2.
d
regeneration studies (in section 3.6) were carried out using 0.1M H2SO4.
The retention of metals in a packed bed column depends, among other factors, on the quantity of the solid sorbent used or, on the bed depth of the column [29, 40-42]. Therefore, the breakthrough curves (at constant influent flow rate of 0.81 mL/s) shown in Figure 6 were plotted to illustrate the effect of bed depth on heavy metal biosorption characteristics of immobilized cassava peel waste pellets. The breakthrough curves for the column were determined by plotting the dimensionless concentration (Ce/C0) against time (where Ce and C0 are the metal ions concentration in the effluent and influent, respectively). From the figure, it can be seen that the breakthrough time and exhaustion time increased with an increase in bed depth. Therefore, the uptake of heavy metals increased with the increase in the bed depth from 5 to 15 cm. The increase in the metal uptake capacity with the increase in bed depth of the column was due to the availability of more binding 9 Page 10 of 38
sites for sorption [23, 29] and an increase in the contact time [1]. In other words, as the bed height increased, more binding sites were available and the ions had more time to contact with the biosorbent resulting in higher removal efficiency of metals in the column thus leading to a decrease in the solute concentration in the effluent. The figure also shows that though the metal uptake
ip t
profiles remained almost identical for different bed depth investigated, probably because uptake capacity strongly depends on the amount of sorbent available for sorption [22, 29]; the slope of
cr
breakthrough curve decreased with increasing bed depth, which resulted in a broadened mass
us
transfer zone [23, 42]. It must be noted that the mass transfer zone in a packed bed column moves from the entrance of the bed and proceed towards the exit [43], i.e., the first portion of the column
an
will adsorb ions until exhaustion; the adsorbing zone then moves through the column until it reaches the outlet. Hence for the same influent concentration in a packed bed system, an increase in
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bed height would create a longer distance for the mass transfer zone to reach the exit, subsequently, resulting in an extended breakthrough time [43]. These results are in agreement with the findings of
Effect of influent flow rate
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3.3.
te
d
similar studies reported in literature [1, 23, 29, 40 - 43].
Influent flow rate is one of the most important parameter for evaluating the performance of an adsorption process, particularly for continuous treatment of waste water at an industrial scale [8, 44, 45]. The effect of influent flow rate on the sorption of Co2+, Cr3+ and V3+ metals from synthetic solution by immobilized cassava peel waste in a fixed bed column was studied by varying the flow rate from 0.83 to 1.61 mL/s, while the bed depth (10 cm) and initial metal ion concentration (100 mg/L) were kept constant. The effect of the flow rate on the biosorption of heavy metals is shown by the breakthrough curves in Figure 7. The breakthrough curves were determined by plotting the dimensionless concentration (Ce/C0) against time (where Ce and C0 are the metal ions concentration in the effluent and influent, respectively). 10 Page 11 of 38
As can be seen from Figure 7, an increase in the flow rate reduced both the breakthrough and exhaustion times resulting in steeper breakthrough curves and shorter zones of mass transfer. The relatively reduced breakthrough and exhaustion time at higher flow rates resulted in comparatively
ip t
less metal uptake and percent removal. In other words, the biosorption efficiency was lower at higher flow rate. The reduction in metal uptake is attributed to insufficient residence time of the
cr
metal ions in the column [22, 23, 46] and the diffusion limitations of the solute into the pores of the
us
sorbent at higher flow rates [29, 47]. In other words, if the residence time of the solute in the column is not large enough for adsorption equilibrium to be reached at the given flow rate, the
an
metal solution leaves the column before the equilibrium occurs [8].
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Just like on the effect of bed depth, these results are also in agreement with the findings of similar
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Competitive biosorption of the metal ions
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3.4.
d
studies reported in literature [8, 22, 23, 29, 46, 47].
Pollution of the environment with toxic metals is essentially a result of many human activities such as mining and metallurgy, thus effluents from such activities would carry admixtures of heavy metal ions in solution [48]. Therefore, when adsorption technique is employed in the removal of heavy metals from industrial wastewater, the adsorbent adsorbs more than one metal which are usually present in the wastewater [22]. This section discusses competitive biosorption of Cr3+, Co2+ and V3+. As can be seen from Figures 6 and 7, V3+ has delayed breakthrough and exhaustion time compared to the other ions; the hierarchy of metal ions followed the order V3+ > Cr3+ > Co2+. This means the biosorption efficiency increased in the order V3+ > Cr3+ > Co2+. These results are in agreement with our previous batch studies [6]. The differences (or variations) are attributed to the
11 Page 12 of 38
metal ions’ affinity towards the biosorbent [1], and it depends on the ionic radius and electropositive charges on the ions [49].
Previous studies have also shown that the presence of other ions in a wastewater system decreases
ip t
biosorption of a particular ion. Vimala et al. [22] studied the behavior of breakthrough curves for biosorption of cadmium (Cd2+) in single and mixed metal systems. The breakthrough and
cr
exhaustion points were found to be earlier for biosorption of Cd2+ in mixed metal system than in a
us
single system. This is due to the competition of co-ions with Cd2+ for the biosorption sites on the
Mathematical modeling of column adsorption
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3.5.
an
biosorbent. We obtained similar results in our batch system studies [6].
Successful design of a column adsorption process requires prediction of the concentration–time
d
profile or breakthrough curve for the effluent [29, 50, 51]. Therefore, various mathematical models
te
have been developed for evaluation of efficiency and applicability of packed beds to large-scale
Ac ce p
operations. Among these, the bed depth service time (BDST) model and the Thomas model were used in this study to analyse the behaviour of the packed column.
3.5.1. Bed depth service time (BDST) model The BDST is one of the most applied models for adsorption of heavy metals in column studies [8]. This model was first proposed by Bohart and Adams [52] and later modified by Hutchins [53]. The BDST is a simple model based on physically measuring the capacity of the bed at different breakthrough values [22, 47]. The model ignores the intraparticle mass transfer resistance and the external film resistance such that the sorbate is sorbed onto the sorbent surface directly [22, 47], i.e., it is based on the surface reaction rate theory [17].
12 Page 13 of 38
The BDST model states that the bed height and service time of a column bears a linear relationship and the equation can be expressed as,
⎞ N 0 Z ⎛ 1 ⎞ ⎛ C0 −⎜ − 1⎟ ⎟ ln ⎜ C0v ⎝ K a C0 ⎠ ⎝ C b ⎠
(1)
ip t
t=
cr
where t is the service time at breakthrough point (h); N0 is the adsorption capacity of the bed
us
(mg/L); Z is the bed depth of column (cm); C0 is the influent or initial concentration of solute (mg/L); Cb is the breakthrough metal ion concentration (mg/L); Ka is the adsorption rate constant
an
(L/mg h); v is the linear flow velocity of feed to bed (cm/h). Equation (1) can be rewritten in the
M
form of a straight line,
(2)
d
t = mZ − n
te
where m and n are the slope and intercept of the BDST line. From Equation (2), it can clearly be
Ac ce p
seen that the bed depth (Z) and the service time of a column are linearly related.
In this study, the column service time was selected when the normalized concentration, Ce/C0 reached 0.05. The plot of service time against bed depth at the flow rate of 0.83 mL/s was found to be linear (results not shown here). The high correlation coefficient values (R2 = 0.871, R2 = 0.942 and R2 = 0.923 for Co2+, Cr3+ and V3+, respectively) indicates the validity of the BDST model to represent the biosorption of heavy metals ions in this study. Assuming that initial concentration (C0) and linear velocity (v) are constant during the column operation, the value of N0 and Ka were evaluated from the slope (N0/C0v) and intercept ((1/KaC0)ln[(C0/Cb)-1]) of the BDST plot. It must be noted that Ka parameter is very important. It characterizes the rate of transfer from the liquid phase to the solid phase and it largely influences the breakthrough phenomenon in any column 13 Page 14 of 38
study [8, 29, 54]. Generally, for smaller values of Ka, a relatively longer bed is required to avoid breakthrough whereas the breakthrough can be eliminated even in a smaller bed when the value of ka is high [8, 29, 54].
ip t
The BDST model constants can be helpful to scale up the process for different flow rates and different initial concentrations without further experimentation [1, 8, 29]. For example, if a value is
cr
determined for one flow rate, values for the other flow rates can be calculated by multiplying the
us
original slope m by the ratio of the original and the new flow rates [55]. It is not necessary to adjust
an
n value, since this term is assumed to be insignificantly affected by changing flow rates [55].
The values of BDST model parameters in this study are presented in Table 1. From Table 1, it can
M
be seen that the value of N0 was higher for V3+ compared to that for Co2+ and Cr3+. This was attributed to the competitive biosorption towards the active site between the metal ions. The V3+
d
ions may have a strong affinity towards the active sites of the sorbent compared to Co2+ and Cr3+.
te
Furthermore, the table also that V3+ has a lower rate constant (Ka) which means that V3+ had more
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contact time with the biosorbent, which resulted in the greater removal of V3+ ion. These results are also in agreement with the findings of our batch studies (i.e., V3+ ions had the highest biosorption rate amongst the other two ions) [6].
3.5.2. Thomas model
Thomas model (or reaction model) is based on the assumption that the process follows Langmuir kinetics of adsorption-desorption with no axial dispersion and mass transfer kinetics [43, 56, 57]. It is derived with the assumption that the rate driving force obeys second-order reversible reaction kinetics [19, 43, 57]. This is the primary weakness of the Thomas solution - its derivation being based on second-order reaction kinetics [19]; adsorption is usually not limited by chemical reaction kinetics, but is often controlled by interphase mass transfer. This discrepancy can lead to some error 14 Page 15 of 38
when this method is used to model adsorption process [19, 58, 59]. Thomas’ solution also assumes a constant separation factor, but it is applicable to either favorable or unfavorable isotherms [19].
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The expression of the Thomas model for an adsorption column is as follows [19, 60]:
Ce 1 = C0 1+exp ( K TH /Q )( q 0 M − C0 Veff )
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cr
(3)
where KTH is the Thomas rate constant (L/mg h); q0 is the maximum solid-phase concentration of
an
the solute (mg/g); m is the amount of adsorbent in the column (g); Q is the volumetric flow rate through the column (L/h); Veff is the volume of effluent; C0 is the initial metal ion concentration
M
(mg/L) and Ce is the effluent metal ion concentration (mg/L) at any time t (min).
te
d
Equation (5) and (6) below are the linearized form of the Thomas model.
(5)
⎛C ⎞ ⎛K q M⎞ ln ⎜ 0 − 1⎟ = ⎜ TH 0 ⎟ − K TH Co t Q ⎠ ⎝ Ce ⎠ ⎝
(6)
Ac ce p
⎛C ⎞ ⎛K q M⎞ ⎛K q M⎞ ln ⎜ 0 − 1⎟ = ⎜ TH 0 ⎟ − ⎜ TH 0 ⎟ Q ⎠ ⎝ Veff ⎠ ⎝ Ce ⎠ ⎝
The experimental data was fitted to the Thomas model by plotting ln [C0/Ce - 1] versus t at a given flow rate (results not shown here). The value of the rate constant (KTH) and the maximum capacity (q0) were evaluated from the slope and intercept of the plots, respectively. The model parameters are listed in Table 2 and 3.
15 Page 16 of 38
From the consistently low values of the regression coefficient (R2) in Tables 2 and 3, it can be concluded that the experimental data did not fit well with the Thomas model. Thus the Thomas model will not be appropriate for describing the sorption process in this study. However, it can be
ip t
seen from the tables that R2 increased with an increase in bed depth and decrease in flow rate.
Furthermore, the results show that as the flow rate increased q0 values decreased for all the metal
cr
ions which is consistent with similar studies by other researchers [42, 43, 50]. The decrease in q0
us
with an increase in flow rate may be attributed to reduced residence time or premature saturation of the active sites. However, as the flow rate increased KTH values increased for Co2+ and V3+, but a
an
reverse trend was observed for Cr3+. An increase in KTH with an increase in flow rate is also in agreement with the studies by Chowdhury et al.[43] and, Malkoc and Nuhoglu [50]. However,
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Yahaya et al. [42] observed a decrease in KTH with an increase in flow rate. Table 3 shows that q0 and KTH increased with an increase in bed height. The effect of bed depth on KTH is in agreement
te Ac ce p
[61].
d
with the studies by Chowdhury et al. [43], but contradicts that by Yahaya et al. [42] and Saadi et al.
Comparing the results from this study and those of similar studies shows a lot of differences. These differences are expected considering the fact that the Thomas model assumes that adsorption is limited by chemical reaction kinetics. It is actually this assumption that leads to some error when this method is used to model adsorption processes [50, 58, 59]. Therefore, it may be argued that the external and internal diffusion are the limiting steps in the biosorption process in this study.
3.6.
Regeneration and reusability studies
The regeneration and subsequent reuse of a biosorbent is very important in industrial practices [38]. It guarantees the continued usage of the biosorbent and thus reduces process costs [36]. Therefore, 16 Page 17 of 38
attempts were made in this study to regenerate and reuse the immobilized cassava peel waste biomass. This was evaluated by comparing the sorption performance of regenerated biomass with the original biomass [29, 45]. The immobilized cassava peel waste biomass regeneration studies were carried out on six consecutive sorption-desorption cycles using column mode operation. In
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other words, six cycles of sorption-desorption were performed to study the reusability of the biosorbent. However, in practical industrial applications, the operation of the biosorption column
cr
would have been discontinued (and the biosorbent is replaced) as soon as the concentration of
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heavy metals (Cr3+, Co2+ and V3+) in the effluent exceeded the regulatory limit [38, 62].
an
Figures 8 and 9 show the efficiencies of the biosorption and desorption cycles, respectively. Figure 8 shows that biosorption efficiency remained reasonably constant irrespective of the number of
M
cycles. However, Figure 9 shows that desorption efficiency was initially constant (first 3 cycles), but declined slightly over the subsequent cycles. The consistent biosorption performance shows that
d
despite repeated usage, there was minimal deterioration of the biosorbent/binding sites. It must be
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noted, however, that loss of biosorption performance is not mainly due to biosorbent damage, but
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due to difficulty in accessing binding sites as the cycles progress [63]. Furthermore, the performance is also strongly depended on the previous elution step, since prolonged elution may destroy the binding sites or inadequate elution may allow metal ions to remain in the sites [29].
Figure 8 also shows that biosorption efficiency increased in the order V3+ > Cr3+ > Co2+ which is in agreement with the order of service times in Figure 6 and 7. However, it is noticed from Figure 9 that desorption efficiency is in the reverse order to the biosorption efficiency trends, i.e., Co2+ > Cr3+ > V3+. This means that weaker bonds were formed between the Co2+ ions and the biosorbent compared to that for Cr3+ and V3+. In other words, Co2+ ions have a lower affinity to the biosorbent so they are easily displaced.
17 Page 18 of 38
4.
Conclusions
The purpose of this study was to investigate the removal efficiency of V3+, Cr3+ and Co2+ by immobilized cassava peel waste biomass in a packed bed column. The performance of the packed
ip t
bed was analysed using the effluent concentration versus time curves. The packed bed column was found to perform better with lower influent rate and higher bed depth. The uptake analysis revealed
cr
a high selectivity for V3+ over Cr3+ and Co2+ at all conditions tested. The study showed that the
us
experimental data best fitted with BSDT model than with the Thomas model for all the conditions tested. The biosorption-desorption results showed that the biosorbent can be used repeatedly
an
without significant loss in sorption capacity reflecting its feasibility for commercial application. Furthermore, the easy availability and low cost would make immobilized cassava peel waste
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Acknowledgements
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biomass an attractive biosorbent option for heavy metals.
Ac ce p
The authors are thankful to all those who provided financial [NRF Scholarship (South Africa) and Friedel Sellschop award (University of the Witwatersrand, South Africa)] or technical support in the course of this research work. In particular, special thanks are due to the Lizzy Seepe for her help with laboratory testing.
Disclaimer
The contents of this paper reflect the views of the authors who are responsible for the facts and accuracy of the data presented herein and do not necessarily reflect the official views or policies of any agency or institute. This paper does not constitute a standard, specification, nor is it intended
18 Page 19 of 38
for design, construction, bidding, or permit purposes. Trade names were used solely for information and not for product endorsement.
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Ac ce p
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22 Page 23 of 38
ip t cr us an
Ac ce p
te
d
M
Fourier transform infrared spectra of immobilised cassava biomass before biosorption
23 Page 24 of 38
ip t cr us an Ac ce p
te
d
M
(a)
(b) Figure 1 - Fourier transform infrared spectra of cassava biomass: (a) before thiolation, (b) after thiolation [6]
24 Page 25 of 38
ip t cr us an
Ac ce p
te
d
M
Figure 2 - Fourier transform infrared spectra of immobilised cassava biomass before biosorption
25 Page 26 of 38
ip t cr us an
Ac ce p
te
d
M
Figure 3 - Fourier transform infrared spectra of immobilised cassava biomass after biosorption
26 Page 27 of 38
ip t cr us
(b)
te
d
M
an
(a)
Ac ce p
(c) (d) Figure 4 - SEM micrograph of cassava biomass:(a) before desorption, (b) 0.1M H2SO4 eluent, (c) 1.5M H2SO4 eluent, (d) 2M H2SO4 eluent
27 Page 28 of 38
ip t cr an
us (b)
Ac ce p
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M
(a)
28 Page 29 of 38
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(c)
Ac ce p
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M
Figure 5 - Fourier transform infrared spectra of cassava biomass for: (a) 0.1 M H2SO4, (b) 1 M H2SO4, (c) 2 M H2SO4
29 Page 30 of 38
ip t cr Ac ce p
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(b)
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M
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(a)
(c) Figure 6 – Breakthrough curves for metal ions adsorption at different bed depth: (a) Co2+, (b) Cr3+, (c) V3+
30 Page 31 of 38
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(b)
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M
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(a)
(c) Figure 7 – Breakthrough curves for metal ions adsorption at different flow rates: (a) Co2+, (b) Cr3+, (c) V3+
31 Page 32 of 38
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Ac ce p
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Figure 8 - Adsorption efficiency of immobilized cassava waste biomass for the adsorptiondesorption cycles.
32 Page 33 of 38
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Ac ce p
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M
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Figure 9 - Desorption efficiency of immobilized cassava waste biomass for the adsorptiondesorption cycles using 0.1 M H2SO4.
33 Page 34 of 38
Table 1 - The BDST model parameters for the biosorption of Co2+, Cr3+ and V3+ on immobilized
cassava waste biomass N0 (mg/L)
Ka (Lmg-1min-1)
R2
Co2+
0.871
0.013
Cr
116.2
0.011
V3+
132.8
0.005
0.942
0.923
Ac ce p
te
d
M
an
us
cr
99.6
3+
ip t
Metal ions
34 Page 35 of 38
Table 2 - Thomas model parameters for the adsorption of Co2+, Cr3+ and V3+ on immobilized
cassava waste biomass at different flow rates and a bed depth 10 cm KTH(mLs-1mg-1)
x (10-4)
(mL/s) 1.25
52.95
2.19
1.61
45.81
2.30
0.83
45.13
2.26
1.25
40.65
1.61
37.78
0.83
36.26
1.25
30.91
1.61
17.88
0.805
us
1.94
0.733
0.608 0.818
1.95
0.771
1.83
0.705
1.70
0.842
1.80
0.716
2.30
0.690
Ac ce p
te
d
V3+
54.62
an
Cr
3+
0.83
M
Co2+
R2
ip t
q0(mg/g)
Flow rate
cr
Metals
35 Page 36 of 38
Table 3 - Thomas model parameters for the adsorption of Co2+, Cr3+ and V3+ on immobilized
Bed depth (cm)
KTH(mLs-1mg-1)
q0(mg/g)
x (10-4)
10
46.499
2.2
15
86.458
2.3
5
25.523
10
37.437
15
61.255
5
28.596
10
34.975
15
61.760
us
1.8
0.621
0.732
0.805
2.1
0.705
2.4
0.770
2.6
0.817
1.9
0.689
2.1
0.715
2.3
0.841
Ac ce p
te
d
V3+
33.164
an
Cr3+
5
M
Co2+
R2
cr
Metals
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cassava waste biomass at different bed depths and a flow rate of 0.83 mL/s
36 Page 37 of 38
Highlights
Ac ce p
te
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M
an
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cr
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A column packed with immobilized cassava biomass was used to remove heavy metals. The column performed better at lower influent rate and higher bed depth. There was higher selectivity for V3+ than for Cr3+ and Co2+ at all conditions. The experimental data fitted the BSDT model better than the Thomas model.
37 Page 38 of 38
S1226-086X(14)00185-3 http://dx.doi.org/doi:10.1016/j.jiec.2014.03.031 JIEC 1977
To appear in: Received date: Revised date: Accepted date:
8-1-2014 6-3-2014 17-3-2014
Please cite this article as: G.S. Simate, S. Ndlovu, The removal of heavy metals in a packed bed column using immobilized cassava peel waste biomass, Journal of Industrial and Engineering Chemistry (2014), http://dx.doi.org/10.1016/j.jiec.2014.03.031 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The removal of heavy metals in a packed bed column using immobilized cassava peel waste
an
us
cr
Geoffrey S. Simate*, Sehliselo Ndlovu
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biomass
School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg,
Ac ce p
te
d
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P/Bag 3, Wits 2050, South Africa
____________________ * Corresponding author. Tel: +27 11 717 7570; Cell: +27 76 112 6959; Fax: +27 11 717 7599; Email: [email protected] (G. S. Simate)
Page 1 of 38
Abstract
Several studies on the removal of heavy metals in batch systems using cassava waste biomass have been reported in literature. However, for practical and large scale operations, packed bed columns
ip t
are preferred. This study investigated the biosorption of heavy metals (Cr3+, Co2+ and V3+) onto immobilized cassava peel waste in a packed bed column. Experiments were conducted with 100
cr
mg/L of combined metal ion solutions under different flow rates (0.83 – 1.61 mL/s) and bed depths
us
(5 – 15 cm). The dynamic behaviour of the process was described in terms of the breakthrough curves. The results showed that the removal efficiency was favoured by low flow rate and high bed
an
depth. Biosorption efficiency was found to increase in the order V3+ > Cr3+ > Co2+ for all conditions tested. Amongst the two well-established column models tested, the bed depth service time (BDST)
M
model with biosorption capacities of 99.6, 116.2 and 132.8 mg/L for Co2+, Cr3+ and V3+, respectively, fitted experimental data very well. The column was regenerated and reused
d
consecutively for six cycles without significant loss in biosorbent capacity signifying its
Ac ce p
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appropriateness for commercial application.
Keywords: heavy metals; wastewater; biosorption; immobilized cassava peel waste biomass; packed bed; breakthrough curve
1.
Introduction
The presence of heavy metals in water and wastewater effluents is one of the greatest challenges in our time. This is because a variety of physiological and neurological damage to the human body are associated with heavy metal exposure [1]. As a result tough regulatory laws that restrict levels of heavy metals present in water or wastewater have been imposed by several nations in the past decades. Therefore, in order to decrease the levels of heavy metals in the environment, it is 1 Page 2 of 38
necessary to treat wastewaters before discharge [2]. At the moment, a wide range of physical and chemical techniques are available for removal of heavy metals [3]. However, these traditional water and wastewater treatment processes have been shown to be prohibitively expensive and ineffective for very low metal concentrations [4-8]. Therefore, the need to find effective and economical
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alternative techniques for removal of heavy metals is taken as a priority in the water and wastewater treatment industry. One such alternative technique that has attracted a lot of attention in recent years
cr
because of its competitiveness, effectiveness and low cost is biosorption [3,6,8 - 15]. Furthermore,
us
biosorption exhibits several other advantages, such as high selectivity and low energy consumption [16]. In recent past, a wide range of biosorbents such as rice husks [17], neem saw dust [8], beer
an
yeast [18], green algae [19] and fly ash [20] have been investigated. More recently [6], we investigated the removal of some heavy metals from waste effluents using cassava peel waste in a
M
batch system.
d
The biosorption capacity of the cassava peel waste [6] and many other biosorbents obtained from
te
batch equilibrium experiments is useful in providing fundamental information about the
Ac ce p
effectiveness of metal-biosorbent system [21]. However, data obtained from batch systems may not be applied directly to most treatment systems (such as column operations) where contact time is not sufficient for the attainment of the equilibrium [8, 21-23]. Furthermore, batch systems are usually limited to the treatment of small quantities of wastewater [21]. Therefore, for practical and large scale operations, a packed column is preferred to a batch system [8, 19, 22]. This is because a packed column makes the best use of the concentration difference known to be the driving force for heavy metal biosorption. This allows for more efficient utilization of biosorbent capacity and also results in better quality of the effluent [8,22,24]. Hence, a column packed with immobilized cassava peel waste pellets was used in our study. In such systems, the concentration profiles in the liquid and biosorbent phases vary in both space and time [19, 25]. This makes the design and optimisation of a packed bed difficult to perform without a quantitative modelling approach [19, 25]. 2 Page 3 of 38
Models have an important role in technology transfer from laboratory-scale to industrial-scale [24]. Appropriate models can help to analyse and explain experimental data, identify process mechanisms, predict answers to changing operational conditions and optimize processes [24,26].
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From the perspectives of process modelling, the dynamic behaviour of a packed column is described in terms of a breakthrough curve [8, 19, 25]. A breakthrough curve, which is S-shaped, is
cr
a plot of effluent solute concentration versus time. Breakthrough is the point on the S-shaped curve
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at which the effluent solute concentration reaches its maximum allowable value [8, 22]. On the other hand, the point where the effluent solute concentration reaches 95% of the influent
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concentration is called the point of column exhaustion [8, 22, 27].
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The shapes of breakthrough curves depend on the nature of the wastewater being treated. If there is only a single adsorbable component in wastewater, the adsorption will be short and the
d
breakthrough curve will be steep [8, 22]. If there is a mixture of components having different
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adsorption capabilities, the sorption zone will be deep and the breakthrough will be flatter.
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Residence time is the major design parameter for the adsorption systems. The optimum residence time determines the size of the adsorbing column and amount of adsorbent.
As stated earlier, the potential of utilising cassava peel waste as a biosorbent for the removal of some heavy metals from aqueous solutions was investigated in our previous study using a batch process [6]. Although the use of cassava biomass had shown much potential in the development of several bioprocesses in the past, there were still some questions that required answers. Therefore, some of the parameters tested in our previous study [6] include biomass modification, and its reusability. Furthermore, the choice of cassava peel waste was also made from an economic standpoint. Since cassava peel waste has no economic value its conversion into an effective biosorbent is expected to increase its market value and ultimately economically benefit the millions 3 Page 4 of 38
of cassava producers [6]. Therefore, the aim of the current study was to use immobilized cassava peel waste pellets as a biosorbent for the removal of heavy metal ions from water and wastewater in
2.1.
Preparation of heavy metal solutions
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Materials and Methods
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2.
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a column mode.
All chemicals used were of analytical grade and were obtained from Merck, South Africa. Synthetic
an
solutions (100 mg/L) with combined metal ions of Cr3+, Co2+ and V3+ were prepared by dissolving required salt quantities of Cr(NO3)3.9H2O, CoSO4.7H2O and VCl3, respectively, in distilled water
M
according to the procedure outlined by American Public Health Association [28]. Relatively low metal ions concentrations of 100 mg/L were used so as to obtain gentle breakthrough curves [29]. It
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must be noted, however, that industrial effluents usually contain higher values than used in many
te
studies [6]. In the previous study by Ndlovu et al. [6], the average optimum pH for biosorption of
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the heavy metals was 4.0. Therefore, the pH of influent solution was adjusted to 4.0 with a pH meter (827 Metrohm) by using 0.1M H2SO4 and /or 0.1 M NaOH accordingly.
2.2.
Preparation of immobilized cassava peel waste biosorbent
The preliminary preparation of cassava peel waste biomass was described in our earlier study [6]. The thiolation (a process of introducing sulfhydryl group (or thiol group), –SH) procedure of the cassava waste was done as described by Abia et al. [9]. Initially, the cellulose biomass was thoroughly washed with 0.3M HNO3 and was then filtered afterwards. The filtrate was discarded and the residue was washed with distilled water until a pH of 7.0 was obtained. Later, the paste obtained was air-dried. 4 Page 5 of 38
The air-dried biomass was divided into 3 equal portions. The first portion was left untreated. The other two portions were treated with 0.5M and 1.0M thioglycollic acid, respectively, as follows: a 25 g portion was mixed with 250 mL of the required concentration of thioglycollic acid solution in
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the presence of hydroxylamine (NH2OH). The mixture was mechanically stirred for 6 hours at 30°C. This allowed the thiolation of the methylene hydroxyl group of the cellulose pyran ring [9].
cr
In other words, the process led to the exchange of the hydroxyl groups by the sulfhydryl groups in
us
the presence of hydroxylamine (NH2OH) as follows:
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B − CH2OH + HSCH2CO2 H + NH2OH → B − CH2 − SH + CH2CO2 H + HONH+ H 2O
(1)
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where B represents the biomass. The mixture was then allowed to settle overnight and then
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centrifuged at 2500 x g for 10 minutes. The supernatant was discarded and the paste was air-dried.
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It must be noted, however, that although grinding of dried biomass may yield stable biosorbent
Ac ce p
particles, generally the free biomass has low mechanical strength not suitable for use in column applications [30]. Furthermore, whilst excessive hydrostatic pressures are required to generate suitable flow rates in packed columns, high pressures can disintegrate the free biomass. These problems can be avoided by the use of immobilized biomass systems [31,32]. Immobilized biomass offers many other advantages including better regeneration and reusability, high biomass loading and minimal clogging in continuous flow systems [33-36].
Therefore, in this study, immobilized cassava peel waste biomass was used. Cassava peel waste biomass was immobilized into small sized pellets by, firstly, mixing the free biomass with 250 mL distilled water and left to hydrate for 10 minutes at room temperature. Secondly, the slurry was mixed with equal volume of 3% (w/v) sterile sodium alginate. Finally, the sodium alginate-biomass 5 Page 6 of 38
mixture was added drop wise through a syringe into 0.2 M calcium chloride (CaCl2) solution so as to get even-sized pellets. The sodium alginate-biomass mixture droplets solidified upon contact with CaCl2, forming pellets and thus entrapping biosorbent particles. The pellets were allowed to harden for 30 minutes and were then washed with 0.9% sodium chloride (NaCl) solution to remove
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Characterisation tests
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2.3.
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excess calcium ions [37]. The generated pellets had a diameter ranging from 3 - 4 mm.
In order to identify the functional groups responsible for the biosorption in the immobilized cassava
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peel waste biomass, Fourier transform infrared (FT-IR, Bruker Tensor 27) spectroscopy analysis was carried out. Scanning electron microscopic (SEM, FEI Quanta 400F) studies were also
Packed bed column tests
te
2.4.
d
M
conducted to observe the surface morphology of the biosorbent.
Ac ce p
The column experiments were conducted in a glass column with an inner diameter of 5 cm and height of 50 cm packed with immobilized cassava peel waste pellets. The column was packed with immobilized cassava peel waste pellets between two supporting layers of glass wool. The combined metal solution of 100 mg/L concentration at a pH of 4 was pumped upward through the column using a peristaltic pump (Watson Marlow 504S) with variable speed adjustment. To study the effect of bed height and flow rate on biosorption, fixed bed studies were carried out with various bed depths of 5, 10 and 15 cm and at flow rates of 0.83, 1.25 and 1.61 mL/s. Samples were collected at the exit of the column at different time intervals and analyzed for metal ion concentrations using flame atomic absorption spectrophotometer (FAAS) (Varian SpectrAA-55B, Varian Techtron (Pty) Ltd).The columns were run till exhaustion of the biosorbent.
6 Page 7 of 38
2.5.
Regeneration and reusability studies
After exhaustion of the immobilized cassava peel waste pellets, it was necessary to regenerate the column for further use. Regeneration was carried out by pumping 0.1M H2SO4 solution through the
ip t
bed in the upward direction for 3 hrs to allow Cr3+, Co2+ and V3+ to be eluted from the biosorbent. Subsequently, the column was washed and rewashed with distilled water and 0.3M HNO3,
cr
respectively. The biomass was rewashed with 0.3M HNO3 to ensure that there were no traces of
us
ions that remained attached (or adsorbed) to the biomass. The column was again fed with influent and sorption studies were carried out. The cycles of sorption followed by desorption, washing and
an
rewashing were repeated to evaluate the biomass sorption capacity.
Results and Discussion
3.1.
Characterisations of immobilized cassava peel waste biomass
te
d
M
3.
Ac ce p
The FT-IR and SEM were the two techniques used in characterisation of immobilized cassava peel waste biomass. The FT-IR spectra before and after thiolation of cassava peel waste biomass are shown in Figure 1. It can be seen from Figure 1 (a) and (b) that the major difference between the two biomass samples is the presence of the sulfhydryl group (-SH). The presence of sulfhydryl group confirmed with FT-IR proves that thiolation process adds sulfhydryl functional groups on the immobilized cassava peel waste biomass. Though –COO, C=O and other functional groups present in cassava biomass do adsorb metal ions, our previous study showed that the thiolation process improved the metal ion sorption [6]. It was thus concluded that the metal ions were incorporated within the biosorbent through interaction with the active sulfhydryl functional groups. In other words, the presence of sulfhydryl functional groups enhanced biosorption.
7 Page 8 of 38
The FT-IR of immobilized cassava peel waste biomass before biosorption is shown in Figure 2. The figure clearly shows that there were no significant changes in the peaks compared to Figure 1 (b) which indicates the presence of the same kind of functional groups in the biosorbent. Figure 3 shows the FT-IR spectra of immobilized cassava peel waste biomass after biosorption of metal ions.
ip t
The FT-IR spectra of Figure 3 (after biosorption) show shifts of several peaks compared to the FTIR spectra of Figure 2 (before biosorption). In other words, the FT-IR spectra of Co2+, Cr3+ and V3+
cr
sorbed immobilized cassava waste biomass showed that the peaks expected at 3363, 2974, 2563,
us
1653, 1443, 1320, 1029 and 613 cm-1 had shifted to 3394,2981, 2630, 1637, 1438, 1325, 1028 and 615 cm-1. This shift may be attributed to the changes in counter ions associated with carboxylate,
an
hydroxylate and -SH anions suggesting that acidic groups such as carboxyl, hydroxyl and thiol are responsible for metal ion uptake. In other words, the changes observed in the spectrum indicate the
M
possible involvement of the stated functional groups found on the surface of the biomass in biosorption process. The shifts (or disappearance) of some peaks in the FT-IR spectra is in
te
d
agreement with findings of similar studies reported in literature [21, 38, 39].
Ac ce p
Desorption of heavy metals from metal-laden biomass was tested by utilizing various concentrations of H2SO4. The best concentration would be the one that would release bound metal from the surface without altering the surface characteristics of the biomass. The SEM is one of the useful techniques applied in the examination of the surface morphology of biosorbents [21] thus it was employed in this study. Figure 4 depicts the surface morphology of immobilized cassava peel waste biomass before and after desorption with different concentration of H2SO4. The pictures indicate that the biosorbent has a rough, irregular and porous surface thus providing large area for metal-surface interaction and biosorption [22]. It is clearly seen that desorption with H2SO4 has brought some visible changes (flake like structures) on the surface of the biosorbent. From the figure it can be seen that as H2SO4 concentration increases, the pellets of immobilized cassava peel waste biomass cluster together resulting in the overall reduction of the surface area. No significant 8 Page 9 of 38
changes in the biosorbent surface were noted after elution with 0.1M H2SO4 such that the biomass remained suitable for reuse. The 1.5M and 2M H2SO4, however, resulted in significant changes in the structure of immobilized cassava peel waste biomass, thus was not suitable for reuse.
ip t
In addition to the SEM images, the FT-IR spectra of the biomass desorbed with different concentrations of H2SO4 are presented in Figures 5. The biomass that was exposed to 0.1M H2SO4
cr
(Figures 5(a)) was similar to the original immobilised biomass (Figure 2), this means that the
us
desorption with low acid concentration did not change the binding sites of biomass and thus it remained suitable for subsequent cycles. As the concentration of H2SO4 increased from 1M to 2M
an
(Figure 5(a) to 5(c)), the structure of the biomass changed and there was a complete loss of –SH group at 2M H2SO4 concentration. The damage or removal of –SH groups at high concentration of
M
H2SO4 is expected to reduce the sorption capacity of cassava. Therefore, all desorption and
te
Effect of column bed depth
Ac ce p
3.2.
d
regeneration studies (in section 3.6) were carried out using 0.1M H2SO4.
The retention of metals in a packed bed column depends, among other factors, on the quantity of the solid sorbent used or, on the bed depth of the column [29, 40-42]. Therefore, the breakthrough curves (at constant influent flow rate of 0.81 mL/s) shown in Figure 6 were plotted to illustrate the effect of bed depth on heavy metal biosorption characteristics of immobilized cassava peel waste pellets. The breakthrough curves for the column were determined by plotting the dimensionless concentration (Ce/C0) against time (where Ce and C0 are the metal ions concentration in the effluent and influent, respectively). From the figure, it can be seen that the breakthrough time and exhaustion time increased with an increase in bed depth. Therefore, the uptake of heavy metals increased with the increase in the bed depth from 5 to 15 cm. The increase in the metal uptake capacity with the increase in bed depth of the column was due to the availability of more binding 9 Page 10 of 38
sites for sorption [23, 29] and an increase in the contact time [1]. In other words, as the bed height increased, more binding sites were available and the ions had more time to contact with the biosorbent resulting in higher removal efficiency of metals in the column thus leading to a decrease in the solute concentration in the effluent. The figure also shows that though the metal uptake
ip t
profiles remained almost identical for different bed depth investigated, probably because uptake capacity strongly depends on the amount of sorbent available for sorption [22, 29]; the slope of
cr
breakthrough curve decreased with increasing bed depth, which resulted in a broadened mass
us
transfer zone [23, 42]. It must be noted that the mass transfer zone in a packed bed column moves from the entrance of the bed and proceed towards the exit [43], i.e., the first portion of the column
an
will adsorb ions until exhaustion; the adsorbing zone then moves through the column until it reaches the outlet. Hence for the same influent concentration in a packed bed system, an increase in
M
bed height would create a longer distance for the mass transfer zone to reach the exit, subsequently, resulting in an extended breakthrough time [43]. These results are in agreement with the findings of
Effect of influent flow rate
Ac ce p
3.3.
te
d
similar studies reported in literature [1, 23, 29, 40 - 43].
Influent flow rate is one of the most important parameter for evaluating the performance of an adsorption process, particularly for continuous treatment of waste water at an industrial scale [8, 44, 45]. The effect of influent flow rate on the sorption of Co2+, Cr3+ and V3+ metals from synthetic solution by immobilized cassava peel waste in a fixed bed column was studied by varying the flow rate from 0.83 to 1.61 mL/s, while the bed depth (10 cm) and initial metal ion concentration (100 mg/L) were kept constant. The effect of the flow rate on the biosorption of heavy metals is shown by the breakthrough curves in Figure 7. The breakthrough curves were determined by plotting the dimensionless concentration (Ce/C0) against time (where Ce and C0 are the metal ions concentration in the effluent and influent, respectively). 10 Page 11 of 38
As can be seen from Figure 7, an increase in the flow rate reduced both the breakthrough and exhaustion times resulting in steeper breakthrough curves and shorter zones of mass transfer. The relatively reduced breakthrough and exhaustion time at higher flow rates resulted in comparatively
ip t
less metal uptake and percent removal. In other words, the biosorption efficiency was lower at higher flow rate. The reduction in metal uptake is attributed to insufficient residence time of the
cr
metal ions in the column [22, 23, 46] and the diffusion limitations of the solute into the pores of the
us
sorbent at higher flow rates [29, 47]. In other words, if the residence time of the solute in the column is not large enough for adsorption equilibrium to be reached at the given flow rate, the
an
metal solution leaves the column before the equilibrium occurs [8].
M
Just like on the effect of bed depth, these results are also in agreement with the findings of similar
te
Competitive biosorption of the metal ions
Ac ce p
3.4.
d
studies reported in literature [8, 22, 23, 29, 46, 47].
Pollution of the environment with toxic metals is essentially a result of many human activities such as mining and metallurgy, thus effluents from such activities would carry admixtures of heavy metal ions in solution [48]. Therefore, when adsorption technique is employed in the removal of heavy metals from industrial wastewater, the adsorbent adsorbs more than one metal which are usually present in the wastewater [22]. This section discusses competitive biosorption of Cr3+, Co2+ and V3+. As can be seen from Figures 6 and 7, V3+ has delayed breakthrough and exhaustion time compared to the other ions; the hierarchy of metal ions followed the order V3+ > Cr3+ > Co2+. This means the biosorption efficiency increased in the order V3+ > Cr3+ > Co2+. These results are in agreement with our previous batch studies [6]. The differences (or variations) are attributed to the
11 Page 12 of 38
metal ions’ affinity towards the biosorbent [1], and it depends on the ionic radius and electropositive charges on the ions [49].
Previous studies have also shown that the presence of other ions in a wastewater system decreases
ip t
biosorption of a particular ion. Vimala et al. [22] studied the behavior of breakthrough curves for biosorption of cadmium (Cd2+) in single and mixed metal systems. The breakthrough and
cr
exhaustion points were found to be earlier for biosorption of Cd2+ in mixed metal system than in a
us
single system. This is due to the competition of co-ions with Cd2+ for the biosorption sites on the
Mathematical modeling of column adsorption
M
3.5.
an
biosorbent. We obtained similar results in our batch system studies [6].
Successful design of a column adsorption process requires prediction of the concentration–time
d
profile or breakthrough curve for the effluent [29, 50, 51]. Therefore, various mathematical models
te
have been developed for evaluation of efficiency and applicability of packed beds to large-scale
Ac ce p
operations. Among these, the bed depth service time (BDST) model and the Thomas model were used in this study to analyse the behaviour of the packed column.
3.5.1. Bed depth service time (BDST) model The BDST is one of the most applied models for adsorption of heavy metals in column studies [8]. This model was first proposed by Bohart and Adams [52] and later modified by Hutchins [53]. The BDST is a simple model based on physically measuring the capacity of the bed at different breakthrough values [22, 47]. The model ignores the intraparticle mass transfer resistance and the external film resistance such that the sorbate is sorbed onto the sorbent surface directly [22, 47], i.e., it is based on the surface reaction rate theory [17].
12 Page 13 of 38
The BDST model states that the bed height and service time of a column bears a linear relationship and the equation can be expressed as,
⎞ N 0 Z ⎛ 1 ⎞ ⎛ C0 −⎜ − 1⎟ ⎟ ln ⎜ C0v ⎝ K a C0 ⎠ ⎝ C b ⎠
(1)
ip t
t=
cr
where t is the service time at breakthrough point (h); N0 is the adsorption capacity of the bed
us
(mg/L); Z is the bed depth of column (cm); C0 is the influent or initial concentration of solute (mg/L); Cb is the breakthrough metal ion concentration (mg/L); Ka is the adsorption rate constant
an
(L/mg h); v is the linear flow velocity of feed to bed (cm/h). Equation (1) can be rewritten in the
M
form of a straight line,
(2)
d
t = mZ − n
te
where m and n are the slope and intercept of the BDST line. From Equation (2), it can clearly be
Ac ce p
seen that the bed depth (Z) and the service time of a column are linearly related.
In this study, the column service time was selected when the normalized concentration, Ce/C0 reached 0.05. The plot of service time against bed depth at the flow rate of 0.83 mL/s was found to be linear (results not shown here). The high correlation coefficient values (R2 = 0.871, R2 = 0.942 and R2 = 0.923 for Co2+, Cr3+ and V3+, respectively) indicates the validity of the BDST model to represent the biosorption of heavy metals ions in this study. Assuming that initial concentration (C0) and linear velocity (v) are constant during the column operation, the value of N0 and Ka were evaluated from the slope (N0/C0v) and intercept ((1/KaC0)ln[(C0/Cb)-1]) of the BDST plot. It must be noted that Ka parameter is very important. It characterizes the rate of transfer from the liquid phase to the solid phase and it largely influences the breakthrough phenomenon in any column 13 Page 14 of 38
study [8, 29, 54]. Generally, for smaller values of Ka, a relatively longer bed is required to avoid breakthrough whereas the breakthrough can be eliminated even in a smaller bed when the value of ka is high [8, 29, 54].
ip t
The BDST model constants can be helpful to scale up the process for different flow rates and different initial concentrations without further experimentation [1, 8, 29]. For example, if a value is
cr
determined for one flow rate, values for the other flow rates can be calculated by multiplying the
us
original slope m by the ratio of the original and the new flow rates [55]. It is not necessary to adjust
an
n value, since this term is assumed to be insignificantly affected by changing flow rates [55].
The values of BDST model parameters in this study are presented in Table 1. From Table 1, it can
M
be seen that the value of N0 was higher for V3+ compared to that for Co2+ and Cr3+. This was attributed to the competitive biosorption towards the active site between the metal ions. The V3+
d
ions may have a strong affinity towards the active sites of the sorbent compared to Co2+ and Cr3+.
te
Furthermore, the table also that V3+ has a lower rate constant (Ka) which means that V3+ had more
Ac ce p
contact time with the biosorbent, which resulted in the greater removal of V3+ ion. These results are also in agreement with the findings of our batch studies (i.e., V3+ ions had the highest biosorption rate amongst the other two ions) [6].
3.5.2. Thomas model
Thomas model (or reaction model) is based on the assumption that the process follows Langmuir kinetics of adsorption-desorption with no axial dispersion and mass transfer kinetics [43, 56, 57]. It is derived with the assumption that the rate driving force obeys second-order reversible reaction kinetics [19, 43, 57]. This is the primary weakness of the Thomas solution - its derivation being based on second-order reaction kinetics [19]; adsorption is usually not limited by chemical reaction kinetics, but is often controlled by interphase mass transfer. This discrepancy can lead to some error 14 Page 15 of 38
when this method is used to model adsorption process [19, 58, 59]. Thomas’ solution also assumes a constant separation factor, but it is applicable to either favorable or unfavorable isotherms [19].
ip t
The expression of the Thomas model for an adsorption column is as follows [19, 60]:
Ce 1 = C0 1+exp ( K TH /Q )( q 0 M − C0 Veff )
us
cr
(3)
where KTH is the Thomas rate constant (L/mg h); q0 is the maximum solid-phase concentration of
an
the solute (mg/g); m is the amount of adsorbent in the column (g); Q is the volumetric flow rate through the column (L/h); Veff is the volume of effluent; C0 is the initial metal ion concentration
M
(mg/L) and Ce is the effluent metal ion concentration (mg/L) at any time t (min).
te
d
Equation (5) and (6) below are the linearized form of the Thomas model.
(5)
⎛C ⎞ ⎛K q M⎞ ln ⎜ 0 − 1⎟ = ⎜ TH 0 ⎟ − K TH Co t Q ⎠ ⎝ Ce ⎠ ⎝
(6)
Ac ce p
⎛C ⎞ ⎛K q M⎞ ⎛K q M⎞ ln ⎜ 0 − 1⎟ = ⎜ TH 0 ⎟ − ⎜ TH 0 ⎟ Q ⎠ ⎝ Veff ⎠ ⎝ Ce ⎠ ⎝
The experimental data was fitted to the Thomas model by plotting ln [C0/Ce - 1] versus t at a given flow rate (results not shown here). The value of the rate constant (KTH) and the maximum capacity (q0) were evaluated from the slope and intercept of the plots, respectively. The model parameters are listed in Table 2 and 3.
15 Page 16 of 38
From the consistently low values of the regression coefficient (R2) in Tables 2 and 3, it can be concluded that the experimental data did not fit well with the Thomas model. Thus the Thomas model will not be appropriate for describing the sorption process in this study. However, it can be
ip t
seen from the tables that R2 increased with an increase in bed depth and decrease in flow rate.
Furthermore, the results show that as the flow rate increased q0 values decreased for all the metal
cr
ions which is consistent with similar studies by other researchers [42, 43, 50]. The decrease in q0
us
with an increase in flow rate may be attributed to reduced residence time or premature saturation of the active sites. However, as the flow rate increased KTH values increased for Co2+ and V3+, but a
an
reverse trend was observed for Cr3+. An increase in KTH with an increase in flow rate is also in agreement with the studies by Chowdhury et al.[43] and, Malkoc and Nuhoglu [50]. However,
M
Yahaya et al. [42] observed a decrease in KTH with an increase in flow rate. Table 3 shows that q0 and KTH increased with an increase in bed height. The effect of bed depth on KTH is in agreement
te Ac ce p
[61].
d
with the studies by Chowdhury et al. [43], but contradicts that by Yahaya et al. [42] and Saadi et al.
Comparing the results from this study and those of similar studies shows a lot of differences. These differences are expected considering the fact that the Thomas model assumes that adsorption is limited by chemical reaction kinetics. It is actually this assumption that leads to some error when this method is used to model adsorption processes [50, 58, 59]. Therefore, it may be argued that the external and internal diffusion are the limiting steps in the biosorption process in this study.
3.6.
Regeneration and reusability studies
The regeneration and subsequent reuse of a biosorbent is very important in industrial practices [38]. It guarantees the continued usage of the biosorbent and thus reduces process costs [36]. Therefore, 16 Page 17 of 38
attempts were made in this study to regenerate and reuse the immobilized cassava peel waste biomass. This was evaluated by comparing the sorption performance of regenerated biomass with the original biomass [29, 45]. The immobilized cassava peel waste biomass regeneration studies were carried out on six consecutive sorption-desorption cycles using column mode operation. In
ip t
other words, six cycles of sorption-desorption were performed to study the reusability of the biosorbent. However, in practical industrial applications, the operation of the biosorption column
cr
would have been discontinued (and the biosorbent is replaced) as soon as the concentration of
us
heavy metals (Cr3+, Co2+ and V3+) in the effluent exceeded the regulatory limit [38, 62].
an
Figures 8 and 9 show the efficiencies of the biosorption and desorption cycles, respectively. Figure 8 shows that biosorption efficiency remained reasonably constant irrespective of the number of
M
cycles. However, Figure 9 shows that desorption efficiency was initially constant (first 3 cycles), but declined slightly over the subsequent cycles. The consistent biosorption performance shows that
d
despite repeated usage, there was minimal deterioration of the biosorbent/binding sites. It must be
te
noted, however, that loss of biosorption performance is not mainly due to biosorbent damage, but
Ac ce p
due to difficulty in accessing binding sites as the cycles progress [63]. Furthermore, the performance is also strongly depended on the previous elution step, since prolonged elution may destroy the binding sites or inadequate elution may allow metal ions to remain in the sites [29].
Figure 8 also shows that biosorption efficiency increased in the order V3+ > Cr3+ > Co2+ which is in agreement with the order of service times in Figure 6 and 7. However, it is noticed from Figure 9 that desorption efficiency is in the reverse order to the biosorption efficiency trends, i.e., Co2+ > Cr3+ > V3+. This means that weaker bonds were formed between the Co2+ ions and the biosorbent compared to that for Cr3+ and V3+. In other words, Co2+ ions have a lower affinity to the biosorbent so they are easily displaced.
17 Page 18 of 38
4.
Conclusions
The purpose of this study was to investigate the removal efficiency of V3+, Cr3+ and Co2+ by immobilized cassava peel waste biomass in a packed bed column. The performance of the packed
ip t
bed was analysed using the effluent concentration versus time curves. The packed bed column was found to perform better with lower influent rate and higher bed depth. The uptake analysis revealed
cr
a high selectivity for V3+ over Cr3+ and Co2+ at all conditions tested. The study showed that the
us
experimental data best fitted with BSDT model than with the Thomas model for all the conditions tested. The biosorption-desorption results showed that the biosorbent can be used repeatedly
an
without significant loss in sorption capacity reflecting its feasibility for commercial application. Furthermore, the easy availability and low cost would make immobilized cassava peel waste
te
Acknowledgements
d
M
biomass an attractive biosorbent option for heavy metals.
Ac ce p
The authors are thankful to all those who provided financial [NRF Scholarship (South Africa) and Friedel Sellschop award (University of the Witwatersrand, South Africa)] or technical support in the course of this research work. In particular, special thanks are due to the Lizzy Seepe for her help with laboratory testing.
Disclaimer
The contents of this paper reflect the views of the authors who are responsible for the facts and accuracy of the data presented herein and do not necessarily reflect the official views or policies of any agency or institute. This paper does not constitute a standard, specification, nor is it intended
18 Page 19 of 38
for design, construction, bidding, or permit purposes. Trade names were used solely for information and not for product endorsement.
ip t
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[61]
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Fourier transform infrared spectra of immobilised cassava biomass before biosorption
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ip t cr us an Ac ce p
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d
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(a)
(b) Figure 1 - Fourier transform infrared spectra of cassava biomass: (a) before thiolation, (b) after thiolation [6]
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ip t cr us an
Ac ce p
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Figure 2 - Fourier transform infrared spectra of immobilised cassava biomass before biosorption
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Figure 3 - Fourier transform infrared spectra of immobilised cassava biomass after biosorption
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ip t cr us
(b)
te
d
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an
(a)
Ac ce p
(c) (d) Figure 4 - SEM micrograph of cassava biomass:(a) before desorption, (b) 0.1M H2SO4 eluent, (c) 1.5M H2SO4 eluent, (d) 2M H2SO4 eluent
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ip t cr an
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(a)
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ip t cr us an
(c)
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Figure 5 - Fourier transform infrared spectra of cassava biomass for: (a) 0.1 M H2SO4, (b) 1 M H2SO4, (c) 2 M H2SO4
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ip t cr Ac ce p
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(b)
d
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(a)
(c) Figure 6 – Breakthrough curves for metal ions adsorption at different bed depth: (a) Co2+, (b) Cr3+, (c) V3+
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(b)
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(a)
(c) Figure 7 – Breakthrough curves for metal ions adsorption at different flow rates: (a) Co2+, (b) Cr3+, (c) V3+
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Figure 8 - Adsorption efficiency of immobilized cassava waste biomass for the adsorptiondesorption cycles.
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Figure 9 - Desorption efficiency of immobilized cassava waste biomass for the adsorptiondesorption cycles using 0.1 M H2SO4.
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Table 1 - The BDST model parameters for the biosorption of Co2+, Cr3+ and V3+ on immobilized
cassava waste biomass N0 (mg/L)
Ka (Lmg-1min-1)
R2
Co2+
0.871
0.013
Cr
116.2
0.011
V3+
132.8
0.005
0.942
0.923
Ac ce p
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99.6
3+
ip t
Metal ions
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Table 2 - Thomas model parameters for the adsorption of Co2+, Cr3+ and V3+ on immobilized
cassava waste biomass at different flow rates and a bed depth 10 cm KTH(mLs-1mg-1)
x (10-4)
(mL/s) 1.25
52.95
2.19
1.61
45.81
2.30
0.83
45.13
2.26
1.25
40.65
1.61
37.78
0.83
36.26
1.25
30.91
1.61
17.88
0.805
us
1.94
0.733
0.608 0.818
1.95
0.771
1.83
0.705
1.70
0.842
1.80
0.716
2.30
0.690
Ac ce p
te
d
V3+
54.62
an
Cr
3+
0.83
M
Co2+
R2
ip t
q0(mg/g)
Flow rate
cr
Metals
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Table 3 - Thomas model parameters for the adsorption of Co2+, Cr3+ and V3+ on immobilized
Bed depth (cm)
KTH(mLs-1mg-1)
q0(mg/g)
x (10-4)
10
46.499
2.2
15
86.458
2.3
5
25.523
10
37.437
15
61.255
5
28.596
10
34.975
15
61.760
us
1.8
0.621
0.732
0.805
2.1
0.705
2.4
0.770
2.6
0.817
1.9
0.689
2.1
0.715
2.3
0.841
Ac ce p
te
d
V3+
33.164
an
Cr3+
5
M
Co2+
R2
cr
Metals
ip t
cassava waste biomass at different bed depths and a flow rate of 0.83 mL/s
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Highlights
Ac ce p
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A column packed with immobilized cassava biomass was used to remove heavy metals. The column performed better at lower influent rate and higher bed depth. There was higher selectivity for V3+ than for Cr3+ and Co2+ at all conditions. The experimental data fitted the BSDT model better than the Thomas model.
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