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Performance Evaluation of Magnetic Polymer-Zeolite Nanocomposite
for Adsorptive Removal of Contaminants from Water
by
NOMCEBO HAPPINESS MTHOMBENI
Submitted in partial fulfilment of the requirements for the degree
DOCTORAL TECHNOLOGIAE ENGINEERING: CHEMICAL
in the
Department of Chemical, Metallurgical and Materials Engineering
FACULTY OF ENGINEERING AND THE BUILT ENVIRONMENT
TSHWANE UNIVERSITY OF TECHNOLOGY
Supervisor: Prof. M. S. Onyango
Co-supervisor: Prof. A. Ochieng
February 2017
i
DECLARATION BY CANDIDATE
“I hereby declare that the dissertation submitted for the degree D Tech: Engineering:
Chemical at Tshwane University of Technology is my own original work and has not
previously been submitted to any other institution of higher education. I further declare that
all sources cited or quoted are indicated and acknowledged by means of a comprehensive
list of references”.
NOMCEBO HAPPINESS MTHOMBENI
ii
DEDICATION
This work is dedicated to
My late parents Emma Mamochini and Ezekial Themba Hadebe. You will forever be in my
heart.
My husband Zwelethu Mthombeni for all the support and love you have given me over the
years during this journey. Thank you my love.
Our amazing children Bongani, Nkateko and Xihlamariso and Rixongile Mthombeni. You
are mommy rock stars.
My grandmother Beauty Motaung, my mother and father Berth and Nelson Mthombeni.
Mawe and Papa I can’t express enough the amount of gratitude I have for to the support
you have given me.
My brother Percy Mthombeni you are a blessing.
My amazing and fabulous sisters: Nombuso, Thandazo, Zandile Hadebe and Duduzile
Mthombeni.
My aunt Khosi Matlala
My brothers Sibusiso Hadebe and Mbusi Mthombeni
To all my friends thank you and everyone who has been there for me
God bless
iii
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to the people and institutions that contributed
to this study
My supervisors, Prof. M.S. Onyango for your guidance and support and the
knowledge that you have imparted on me during the study and most importantly
the virtues of hard work and dedication
My co-supervisor Prof. A Ochieng for the support
Ms Sandy Mbakop and Ms Busisiwe Nzolo
Tshwane University of Technology for the opportunity to study
National Research Foundation (NRF) for funding
University of South Africa for funding under AQIP
Fellow students : Thabo, Alice, Mokgadi, Xoliswa, Anthony , Jacob, Keletso
MOST IMPORTANTLY OUR HEAVENLY FATHER ABOVE AND HIS SON JESUS
CHRIST
iv
Abstract
Polymer network can be easily incorporated inside the structure of zeolite due to the
presence of highly ordered pores, channels and cages in different forms and sizes in the
structure of zeolite. Polymer–zeolite nanocomposite has the potential to overcome the
limitations of both materials while combining the advantages of polymers and zeolites.
Consequently, this work reports on the preparation of zeolite-clay and zeolite-polymer
based nanocomposites and their application in the remediation of water contaminated with
metals. In particular, zeolite-bentonite (B/Z) and zeolite- polypyrrole (MZPPY)
nanocomposites were examined to evaluate their performance for the removal of Cr(VI),
V(V) and Mn(II) in both batch and column experiments. Polypyrrole and natural zeolite
was prepared by chemical polymerization of pyrrole in the presence of magnetized zeolite
dispersed in aqueous solution. Bentonite/zeolite adsorbent was prepared by physically
mixing calcium bentonite and natural zeolite. The structure and morphology of the
prepared adsorbents were analyzed with the Fourier transform infrared (FTIR), field-
emission microscope (FE-SEM), energy dispersive X-ray (EDX), high resolution
transmission electron microscope (HR-TEM) and X-ray diffraction (XRD). The effects of
magnetic zeolite loading within the composite, initial pH, sorbent dosage, temperature and
initial solution concentration on metal ions removal efficiency were investigated. Batch
sorption isotherms data at solution pH 2 and pH 4-5 for Cr(VI) and V(V), respectively,
were satisfactorily described by the Langmuir isotherm model with a maximum sorption
capacity of 344 mg/g and 65 mg/g for Cr(VI) and V(V), respectively, at 25 °C.
Equilibrium data for sorption of Mn(II) using bentonite /zeolite adsorbent fitted well to the
Freundlich isotherm model. Meanwhile, the kinetic data for all ions fitted to the pseudo-
second order kinetic model. From thermodynamic studies, it was revealed that the
adsorption process is spontaneous and endothermic in nature. The presence of co-existing
v
ions affected the Mn(II) removal efficiency when using the B/Z adsorbent while in the
presence of SO42-
ions the performance of MZPPY was significantly affected during
Cr(VI) removal. In a continuous fixed-bed operation undertaken to evaluate the efficiency
of bentonite/zeolite and magnetic zeolite polypyrrole as adsorbents for the removal of
Cr(VI), V(V) and Mn(II) from aqueous solution under the effect of various process
parameters like bed mass, flow rate and initial metal ion concentrations, results indicated
that the column performed well at low flow rate. Also, breakthrough time increased with
increasing bed mass. The Yoon–Nelson, Thomas and Bohart–Adams models were applied
to experimental data to predict the breakthrough curves and to determine the characteristic
parameters of the column that are useful for process design. The Yoon–Nelson and
Thomas models predictions were in very good agreement with the experimental results at
all the process variables studied. The adsorption-desorption studies indicated that MZPPY
retained its original adsorption capacity up to two consecutive cycles. Based on
performance data, it can be concluded that the MZPPY composite is a competitive sorption
media for Cr(VI) and V(V) removal from aqueous environments. High values of AER in
column studies and low adsorption capacity in batch studies indicate that B/Z is not
effective in treating water contaminated with Mn(II) and hence not recommended .
Keywords: natural zeolite, bentonite, polypyrrole, chromium , manganese , vanadium ,
adsorption
vi
TABLE OF CONTENTS
DECLARATION BY CANDIDATE ..................................................................................... i
DEDICATION....................................................................................................................... ii
ACKNOWLEDGEMENTS.................................................................................................. iii
Abstract ............................................................................................................................. iv
List of figures .................................................................................................................. xiv
List of Tables .................................................................................................................. xix
List of outputs ..................................................................................................................... xxi
Chapter 1................................................................................................................................ 1
1. Introduction ............................................................................................................... 1
1.1. Problem statement and motivation ........................................................................ 5
1.2. Hypothesis ............................................................................................................. 6
1.3. Research Aims ....................................................................................................... 7
Chapter 2................................................................................................................................ 8
2. Introduction ............................................................................................................... 8
2.1. Heavy metals in water and health impacts ........................................................ 9
2.1.1. Vanadium................................................................................................. 10
2.1.2. Chromium ................................................................................................ 13
2.1.3. Manganese ............................................................................................... 15
2.2. Conventional methods of heavy removal from water ...................................... 17
2.3. Adsorption of heavy metals from wastewater .................................................. 20
2.4. Models for adsorption .................................................................................. 22
vii
2.4.1. Kinetics .................................................................................................... 22
2.4.1.1. Pseudo-first-order rate equation ......................................................... 23
2.4.1.2. Pseudo Second-Order Equation .......................................................... 24
2.4.1.3. Elovich model ...................................................................................... 25
2.4.1.4. Ritchie's equation ................................................................................ 26
2.4.2. Diffusion models .................................................................................. 26
2.4.2.1. Intraparticle diffusion model .......................................................... 27
2.4.2.2. Film Diffusion ................................................................................ 28
2.4.3. Adsorption isotherms ............................................................................... 28
2.4.3.1. Langmuir isotherm .............................................................................. 29
2.4.3.2. Freundlich isotherm ............................................................................ 30
2.4.3.3. Dubinin–Radushkevich isotherm model .............................................. 30
2.4.3.4. Redlich-Peterson isotherm .................................................................. 32
2.4.3.5. BET isotherm ....................................................................................... 33
2.4.3.6. Thermodynamic parameters ................................................................ 33
2.4.4. Fixed bed column .................................................................................... 35
2.4.5. Breakthrough curves modelling........................................................... 37
2.4.5.1. Thomas model ................................................................................ 37
2.4.5.2. Bohart–Adams model ..................................................................... 38
2.4.5.3. Yoon–Nelson model ....................................................................... 38
2.5. Removal of heavy metals from water by adsorption........................................ 38
2.5.1. Activated carbon and low cost activated carbon adsorbents ................... 38
viii
2.5.1.1. Removal of chromium using activated carbon and low cost waste
adsorbents ............................................................................................................ 39
2.5.1.2. Activated carbon as an adsorbent for manganese............................... 44
2.5.1.3. Removal of vanadium using activated carbon and other agricultural
water and byproducts .......................................................................................... 46
2.5.2.Low cost byproducts adsorbents and biosorbents for manganese removal ... 48
2.5.2.1. Agricultural waste ........................................................................... 48
2.5.2.2. Removal of heavy metals using chitosan ....................................... 53
2.5.3. Clay and zeolite based adsorbents ........................................................... 57
2.5.3.1. Chromium removal using clays and zeolite based adsorbents ............ 57
2.5.3.2. Manganese removal by clays and zeolite based adsorbents ............... 63
2.5.3.3. Vanadium removal by clay based absorbents ..................................... 66
2.6. Nanotechnology ............................................................................................... 67
2.6.1. Polymer based nanoadsorbents ................................................................ 68
2.6.1.1. Removal of chromium with polymer based nanoadsorbents ............... 68
2.6.1.2. Removal of manganese using polymer based nanoadsorbents ........... 71
2.6.2. Magnetic nanoadsorbents ........................................................................ 73
2.6.3. Polymer clay based nanoadsorbents ........................................................ 78
2.7. Conclusion ........................................................................................................... 80
Chapter 3.............................................................................................................................. 82
Adsorption of Hexavalent Chromium Onto Magnetic Natural Zeolite-Polymer
Composite ........................................................................................................................ 82
ix
3. Introduction ............................................................................................................. 82
3.1. Materials and Methods .................................................................................... 85
3.1.1. Materials .................................................................................................. 85
3.2. Methods ....................................................................................................... 85
3.2.1. Synthesis of magnetic zeolite ............................................................... 85
3.2.2. Synthesis of the magnetic zeolite-polypyrrole composite (MZPPY) .... 86
3.2.3. Characterization .................................................................................. 86
3.2.4. Batch adsorption experiments ............................................................. 87
3.2.4.1. Effect of magnetic zeolite loading within the composite, pH and
sorbent dosage ................................................................................................. 87
3.2.4.2. Effect of co-existing ions ................................................................ 87
3.2.4.3. Regeneration studies ....................................................................... 88
3.2.4.4. Kinetics and equilibrium isotherm studies. .................................... 88
3.3. Results and discussion ................................................................................. 89
3.3.1. Characterization of the adsorbent. ....................................................... 89
3.3.2. Effect of magnetic zeolite loading within the composite, pH and
sorbent dosage on Cr(VI) removal ...................................................................... 96
3.3.3. Effect of co-existing ions ...................................................................... 99
3.3.4. Regeneration studies.......................................................................... 100
3.3.5. Kinetic studies.................................................................................... 101
3.3.6. Equilibrium isotherm and thermodynamic studies ............................ 105
Conclusion .................................................................................................................. 112
x
Chapter 4............................................................................................................................ 114
Vanadium (V) adsorption isotherms and kinetics using polypyrrole coated magnetized
natural zeolite ................................................................................................................. 114
4. Introduction ........................................................................................................... 114
4.1. Experimental .................................................................................................. 117
4.1.1. Chemicals and methods ......................................................................... 117
4.1.2. Methods ................................................................................................. 117
4.1.2.1. Characterization ................................................................................ 118
4.1.2.2. Batch adsorption, isotherms and kinetics studies .............................. 118
4. Results and Discussion ...................................................................................... 120
4.2.1. Characterization of the adsorbent .......................................................... 120
4.2.2. Effect of pH ........................................................................................... 128
4.2.3. Adsorption kinetics of V(V) onto MZPPY............................................ 129
4.2.4. Adsorption isotherm .............................................................................. 131
4.2.5. Effect of co-existing ions and desorption studies .................................. 138
4.3. Conclusions ................................................................................................... 140
Chapter 5............................................................................................................................ 142
Fixed bed Column studies for Chromium (VI) and Vanadium (V) removal from aqueous
using of Magnetic Zeolite Polypyrrole .......................................................................... 142
5. Introduction ........................................................................................................... 142
5.1. Materials and methods .............................................................................. 144
5.2. Column studies .......................................................................................... 144
xi
5.2.1. Effect of bed mass of magnetic zeolite polypyrrole ........................... 145
5.2.2. Effect of flow rate .............................................................................. 145
5.2.3. Effect of initial metal concentration .................................................. 146
5.2.4. Environmental sample ....................................................................... 146
5.2.5. Breakthrough analysis ....................................................................... 147
5.3. Results ....................................................................................................... 148
5.3.1. Effect of bed mass .............................................................................. 148
5.3.2. Effect of flowrate ............................................................................... 152
5.3.3. Effect of initial concentration ............................................................ 154
5.3.4. Removal of Cr(VI) from environmental water sample ...................... 157
5.3.5. Breakthrough curves modelling......................................................... 158
5.3.5.1. Thomas model .............................................................................. 158
5.3.5.2. Bohart-Adams model .................................................................... 159
5.3.5.3. Yoon-Nelson model ...................................................................... 160
5.4. Conclusions .................................................................................. 162
Chapter 6............................................................................................................................ 163
Adsorptive removal of manganese from aqueous solution using batch and column
studies ............................................................................................................................ 163
6. Introduction ........................................................................................................... 163
6.1. Materials and Methods .................................................................................. 166
6.1.1. Chemicals and reagents ......................................................................... 166
6.1.2. Methods ................................................................................................. 166
xii
6.1.3. Batch adsorption experiments ............................................................ 167
6.1.3.1. Effect of pH and sorbent dosage .................................................... 167
6.1.3.2. Kinetics and equilibrium isotherm studies .................................. 167
6.1.4. Column studies ...................................................................................... 169
6.1.4.1. Effect of bed mass, flow rate and initial concentration .................... 170
6.2. Results and discussions ................................................................................. 170
6.2.1. Characterization ..................................................................................... 170
6.2.2. Effect of sorbent dosage and pH.......................................................... 172
6.2.3. Effect of co-existing ions and desorption studies .................................. 175
6.2.5. Adsorption isotherm .............................................................................. 181
6.2.6. Column studies ...................................................................................... 185
6.2.7. Effect of bed mass ................................................................................. 186
6.2.8. Effect of flowrate ................................................................................... 187
6.2.9. Effect of initial concentration ................................................................ 189
6.2.10. Removal of Mn (II) from Acid mine water sample ............................... 190
6.3. Breakthrough curves modelling................................................................. 193
6.3.1. Thomas model ....................................................................................... 193
6.3.2. Bohart–Adams model ............................................................................ 194
6.3.3. Yoon–Nelson model .............................................................................. 195
4. Conclusion ..................................................................................................... 196
Chapter 7............................................................................................................................ 198
Conclusions and Recommendations .............................................................................. 198
xiii
REFERENCES .............................................................................................................. 200
Appendix ........................................................................................................................... 274
xiv
List of figures
Figure 2-1. Eh-pH diagram for the system V-O2-H2O, for dissolved EV<10-4 mol/kg (11.5
mg/L as VO4) (Langmuir et al., 2004)................................................................................. 12
Figure 2-2: The Redox potential (Eh)-pH diagram for Cr-O-H systems (Palmer and
Wittbrodt, 1991, Lukman et al., 2014) ................................................................................ 14
Figure 2-3. Eh–pH diagram for the Mn–O2–H2–H2O system at 25 °C (Lu et al., 2013) .... 15
Figure 2-4 . The structure of montmorillonite (Bhattacharyya and Gupta, 2008) ............... 58
Figure 2-5. Structure of zeolite (2014) ................................................................................ 61
Figure 2-6. Binding of building units (PBU and SBU) in three-dimensional zeolite-
clinoptilolite structure (Margeta et al., 2013). ..................................................................... 62
Figure 2-7. Schematic fabricating of RHC-mag-CN nanocomposite.................................. 75
Figure 3-1. FTIR spectra of the MZPPY (a) before and (b) after adsorption with Cr(VI). . 90
Figure 3-2. XRD pattern of magnetic zeolite (a) and MZPPY before (b) after (c) Cr(VI)
adsorption and (d) after regeneration process ...................................................................... 91
Figure 3-3.Scanning electron microscopic images of (a) magnetic zeolite and (b) MZPPY
before and (c) after adsorption of Cr(VI). ........................................................................... 93
Figure 3-4. Transmission electron microscopic images of (a) magnetic zeolite and MZPPY
((b)–(d)). .............................................................................................................................. 93
Figure 3-5. Energy dispersive X-rays (EDX) spectra of MZPPY (a) before and (b) after
Cr(VI). ................................................................................................................................. 94
Figure 3-6. Zeta potential of the MZPPY at different pH values. ....................................... 95
Figure 3-7. Effect of magnetic zeolite loading onto PPY. Initial concentration: 300 mg/L;
Temperature : 25 ͦ C; dose: 2 g/L; contact time : 24 hrs ...................................................... 97
Figure 3-8. Effect of pH on the adsorption of Cr(VI) by MZPPY and magnetic zeolite. 300
mg/L; Temperature : 25 ͦ C; dose: 2 g/L; contact time : 24 hrs ........................................... 98
xv
Figure 3-9. Effect of dosage of adsorbent on the adsorption of Cr(VI). 300 mg/L;
Temperature : 25 ͦ C; contact time : 24 hrs .......................................................................... 99
Figure 3-10. Effect of coexisting ions on the adsorption of Cr(VI) onto MZPPY. ........... 100
Figure 3-11. Adsorption–desorption cycles ....................................................................... 101
Figure 3-12.Adsorption kinetics for adsorption of Cr(VI) adsorption onto MZPPY. ....... 102
Figure 3-13.The pseudo-second order kinetic plot for adsorption of Cr(VI) adsorption onto
MZPPY. ............................................................................................................................. 103
Figure 3-14. Intraparticle diffusion model for adsorption of Cr(VI) adsorption onto
MZPPY. ............................................................................................................................. 105
Figure 3-15. Nonlinear isotherm plots: (-) Langmuir model, (—) Freundlich model at
different temperatures of Cr(VI) sorption onto MZPPY. .................................................. 106
Figure 3-16. Equilibrium data fit to linear Langmuir isotherm of Cr(VI) sorption onto
MZPPY. ............................................................................................................................. 107
Figure 3-17. Equilibrium data fit to linear Freundlich isotherm (c) of Cr(VI) sorption onto
MZPPY. ............................................................................................................................. 108
Figure 3-18. Plot to determine thermodynamic parameters of Cr(VI) adsorption onto
MZPPY. ............................................................................................................................. 111
Figure 4-1. FTIR spectra of the magnetized zeolite (a) and MZPPY (b) before and (c) after
adsorption with V(V). ........................................................................................................ 122
Figure 4-2. Scanning electron microscopic images of natural zeolite (a), magnetized
zeolite (b), magnetic zeolite–polypyrrole media before (c) and after adsorption (d). ....... 124
Figure 4-3. Transmission electron microscopic images of MZPPY. ................................ 125
Figure 4-4. Energy dispersive X-rays (EDX) spectra of MZPPY before V(V) adsorption
........................................................................................................................................... 125
Figure 4-5. Energy dispersive X-rays (EDX) spectra of MZPPY after V(V) adsorption . 126
xvi
Figure 4-6. XRD pattern of natural zeolite and magnitezed zeolite .................................. 127
Figure 4-7. Effect of pH on the adsorption of V(V) by MZPPY, polypyrrole, magnetite and
zeolite................................................................................................................................. 128
Figure 4-8. Adsorption kinetics plot of V(V) onto MZPPY at three different
concentrations and with experimetal data fitted to kinectic models .................................. 130
Figure 4-9. Effect of temperature of V(V) sorption onto MZPPY .................................... 133
Figure 4-10. Equilibrium isotherms of V(V) sorption onto MZPPY and nonlinear isotherm
plots: (-) Langmuir model, (---) Freundlich model at 298 K ,(···) Langmuir Freundlich, (-..-
..) RedlichPeterson, (-
.-.) Dubinin–Radushkevich .............................................................. 134
Figure 4-11. Equilibrium isotherms of V(V) sorption onto MZPPY and nonlinear isotherm
plots: (-) Langmuir model, (---) Freundlich model at 308 K,(···) Langmuir Freundlich, (-..-
..) RedlichPeterson, (-
.-.) Dubinin–Radushkevich .............................................................. 134
Figure 4-12. Equilibrium isotherms of V(V) sorption onto MZPPY and nonlinear isotherm
plots: (-) Langmuir model, (---) Freundlich model at 318 K ,(···) Langmuir Freundlich, (-..-
..) RedlichPeterson, (-
.-.) Dubinin–Radushkevich .............................................................. 135
Figure 4-13. Effect of coexisting ions on the adsorption of V(V) onto MZPPY ............. 139
Figure 4-14. Adsorption–desorption cycles of V(V) onto MZPPY .................................. 140
Figure 5-1. Schematic diagram of a fixed-bed adsorption column set up ......................... 145
Figure 5-2. Breakthrough curves for adsorption of Cr(VI) from water using MZPPY at
different bed masses. Flow rate of 1.5 mL/min and initial concentration of 20 mg/L at pH
2. ........................................................................................................................................ 149
Figure 5-3.Breakthrough curves for adsorption of V(V) from water using MZPPY at
different bed masses. Flow rate of 1.5 mL/min and initial concentration of 47 mg/L at pH
4.5. ..................................................................................................................................... 150
xvii
Figure 5-4. Breakthrough curves for the removal Cr(VI) from water using MZPPY at
different flow rates. Initial concentration of 20 mg/L, bed mass of 2.5 g. ........................ 153
Figure 5-5. Breakthrough curves for the removal V(V) from water using MZPPY at
different flow rates. Initial concentration of 47 mg/L, bed mass of 2.5 g. ........................ 154
Figure 5-6. Effect of initial concentration of Cr(VI) on breakthrough performance MZPPY
........................................................................................................................................... 155
Figure 5-7. Effect of initial concentration of V(V) on breakthrough performance of
MZPPY at pH 4.5. ............................................................................................................. 156
Figure 5-8. Breakthrough curves for the removal of Cr(VI) from environmental water
sample with initial concentration of 5 mg/L; at flow rate 1.5 mL/min; bed mass 2.5 g and
pH 2. .................................................................................................................................. 157
Figure 6-1. XRD patterns of (a) raw zeolite, (b) Ca-bentonite and (c) Ca-betonite/Zeolite
before and after adsorption ................................................................................................ 171
Figure 6-2. FTIR spectra of the bentonite/zeolite (a) before and (b) after adsorption
with Mn(II) ions. ............................................................................................................. 172
Figure 6-3. Removal effeciency of zeolite, bentonite and bentonite/zeolite mixture ........ 173
Figure 6-4. The effect of pH on the adsorption of Mn(II) by B/Z adsorbent . ........... 174
Figure 6-5. Effect of B/Z adsorbent dosage on the removal of Mn(II) ions at an initial
Mn(II) concentration of 50 mgL, at pH 6 and a temperature of 298 K. ............................ 175
Figure 6-6. Effect of coexisting ions on the adsorption of Mn(II) onto B/Z. .................... 176
Figure 6-7. The effect of desorption on Mn(II) from the adsorbent using different eluent at
three different concentrations ............................................................................................ 177
Figure 6-8. Adsorption–desorption cycles on the adsorption of Mn (II) onto B/Z. 177
Figure 6-9. Adsorption kinetics plot of Mn (II) onto B/Z at three different initial
manganese ion concentrations. .......................................................................................... 178
xviii
Figure 6-10. Adsorption kinetics plot of Mn (II) onto B/Z using AMD samples with
experimental data fitted to Pseudo first and second order kinetic models as well as Elovich
kinetic model. .................................................................................................................... 180
Figure 6-11. Equilibrium isotherms of Mn(II) adsorption nonlinear onto B/Z adsorbent.
........................................................................................................................................... 183
Figure 6-12. Effect of mass on breakthrough curves for adsorption of Mn (II) onto B/Z
adsorbent at a flow rate of 5mL/min and initial concentration of 50 mg/L....................... 187
Figure 6-13. Effect of flowrate on breakthrough curves for adsorption of Mn (II) onto B/Z
adsorbent at a bed mass of 10 g and initial concentration of 50 mg/L .............................. 188
Figure 6-14. Effect of initial concentration on breakthrough curves for adsorption of Mn
(II) onto B/Z adsorbent at a bed mass of 10 g and flowrate of 5 mL/min ......................... 190
Figure 6-15. Breakthrough curve for Mn(II) removal from mine water using
bentonite/zeolite clay ......................................................................................................... 192
xix
List of Tables
Table 2-1. Comparison of activated carbons and low cost adsorbents sorption capacities of Cr(VI)
ions ................................................................................................................................................... 41
Table 2-2. Mn adsorption studies using activated carbon .............................................................. 45
Table 2-3. Mn uptake using low cost agricultural waste through sorption ..................................... 50
Table 2-4. Clinoptilolite as an adsorbent for removal of Cr(VI)ions ............................................ 63
Table 2-5. Maximum sorption capacities of Mn ions on clay mineral ............................................ 64
Table 2-6. Adsorption capacities of Cr(VI) ions on polymer based nanoadsorbents ..................... 70
Table 3-1. Kinetics parameters for Cr(VI) adsorption onto MZPPY ............................................ 104
Table 3-2. Langmuir and Freundlich isotherms parameters for Cr(VI) adsorption onto MZPPY 109
Table 3-3. Comparison of maximum sorption capacity of other adsorbents for hexavalent
chromium adsorption at room temperature. ................................................................................... 109
Table 3-4. Thermodynamics parameters for the adsorption of Cr(VI) on MZPPY composite. .... 112
Table 4-1. Kinetics parameters for V(V) adsorption onto MZPPY .............................................. 131
Table 4-2. Isotherms parameters for vanadium adsorption onto MZPPY ..................................... 136
Table 4-3. Comparison of maximum sorption capacity of other adsorbents for vanadium removal
........................................................................................................................................................ 137
Table 4-4. Thermodynamics parameters for the adsorption of V(V) on MZPPY composite. ...... 138
Table 5-1. Selected physico-chemical property of mine water from Lanxess Chrome Mine
(Rustenburg) ................................................................................................................................... 146
Table 5-2. Summary of results at breakthrough point for Cr(VI) removal using MZPPY ........... 151
Table 5-3. Summary of results at breakthrough point for V(V) removal using MZPPY .............. 152
Table 5-4. Thomas, Adams–Bohart and Yoon–Nelson models constants for the Cr(VI) sorption 160
Table 5-5. Thomas, Adams–Bohart and Yoon–Nelson models constants for the V(V) adsorption
........................................................................................................................................................ 161
Table 6-1. Kinetics parameters for Mn(II) adsorption onto B/Z adsorbent using synthetic water 179
Table 6-2. Kinetics parameters for Mn(II) adsorption onto B/Z adsorbent using AMD water
samples ........................................................................................................................................... 180
xx
Table 6-3. Maximum sorption capacities of Mn ions on clay mineral .......................................... 182
Table 6-4. Langmuir and Freundlich isotherms parameters for Mn(II) adsorption onto B/Z
adsorbent ........................................................................................................................................ 184
Table 6-5. Summary of results at breakthrough point ................................................................... 191
Table 6-6. Chemical composition of mine water. ......................................................................... 192
Table 6-7. Thomas, Adams–Bohart and Yoon–Nelson models constants for Mn(II) adsorption onto
B/Z adsorbent. ................................................................................................................................ 195
xxi
LIST OF OUTPUTS
Publications
• MTHOMBENI, N. H., MBAKOP, S., OCHIENG, A. & ONYANGO, M. S. 2016.
Vanadium (V) adsorption isotherms and kinetics using polypyrrole coated
magnetized natural zeolite. Journal of the Taiwan Institute of Chemical Engineers,
66, 172-180.
• MTHOMBENI, N. H., ONYANGO, M. S. & AOYI, O. 2015. Adsorption of
hexavalent chromium onto magnetic natural zeolite-polymer composite. Journal of
the Taiwan Institute of Chemical Engineers, 50, 242-251.
• MTHOMBENI N. H., ONYANGO, M. S. & AOYI, O. Fixed-bed Column
Operation for Chromium (VI) Removal from Aqueous Solution using Magnetic
Zeolite- Polypyrrole Composite (To be submitted )
Conferences
• MTHOMBENI, N. H., MBAKOP AND, S. & ONYANGO, M. S. Adsorptive
Removal of Manganese from Industrial and Mining Wastewater. 2016 Sustainable
Research and Innovation (SRI) Conference May 4-6; 2016, Nairobi, Kenya
• MTHOMBENI, N. H., MBAKOP, S. & ONYANGO, M. S. 2015. Magnetic
zeolite-polymer composite as an adsorbent for the remediation of wastewaters
containing vanadium. 2nd International Conference on Environment Pollution and
Prevention. November 12-13; 2014, Auckland, New Zealand
1
CHAPTER 1
1. Introduction
Water is acknowledged as the most important and indispensable of all the natural resources and
it is critical in all aspects of life (Kamika and Momba, 2012). One of the world's greatest
challenges in recent years is water shortages which are due to climate change, (Masese et al.,
2013, Dudgeon, 2010) as well as population growth, increased levels of urbanization, mounting
demand from user sectors, a lack of investment in infrastructure, deteriorating water quality due
to pollution and the effects of climate change, the significance of water in economic and social
development is increasingly being recognized (Zhuwakinyu, 2012).
South Africa is a water stressed country and water availability is not consistent across the South
African landscape and varies greatly between different catchments (SAEO, 2012 ).The effects of
variable rainfall patterns and different climatic regimes are compounded by high evaporation
rates across the country. It is estimated that the effect of climate change on water resources
suggests that South Africa may experience a reduction of 10% in average rainfall reducing
surface water runoff up to 50-75% by 2025 (UNEP-FI, 2009).
Mining has played a central role in the development and growth of the South African economy
but impacts water resources negatively (UNEP-FI, 2012). Mining and metallurgical industries
are associated with heavy metals wastewaters that are directly or indirectly discharged into the
environment increasingly. There has been an increasing ecological and global public health
concern associated with environmental contamination by heavy metals (Tchounwou et al., 2012).
Heavy metals are not biodegradable and tend to accumulate in living organisms and many heavy
2
metal ions are known to be toxic or carcinogenic (Hu et al., 2005). There a lot of metals which
are have been found to be toxic to human beings and ecological environments, such as Cr, Cu,
Pb, V, Hg, Zn, Mn, Ni and other metals (Ojedokun and Bello, 2016, Meena et al., 2008). Several
treatment methods to remove heavy metals from mining wastewaters have been reported. Heavy
metal ions in wastewater are commonly removed by chemical treatment, oxidation and
hydrolysis, ion-exchange, bio-sorption and adsorption (Patterson, 1985). Most recently, many
efforts have been addressed on the developing of new technologies in which technological,
environmental and economic constraints are taken into consideration. The purification methods
have to avoid generation of secondary waste and to involve materials that can be recycled and
easily used on an industrial scale.
Adsorption compared with other methods appears to be an attractive process in view of its
efficiency and capacity of removing heavy metal ions over wide range of pH and to a much
lower level, ability to remove complex form of metals that is generally not possibly by other
methods. It is also environmentally friendly, cost effective and its ease of operation compared to
other processes with which it can be applied in the treatment of acid mine and heavy metal
containing wastewater (Rao et al., 2010). In the past several investigations have been undertaken
for the removal of heavy metals from wastewater using different low-cost materials. These
include amongst many others activated carbon made from agricultural wastes, alumina and
natural zeolite (Ngomsik et al., 2006). Adsorption technique though robust in nature suffers from
massive mass transfer resistance due to inherently large surface areas and large diffusion lengths
of the adsorbents. To overcome limiting factors in adsorption, nanomaterial adsorption media
could be designed to maximize mass transport kinetics by providing contaminants with rapid
3
access to high surface area and by promoting internal mass transport (Hristovski et al., 2007).
Because of their size, nanomaterials exhibit novel properties as large surface area, potential for
self-assembly, high specificity, high reactivity, and catalytic potential make nanoparticles
excellent candidates for wastewater treatment applications (Zhang et al., 2006)..
Clays have been considered to be excellent adsorbent materials due to large surface area,
chemical and mechanical stability, layered structure, and high cation exchange capacity
(Bhattacharyya and Gupta, 2008). Natural zeolites are widely available and have been studied
extensively due to their properties such as non-toxicity, their robustness, high selectivity and
easy removal, which are crucial factors for a successful water management (Margeta et al.,
2015). However, natural zeolite has poor capacity for most contaminants. To improve
performance of natural zeolites, the surface needs to be modified. Conducting polymers such as
polypyrrole offer good prospects for functionalization due to their nontoxicity, environmentally
stability, ease of preparation and high efficiency for removal of toxic metals in water (Bhaumik
et al., 2011a, Bhaumik et al., 2011b, Bhaumik et al., 2012, Bhaumik et al., 2013a, Bhaumik et
al., 2013b, Setshedi et al., 2014, Setshedi et al., 2015) [26-32]. Polymers have very low density
and therefore in real industrial applications, large volume of fixed beds would be required thus
limiting their use.
The incorporation of a conducting polymer into an inorganic host such as zeolite has been
considered as an important method to generate a new type of hybrid materials that have unique
properties. Zeolites are considered the most suitable host materials due to their well-ordered
pore systems that can serve as substrate for growing of conducting polymers (Yu et al., 2014).
Polypyrrole carries charges in the polymer matrix, and has some positively charged nitrogen
4
atoms which provide a good prospect for its applications in adsorption or filtration (Bhaumik et
al., 2011b, Zhang and Bai, 2003).
Both natural zeolite and polymers are bulk adsorption materials which have inherent limitations
as they suffer from massive mass transfer resistance due to large surface areas and large
diffusion lengths. These limitations could be overcome by developing nanocomposites in which
nanoparticles are imbedded in bulk materials (Hristovski et al., 2007).
Separation of these nanomaterials from water is still a challenge though. Magnetic adsorbents
have gained much attention recently in this respect because upon use they can easily be retrieved
from solution with applied magnetic field (Bhaumik et al., 2011a, O’Handley, 2000).Magnetic
separation has been combined with adsorption for heavy metal removal from contaminated water
at laboratory scales (Hu et al., 2005, Mayo et al., 2007, Yavuz et al., 2006, Cheng et al., 2012,
Bhaumik et al., 2011b). Magnetic separation is especially desirable in industry because it
overcomes many of the issues present in filtration, centrifugation or gravitational separation, and
requires much less energy to achieve a given level of separation (Wang et al., 2009, Wang and
Lo, 2009).
Adsorption process can be configured in batch and continuous fixed bed mode. Batch adsorption
studies provide fundamental information about effectiveness of adsorption process using specific
adsorbent and to determine the maximum adsorption capacity. For industrial wastewater
treatment systems the continuous fixed-bed column adsorption is often more applicable (Karimi
et al., 2012).
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Therefore, this study focuses on the adsorption of Cr(VI) and V(V) using magnetic zeolite
polypyrrole (MZPPY) and removal of Mn(II) using zeolite bentonite (B/Z) adsorbent. Batch
adsorption studies will be used to evaluate process parameters such as pH, sorbent dosage, initial
metal ion concentration, time and temperature. Furthermore, fixed-bed column studies will be
utilized to simulate the industrial continuous adsorption processes while varying the influent
metal ion concentration, bed mass and influent flow rate.
1.1. Problem statement and motivation
South Africa is blessed with the occurrence of many minerals, often in large quantities and of
strategic importance to it and to other nations. Mining and metallurgical industries, forming the
backbone of the South African economy, has resulted in extensive environmental pollution
problems. One such problem is heavy metal pollution, which is one of the most destructive forms
of pollution in the world, and is widely accepted as responsible for costly environmental and
socio-economic impacts.
High concentrations of heavy metals and other toxic elements can severely contaminate surface
and groundwater, as well as soils. Most rural communities depend solely on these surface and
groundwater wells, which contain high levels of water contaminants such as heavy metals that
have harmful effects on the environment and humans. As such industrial and mining discharge
streams must be treated using appropriate techniques. Industrial effluent treatment processes are
designed to ensure that when wastewaters are discharged into natural water sources, any adverse
effect are reduced or prevented, and where permitted treated water recycled.
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Various treatment methods such as mineral precipitation and co-precipitation, reverse osmosis,
insoluble hydroxide formation, ion exchange, solvent extraction, coagulation and adsorption are
utilized to treat wastewaters. Of all these methods, an adsorption technique is the most versatile
and cost- effective method for the removal of pollutants in wastewater. However, most
traditional adsorbents suffer from diffusion limitation within the particles which leads to a
decrease in the adsorption rate and available capacity. Recently, nanostructured materials have
proven advantageous over traditional adsorbents due to very large surface area, accessible active
sites and a short diffusion length, which result in high adsorption capacity, rapid extraction
dynamics and high adsorption efficiencies. The application of magnetic nanoadsorbents has
attracted attention to solve environmental problems because they have large surface area, are
highly dispersible in water and are easily separable from aqueous solution by the application of
external magnetic field. Natural zeolite and clays which are widely available, cheap and non-
toxic are commonly used as adsorbents for water treatment.
Consequently, this study explores the application of magnetic zeolite and clay nanostructured
adsorbent for treatment of mining wastewater with intent to recycle the treated water to be reused
in the process. The effect of several process variables will be explored.
1.2. Hypothesis
It is hypothesized that zeolite-clay and zeolite-polymer nanostructured materials are
robust and stable in aqueous environments
It is then hypothesized that zeolite-clay and zeolite-polymer based nanomaterials are
capable of remedying mining wastewater to acceptable standard for various applications
It is also hypothesized that the performance of zeolite-clay and zeolite-polymer based
nanomaterials are influenced by the water quality parameters and process conditions
7
It is further hypothesized that the interaction between nanostructured adsorbents and
water contaminants is chemical in nature and hence the adsorption data can be modeled
using chemical adsorption based models
It is finally hypothesized that the nanostructured adsorbents can be regenerated and
reused
1.3. Research Aims
The goal of the research is to explore the application of nanostructured materials in mining
wastewater with intent to recycle the treated water to be reused in the process. The goal of the
research is to protect the environment and human health by minimizing exposure of toxic metals
found in mining wastewater by treatment with these clay nanomaterials. It is expected that the
results will improve the quality of water and reduce the impact of mine wastewater in the
environment.
Consequently, the specific objectives of this project are to:
Develop and characterize zeolite based nanomaterials for mining wastewater treatment.
To establish selectivity, robustness and stability of the nanomaterials in treatment of
mining wastewater contaminants using batch tests and generate equilibrium and kinetic
data for various pollutants in mining wastewater
Evaluate the performance of the nanomaterials for various pollutants sorption in a fixed-
bed column
Model batch and column data using chemical reaction mechanism as the dominating step
in order to extract preliminary adsorption design data
Test reusability of the developed media as a means of establishing cost implications of
the new material
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CHAPTER 2
2. Introduction
Global water demand for freshwater resources is largely influenced by population growth,
urbanization, food and energy security policies, and macro-economic processes such as trade
globalization, industrialization, changing diets and increasing production and consumption
(Connor, 2015). These also contributes to the polluting of water resources, further reducing their
immediate accessibility and thus compromising the capacity of ecosystems and the natural water
cycle to satisfy the world’s growing demand for water (MEA, 2005) . It is projected that the
water demand will increase by 55% by 2050, mainly due to growing demands from
manufacturing, thermal electricity generation and domestic use (Connor, 2015, MEA, 2005).
South Africa is classified as a water-stressed country with an annual fresh water availability of
less than 1 700 m3 per capita (the index for water stress) (Otieno and Ochieng, 2004). There is
uneven distribution of rainfall across South Africa with the eastern part of the country being
much wetter than the western part due to the nature of the weather conditions (Turpie and Visser,
2013). There are also alternating periods of droughts and floods which affect the amount of water
across South Africa (Water, 2011). Climate change is expected to alter the water cycle and will
subsequently impact water availability and demand (Haddeland et al., 2014).
Also mining, mineral processing and extractive-metallurgical operations generate toxic liquid
effluents and large volumes of solid waste that results in serious environmental consequences
(Azapagic, 2004, Ahluwalia and Goyal, 2007). Industrial and mining activities such
electroplating, metal smelting and chemical industries, and manufacturing processes are few
sources of anthropogenic heavy metals in water (Chowdhury et al., 2016, He et al., 2008).
9
Mining minerals are associated with acid drainage problems that have a long-term impact to
waterways and biodiversity (Akcil and Koldas, 2006). Domestic, industrial, and agricultural
wastewater containing high concentrations of metals are often discharged into the environment in
many developing countries due to poor treatment of this water (Gupta, 2008).
2.1. Heavy metals in water and health impacts
The sources of drinking water such as surface water, groundwater, and seawater are likely to be
polluted by heavy metals (Bryan and Langston, 1992, Chowdhury et al., 2016). Wastewater
streams discharged from mining, paint manufacture, petroleum refining, battery manufacture,
chemical, plating and smelting industries contain heavy metals contaminants. Mn and other
metals such as Fe, V, Al, Zn and Cu are often present in high concentrations in mine drainage
waters than in unpolluted streams and groundwater (Banks et al., 1997, Hallberg and Johnson,
2005, Luptakova et al., 2012).
Concentrations of heavy metals and other solutes in acid mine drainage are commonly elevated
due to aggressive dissolution of carbonate, oxide, and aluminosilicate minerals by acidic water
along paths down flow from oxidizing pyrite (Blowes and Ptacek, 1994, Cravotta III, 1994). The
study conducted by Hansen (Hansen, 2015) indicated that seepage from the tailings
impoundments in the Witwatersrand basin in South Africa are acidic and contain elevated
concentrations of trace metal concentrations, specifically Mn, Al, Cr, Co, Cu, Ni, Zn and U, as
well as SO4. The AMD from the gold mines in West Rand have been also found to contain high
concentrations of these metals (Durand, 2012). This chapter gives an overview of the occurrence
and speciation of vanadium, chromium and manganese in the environment, whereby various
10
conventional treatment techniques commonly used for treatment of these heavy metals are
discussed. Moreover, insight about adsorption technology and various adsorbents previous used
for adsorption are presented.
2.1.1. Vanadium
Vanadium is a trace metal that is found naturally both in soil and water (Cran and Tracey, 1998)
and does not occur in nature as metallic vanadium. It is a corrosion-resistant, steel-grey metal;
existing in oxidation states ranging from −1 to +5. Its most common valence states are +3, +4,
and +5 (Barceloux and Barceloux, 1999) . Vanadium pentaoxide (V2O5) is the most common
existing and used form of vanadium. Ammonium metavanadate (NH4VO3), sodium
metavanadate (NaVO3) and sodium orthovanadate (Na3VO4) are also common forms of
vanadium (Imtiaz et al., 2015, Cran and Tracey, 1998).
Vanadium is very widely distributed and is mined in South Africa, Russia, and China. Vanadium
is used as a steel additive and most of the vanadium consumption is as a ferrovanadium (a
mixture of iron and vanadium). Vanadium is also used in titanium alloys, batteries, catalyst in
chemical and polymer industries (Navarro et al., 2007, Imtiaz et al., 2015, Moskalyk and
Alfantazi, 2003). Vanadium pentoxide is formed during the smelting of iron ore, from uranium
ores and by a salt roast process from boiler residues or residues from elemental phosphate plants.
During the burning of fuel oils in boilers and furnaces, vanadium pentoxide is present in the solid
residues, soot, boiler scale, and fly ash (Costigan et al., 2001).
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Vanadium is a heavy metal pollutant for ground waters as well as surface water impacted by
mostly mining and can also be observed in water resources such rivers, lakes and seas (Imtiaz et
al., 2015).
There are three main exposure routes to vanadium: air, drinking water and the food chain. The
concentration of vanadium in fresh water highly depends on the geographical location (WHO,
2000). Vanadium is a heavy metal pollutant for ground waters as well as surface water impacted
by mostly mining can also observed in water resources such rivers, lakes and seas (Imtiaz et al.,
2015). At oxic water conditions and about neutral to slightly alkine pH soluble vanadate is the
most dominant species. Under non-oxic conditions sparingly available oxidovadium (IV)
hydroxide is generated and transported in water in the form of colloids. A decrease in pH and
addition of phosphate based corrosion inhibitors to drinking water can re-mobilize vanadate thus
eventually increasing vanadate concentrations beyond tolerable levels (Sigel et al., 2014).
The toxicity of vanadium compounds generally increases as valence increase. Pentavalent
compounds are the most toxic. The pH of the aqueous medium has a great impact on vanadium
toxicity (Crans, 2005). Adverse respiratory effects have been reported in humans and animals
exposed to vanadium compounds at concentrations much higher than those typically found in the
environment. The gastrointestinal system is becomes sensitive to toxicity following oral
exposure and hematological system following inhalation or oral exposure (Barceloux and
Barceloux, 1999).
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Figure 2-1. Eh-pH diagram for the system V-O2-H2O, for dissolved EV<10-4 mol/kg (11.5 mg/L
as VO4) (Langmuir et al., 2004).
The Eh-pH diagram in Figure 2-1 depicts speciation of V (III)-V(V)at different pH, most
aqueous V(V)solutions are dilute and only mononuclear (monomeric) species are expected:
VO2+, VO (OH)3 (aq.), VO2(OH)2
¯, VO3(OH)
¯2, and VO4
3¯, depending on pH. At pH 2 to 6
decavanadates may be present, from pH 6 to 9, metavanadates, (VO3)xx-
, and form slowly at 25
°C from depolymerization of decavanadates. At pH 9-12 the pyrovanadate is formed. The
orthovanadate species, VO43¯, exists in aqueous solution above pH (Baes and Mesmer, 1977)
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2.1.2. Chromium
Chromium is the 21st most abundant element in the Earth’s crust. It is a hard, brittle metal that
with difficulty can be forged, rolled and drawn unless in a pure form. It is easier to work with
and it is an excellent alloying metal with iron. The bright silvery property makes it an
appropriate metal to provide reflective, non-corrosive attractive finish to electroplating (Krebs,
2006). The principal chromium ore is ferric chromite, FeCr2O4, found mainly in South Africa
(with 96% of the world’s reserves), Russia and the Philippines (Mohan and Pittman Jr, 2006).
The oxidation states of chromium are +6 in chromates (CrO42-
) and dichromates (Cr2O72-
), +3
which is most stable and +2 (Daintith, 2014). The hydrolysis of Cr(III) is complex. It produces
mononuclear species CrOH2+
, Cr(OH)2+, Cr(OH)4
−, neutral species Cr(OH)3
0 and polynuclear
species Cr2(OH)2 and Cr3(OH)45+
. The hydrolysis of Cr6+
produces only neutral and anionic
species, predominately CrO42−
, HCrO42−
, Cr2O72−
(Mohan et al., 2005a, Mohan et al., 2006,
Mohan and Pittman Jr, 2006). At low pH and high concentrations Cr2O72-
predominates while at
pH greater than 6.5 Cr(VI) exist as CrO42-
(Mohan et al., 2005a).
On a worldwide basis, about 80% of the chromium mined goes into metallurgical applications
mainly in manufacturing of stainless steel, 15% is used in chromium chemicals manufacture and
the remainder refractory applications (Barnhart, 1997) . Chromium is considered an essential
nutrient and also has detrimental health hazard properties. Cr(VI), is considered harmful even in
small intake quantities , however Cr(III), is considered essential for good health in moderate
intake (Guertin, 2005).
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Figure 2-2: The Redox potential (Eh)-pH diagram for Cr-O-H systems (Palmer and Wittbrodt,
1991, Lukman et al., 2014)
Industries like paint and pigment manufacturing, stainless steel production, corrosion control,
textile, leather tanning, chrome electroplating, metal finishing industries, wood preservation, and
photography discharge effluent containing hexavalent chromium, Cr(VI) to surface water (El
Nemr et al., 2015). i, 1982). However, due to the dynamic interconversion of chromium (III) and
(VI) in aqueous environments, the availability of chromium (III) may lead to a health risk if this
conversion to chromium (VI) occurs. The impacts of chromium (VI) to human health are lung
cancer, respiratory irritation, dermatosis, dermatitis, and kidney and liver damage and
gastrointestinal tract. Dermatosis, nephritis, and liver damage are the results of absorption of
very large quantities of chromium due to exposure over a long period and a relatively small risk
15
of stomach cancer is associated with chromium (VI), due dynamic interconversion of chromium
(III) and (VI) in aqueous environments (Kimbrough et al., 1999).
2.1.3. Manganese
Manganese is a brittle, gray-pink metal with an atomic weight of 54.938. It is too brittle to be
used unless alloyed. Manganese has only one stable natural isotope, 55Mn. Manganese occurs
naturally and is the third most abundant transition metal on the earth crust (9.5 × 102 ppm)
(Tavlieva et al., 2015, Cox, 1995). It can exist in 11 oxidation states with manganese
compounds Mn(II), Mn(IV) and Mn(VII) being the most environmentally and biologically
important (Mahmoud et al., 2012, USEPA, 1994). Manganese most common minerals are
oxides [pyrolusite (MnO2)] and hydro-oxides [psilomelane BaMn2+
Mn4+
8O16(OH)4] and
braunite (Mn2+
Mn3+
6)(SiO12), and to a lesser extent as rhodochrosite (MnCO3).
Figure 2-3. Eh–pH diagram for the Mn–O2–H2–H2O system at 25 °C (Lu et al., 2013)
16
Mn(II) compounds are stable in acid solution but are readily oxidized in alkaline medium. The
Eh-pH diagram of Mn in Figure 3 show Mn2+
is formed at low pH, which progressively react
with OH- as pH increases to produce metal hydroxides (Mn(OH)2) or oxides (Huang, 2016).
South Africa has the world’s largest resources of manganese which is estimated to be around
80%. Manganese is used in the manufacture of iron and steel alloys, batteries, glass and
fireworks, a significant fertilizer for plants, food additive for stocks and catalyst for organic
synthesis (Li et al., 2010). Manganese is also found in the effluents of mine waters, either
neutral or acid (AMD) (Silva et al., 2012). Mn is one of the most widely used metals in the
world and one of the important indexes of water pollutants (Ma et al., 2013). Exposure to excess
manganese may lead to Mn intoxication that may result in the onset of a neurological phenotype
known as manganism which present with motor symptoms resembling Parkinson’s disease
(Erikson et al., 2007, Martinez-Finley et al., 2013, Roels et al., 2012).
Manganese occurs naturally in many surface water and groundwater sources and in soils that
may erode manganese into waters. Improper disposal of dry-cell batteries or other toxic wastes
from industries can yield higher manganese concentrations well above those normally found in
environmental water (WHO, 2011) and cause significant harm to public health (Frisbie et al.,
2012). Manganese at lower doses is an essential nutrient for humans and animals. Mn at elevated
concentrations is a powerful neurotoxin which affects the nervous system and causes learning
disabilities and intellectual impairment in children (Bouchard et al., 2007, Bouchard et al.,
2011). Studies have shown that children exposed to 240–350 µg manganese/L in water had
elevated manganese concentration in their hair and exhibited impaired manual dexterity and
17
speed, short-term memory, and visual identification when compared with children from areas
which manganese was controlled (He et al., 1994). Children exposed to manganese intoxication
from water containing above 1,0 µg manganese/L, had attention and memory impairments
conditions (Woolf et al., 2002) and others presented neurologic symptoms including a repetitive
stuttered speech, poor balance, coordination, and fine motor skills (Sahni et al., 2007).
Manganese intoxication is also linked to Mn induced Parkinsonism (Lucchini et al., 2009,
Aschner et al., 2009) , low fetal weight (Grazuleviciene et al., 2009, Zota et al., 2009) , infant
mortality (Hafeman et al., 2007, Spangler and Spangler, 2009) and increased cancer rates
(Spangler and Reid, 2010).
2.2.Conventional methods of heavy removal from water
Several treatment methods to remove Mn, Cr(VI) and V(V) from wastewaters have been
reported. Heavy metals such as manganese, chromium and vanadium and in wastewater is
commonly removed by precipitation (Silva et al., 2012, Pakarinen and Paatero, 2011, Silva et al.,
2010, Zhang et al., 2010b, Nishimura and Umetsu, 2001, Golbaz et al., 2014, Golder et al., 2011,
Kongsricharoern and Polprasert, 1995, Kongsricharoern and Polprasert, 1996, Geldenhuys et al.,
2003), ion-exchange (Haghsheno et al., 2009, White and Asfar-Siddique, 1997, Alvarado et al.,
2013, Cavaco et al., 2007, Fan et al., 2013) , oxidation and filtration (Roccaro et al., 2007,
Piispanen and Sallanko, 2010), coagulation and flocculation (Fu-Wang et al., 2009) , biosorption
(Mohan et al., 2005b, Hou et al., 2012, Dittert et al., 2014, Febrianto et al., 2009, Khambhaty et
al., 2009, Li et al., 2008, Vijayaraghavan et al., 2011), reverse osmosis (Afonso et al., 2004,
Benito and Ruíz, 2002, Bhattacharya et al., 2013, Bódalo et al., 2005, Richard, 2004, Li et al.,
2014), ultrafiltration (Aroua et al., 2007, Chakraborty et al., 2014), electrochemical (Almaguer-
18
Busso et al., 2009, Lakshmipathiraj et al., 2008, Liu et al., 2011, Ouejhani et al., 2008), and
adsorption (Chen and Liu, 2014, Al-Rashdi et al., 2011, Anirudhan et al., 2013, Anupam et al.,
2011, Baccar et al., 2009, Baral et al., 2006, Behnajady and Bimeghdar, 2014, Chen et al., 2014).
Precipitation is widely used in process industries because it is cheap and relatively easy to
operate (Fu and Wang, 2011). Hydroxide precipitation technique is the most widely used
chemical precipitation technique due to its relative simplicity, low cost and ease of pH control.
Although most metals are precipitated as hydroxides other methods such as sulfide and carbonate
precipitation are also used (Wang et al., 2005). Lime and limestone are the most commonly
employed precipitant agents due to their availability and are inexpensive in most countries and
have been employed to remove manganese and other heavy metals in water (Aziz and Smith,
1996, Aziz and Smith, 1992, Aziz et al., 2008, Barakat, 2011). The major expense associated
with precipitation is the treatment cost of the chemicals and the precipitated sludge that is
produced (Wang et al., 2005). Excessive sludge production requires further treatment, and the
problems associated with precipitation are slow metal precipitation, poor settling, and the
aggregation of metal precipitates have long term environmental effects for sludge disposal (Aziz
et al., 2008).
Ion exchange in wastewater treatment is used for demineralization. Ion-exchange processes have
been widely used to remove manganese and other heavy metals from wastewater because it is
very effective to remove various heavy metals and can be easily recovered and reused by
regeneration operation (Lee et al., 2007). Ion exchange is the exchange of ions between the
substrate and surrounding medium. Ion exchange resins are suitable to use at wide range of
19
different pH values and at high temperatures and are insoluble in most organic and aqueous
solutions (Goher et al., 2015) . They contain a covalent bonding between the charged functional
groups and the cross linked polymer matrix (Sherrington, 1998). The disadvantage of using this
technology is that ion exchange media is easily fouled by organics and other solids in the
wastewater.
The electrochemical method is one of the technologies that are used as alternative option for the
remediation of water and wastewaters mainly due to its advantages such as environmental
compatibility, versatility, high energy efficiency, amenability of automation and safety.
Electrochemical methods include electrocoagulation, electro-oxidation and electro-reduction
(Pulkka et al., 2014). This process uses an electric field as the driving force for the separation of
ions using ion exchange membranes or plates to pass a current through an aqueous metal-bearing
solution containing a cathode plate and an insoluble anode. Metallic ions that are positively
charged cling to cathodes that is negatively charged and the metal deposit can be stripped and
recovered. Corrosion of the electrodes is the main disadvantage of using this method because
electrodes would frequently have to be replaced (Barakat, 2011). Electrochemical water or
wastewater technologies have not been widely used because they involve relatively large capital
investment and expensive electricity supply (Chen and Hung, 2007).
Coagulation and flocculation are important pretreatment processes. The coagulants that are used
often in water treatment are aluminum and ferric salts. The purposes of coagulation are to
destabilize particles present in the raw water and to convert dissolved natural organic matter into
particles (Edzwald, 2007). The main objects of coagulation are the hydrophobic colloid and
20
suspended particle which consist of insoluble particles (Chang and Wang, 2007). Flocculation
utilizes polymers to form bridges between the flocs and bind the particles into large agglomerates
or clumps that can be removed by filtration, straining or floatation (Wang et al., 2005) . Heavy
metals that are soluble in water after coagulation cannot be effectively removed and coagulation
units must be followed by other treatment techniques (Chang and Wang, 2007).
Membrane filtration technologies that are used for removal of heavy metals include
microfiltration, ultrafiltration nanofiltration and reverse osmosis depending on the size of the
particle that can be retained. Nanofiltration is the intermediate process between ultrafiltration and
reverse osmosis and the advantages of this process include ease of operation, reliability and
comparatively low energy consumption (Eriksson, 1988). It also helps to minimize scale
formation on the equipment involved in both reverse osmosis and thermal desalination processes
(Izadpanah and Javidnia, 2012). Membrane filtration technology can remove heavy metal ions
with high efficiency but high costs, process complexities, membrane fouling, and low permeate
flux have limited its use for heavy metal removal (Fu and Wang, 2011).
2.3.Adsorption of heavy metals from wastewater
Adsorption is a common method of heavy metal removal from water. Several physicochemical
properties such as specific surface area, pore structure, and surface chemistry of adsorbents
regulate the adsorption efficiency, selectivity, equilibrium time of adsorption, regeneration
capacity, and their stability in aqueous solutions (Islam et al., 2015, Yang et al., 2008, Xue and
Li, 2008, Faust and Aly, 1987). The adsorptive capacity of the adsorbent is also influenced by
the adsorbate properties of group functionality, branching or geometry, polarity, hydrophobicity,
21
dipole moment, molecular weight and size, and aqueous solubility. The solution conditions such
as pH, temperature, adsorbate concentration, ionic strength, and competitive solutes also have an
effect on the adsorption capacity (Faust and Aly, 1987).
Physico-chemical treatment methods have important role to play in today’s wastewater treatment
contaminated with Mn, Cr, V and other heavy metal ions and will continue to have increased
use because of established practice and continued improvisation. Adsorption, ion exchange, and
coagulation, continue to dominate the wastewater treatment mainly through improved materials,
devices, and practices (Bhandari and Ranade, 2014). They offer various advantages such as their
rapid process, ease of operation and control, flexibility to change of temperature. Unlike in
biological system, physico-chemical treatment can accommodate variable input loads and flow
such as seasonal flows and complex discharge. Chemical plants can be modified if required and
the treatment system generally requires a lower space and installation cost (Selvam et al., 2015,
Barakat, 2011).
Adsorption technique is one of the wastewater treatment technologies that is recognized as an
effective, economically favorable, and easy to operate method for heavy metal wastewater
treatment and has a wide range of applications (Gupta and Ali, 2013, Le Cloirec and Faur-
Brasquet, 2008). It offers flexibility in design and operation and mostly it will produce treated
effluent that is odorless, free of color and is suitable for reuse. Adsorption process is usually
reversible and the regeneration of the adsorbent will result in economical desorption processes
(Gautam et al., 2013, Dinu and Dragan, 2014).
22
2.4.Models for adsorption
Sorption is used to describe every type of capture of a substance from the external surface of
solids, liquids, or mesomorphs as well as from the internal surface of porous solids or liquids
(Poulopoulos and Inglezakis, 2006). The molecule that accumulates or adsorbs at the interface is
called an adsorbate, and the solid on which the adsorption occurs is called the adsorbent.
Adsorption is basically an attraction of adsorbate molecules, a gaseous or liquid component, to
an adsorbent surface, a porous solid. Interaction between adsorbate and adsorbent is comprised
of molecular forces including permanent dipole, induced dipole, and quadrupole electrostatic
effects, also called Van der Waals forces (Thomas and Crittenden, 1998).
2.4.1. Kinetics
Adsorption equilibrium is not established instantaneously particular for porous adsorbents. The
mass transfer from the solution to the adsorption sites within the adsorbent particles is
constrained by mass transfer resistances that determine the time required to reach the state of
equilibrium. The rate of adsorption is usually limited by diffusion processes toward the external
adsorbent surface and, in particular, within the porous adsorbent particles. The mass transfer
parameters and equilibrium data are important input data for the determination of the required
contact times in slurry reactors as well as for fixed-bed design. The adsorption process can be
characterized by the following four consecutive steps:
Transport of the adsorbate from the bulk liquid phase to the hydrodynamic boundary
layer localized around the adsorbent particle
Transport through the boundary layer to the external surface of the adsorbent, termed film
diffusion or external diffusion
23
Transport into the interior of the adsorbent particle (termed intraparticle diffusion or
internal diffusion) by diffusion in the pore liquid (pore diffusion) and/or by diffusion in
the adsorbed state along the internal surface (surface diffusion)
Energetic interaction between the adsorbate molecules and the final adsorption sites
(Worch, 2000)
It is commonly assumed that the first and the fourth step are very fast and the total rate of the
adsorption process is determined by film and/or intraparticle diffusion. Since film diffusion and
intraparticle diffusion act in series, the slower process determines the total adsorption rate
(Worch, 2000). Several mathematical models have been applied to describe adsorption kinetic
process, which can generally be classified as adsorption reaction models and adsorption diffusion
models (Qiu et al., 2009).
2.4.1.1.Pseudo-first-order rate equation
The pseudo-first-order model is well known model to correlate data for various solutes. The
Lagergren pseudo-first-order model is the earliest known equation to describe the adsorption rate
in the liquid phase. It is expressed as:
𝑑𝑞
𝑑𝑡= 𝑘1(𝑞𝑒 − 𝑞) (2-1)
Integration of the above equation with following boundary condition: 𝑡 = 0 → 𝑞𝑡 = 0, 𝑎𝑛𝑑 𝑡 =
𝑡 → 𝑞𝑡 = 𝑞𝑡 results to the following expression:
ln(𝑞𝑒 − 𝑞𝑡) = ln 𝑞𝑒 − 𝑘1𝑡 (2-2)
24
log (𝑞𝑒 − 𝑞𝑡) = log 𝑞𝑒 −𝑘1
2.303𝑡 (2-3)
where qe and qt refer to the amount of ion adsorbed (mg/g) at equilibrium and at any time, t
(min), respectively, and k1 is the equilibrium rate constant of pseudo first-order adsorption
(l/min) (Ismadji et al., 2015).
2.4.1.2.Pseudo Second-Order Equation
(Ho and McKay, 2000) made the assumption that the process may be pseudo-second order and
the rate limiting step may be chemical sorption or chemisorption involving valence forces
through sharing or the exchange of electrons in order to develop mathematical model to describe
sorption process during agitation. It must also be assumed that the sorption follows Langmuir
model. The kinetic rate equation is expressed as:
𝑑𝑞𝑡
𝑑𝑡= 𝑘(𝑞𝑒 − 𝑞𝑡) (2-4)
Integrating this for the boundary conditions 𝑡 = 0 𝑡𝑜 𝑡 = 𝑡 𝑎𝑛𝑑 𝑞𝑡 = 0 𝑡𝑜 𝑞𝑡 = 𝑞𝑡, gives:
1
(𝑞𝑒−𝑞𝑡)=
1
𝑞𝑒+ 𝑘𝑡 (2-5)
With the linear form given as:
𝑡
𝑞𝑡=
1
𝑘𝑞𝑒2 +
1
𝑞𝑒 𝑡 (2-6)
If the initial sorption rate is
ℎ0 = 𝑘𝑞𝑒2 (2-7)
Then
𝑞𝑡 =𝑡
1ℎ⁄ +𝑡
𝑞𝑒⁄ (2-8)
25
This can be rearranged to this expression
𝑡
𝑞𝑡=
1
ℎ0+
1
𝑞𝑒 𝑡 (2-9)
ℎ0 can be determined experimentally by plotting of 𝑡/𝑞𝑡 against 𝑡.
2.4.1.3.Elovich model
Elovich’s equation is another rate equation based on the adsorption capacity
𝑑𝑞
𝑑𝑡= 𝑎 𝑒−𝛼𝑞 (2-10)
where q is the quantity adsorbed during the time t, α the initial adsorption rate, and a is the
desorption constant during any one experiment (Ho, 2006).
The integrated form can be rewritten as:
𝑞 = (2.3
𝛼) 𝑙𝑛(𝑡 + 𝑡0) − (
2.3
𝛼) 𝑙𝑛 𝑡0 (2-11)
if
𝑡0 =1
𝛼𝑎 (2-12)
the plot of q as a function of ln (t + t0) should yield a straight line with a slope of 2.3/α. Elovich
equation is commonly applied to determine the kinetics of chemisorption of gases onto
heterogeneous solids, and it is restricted, as it only describes a limiting property ultimately
reached by the kinetic curve (Ho, 2006).
(Chien and Clayton, 1980) assumed that aαt ≫ 1 and by integrating and applying the boundary
conditions of q = 0 at t = 0 and q = q at t = t, then the equation becomes
𝑞 = 𝛼 𝑙𝑛(𝑎𝛼) + 𝛼 𝑙𝑛(𝑡) (2-13)
26
the constants can be obtained from the slope and the intercept of a straight line plot of q against
𝑙𝑛(t) (Ho, 2006).
2.4.1.4.Ritchie's equation
(Ritchie, 1977) made assumptions that θ is the fraction of surface sites which are occupied by an
adsorbed gas, and that n the number of surface sites occupied by each molecule of the adsorbed
gas, and α is the rate constant. Also assuming that the rate of adsorption depends solely on the
fraction of sites which are unoccupied at time t, and then the equation can be expressed as
𝑑𝜃
𝑑𝑡= 𝛼(1 − 𝜃)𝑛 (2-14)
Integrating equation
1
(1−𝜃)𝑛−1 = (𝑛 − 1)𝛼𝑡 + 1 𝑓𝑜𝑟 𝑛 ≠ 1 (2-15)
or
𝜃 = 1 − 𝑒−𝛼𝑡 𝑓𝑜𝑟 𝑛 = 1. (2-16)
It is assumed that no site is occupied at t = 0 when introducing the amount of adsorption, q, at
time t, then equation 2- 15 becomes
𝑞∞𝑛−1
(𝑞∞−𝑞)𝑛−1 = (𝑛 − 1)𝛼𝑡 + 1 (2-17)
Equation 16 becomes
𝑞 = 𝑞∞(1 − 𝑒−𝛼𝑡) (2-18)
where q∞ is the amount of adsorption after an infinite time (Ho, 2006)
2.4.2. Diffusion models
27
2.4.2.1.Intraparticle diffusion model
The fractional approach to equilibrium change is done according to a function of (Dt/r2)1/2
,
where r is the radius of adsorbent particle and D is the effective diffusivity of solute within the
particle. The initial rate of Intraparticle diffusion is obtained by curve linearization of
qt = f (t1/2
), which is expressed as
𝑞𝑡 = 𝑘𝑝𝑡1/2 + 𝐶 ( 2-19)
where kp is the IPD rate constant (mg/ (g min1/2
)) and C is a constant for any experiment (mg/g).
To obtain the intercept it has been indicated that extrapolation of the linear portion of the plot
back to the axis provides intercepts which are proportional to the extent of the boundary layer
thickness, that is, the larger the intercept the greater the boundary layer effect (McKay et al.,
1980, Wu et al., 2009).
The initial adsorption behavior is determined by the following equations:
𝑞𝑖 = 𝑘𝑝𝑡𝑖1/2
+ 𝐶 (2-20)
where ti is the longest time in adsorption process and q is the solid phase concentration at
time t = ti for an adsorption system. Therefore subtracting the equations
𝑞𝑖 − 𝑞𝑡 = 𝑘𝑝(𝑡𝑖1/2
− 𝑡1/2) (2-21)
Rearrangement yields
(𝑞𝑡
𝑞𝑖) = 1 − 𝑅𝑖 [1 − (
𝑡
𝑡𝑖)
1/2
] (2-22)
28
where 𝑅𝑖 =𝑘𝑝𝑡𝑖
1/2
𝑞𝑖⁄ , which is defined as the initial adsorption factor of the intraparticle
diffusion model. The characteristic curve based on intraparticle diffusion model can be obtained
from the following equation
𝑅𝑖 =𝑞𝑖−𝐶
𝑞𝑖= 1 − (
𝐶
𝑞𝑖) (2-23)
Ri can be represented in terms of the ratio of the initial adsorption amount (C) to the final
adsorption amount (qi). When there is no initial adsorption behavior in an adsorption system
Ri = 1 then equation 2- 20 passes through the origin. When C = qi when adsorption occurs right
at the beginning of the process Ri = 0 (Wu et al., 2009).
2.4.2.2.Film Diffusion
The film diffusion mass transfer rate equation can be described as follows;
𝑙𝑛 (𝐶𝑡
𝐶𝑜) = −𝑘𝑒𝑑 (
𝐴
𝑉) 𝑡 (2-24)
where, ked is external diffusion rate constant (cm/min), Ct is the concentration at time t = t, C0 is
the concentration at time t = 0, A/V is external sorption area to the total solution volume and t is
time. The plots of ln (Ct/C0) versus time can be used to describe whether the sorption proceeds
sequentially through the sorbent particle and in the bulk of the solution by diffusion (Shah et al.,
2014, Gao et al., 2008, Gupta et al., 2009).
2.4.3. Adsorption isotherms
The equilibrium in adsorption systems is commonly represented by the equilibrium isotherm.
The equilibrium isotherm represents the distribution of the adsorbed material between the
29
adsorbed phase and the solution phase at equilibrium, and is characteristic for a specific system
at a particular temperature (Poulopoulos and Inglezakis, 2006).
2.4.3.1.Langmuir isotherm
The Langmuir equation introduced a clear concept of the monomolecular adsorption on the
energetically homogeneous surfaces. This isotherm describes adsorbate-adsorbent systems in
which the adsorbents that exhibit the Langmuir isotherm behavior are supposed to contain fixed
individual sites, each of which equally adsorbs only one molecular, forming thus forming a
monolayer, namely, a layer with the thickness of the molecule (Cecen and Aktas, 2011) .
Langmuir further supposed that the rate of desorption from the surface is directly proportional to
the fractional surface coverage and that the rates of adsorption and desorption are equal at
equilibrium (Thomas and Crittenden, 1998). Langmuir adsorption isotherm normally is
expressed as follows:
𝑞 =𝑞𝑚𝑎𝑥𝑏𝐶𝑒
(1+𝑏𝐶𝑒) (2-25)
where Ce (mg/L) is the equilibrium adsorbate concentration in solution, qmax (mg/g) is the solute
adsorbed per unit weight of adsorbent in forming a complete monolayer on the surface, b is the
Langmuir constant related to energy of adsorption and q (mg/g) is the adsorption capacity ( mg
adsorbate adsorbed per g of adsorbent ).
The linearized equation form of the Langmuir equation is given as:
𝟏
𝒒=
𝟏
𝒒𝒎𝒂𝒙+ (
𝟏
𝒃𝒒𝒎𝒂𝒙) (
𝟏
𝑪𝒆) (2-26)
The constants in the Langmuir isotherm can be determined by plotting 1/q versus 1/Ce which
gives a linear plot whereby Langmuir parameters can be extracted. The essential features of
30
Langmuir isotherm can be expressed in terms of a dimensionless separation factor or equilibrium
parameter RL:
𝑅𝐿 =1
(1 + (𝑏 × 𝐶𝑂)
where Co is the initial solute concentration of in mg/L. The value of RL indicates the shape of the
isotherm whether the adsorption is favorable (0<RL<1), unfavorable (RL>1), linear (RL=1) or
irreversible (RL=0).
2.4.3.2.Freundlich isotherm
The Freundlich equation is usually applied in a case of heterogeneous surface energies consisting
of sites with different adsorption potentials, and each type of site is assumed to adsorb molecules
(Poulopoulos and Inglezakis, 2006, Cecen and Aktas, 2011). The model has the following form
(Freundlich, 1906):
𝑞 = 𝐾𝐹𝐶𝑒1 𝑛⁄
(2-27)
The linearized form of the equation is expressed as:
log 𝑞 = log 𝐾𝐹 +1
𝑛log 𝐶𝑒 (2-28)
KF (mol1-n
Lng
-1) and n are Freundlich constant which represent the adsorption capacity and the
adsorption intensity respectively (Saini, 2015). A plot of log 𝐶𝑒 vs. log 𝑞𝑒 gives a linear curve
with the intercept value of 𝐾F and the slope of 1/𝑛.
2.4.3.3.Dubinin–Radushkevich isotherm model
This is an empirical model initially developed to describe isotherms for the adsorption of
subcritical vapors onto micropore solids following a pore filling mechanism (Dubinin et al.,
31
1947, Nguyen and Do, 2001, Foo and Hameed, 2010). The approach was usually applied to
distinguish the physical and chemical adsorption of metal ions (Foo and Hameed, 2010).
It is more generally applicable than the Freundlich isotherm since it is not limited by the
homogenous surface and constant adsorption potential assumption (Saini, 2015). The D-R
equation is expressed as:
𝑞 = (𝑞𝑚)exp (−𝐾𝐷𝑅𝜀2) (2-29)
and the linear form is shown as :
ln 𝑞 = ln 𝑞𝑚 − 𝐾𝐷𝑅𝜀2 (2-30)
KDR is the activity constant related to average adsorption energy (mol2/kJ
2) and 𝜺 is the Polanyi
potential (kJ/mol) which can be calculated from this equation:
𝜀 = 𝑅𝑇 ln (1 +1
𝐶𝑒) (2-31)
E per molecule of adsorbate (for removing a molecule from its location in the sorption space to
the infinity) can be calculated by the following relationship:
𝐸 = (2𝐾𝐷𝑅)−1
2⁄ (2-32)
2.1.1. Temkin isotherm model
This isotherm describes the adsorbent –adsorbate interactions assuming that the adsorption heat
decreases linearly rather than logarithmic as the coverage proceeds (Saini, 2015). The derivation
of the isotherm equation is characterized by a uniform distribution of binding energies (Foo and
Hameed, 2010, Saini, 2015). The equation for the isotherm expressed in the following form:
𝑞 =𝑅𝑇
𝑏𝑇ln 𝐴𝑇𝐶𝑒 (2-33)
The linear equation is expressed as:
32
𝑞 =𝑅𝑇
𝑏𝑇ln 𝐴𝑇 + (
𝑅𝑇
𝑏𝑇) ln 𝐶𝑒 (2-34)
2.1.2. Modified Langmuir isotherm
Langmuir isotherm can be modified in several ways to improve its fit with experiments. The
Langmuir-Freundlich equations combine Langmuir and Freundlich equations :
𝑞 =𝐾𝐿𝐹 𝐶𝑒
1𝑛
1+ 𝑎𝐿𝐹 𝐶𝑒
1𝑛
(2-35)
where KLF is the Langmuir–Freundlich constant and aLF is the affinity coefficient (Bhaumik et
al., 2011a).
2.4.3.4.Redlich-Peterson isotherm
Redlich–Peterson isotherm amends inaccuracies of two parameter Langmuir and Freundlich
isotherm equations in some adsorption systems (Wu et al., 2010). It is a hybrid isotherm which
incorporates three parameters into an empirical equation. It has a linear dependence on
concentration in the numerator and an exponential function in the denominator to represent
adsorption equilibria over a wide concentration range, that can be applied either in homogeneous
or heterogeneous systems due to its versatility (Foo and Hameed, 2010).
The equation is represented as:
𝑞𝑒 =𝐾𝑅𝑃𝐶𝑒
1+𝑎𝑅𝑃𝐶𝑒𝑏𝑅𝑃
(2-36)
where KRP and aRP are Redlich–Peterson constants and bRP is the Redlich–Peterson model
exponent.
33
2.4.3.5.BET isotherm
Brunauer–Emmett–Teller developed an equation capable of correlating multilayer adsorption
systems and is a theoretical model most widely applied in the gas–solid equilibrium systems
(Foo and Hameed, 2010, Brenner, 2013). The derivation of the equation follows Langmuir
approach except that the adsorption takes place in the multilayers even before monolayer
coverage is complete. The model assumes that for each adsorption site an arbitrary number of
adsorbate molecules may be accommodated. The rate of adsorption in the first layer is different
from those other layers (Brenner, 2013). The liquid–solid interface equation is exhibited as:
𝒒𝒆 =𝒒𝒔𝑪𝑩𝑬𝑻𝑪𝒆
(𝑪𝒔−𝑪𝒆)[𝟏+(𝑪𝑩𝑬𝑻−𝟏)(𝑪𝒆 𝑪𝒔⁄ )] (2-37)
where CBET, Cs, qs and qe are the BET adsorption isotherm (L/mg), adsorbate monolayer
saturation concentration (mg/L), theoretical isotherm saturation capacity (mg/g) and equilibrium
adsorption capacity (mg/g), respectively (Foo and Hameed, 2010).
2.4.3.6.Thermodynamic parameters
Activation energy in adsorption process, is defined as the energy that must be overcome by the
adsorbate ion to interact with the functional groups on the surface of the adsorbent. It is the
minimum energy needed for a specific adsorbate-adsorbent interaction to take place, even though
the process may already be thermodynamically possible. The activation energy (Ea) can be
determined from experimental measurements of the adsorption rate constant at different
temperatures according to the Arrhenius equation as follows (Saha and Chowdhury, 2011):
ln 𝑘 = ln 𝐴 −𝐸𝑎
𝑅𝑇 (2-38)
34
where k is the adsorption rate constant, A is the frequency factor constant, Ea is the activation
energy (kJ.mol-1
), R is the gas constant (8.314 J/mol/K) and T is the temperature (K). Values of
Ea and A can be determined from the slope and the intercept by plotting ln k versus 1/T.
Thermodynamic considerations of an adsorption process are necessary to ascertain whether the
process is spontaneous or not. The Gibb’s free energy change, ΔG0, is a fundamental indication
of spontaneity of a chemical reaction. Enthalpy (ΔH0) and entropy (ΔS
0) parameters must be
considered in order to determine the Gibb’s free energy of the process. The free energy of an
adsorption process is related to the equilibrium constant by the classical Van’t Hoff equation
(Saha and Chowdhury, 2011):
∆𝐺 ͦ = −𝑅𝑇 𝑙𝑛 𝑘𝑑 (2-39)
ln 𝑘𝑑 = −∆𝐻 ͦ
𝑅𝑇+
∆ 𝑆 ͦ
𝑅 (2-40)
where ΔG0 is the Gibb’s free energy change (kJ/mol), R is the ideal gas constant
(8.314J/mol/K), and T is temperature (K) and kD is the single point or linear sorption distribution
coefficient which is defined as:
𝑘𝑑 =𝐶𝑎
𝐶𝑒 (2-41)
where Ca is the equilibrium adsorbate concentration on the adsorbent (mg /L) and Ce is the
equilibrium adsorbate concentration in solution (mg/L).
a decrease in the negative value of ΔG0 with an increase in temperature indicates that the
adsorption process is more favorable at higher temperatures. This could be possible because the
mobility of adsorbate in the solution increase with increase in temperature and that the affinity of
adsorbate on the adsorbent is higher at high temperatures. On the contrary, an increase in the
negative value of ΔG0 with an increase in temperature implies that lower temperature makes the
35
adsorption easier. Also reactions occur spontaneously at a given temperature if ΔG0 is a negative
quantity (Saha and Chowdhury, 2011).
2.4.4. Fixed bed column
Adsorption in a fixed-bed adsorber is a time and distance dependent process. Each adsorbent
particle in the bed accumulates adsorbate from the percolating solution as long as the state of
equilibrium is reached, during the adsorption process. This equilibration process proceeds
successively, layer by layer, from the column inlet to the column outlet (Worch, 2012). Batch
adsorption test are unable provide adequate scaled up data that can be used to design flow
columns. They are inadequate to estimate the breakthrough behavior of adsorbents, an
indispensable parameter for appropriate design of an adsorption column. Batch adsorption is
economically infeasible in contrast to contaminant adsorption using dynamic columns. Dynamic
column experiments aids in resolving the limitations of laboratory and field pilot testing, and in
compiling relevant data for scale-up design (Rahaman et al., 2015).
The performance of continuous packed beds is described through the concept of the
breakthrough curve. The breakthrough point and the breakthrough curve depend on the nature of
the adsorbate and the adsorbent, the operating conditions and the geometry of the column (Hung
et al., 2012). The breakthrough curve is usually expressed by Ct/C0 as a function of time or
volume of the effluent for a given bed depth. Ct is the effluent concentration from the column
and C0 the influent concentration. Effluent volume (Veff) can be calculated as follows:
𝑉𝑒𝑓𝑓 = 𝐹 × 𝑡𝑒 (2-42)
36
where te is the bed exhaustion time at which concentration in the effluent reached 99.5% of
initial concentration and F is the volumetric flow rate (Tamez Uddin et al., 2009) . The total
quantity of contaminant adsorbed in the column (mad) is calculated from the area above the
breakthrough curve multiplied by the flow rate (Chen et al., 2012).
𝑀𝑎𝑑 =𝐹
1000∫ 𝐶𝑎𝑑𝑑𝑡
𝑡=𝑡𝑜𝑡𝑎𝑙
𝑡=0 (2-43)
where Cad is the concentration of metal removal (mg/L)
Total amount of contaminant passed through the column is calculated by the following
expression
𝑚𝑡𝑜𝑡𝑎𝑙 =𝐶0×𝐹 ×𝑡𝑒
1000 (2-44)
Then the total removal percent with respect to flow volume can be calculated from this equation
𝑇𝑜𝑡𝑎𝑙 𝑟𝑒𝑚𝑜𝑣𝑎𝑙(%) =𝑚𝑎𝑑
𝑚𝑡𝑜𝑡𝑎𝑙× 100 (2-45)
Equilibrium metal uptake or maximum capacity of the column, qeq (mg/g), in the column is
calculated as the following (Chen et al., 2012) :
𝑞𝑒 = 𝑀𝑎𝑑
𝑚 (2-46)
The capacity of the bed at breakthrough point is determined from the following equation:
𝑞𝑏 =𝐶0
𝑚∫ (1 −
𝐶𝑡
𝐶0)
𝑉𝑏
0𝑑𝑉 (2-47)
where qb is the bed capacity at breakthrough point (mg/g), m is the bed mass (g), C0 is the initial
adsorbate concentration (mg/L), Ct is the concentration of treated water of the outlet solution
(mg/L)at any time t and Vb is the volume processed at breakthrough point (L).
37
The performance of a fixed bed column is directly related to the number of bed volumes (BV)
processed before the breakthrough point is reached (Onyango et al., 2009, Mthombeni et al.,
2012). The number of bed volumes at breakthrough point is given by the following equation:
𝐵𝑉 =𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑎𝑡 𝑏𝑟𝑒𝑎𝑘𝑡ℎ𝑟𝑜𝑢𝑔ℎ 𝑝𝑜𝑖𝑛𝑡(𝐿)
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑏𝑒𝑑(𝐿) (2-48)
The adsorbent exhaustion rate (AER) per volume of water treated at breakthrough point and is
expressed as:
𝐴𝐸𝑅 =𝑀𝑎𝑠𝑠 𝑜𝑓 𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 (𝑔)𝑖𝑛 𝑐𝑜𝑙𝑢𝑚𝑛
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑡𝑟𝑒𝑎𝑡𝑒𝑑(𝐿) 2-49)
The empty bed contact time (EBCT), is used to determine the optimum adsorbent usage in a
fixed-bed column. It is expressed as follows (Ma et al., 2014).
𝐸𝐵𝐶𝑇 =𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑏𝑒𝑑 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
𝑓𝑙𝑜𝑤𝑟𝑎𝑡𝑒 (𝑚𝐿/𝑚𝑖𝑛) 2-50)
2.4.5. Breakthrough curves modelling
2.4.5.1.Thomas model
Thomas model is developed on the assumption that (1) the adsorption is not limited by chemical
interactions but by mass transfer at the interface and (2) the experimental data follows Langmuir
isotherms and second-order kinetics (Foo et al., 2013, Wang et al., 2015, Nguyen et al., 2015a).
The model can be described by the following expression:
𝐶𝑡
𝐶0=
1
1+exp (𝑘𝑇ℎ𝑞0𝑚
𝐹−𝑘𝑇ℎ𝐶0𝑡)
(2-51)
where kTh is the Thomas rate constant (mL/min mg), qo is the adsorption capacity (mg/g), Co is
the inlet concentration (mg/L), Ct is the outlet concentration at time t (mg/L), m is the mass of
adsorbent (g), F is the feed flow rate (mL/min), and t is time (min).
38
2.4.5.2.Bohart–Adams model
The model assumes that equilibrium is not instant and the adsorption rate is controlled by
external mass transfer (Quintelas et al., 2013, Nguyen et al., 2015a). The model can be expressed
as:
𝐶𝑡
𝐶0= 𝑒𝑥𝑝 (𝑘𝐴𝐵𝐶0𝑡 − 𝑘𝐴𝐵𝑁0
𝑍
𝑈0) (2-52)
where kBA is the kinetic constant (L/mg min), No is the saturation concentration (mg/L), z is the
bed depth of the fixed bed column (cm) and U0 is the superficial velocity (cm/min) defined as the
ratio of the volumetric flow rate F (cm3/min) to the cross-sectional area of the bed (cm
2).
2.4.5.3.Yoon–Nelson model
This model is based on the assumption that the rate of decrease in the probability of adsorption
for each adsorbate molecule is proportional to the probability of adsorbate adsorption and the
probability of adsorbate breakthrough on the adsorbent (Bhaumik et al., 2013b, Chen et al., 2012,
Setshedi et al., 2014). Yoon–Nelson model for a single component system can be expressed as :
𝐶𝑡
𝐶0−𝐶𝑡= exp (𝑘𝑌𝑁𝑡 − 𝜏𝑘𝑌𝑁) (2-53)
where kYN is the rate constant in (min-1
), τ is the time required for 50% adsorbate breakthrough
(min) and t is the processing time (min).
2.5. Removal of heavy metals from water by adsorption
2.5.1. Activated carbon and low cost activated carbon adsorbents
Activated carbon has been found to be a good adsorbent due to its high capacity of adsorption
because of small particle sizes and active free valences. However activated carbon as an
39
adsorbent for water treatment is restricted due to its high production cost and the regeneration
process that involves costly chemicals (Gupta and Ali, 2013). To decrease the high cost of
activated carbon several studies on using waste materials have been conducted to find a low-cost
adsorbent (Dias et al., 2007) . A variety of low-cost activated carbons from agricultural waste
materials such as coconut shell fibers and coconut shell developed and used for the removal of
heavy metals from synthetic wastewater (Mohan et al., 2005a, Mohan et al., 2006). The use of
biomass waste-derived activated carbon to remove heavy metals has also been explored
(Budinova et al., 2009). Several studies have reported that process parameters such as pH, heavy
metal ion concentration, temperature and sorbent particles size significantly influence the
adsorption process. Since the choice of the sorbent material for any adsorption process is
important and depends on its cost, availability and suitability to remove the given pollutant;
scattered studies have been conducted for the past years to develop adsorbents mainly from
several agricultural and industrial waste materials with the dual aim of low cost as well as
effectiveness for adsorption process optimization (Mthombeni et al., 2015a).
2.5.1.1.Removal of chromium using activated carbon and low cost waste adsorbents
A variety of low-cost activated carbons from agricultural waste materials such as coconut shell
fibers and coconut shell have been developed and used for the removal of heavy metals from
synthetic wastewater (Mohan et al., 2005a, Mohan et al., 2006). Activated carbon obtained from
Pterocladia capillacea, a red marine macroalgae, was tested for its ability to remove toxic
Cr(VI) from aqueous solution (El Nemr et al., 2015). Batch equilibrium tests at different pH
conditions showed that at pH 1.0, a maximum chromium uptake was observed for both
inactivated dried red alga P. capillacea and its activated carbon. The Langmuir maximum
sorption capacities for dried red alga and its activated carbon were 12 and 66 mg/g, respectively.
40
Sugashini and Begum (2015) prepared activated carbon (AC) from carbonized rice husks using
ozone as an activating agent and used for the adsorption of Cr(VI) ions. The Brunauer-Emmett-
Teller surface area of the carbons was increased from 20 to 380 m2/g by the activation. The
authors observed that the silica attached to the carbonaceous material is loosened, leading to a
release of carbon during the ozone activation which exists as both molecular and atomic oxygen
on the surface of carbon. Atomic oxygen oxidizes the carbon surface into acidic functional
groups such as carboxylic, ketonic and phenolic. A maximum removal percentage (94%) of
Cr(VI) ions was obtained for a 100 mg/L aqueous solution at the optimized conditions of pH
value of 2.0, adsorbent dosage of 0.2g, time of 2.5 h and stirring speed of 300 r/min. They found
that the adsorption isotherms fitted well to the Freundlich equation and the adsorption rate
followed pseudo second order kinetics which indicated that the adsorption was spontaneous and
exothermic.
Batch adsorption removal of Cr(VI) ions from aqueous solutions onto black rice husk ash was
investigated by Georgieva et al. (2015) under different conditions. The most favorable
conditions for removing Cr(VI) from aqueous solutions via adsorption onto black rice husk ash
were found to be pH 2, adsorbent dosage level of 15 g/L in the concentration range 25–
200 mg L− 1
, temperature interval 10–30 °C and contact time 30–120 min. The percent of
removal of Cr(VI) ions at initial concentration 50 mg/L at 30 °C was 98.46%. The value of
activation energy (41.57 kJ mol− 1
) is on limit between physical and chemical adsorption, but the
physisorption was the predominant adsorption mechanism for Cr(VI) removal by the adsorbent .
Sulfuric acid modified avocado seed as a low-cost carbonized adsorbent, was investigated by
Bhaumik et al. (2014b) for the removal of toxic Cr(VI) from water/wastewater in batch
41
experiments. A low temperature of 100 °C chemical carbonization treatment was used for the
production of the adsorbent. It was revealed that there was formation of agglomerated and
rodlike structured particles after carbonization of avocado seed and there was the presence of
oxo-functional groups on the surface of sulfuric acid modified avocado seed. Adsorption of
Table 2-1. Comparison of activated carbons and low cost adsorbents sorption capacities of
Cr(VI) ions
Adsorbent Mode of
operation
:Initial
concentration
(mg/L)
Adsorbent
dosage (g/L)
pH Rem
oval
%
Adsorption
capacity
Model used to
calculate
adsorption
capacity (mg/g )
References
Rice husk
activated
carbon
Batch : 100 4 2 94 62.9 mg/g Langmuir (Sugashini and
Begum, 2015)
Black rice
husk ash
Batch : 50 15 2 99 n/a n/a (Georgieva et al.,
2015)
Activated
rice husk
Activated
alumina
Batch :10
Batch: 10
14
14
2
4
93
99
n/a Freudlich (Bishnoi et al.,
2004)
Red marine
macroalgae
(Pterocladia
capillacea)
-based
activated
carbon
Batch : 100
2
12 mg/g
66 mg/g
Langmuir
Langmuir
(El Nemr et al.,
2015)
Green honey
myrtle
(Melaleuca
diosmifolia)
Batch : 250 5 2-10 97-
99
62.5 mg/g Langmuir (Kuppusamy et
al., 2016)
42
Acid–base
modified
activated
carbon
Batch : 5-50
2
n/a n/a 13.9 mg/g Langmuir (Liu et al., 2007)
Activated
carbon from
lagoon seeds
Batch: 100 2 3 n/a 35.2 mg/g Langmuir (Yang et al.,
2015)
Fe-modified
Trapa
natans husk
activated
carbon
Batch: 20 1.5 2-3 95 11.83 mg/g Langmuir (Liu et al., 2010)
Quaternary
amine-
anchored
activated
carbons
AC
AC-T
AC-E
Batch: 10-150 1
5.5
99%
10.57 mg/g
26.247
mg/g
112.36
mg/g
Langmuir (Wang et al.,
2016)
Activated
carbon-Fe
Activated
carbon-Al
Activated
carbon-Mn
Activated
carbon
Batch : 5.7–
63.3
1 5.38 n/a 34.39 mg/g
33.74 mg/g
33.67 mg/g
15.07 mg/g
Langmuir (Sun et al., 2014b)
Powderd
activated
carbon
Batch : 0.05-
0.25
0.25-2.5
0.1 4 99.4 46.9 mg/g Langmuir (Jung et al., 2013)
Activated
carbons
derived from
peanut shells
Batch : 10-100 n/a 4 46.3 16.26 mg/g Langmuir (Al-Othman et al.,
2012)
Algae bloom Batch: 200 1 1 99 155.52 Langmuir (Zhang et al.,
43
residue
derived
activated
carbon
mg/g 2010a)
Activated
carbon-
Jatropha
wood
(JKV2T700)
Peanut
shells
(CHV100T4
00)
Batch: 10-100 0.6 2
97
68
140 mg/g
106 mg/g
Langmuir
(Gueye et al.,
2014)
Acrylonitrile
-
divinylbenze
ne activated
carbon
Batch 1.2 2 n/a 101.2 mg/g Langmuir (Duranoğlu et al.,
2012)
Prawn shell
activated
carbon
Batch: 25-125
1.6 n/a 99 100 mg/g Langmuir (Arulkumar et al.,
2012)
Activated
carbon
derived from
E. crassipes
root biomass
Batch: 10-100
Column : 10
0.2 g
4.5
4.5
77-
92
36.34 mg/g Langmuir (Giri et al., 2012)
n/a= not available
Cr(VI) onto surface of sulfuric acid modified avocado seed was highly pH dependent and found
to be an optimum at pH 2.0. Adsorption isotherm results suggested that the capacity increases
with an increase in temperature. Nonlinear regression analysis revealed that the Freundlich
isotherm model provided a better correlation than the Langmuir isotherm model for Cr(VI)
44
adsorption onto sulfuric acid modified avocado seed. The maximum Cr(VI) adsorption capacity
of 333.33 mg/g was obtained at 25 °C.
Adsorption kinetics was best described by the pseudo-second-order model. The presences of
coexisting ions were found to slightly affect the Cr(VI) removal efficiency of sulfuric acid
modified avocado seed. Experiment with real wastewater sample containing 47.34 mg/L of
Cr(VI) demonstrated that by the use of only 0.03 g/25 mL of sulfuric acid modified avocado
seed, almost 100% removal at pH 2.0 was achieved . Studies on the adsorption of Cr(VI) on
activated carbon and low cost waste derived activated carbon is summaries in Table 2-1.
2.5.1.2.Activated carbon as an adsorbent for manganese
The removal of manganese and other metal ions from industrial wastes using granular activated
carbon which recorded an adsorption capacity of 2 mg/g for manganese ion removal has been
investigated Goher et al. (2015). Modification and impregnation of the activated carbon has also
been used to increase the surface adsorption and removal capacity and to add selectivity to
activated carbon. Granulated activated carbon modified with tannic acid (Üçer et al., 2006), Fe3+
impregnated activated carbon (Mondal et al., 2008) were used for the removal of manganese and
other heavy metals ions.
Budinova et al. (2009) explored the use of biomass waste-derived activated carbon to remove
arsenic and manganese. The maximum sorption capacity for Mn(II) ions was 23.4 mg/g. The
removal of manganese ionic species was observed to increase sharply between pH 2 and 4, and
the maximum uptake was attained and remained constant at pH values above 4. This was
attributed to partial hydrolysis of Mn (II) ions with increasing pH, resulting in the formation of
45
complexes with OH- such as Mn(OH)
+, Mn(OH)2, Mn2(OH)3
+, Mn2OH
3+, Mn(OH)4
2- species in
solution.
Adsorption of manganese (II) ions from aqueous solution using activated carbon derived
Ziziphus a spina-christi seed was investigated by Omri and Benzina (2012). It was suggested that
the adsorption mechanism involved chemical bonding and cation exchange on the surface of the
prepared activated carbon which contains functional groups of oxygen and aromatic compounds.
Table 2-2. Mn adsorption studies using activated carbon
Sorbent Experimental
conditions
Initial
concentration
(mg/L)
Adsorbent
dosage (g/L)
pH Removal
efficiency
(%)
Adsorption
capacity
(mg/g)
Reference
Surfactant
modified
carbon
unmodified
mesoporous
carbon
*cetyltrimeth
yl ammonium
bromide
*sodium
dodecyl
sulfate
Batch
experiments
Temp: 70±℃
Time : 420
min
50
50
50
1
1
1
7
7
7
56.8
82.2
70.5
40
43
47
(Anbia and
Amirmah
moodi,
2011)
Activated
carbon-
granular
Batch
experiments
Temp: Room
Temp
Time: 6hrs
n/a 0.1-.6
0.2-0.9
n/a n/a 2.5 (bin Jusoh
et al.,
2005)
n/a : not available
46
The possible mechanisms of ion exchange considered was that a manganese ion (Mn2+
) attaches
itself to two adjacent hydroxyl groups and two-oxyl groups which donate two pairs of electrons
to metal ions, forming four coordination number compounds and releasing two hydrogen ions
into solution. The Langmuir adsorption capacity was found to be 172.41 mg/g.
Akl et al. (2013) demonstrated the potential use of activated carbons derived from olive stones
for the removal of Fe (Ш) and Mn (П) from aqueous solutions. Further results obtained in some
other studies on use of activated carbon are summarized in Table 2-2.
2.5.1.3.Removal of vanadium using activated carbon and other agricultural water and
byproducts
It has been reported that activation of carbon with ZnCl2 in coir pith generated more interspaces
between carbon layers leading to more microporosity and more surface area compared to the
carbon prepared in the absence of ZnCl2 (Namasivayam and Sangeetha, 2006). The study
reported ZnCl2 activated carbon to be an effective adsorbent for the removal of vanadium (V)
from aqueous solution with successful regeneration of the adsorbent. The Langmuir adsorption
capacity was found to be 24.9 mg/g. Maximum removal of V(V) occurred in the pH range 4.0 to
9.0. Desorption of V(V) was observed to increase with increase in pH due to the increased
concentration of OH−. The authors suggested that the effect of pH and desorption studies show
that ion exchange mechanism is operative in the adsorption process.
Bhatnagar et al. (2008) investigated the potential of metal sludge which as a waste product of
electroplating industry in removing vanadium from water. The adsorption capacity of metal
sludge for vanadium was found 24.8 mg/g at 25 ◦C. The adsorption process was found to be
endothermic and experimental data conform to Langmuir model. The analysis of kinetic data
47
indicated that adsorption system was a pseudo-first-order process and it was controlled by
intraparticle diffusion.
A novel adsorbent grafted tamarind fruit shell bearing –NH3+
Cl− bearing functional moiety was
prepared by Anirudhan and Radhakrishnan (2010). It was synthesized from tamarind fruit shell
(TFS) through graft copolymerization of hydroxyethylmethacrylate onto tamarind fruit shell in
the presence of N,N΄-methylenebisacrylamide as a cross linking agent using K2S2O8/Na2S2O3
initiator system, followed by transmidation and hydrochloric acid treatment. The maximum
adsorption was observed in the pH range 3.0–6.0. The adsorption was found to be dependent on
the initial concentration and exothermic. The adsorption followed the reversible second-order
kinetics and the low Ea (−4.06 kJ/mol) indicated that the process was controlled by diffusion.
Equilibrium adsorption studies demonstrated that the adsorption process obeys Langmuir
isotherm model and the maximum adsorption capacity of V(V) was 45.86 mg/g at 30◦C. The
values of change in entropy (∆S°) and heat of adsorption (∆H°) for V(V) adsorption on
functionalized grafted tamarind fruit shell were estimated as −47.20 J/mol K and −15.64 kJ/mol,
respectively. The values of isosteric heat of adsorption (∆Hi) were found to be constant (~ 26.0
kJ/mol) which indicated that adsorbent used possessed nearly homogeneous surface sites. The
spent adsorbent regenerated with 0.2 M NaOH, achieved up to 96% recovery and can be reused
in four adsorption–desorption cycles without any substantial loss in adsorption capacity.
Hu et al., (2014) evaluated the potential of dried orange juice residue was as an economical and
environmentally benign sorbent through chemical loading with zirconium (IV). The adsorption
properties for vanadium (V) and chromium (VI) from synthetic and actual waste water onto the
Zr(IV)-loaded orange juice residue were investigated. The results indicated that the adsorption of
48
V(V) is strongly dependent on solution pH, and the maximum separation coefficient (βV/Cr = 45)
between V(V) and Cr(VI) takes place at pH 2.85. It was proposed that the anion exchange or
nucleophilic substitution reaction between OH- and metal oxo-anions was the main adsorption
mechanism. The authors indicated that adsorption of V(V) onto the gel can be described well by
Langmuir adsorption and pseudo-second-order rate equations. The maximum adsorption
capacities for V(V) and Cr(VI) were estimated as 51.0943245 and 14.9748768 mg/g,
respectively.
The adsorbed vanadium was effectively desorbed using a diluted NaOH solution. It was reported
that there is a negligible interference of coexisting anions, such as nitrates and chlorides. It was
also demonstrated that the bed was not completely saturated until 320 bed volumes for V(V)
were treated , while for Cr(VI), Ct became equal to C0 only at 30 bed volumes, suggesting that
there was a larger adsorption capacity of Zr(IV)-SOW toward V(V) over Cr(VI).
2.5.2. Low cost byproducts adsorbents and biosorbents for manganese removal
2.5.2.1. Agricultural waste
Industrial byproducts and agricultural wastes locally available in certain regions have been
utilized as low-cost adsorbents. Of the different biological methods, bioaccumulation and
biosorption have been demonstrated to possess good potential to be an alternative replace most
of conventional physicochemical treatment methods for the removal of metals (Khoo and Ting,
2001, Vijayaraghavan and Yun, 2008). Biosorption is the binding of metals to organic mater; it is
a passive metabolic independent process (Acheampong et al., 2010). Biomaterials of microbial
and plant origin interact effectively with heavy metals (Vijayaraghavan and Yun, 2008) and the
sorption on these biomaterials is attributed to their constituents, which are mainly proteins,
carbohydrates and phenolic compounds, since they contain functional groups such as
49
carboxylates, hydroxyls and amines, which are able to attach to the metal ions (Lesmana et al.,
2009). Biosorbent materials that are easily available include three groups: algae, fungi, and
bacteria (Wang and Chen, 2009, Volesky and Holan, 1995).
Adsorption of Mn(II) from water was investigated using a thermally decomposed leaf (Li et al.,
2010). The adsorbent dosage used was 10g/L for a concentration of 100 mg/L. According to their
results the removal percentage of manganese increased from 0-99% for pH values between 1.8 -
3.84, with the adsorption capacity determined at 61–66 mg/g. The adsorption on the thermally
decomposed leaf involved the chemisorption relevant to phosphate, ferrous oxide and carbonate
while physisorption was attributed to carbon black containing abundant microspheres. Maize
husk, which is an abundant agricultural waste was used to prepare a biosorbent (Adeogun et al.,
2013) for Mn(II) ions. In another study green tomato husk modified and unmodified with
formaldehyde was evaluated for simultaneous removal of iron and manganese (García-Mendieta
et al., 2012).
Rice husk is the by-product of the rice milling industry produced in large quantities as a waste.
It consists of crude protein, ash (including silica), lignin, hemicellulose, and cellulose. It is
insoluble in water, has good chemical stability, high mechanical strength and possesses a
granular structure, making it a good adsorbent material suitable for removing heavy metals from
wastewater (Peng and Sun, 2010). It has been suggested that the removal of Mn(II) ions by rice
husk from aqueous solutions using is a chemisorption process, where there is a chemical
reaction between surface OH-groups of SiO2 and Mn (II), releasing H+ ions in the solution
(Tavlieva et al., 2015). Another study using rusk husk in which Langmuir maximum adsorption
50
capacity 8.3 mg/g was reported by Krishnani et al. (2008). The results of rice husks and other
agricultural by-products are shown in Table 2-3.
Table 2-3. Mn uptake using low cost agricultural waste through sorption
Sorbent Mode of
operation
Initial
concentration
(mg/L)
Adsorbent
dosage
(g/L)
pH Removal
efficiency
(%)
Adsorption
capacity
(mg/g)
Reference
Thermally
decomposed
leaf
Batch
experiments
100 10 3.8
4
99 61-66 (Li et al.,
2010)
Maize husk Batch
experiments
100 0.8 2 93.4 9 (Adeogun et
al., 2013)
Rice husk Batch
experiments
100 2.5 7 26.62 12-18 (Tavlieva et
al., 2015)
Rice husk Batch and
column
experiments
200 3 6 Not
available
7.7 (Krishnani
et al., 2008)
Banana peel Batch
experiments
10 4 4 90 3.6 (Ali and
Saeed,
2015)
Green
tomato husk
Batch
experiments
10 0.1 6 84.8 15.2 (García-
Mendieta et
al., 2012)
n/a : not available
Saccharomyces cerevisiae has received increasing attention due to its unique nature and its metal
sorption capacity (Wang and Chen, 2006, Das et al., 2008, Chen and Wang, 2008, Fadel et al.).
Eleven S. cerevisiae yeast strains in alive and dead forms were screened and studied by (Fadel et
al.) for biosorption and bioaccumulation of manganese from synthetic aqueous solution. It was
found that S. cerevisiae F-25 in alive form was the most effective biosorbent for Mn(II) and
biosorbed 22.5 mg/g yeast biomass. The results revealed that environmental conditions that
51
favored maximum Mn(II) concentrations biosorption by S. cerevisiae F-25 in alive form were
4.8 mg/l after 30 min at pH 7, agitation 150 rpm and yeast biomass concentration 0.1 gm/l at
30℃.
Hasan et al. (2012) studied the biosorption isotherms of Bacillus sp. and sewage activated sludge
in laboratory-scale experiments using the isotherm models developed by Langmuir, Freundlich,
Dubinin-Radushkevich, Temkin, and Redlich-Peterson (R-P). It was revealed that Bacillus sp. is
a more effective biosorbent than sewerage activated sludge. Biosorption of manganese by
Bacillus sp. was found to be significantly better fitted to the Langmuir-1 isotherm than the other
isotherms, while the Dubinin-Radushkevich isotherm was the best fit for sewage activated
sludge. The Langmuir maximum biosorption capacities of manganese onto Bacillus sp. and
sewage activated sludge were 43.5 mg Mn2+
/g biomass and12.7 mg Mn2+
/g biomass,
respectively. The D-R isotherm showed that the biosorption processes by both Bacillus sp. and
sewage activated sludge occurred via the chemical ion-exchange mechanism between the
functional groups and Mn2+
ion.
Mn(II) ions biosorption was studied by Kamarudzaman et al. (2015) using Pleurotus spent
mushroom compost performed in a fixed-bed column. Mn(II) ions biosorption in fixed-bed
column achieved better performance at a flow rate of 1 mL/min, bed height of 30 cm and initial
Mn(II) concentration of 10 mg/L. The breakthrough time and exhaustion time were obtained at
16.5 hours and 26.7 hours, respectively.
The ability of in vitro roots cultures of Typha latifolia and Scirpus americanus to remove Mn and
other metal ions was studied by Santos-Díaz and Barrón-Cruz (2011). T. latifolia roots were able
to uptake 1.680 mg /g, while the S. americanus roots removed 4.037 mg/g. The potential of
52
macrophyte Spirodela polyrhiza (L.) Schleiden from the phoomdi biomass of Loktak lake, India
as an adsorbent to remove Mn(II) and other metal ions from metal solution system was
investigated by Meitei and Prasad (2014). Metal adsorption was fast and equilibrium was
attained within 120 min. Langmuir isotherm best described the equilibrium with maximum
adsorption capacities of 35.7 mg/g for Mn (II) ions. Adsorption of the specific metal ions from
binary and ternary metal solution system showed antagonistic nature due to screening effect and
competition between the metal ions for active sites on the biomass.
Hexavalent chromium biosorption by raw and acid-treated Oedogonium hatei from aqueous
solutions has been studied by Gupta and Rastogi (2009). The nature of biomass–metal ions
interactions showed that the participation of −COOH, −OH and −NH2 groups in the biosorption
process. Biosorbents could be regenerated using 0.1 M NaOH solution, with up to 75% recovery.
A novel biosorbent Caryota urens inflorescence waste biomass (CUIWB) was used for the
removal of hexavalent chromium from aqueous solutions. Investigations carried out proved that
CUIWB is a good potential biosorbent for the treatment of hexavalent chromium in aqueous
media with a Langmuir capacity of 100 mg/g (Rangabhashiyam and Selvaraju, 2015).
The use of dried and re-hydrated biomass of the seagrass Posidonia oceanica was investigated
by (Pennesi et al., 2013) as an alternative and low cost biomaterial for removal of vanadium(III)
from wastewaters. For the single-metal solutions, the optimum was at pH 3, where a significant
proportion of vanadium was removed (ca. 70%)
Kaczala et al. (2009) investigated batch sorption with untreated Pinus sylvestris sawdust after
settling/sedimentation phase to remove vanadium and lead from a real industrial wastewater. It
53
was found that when the initial pH was reduced from 7.4 to 4.0, the sorption efficiency increased
from 43% to 95% for V.
2.5.2.2.Removal of heavy metals using chitosan
Chitosan occurs naturally and as a polysaccharide which is found in insects, arthropods and
crustaceans, fungal biomass and algae by deacetylation of chitin, and has an ability to
effectively remove heavy metal ability due to its amino groups and hydroxy groups (Lu et al.,
2001, Padilla-Rodríguez et al., 2015). It is inexpensive, abundant, biodegradable, and widely
available from sea food-processing wastes (Bailey et al., 1999, de Castro Dantas et al., 2001, Pal
and Banat, 2014, Jung et al., 2013).
Abdeen et al. (2015) in their study explored the ability of polyvinyl alcohol/chitosan binary dry
blend as an adsorbent for removal of Mn(II) ion from aqueous solution. The chitosan material
was made from shell from shrimps collected from sea food shops. It was found that the
biosorption capacity of the polyvinyl alcohol/chitosan adsorbent increases from 0.596 to 9.22
mg/g with increasing metal ion concentrations from 5 to 100 mg/L. They also concluded from
their study that the optimum pH for biosorption of Mn (II) ion on polyvinyl alcohol/chitosan was
5.0. In a study done by Al-Wakeel et al. (2015) the adsorption capacity of glycine onto modified
chitosan resin was found to be 71.4mg/g at pH 6. Reiad et al. (2012) investigated the adsorption
of manganese using microporous chitosan/polyethylene glycol blend membrane which showed
adsorption capacity of up to 18 mg/g for manganese ions at pH 5.9.
(Robinson-Lora and Brennan, 2010) evaluated the role of chitin derived from processed crab
shells and its associated proteins in the removal of manganese from mine impacted waters under
abiotic and anoxic conditions using sorption processes. According to their results the maximum
54
sorption capacity was found to depend greatly on the pH of the solution, with very minimal or no
adsorption observed at pH less than 5. At pH 5.4 value the adsorption capacity was 0.165 mg/g
and 0.981 mg/g at pH 8.7.
Crab shells were found to possess better uptake capacities of 69.9 mg/g for Mn(II). The removal
of Mn(II) by crab shell was found to be pH dependent, with optimum sorption occurring at pH 6.
The mechanism of metal removal by crab shell was identified as micro-precipitation of metal
carbonates followed by adsorption onto chitin at the surface of crab shell (Vijayaraghavan et al.,
2011).
Masukume et al. (2014) found that the adsorption of Mn sea shells as adsorbents from acid mine
water is very low. The removal efficiencies of manganese ranged from 12.1% (sorbent mass, 0.1
g/50mL) to 54.4% (sorbent mass, 1 g/50 mL).
Ananpattarachai and Kajitvichyanukul (2016) prepared a novel bio-catalyst by addition of
titanium dioxide in the mixture solution of chitosan and xylan in acetic acid and evaluated
photocatalytic reduction of Cr(VI)in aqueous solution under ultraviolet irradiation. From the
rough surface modification from xylan addition and the chelating ability of chitosan, the
enhancement of Cr(VI) removal by adsorption and photocatalysis of this bio-catalyst was
pronounced. The photocatalytic reduction rate of Cr(VI) was 0.56 × 10-3
and 0.03 × 10-3
ppm-
min for the titania bio-catalyst and titania nanopowder, respectively. Significant dependence of
Cr(VI) removal on the titania and chitosan loading could be explained in terms of the
relationship between kinetics of Cr(VI) photocatalytic reactions and the loading amount of each
chemical. The authors suggested that according to the Langmuir–Hinshelwood model, the
55
photocatalytic rate constant of surface reaction of Cr(VI) was increased with an increasing of
chemical loading in bio-catalyst.
A carbonaceous chitosan adsorbent with good stability in acid solution the amine groups were
retained completely after simple and green hydrothermal carbonization treatment and was used
effectively to remove Cr(VI) (Shen et al., 2016). Under optimal conditions, the adsorption
capacity of the chitosan for Cr(VI)reached as high as 388.60 mg/g and the prepared chitosan
adsorbent could be reused at least five times with adsorption efficiency more than 92%.
Zhang et al. (2014) prepared and investigated an inorganic-biopolymer composite based on
chitosan-zirconium (IV) as a biosorbent for the removal of vanadium (V) ions from aqueous
solution. Batch experiments were performed to evaluate various relevant parameters affecting the
adsorption capacity such as pH, initial concentration, and contact time, temperature and co-
existing ions. The authors reported that the optimum pH was 4.0 and the equilibrium was
achieved after 4 h for V(V) adsorption. The Langmuir isotherm model described well the
adsorption of V(V), with the maximum adsorption capacity of 208 mg/g at 30℃. The kinetics
data fitted well to pseudo-second-order equation, indicating that chemical sorption as the rate-
limiting step of adsorption mechanism. Co-existing ions such as nitrate, chloride and sulfate had
a certain effect on the uptake of V(V). It was reported that the V(V) loaded chitosan-zirconium
(IV) composite could be regenerated by 0.01 mol/L sodium hydroxide, with efficiency greater
than 95%.
Liu and Zhang (2015) prepared a new chitosan bead modified with titanium ions was prepared
and studied the adsorption of vanadium ions from aqueous solutions using the prepared chitosan
bead. Batch adsorption experiments revealed a maximum of adsorption capacity of 124.5 mg/g at
56
pH 4. It was suggested that at pH < 4, there were more H+
ions enough to restrict the free
movement of V(V)ions toward the adsorption sites. At > pH 4, there was some competition in
the adsorption process of V(V)ions with the adsorbent. The batch experiments also revealed that
the adsorption capacity of V(V)immobilized on the adsorbent was enhanced with the increase of
initial vanadium concentration and reached a maximum value (207 mg/g). . t The adsorption of
vanadium followed the pseudo second-order kinetic and the Langmuir isotherm model, with a
maximum adsorption capacity of 210 mg/g. The thermodynamic parameters revealed that the
nature of adsorption was feasible, spontaneous (∆G° < 0) and endothermic process (∆H° > 0). It
suggested that the mechanisms of adsorption were possibly attributed to electrostatic attraction.
Padilla-Rodríguez et al. (2015) synthesized protonated chitosan flakes (PCF) and evaluated them
for the effective removal of vanadium (III, IV, and V) oxyanions from aqueous solutions. The
authors showed that the adsorption capacity of vanadium increased when using protonated
chitosan flakes instead of chitosan beads. 99-100% vanadium oxyanions removal was achieved
during the first 2 hrs. of contact when using 5 g/L protonated chitosan flakes adsorbent and the
initial vanadium concentration of 0.500 mg/L, while chitosan beads only removed 4-18%.
Vanadium (III, IV and V) adsorption described the Langmuir's model. Even when common
anions were added, such as chloride, carbonate, sulfate and phosphate vanadium ions were still
removed effectively.(Nakajima, 2002) studied the aspects and the mechanism of vanadium
adsorption by the persimmon tannin gel using the ESR spectroscopy. The persimmon tannin gel
can adsorb vanadium effectively from aqueous solutions containing VOCl2 and NH4VO3,
respectively. The adsorption of vanadium from the VOCl2 solution was at maximum at around
pH 5–6, while that from the NH4VO3 solution, and was maximum at pH 3.75 .
57
Biosorbents have a shorter life span compared with conventional sorbents and have low
adsorption capacities (Gadd, 2008, Alfarra et al., 2014) Furthermore the separation of
biosorbents would be difficult after adsorption (Fu and Wang, 2011).
2.5.3. Clay and zeolite based adsorbents
2.5.3.1.Chromium removal using clays and zeolite based adsorbents
Clays are hydrous aluminosilicates and may be composed of mixtures of fine grained clay
minerals and clay-sized crystals of other minerals such as quartz, carbonate and metal oxides.
Clays play an important role in the environment by acting as a natural scavenger of pollutants by
taking up cations and anions either through ion exchange or adsorption or both. Thus, clays
invariably contain exchangeable cations and anions held to the surface (Bhattacharyya and
Gupta, 2008). Bentonite aluminium phyllosilicate (clay) mainly consists of montmorillonite
(smectite group) and is chosen because of its favorable swelling capacities, its high cation
adsorption capacities and its strong mechanical stability (Norrfors et al., 2016, Malamis and
Katsou, 2013). The crystal structure of montmorillonite is composed of units made up of two
layers of silica tetrahedral with a central layer of alumina octahedron sheet between them. The
tetrahedral and octahedral sheets combine in such a way that the tips of the tetrahedral of each
silica sheet and one of the hydroxyl layers of the octahedral sheet form a common layer. The
atoms in this layer, which are common to both sheets, become oxygen instead of hydroxyl. It is
thus referred to as a three-layered clay mineral with T–O–T layers making up the structural unit
(Bhattacharyya and Gupta, 2008). The structure of montmorillonite is presented in Figure 2-4
58
Figure 2-4 . The structure of montmorillonite (Bhattacharyya and Gupta, 2008)
Montmorillonite, due to its large surface area and high cation exchange capacity, has been
studied for potential applications as an environmental remediation agent to remove Cr(VI) (Hu
and Luo, 2010).
Montmorillonite modified with aluminum hydroxypolycation and cetyltrimethylammonium
bromide was studied by (Hu and Luo, 2010). The authors found that aluminum
hydroxypolycation and cetyltrimethylammonium bromide had either entered the interlayer or
sorbed on the external surface of the clay. Different intercalation orders resulted in different
structures. The experimental data revealed that if aluminum hydroxypolycation was intercalated
before cetyltrimethylammonium bromide, the adsorption capacity was better than that of
intercalated simultaneously or cetyltrimethylammonium bromide pre-intercalated. The pH of the
solution and environmental temperature had significant influences on the adsorption of Cr(VI).
The optimal pH for the removal was about 4, and the temperature of 25℃ was best suitable.
They found that all adsorption processes were rapid during the first 5 min and reached
59
equilibrium in 20 min. The adsorption kinetics could be described quite well by pseudo-second-
order model. The adsorption rates of 3.814 mg/g/min and the adsorption capacities11.97 mg/g
was achieved. The adsorption of modified montmorillonite was suggested to be mainly induced
by the surface charge and the complexation reaction between cetyltrimethylammonium bromide
and hexavalent chromium species at the edge of the clay particle.
Yuan et al. (2009) prepared montmorillonite-supported magnetite nanoparticles by co-
precipitation and hydrosol method and to investigate the removal mechanism of hexavalent
chromium by synthesized magnetite nanoparticles. The montmorillonite supported magnetite
nanoparticles were found to exist on the surface or inside the antiparticle pores of clays, with
better dispersing and less coaggregation than the ones without montmorillonite support. It was
revealed that the Cr(VI) uptake was mainly governed by a physico-chemical process, which
included an electrostatic attraction followed by a redox process in which Cr(VI) was reduced into
trivalent chromium. The adsorption of Cr(VI) was found to be highly pH-dependent and the
kinetics of the adsorption followed the pseudo-second-order model. The adsorption data of
unsupported and clay-supported magnetite nanoparticles fitted well with the Langmuir and
Freundlich isotherm equations. The montmorillonite-supported magnetite nanoparticles showed
a much better adsorption capacity per unit mass of magnetite (15.3 mg/g) than unsupported
magnetite (10.6 mg/g), and were more thermally stable than their unsupported counterparts.
Santhana Krishna Kumar et al. (2012) reported the detailed study on the adsorption of hexavalent
chromium using dodecylamine intercalated sodium montmorillonite as a potential adsorbent. The
adsorption involved primarily the electrostatic interaction of hydrogentetraoxochromate (VI)
60
anion with the protonated dodecylamine and the surface hydroxyl groups of the clay material.
The pseudo second order kinetic model fitted the experimental data and an adsorption capacity of
23.69 mg/g was obtained from the Langmuir isotherm model. Adsorption process was found to
be exothermic and favorably spontaneous.
Attapulgite clay-carbon nanocomposite is an exceptionally promising candidate as a low-cost,
sustainable, and effective adsorbent for the removal of toxic ions from water. Attapulgite clay,
which is a magnesium aluminum silicate that is abundant in nature, and glucose, which is a green
chemical obtained from biomass are cheap and ecofriendly materials. Compared to carbon-based
materials, the nanocomposite exhibited high adsorption ability for Cr(VI) ions with maximum
adsorption capacities of 177.74 mg/g. (Chen et al., 2011a).
Zeolites are defined as hydrated aluminosilicate minerals with crystalline microporous structure.
The structure consists of three-dimensional frameworks of SiO4 and AlO4 tetrahedral that are
linked to each other through oxygen atoms (Figure 2-5) (Broach et al., 2000). Their intricate
structural arrangement is also responsible for the high specific surface area (>700 m2/g). The
isomorphic framework substitution of tetravalent silicon by trivalent aluminum in the mineral’s
structure creates a net negative charge that is counterbalanced by the presence of cations (usually
Ca2+,
Na+ and K
+) which are situated in cavities. These cations species promotes the ion
exchange capabilities with other cations including heavy metals. The cavities are occupied by
large ions and water molecules which are able to move, allowing ion exchange necessary to
balance the negative charge in the framework (Malamis and Katsou, 2013, Broach et al., 2000,
Figueiredo and Quintelas, 2014).
61
Figure 2-5. Structure of zeolite (2014)
Natural zeolites have advantages over other cation exchange materials such as commonly used
organic resins, because they are cheap, they exhibit excellent selectivity for different cations at
low temperatures, which is accompanied with a release of non-toxic exchangeable cations
(K+,Na
+, Ca
2+ and Mg
2+) to the environment, they are compact in size and they allow simple and
cheap maintenance in the full-scale applications. Clinoptilolite is one of the most investigated
zeolite in basic and applied research. In the structure of clinoptilolite, there are three types of
channels, of which two are parallel, and made of ten and eight-membered rings of Si/AlO4,
while one, defined by eight-membered rings, is vertical Figure 2-6 (Margeta et al., 2013).
Leyva-Ramos et al. (2008) prepared a surface modified zeolite by adsorbing the cationic
surfactant hexadecyltrimethylammonium (HDTMA) bromide on the external surface of the
zeolite. It was found that the surface area and pore volume were reduced due to pore blocking
caused by the surfactant molecules adsorbed on the zeolite. The Cr(VI) was adsorbed
considerably on surface modified zeolite but not on the natural zeolite. The Cr(VI) adsorption
capacity of surface modified zeolite showed a maximum at pH 6 and diminished 18 and 2.7
times with an increase in pH from 6 to 10 and decreasing pH from 6 to 4, respectively. The
62
adsorption capacity reduced when temperature from was increased 15 to 25 ◦C since the
adsorption of Cr(VI) on surface modified zeolite was due to an exothermic reaction. Desorption
studies showed that Cr(VI) was irreversibly adsorbed on surface modified corroborating that
Cr(VI) was chemisorbed on the surface modified zeolite.
Figure 2-6. Binding of building units (PBU and SBU) in three-dimensional zeolite- clinoptilolite
structure (Margeta et al., 2013).
Salgado-Gómez et al. (2014) investigated the zeolite-rich tuff from the state of Chihuahua, which
was conditioned sodium chloride solution and modified with a hexadecyltrimethylammonium
bromide solution. Then was used to evaluate the removal of Cr(VI) from aqueous solution. The
removal efficiency of chromium ions from aqueous solution was found to be influenced by pH
and the specific chromium species involved plays involved in the adsorption process. A number
of important parameters can influence the sorption process of Cr(VI)ions on natural zeolite such
as: conductivity, pH, temperature of treated water, ionic strength, initial concentration of
chromium in solution, zeolite mass, and zeolite particle size. Table 2-4 presents the achievements
of clinoptilolite in the adsorption of Cr(VI).
63
Table 2-4. Clinoptilolite as an adsorbent for removal of Cr(VI)ions
Modification Initial
concentratio
n (mg/L)
Adsorbent
dosage (g/L)
pH Adsorption
capacity
References
Surface modification
by Fe(II)
2.5- 350 200 4-
11
0.3 mg/g (Lv et al., 2014a)
Bacteria loaded
clinoptilolite
10-300 10 5-6 n/a (Erdoğan and Ülkü,
2012)
Hexadecyltrimethylam
monium bromide
32-225 25 5 3.55 mg/g (Zeng et al., 2010)
Hexadecyltrimethylam
monium bromide
1-740 12.5 3 13.622 mg/g (Bajda and Kłapyta,
2013)
Fe3O4-polypyrrole 300-1100 2 2 344.83 mg/g (Mthombeni et al.,
2015b)
n/a = not available
2.5.3.2. Manganese removal by clays and zeolite based adsorbents
Natural materials clays and zeolites have been explored for treating wastewater contaminated
with metals due to their metal-binding capacity (Acheampong et al., 2010) , low cost and
abundance. Dimirkou and Doula (2008) studied the removal of Mn 2+
in drinking water using
clinoptilolite (natural zeolite).
64
Table 2-5. Maximum sorption capacities of Mn ions on clay mineral
Clay Sorbent Initial
concentration
(mg/L)
Adsorbent
dosage (g/L)
pH Adsorption
capacity
Reference
Greece
Clinoptilolite
Fe- Clinoptilolite
0.2 -1000 1
1
7.53
8.14
7.69
27.1 mg/g
(Doula, 2006,
Dimirkou and
Doula, 2008)
Clinoptilolite
Vermiculite
0.5 40 7.1±
0.2
N/a (70 % Mn
removal )
N/a (80 % Mn
removal )
(Inglezakis et
al., 2010)
Serbian natural
zeolite
400 10 5.5 10 mg/g (Rajic et al.,
2009)
Brazilian
vermiculite
550 2.5 6.4 31.53 mg/g (da Fonseca et
al., 2006)
Kaolite N/a 10 N/a 0.446 mg/g (Yavuz et al.,
2003)
Synthetic zeolite
made from fly ash
10 100 2-12 N/a (Belviso et al.,
2014)
Natural zeolite
Al-Natural zeolite
NH4-Natural
zeolite
25-250 N/a 6
6
6
7.69 mg/g
25.12 mg/g
24.33 mg/g
(Ates, 2014)
Turkey natural
zeolite
20 37 2.5
3.5 4.5
0.37 mg/g
0.52 mg/g
0.52 mg/g
(Motsi et al.,
2009)
Natural zeolite –
Slovakia
14.4 40 7 0.076 mg/g (Shavandi et
al., 2012)
Brazilian natural
scolecite
50 16.7 6 2.1 mg/g (Dal Bosco et
al., 2005)
65
Chilean zeolites
*natural zeolite
*activated zeolite-
NaOH
Na2CO3
NH4Cl
NaCl
100 2.5 6-6.8
0.256 meq/g
0.761 meq/g
0.718 meq/g
0.675 meq/g
0.774 meq/g
(Taffarel and
Rubio, 2010)
Mexican natural
zeolite
Na modified
natural zeolite
10 10 6 138.8 meq/kg
232.55 meq/kg
(García-
Mendieta et al.,
2009)
Nigerian kaoline
clay
100-500 1 6 111.11 mg/g (Dawodu and
Akpomie,
2014)
Magnesium
enriched kaolinite-
bentonite ceramics
0.53-530 108 3 N/a (80% Mn
removal)
(Cvetković et
al., 2010)
Fly ash 1.5 20 3-8.5 N/a (74.2 % Mn
removal)
(Sharma et al.,
2007)
Coal 0.05-100 4 6.85-
7.35
N/a (63 % Mn
removal
(Vistuba et al.,
2013)
Lignite 100 6 6 3.4 & 18.7 mg/g (Mohan and
Chander, 2006)
Surfactant
modified
alumina
10-70 20 6 1.31 mg/g (Khobragade
and Pal, 2016)
n/a : not available
According to them clinoptilolite modified with Fe adsorbs more manganese (27.1 mg/g) than
unmodified clinoptilolite (7.69 mg/g,). Doula (2006) noted that high manganese adsorption
66
capacity of clinoptilolite modified with Fe was due to Fe-clusters located on the surface and to
high surface negative charge. The removal of metals by zeolite is known to be a complex process
which involves ion exchange and adsorption (Perić et al., 2004). (Yavuz et al., 2003), explored
the removal of manganese from aqueous solution by adsorption on natural kaolinite. The
Langmuir adsorption capacity for Mn was found to be 0.446 mg/g. Several studies have been
conducted on metal uptake including manganese using clays and minerals and the results are
shown in Table 2-5.
2.5.3.3.Vanadium removal by clay based absorbents
Layered double hydroxides have recently attracted significant attention for their potential
applications such as ion exchangers and adsorbents (Li and Duan, 2006, Song et al., 2013). The
adsorption of vanadium ions onto Mg/Al layered double hydroxides has been reported (Song et
al., 2013, Wang et al., 2012). Mg/Al layered double hydroxides were prepared by co-
precipitation and the calcination of the substance and transforms into mixed oxides. Song et al.,
2013 suggested that the small particle size which was reported to range from nano to microns
provide good adsoprtion capacity for vanadate. The study results proved that vanadate can be
removed at optimum experimental conditions with a removal rate of 99.9% for 100 mg/L. Wang
et al. 2014 reported that the maximum adsorption was achieved at pH 2.5. They suggested that
because vanadium (V) in aqueous solution exists predominately as H3V2O7− in the pH range of
2.5–7.0. At pH 2.0, vanadium in the (+5) oxidation state occurs as the VO2+
cation, and lower
adsorption capacity is due to anion preferable of calcined hydrotalcite for anion vacancy in the
layer after calcination. They also noted that the decrease in adsorption capacity at higher pH
values was due to competition between OH− and vanadium anions for available surface sites of
adsorbents.
67
Manohar et al. (2005) investigated the possibility of using natural bentonite clay as a precursor to
produce aluminum-pillared clay for the removal of vanadium (IV) from aqueous solutions. They
performed batch experiments as a function of solution pH, contact time, vanadium (IV)
concentration, adsorbent dose, ionic strength, and temperature. The authors reported that the
maximum adsorption capacity was observed in the pH range 4.5-6.0. The maximum vanadium
removal of 99.8 and 88.5% was reported to occur at pH 5.0 from an initial concentration of 5 and
10 mg/L, respectively. It was concluded that the intraparticular diffusion of metal that occurs
through pores in the clay is the main rate-limiting step. The temperature dependence indicated
that the nature of adsorption was endothermic. It was found that the percentage removal of
vanadium (IV) decreased with increasing ionic strength. The desorption data showed that the
spent PILC can be regenerated for further use by 0.1 M HCl.
2.6.Nanotechnology
Recent advances in nanotechnology offer opportunities to develop next-generation water supply
systems. The unique properties of nanomaterials such as high surface area, photosensitivity,
catalytic and antimicrobial activity, electrochemical, optical, and magnetic properties, and
tunable pore size and surface chemistry, provide useful features for many applications.
Nanomaterials can be applied as sensors for water quality monitoring, specialty adsorbents, solar
disinfection/decontamination, and high performance membranes. the modular, multifunctional
and high-efficient processes enabled by nanotechnology also provide a high performance to
aging infrastructure and to develop high performance, low maintenance decentralized treatment
systems including point-of-use devices (Qu et al., 2013b).
Nanomaterials are typically defined as materials smaller than 100 nm in at least one dimension.
At this scale the physical and chemical properties of nanomaterials can become very different
68
from those of the same material in larger bulk form, many of which have been explored for
applications in water and wastewater treatment. Some of these applications utilize the smoothly
scalable size-dependent properties of nanomaterials which relate to the high specific surface
area, such as fast dissolution, high reactivity, and strong sorption and other discontinuous
properties, such as superparamagnetism, localized surface plasmon resonance, and quantum
confinement effect (Qu et al., 2013a).
2.6.1. Polymer based nanoadsorbents
2.6.1.1.Removal of chromium with polymer based nanoadsorbents
Various polymers have been used as adsorbents in the removal of Cr(VI) from wastewaters.
Conducting polymers such as polythiophene (PTh), polypyrrole (PPy), polyaniline (PANI), and
their derivatives stand out as the most promising members of the conjugated polymer family
because of their unique electrical behavior, excellent environmental and thermal stability, low-
cost synthesis, and mechanical strength (Jaymand, 2014, Wang et al., 2010, Hatamzadeh et al.,
2014, Wu and Lin, 2006). Polypyrrole find applications in various fields such as
microelectronics, composite materials, optics and biosensors as adsorbent (Karthikeyan et al.,
2009). Polypyrrole and its derivatives have attracted a great deal of attention in its application
for water treatment. The ion-exchange capacities depends on the polymerization conditions , the
type and size of the counter ions incorporated during the polymerization process as well as on
the ions present in the electrolyte solution, the polymer thickness and the ageing of the polymer
(Weidlich et al., 2001). Polymer matrix based nanocomposites has emerged initially with
interesting observations which involves exfoliated clay and more recently studies with carbon
nanotubes, carbon nanofibers, exfoliated graphite (graphene), nanocrystalline metals, nanoscale
inorganic fillers or fiber modifications have also emerged (Paul and Robeson, 2008).
69
(Bhaumik et al., 2011d) prepared a Fe3O4 coated polypyrrole (PPy) magnetic nanocomposite via
in situ polymerization of pyrrole monomer for the removal of highly toxic Cr(VI). Up to 100%
adsorption removal was found with 200 mg/L Cr(VI) aqueous solution at pH 2. They suggested
that that ion exchange and reduction on the surface of the nanocomposite may be the possible
mechanism for Cr(VI) removal by the PPy/Fe3O4 nanocomposite according to the XPS studies.
Adsorption results showed that Cr(VI) removal efficiency by the nanocomposite decreased with
an increase in pH. Adsorption kinetics data was found to be best described by the pseudo-
second-order rate model while isotherm data fitted well to the Langmuir isotherm model. The
authors concluded that thermodynamic study revealed that the adsorption process is endothermic
and spontaneous in nature. They also indicated that in spite of the very poor recovery of the
adsorbed Cr(VI); the regenerated adsorbent can be reused successfully for only two successive
adsorption–desorption cycles without appreciable loss of its original capacity. The authors’ also
demonstrated the effectiveness of the nanocomposite in a fixed bed column using environmental
water. They processing 5.04 L water with initial 76.59 mg/L Cr(VI) concentration using only 2 g
of adsorbent mass to below acceptable level (Bhaumik et al., 2013b). Removals of Cr(VI) by
polymer based adsorbents are summarized in the Table 2-6 below.
70
Table 2-6. Adsorption capacities of Cr(VI) ions on polymer based nanoadsorbents
Adsorbent Synthesis:
oxidant
Ci Cr(VI)
(mg/L)
Adsorbent
dosage
(g/L)
pH Temperature :
Adsorption
capacity or
removal
percentage
Model used
to calculate
adsorption
capacity
References
PPy/Fe3O4
nanocomposite
FeCl3 200 2 2 25℃ :169.49
mg/g
Langmuir (Bhaumik et
al., 2011d)
Glycine doped
polypyrrole
Ammonium
peroxydisulfate
200-500 2 2 25℃ :217.39
mg/g
Langmuir (Ballav et al.,
2012)
Polypyrrole-
coated halloysite
nanotube clay
nanocomposite:
FeCl3 100-500 1.5 2 25℃ :149.25
mg/g
Langmuir (Ballav et al.,
2014a)
Fe3O4 coated
glycine doped
polypyrrole
magnetic
nanocomposites
Ammonium
peroxydisulfate
200-650 2 2 25℃ : 238
mg/g
Langmuir (Ballav et al.,
2014b)
Polyacrylonitrile/
polypyrrole
core/shell
nanofiber mat
FeCl3 100-200 3.33 2 25℃ : 61.8
mg/g
Langmuir (Wang et al.,
2013)
Polypyrrole-
polyaniline.
nanofibers
FeCl3 100-400 1 2 25℃ : 227.27
mg/g
Langmuir (Bhaumik et
al., 2012)
Graphene oxide-
alpha
cyclodextrin-
polypyrrole
nanocomposites
FeCl3 100-700 0.5 2 25℃ : 606.06
mg/g
Langmuir (Chauke et
al., 2015)
71
Polypyrrole
coated secondary
fly ash–iron
composites
FeCl3 25-100 5 2 25℃ : 66.93
mg/g
Langmuir (Zhou et al.,
2016)
Polypyrrole–
titanium(IV)
phosphate
nanocomposite
FeCl3 200-1000 10 2-7 30℃ : 31.64
mg/g
Langmuir (Baig et al.,
2015)
Polyaniline/poly
ethylene glycol
Poly ethylene
glycol
50 1 5 Room
tempertaure :
68.97 mg/g
Langmuir (Samani et al.,
2010)
Polyaniline/silica
gel
Ammonium
peroxydisulfate
50-125 2 4.2 30℃ : 31.64
mg/g
Langmuir (Karthik and
Meenakshi,
2014)
Polyaniline–
magnetic
mesoporous silica
composite
Ammonium
persulfate
100-500 0.8 2 25℃ : 193.85
mg/g
Langmuir (Tang et al.,
2014)
n/a= not available
2.6.1.2.Removal of manganese using polymer based nanoadsorbents
Other adsorbents such as poly(sodium acrylate)–graphene oxide double network hydrogel
adsorbent have been prepared and studied for the removal of Mn2+
. The maximum Langmuir
adsorption capacity for Mn2+
was found to be 165.5 mg/g at pH 6 and a temperature of 303 K.
The adsorbent indicated good reusability performance (Xu et al., 2015).
Idris, (2015) studied functionalized mesoporous silica employed as adsorbent for Mn(II) from
aqueous solutions. The surface area of functionalized mesoporous silica and diethylenetriamine
72
functionalized were found to 760 and 318 m2 /g (N2 adsorption). Mn(II) adsorption was found to
be pH dependent and best results were obtained at pH 6.5–7. The adsorption onto the
diethylenetriamine functionalized- mesoporous silica followed the pseudo-second-order kinetic
model. The equilibrium data fitted well to the Langmuir isotherm model and the maximum
adsorption capacity of Mn(II) was found to be 88.9 mg/g. The authors concluded that the
adsorption occurred on a homogeneous surface by monolayer sorption without interaction
between the adsorbed ions.
The adsorption of manganese ions from aqueous solution by polyaniline/sawdust nanocomposite
was experimentally investigated by Qomi et al. (2014). The experiments evaluated the effect of
various experimental parameters i.e., pH, adsorbent dosage and contact time on the removal
efficiency. The results showed that optimum conditions for manganese removal are at pH 10,
adsorbent dosage of 10 g/L and equilibrium contact time of 30 min. The adsorption capacity for
manganese ions was obtained to be 58.824 mg/g.
Moawed et al. (2013) modified the polyurethane foam with polyhydroxyl and used it as a new
sorbent for separation of manganese and iron ions in natural samples. The maximum sorption of
Mn(II) was found in the pH range of 6–8. The kinetics of sorption of the Mn(II was found to be
fast with an average value of half-life of 11.7 min. The sorption capacity of polyhydroxyl
polyurethane foam was 8.7 µmol/g.
Khan et al. (2014) performed comparative sorption study of dissolved manganese and cobalt ions
onto alginate beads and thermally activated nano-carbon beads. Optimum metal uptake was
observed at pH 8 with about 80–92% metal ions adsorbed within 4 h, followed by a slower
73
adsorption stage. It was revealed that increases in initial Mn(II) concentration from 52.28 to
891.5 mg/L increased sorption on alginate beads and activated nano-carbon beads from 18.07 to
63.7 mg/g and 23 to 78 mg/g, respectively.
Islam et al. (2015) prepared porous phosphine-functionalized electrospun poly(vinyl
alcohol)/silica composite nanofiber using a facile electrospinning technique. The composite
nanofibers with 0.5 g/L fiber loading was found to be able to remove almost completely 96% the
Mn2+
+
ions from aqueous solutions with an initial concentration of 120 mg/L, at pH 6 within 15
min of contact time. A reasonably simple acid treatment with a 1 M HCl solution was found to
be extremely effective in regenerating the nanofiber adsorbent, and 92.5% of the metal ions were
removed from the adsorbent even in the fifth regeneration/reuse cycle. The adsorption
equilibrium studies revealed the excellent adsorption capacity of the nanofiber for Mn2+
ions to
be 234.7 mg/g.
2.6.2. Magnetic nanoadsorbents
After adsorption, it can be difficult to separate powder adsorbents from the aqueous solution
using conventional separation methods such as filtration and sedimentation, because adsorbents
may block filters or be lost. Separation technologies utilizing magnetic adsorbents as an
alternative method for water treatment has received considerable attention in recent years (Sun et
al., 2014a). For ease of separation and regeneration of adsorbents, recently research has been
focused on magnetic separation technology (Reddy and Lee, 2013). Magnetic particles can be
used to adsorb and remove contaminants from aqueous effluents, and after adsorption process,
can be separated from the medium by a simple magnetic process (Yao et al., 2014).
74
Sun et al., (2014a) prepared novel amino-functionalized magnetic cellulose composite and tested
for its ability to remove Cr (VI). This was prepared a by a 4 step process involving synthesis of
magnetic silica nanoparticles using co-precipitation method followed by the hydrolysis of
sodium silicate, then coating with cellulose through the regeneration of cellulose dissolved in 7
wt.% NaOH/12 wt.% urea aqueous solvent, grafting of glycidyl methacrylate using cerium
initiated polymerization and ring-opening reaction of epoxy groups with ethylenediamine to
yield amino groups. The results demonstrated that Cr(VI) adsorption was highly pH dependent
and the optimized pH value was 2.0. The adsorption isotherms of the adsorbent fitted well the
Langmuir model, with the maximum adsorption capacity of 171.5 mg/g at 25 ͦ C. The adsorption
rate was considerably fast, and the adsorption reached equilibrium within 10 min. The
thermodynamic parameters obtained showed that the adsorption of Cr(VI) onto the adsorbent
was an exothermic and spontaneous process. The Cr(VI) ions could be effectively desorbed
using a 0.1 mol/L NaOH solution . They concluded that composite material could be promising
adsorbent for Cr(VI) removal, with the advantages of high adsorption capacity, rapid adsorption
rate and convenient recovery under magnetic field.
Mesoporous magnetic Fe3O4@C nanoparticles was synthesized by a one-pot approach and used
as adsorbents for removal of Cr (IV) from aqueous solution by Zhang et al. (2013).
Their results revealed that the mesoporous magnetic Fe3O4@C nanospheres have excellent
adsorption efficiency and be easily isolated by an external magnetic field. In comparison with
magnetic Fe3O4 nanospheres, the mesoporous magnetic Fe3O4@C exhibited 1.8 times higher
removal rate of Cr (VI). They suggested that the mesoporous structure and an abundance of
hydroxy groups on the carbon surface may have been responsible for high absorbent capability.
75
Li et al. (2013c) prepared N-doped porous carbon with magnetic nanoparticles formed in situ
(RHC-mag-CN) fabricated through simple impregnation then polymerization and calcination
(Figure 2-7). The doped nitrogen in RHC-mag-CN was in the form of graphite-type layers with
the composition of CN. The resultant nanocomposite maintained a high surface area of 1136
m2/g with 18.5 wt. % magnetic nanoparticles (Fe3O4 and Fe) inside, which showed a saturation
magnetization of 22 emu/g. It was reported that 92% of Cr(VI) could be removed within 10 min
for dilute solutions at 2 g/L adsorbent dose. They suggested that the high adsorption was related
to the synergetic effects of physical adsorption from the surface area and chemical adsorption
from complexation reactions between Cr(VI) and Fe3O4.They also noted that the basic CNs in
RHC-mag-CN increase the negative charge density and simultaneously increase the adsorption
of metallic cations, such as Cr(III) formed in the acid solution from the reduction of Cr(VI).
They concluded that the formation of magnetic nanoparticles inside not only supplied
complexing sites for the adsorption of Cr(VI), but also showed perfect magnetic separation
performance from aqueous solution.
Figure 2-7. Schematic fabricating of RHC-mag-CN nanocomposite.
76
Thinh et al. (2013) prepared magnetic chitosan nanoparticles by co-precipitation via
epichlorohydrin cross-linking reaction. The average size of magnetic chitosan nanoparticles was
estimated at ca. 30 nm. They found that the adsorption of Cr(VI) was highly pH-dependent and
the kinetics follows the pseudo-second-order model. Langmuir maximum adsorption capacity at
pH 3 and room temperature was calculated as 55.80 mg/g. The authors suggested that magnetic
chitosan nanoparticles could serve as a promising adsorbent for the removal of Cr(VI) nanoscale
zero-valent iron assembled on magnetic Fe3O4/graphene nanocomposite was synthesized by (Lv
et al., 2014b) for Cr(VI) removal from aqueous solution. Nanoscale zero valent iron particles
were found to be perfectly dispersed either among Fe3O4 nanoparticles or above the basal plane
of graphene. The composite material showed Cr(VI) removal efficiency of 83.8%, which was
much higher than those of individual materials (18.0% for nanoscale zero valent iron, 21.6% for
Fe3O4 nanoparticles and 23.7% for graphene) . Maximum Cr(VI) adsorption capacity varied
from 66.2 to 101.0 mg/g with decreasing pH from 8.0 to 3.0 at 30 ͦ C. Thermodynamic studies
indicated spontaneous tendency and exothermic nature of the adsorption process .The authors
suggested that the robust performance of nanoscale zero-valent iron assembled on magnetic
Fe3O4/graphene nanocomposites arises from the formation of micro- nanoscale zero-valent iron -
graphene/ nanoscale zero-valent iron Fe3O4 batteries and strong adsorption capability of broad
graphene sheet/Fe3O4 surfaces. They also suggested that the electrons released by nanoscale
zero-valent iron spread all over the surfaces of graphene and Fe3O4, and the adsorbed Cr(VI) ions
on them capture these floating electrons and reduce to Cr(III). Fe3O4 NPs also served as
protection shell to prevent Nanoscale Zero Valent Iron from agglomeration and passivation.
77
Pang et al. (2011) developed A stable magnetic nanoparticle with a shell core structure of γ-
Fe2O3@Fe3O4. According to their results the adsorbent was able to effectively remove anionic
Cr(VI) in the pH range of 2–3 which was attributed to the large amount of protonated imine
groups on its surface, and the adsorbent could be magnetically separated from liquid quickly.
Adsorption equilibrium was reached within 30 min and independent of initial Cr(VI)
concentration. The calculated thermodynamic parameters indicated that adsorption of Cr(VI)
was spontaneous and exothermic in nature. Competition from coexisting ions (K+, Na
+, Ca
2+,
Cu2+
, Cl−, and NO3
−) was found to be insignificant. The adsorbent had satisfying acid–alkali
stability and could be regenerated by 0.02 mol/LNaOH solution.
Chen et al. (2013) prepared a magnetically separable adsorbent, named
chitosan/montmorillonite–Fe3O4 (CTS/MMT–Fe3O4) microsphere by microemulsion process
The microspheres were applied as adsorbents for the removal of Cr(VI) from aqueous solution.
They investigated the effects of montmorillonite content (wt %), the initial concentration of
Cr(VI) solution, initial pH value of Cr(VI) solution and the adsorption dose on the adsorption
capacity of the microspheres. Experiments showed that the adsorption capacity of CTS/xMMT–
Fe3O4 microspheres for Cr(VI) is higher than the mean value of those of chitosan and
montmorillonite. The optimum pH value for Cr(VI) adsorption was found at pH 2, and the
adsorption capacity increased with an increase in adsorption temperature. The adsorption kinetics
was described by the pseudo-second order equation, while the isotherm was described by
Langmuir equation. Furthermore the material could be reused in a number of cycles meanwhile
Tang et al. (2014), synthesized a magnetic mesoporous silica composite with high uniformity
and polyaniline (PANI) grafting and successfully applied to removed Cr(VI). They indicated
78
that with the assistance of high adsorption capacity of protonated PANI and the magnetic
mesoporous composite at low pH, Cr(VI) was adsorbed and then reduced to less toxic Cr(III) by
PANI. The data obtained from the kinetic study fitted well to the pseudo-second-order model
and Langmuir model well described the sorption isotherms with the maximum adsorption
capacity of 193.85 mg/g. Competition from coexisting ions (K+, Na
+, Ca
2+, Cl
-, SO4
2-, NO
-3) was
proved to be insignificant. Moreover, the exhausted adsorbent could be well regenerated and
kept above 83% removal efficiency in the first three cycles, which demonstrated that it was cost-
effective. The authors concluded that this novel magnetic adsorbent may offer a simple
adsorption and synergistic reduction treatment option to remove Cr(VI) contaminant from
industrial wastewater and natural water bodies. The use of magnetic adsorbents in removing
Cr(VI)from aqueous offers a significant advantage over other adsorbents which is the ability to
separate them from an aqueous solution on application of a magnetic field.
2.6.3. Polymer clay based nanoadsorbents
Clay minerals have low surface area and poor affinity to pollutants due to some of the
inaccessible sites of the stacked lamellar sheets and hydration of the clay mineral surface tends to
reduce accessibility of the interlayer spaces (Setshedi et al., 2013, Unuabonah and Taubert,
2014). Polymer clay based nanocomposites include the use of smectite-type clays, such as
hectorite, montmorillonite, and synthetic mica, as fillers to enhance the properties of polymers.
The principle used in polymer clay based nanocomposites is to separate not only clay aggregates
but also individual silicate layers in a polymer which then enables the excellent mechanical
properties of the individual clay layers to function effectively, while the number of reinforcing
components also increases dramatically because each clay particle contains hundreds or
thousands of layers (Gao, 2004). The modification of the two-dimensional clays using
79
conductive polymer attracted increasingly attention in the field of water treatment owing to the
high cation exchange capacity, t abundant active adsorption sites and organic functional groups
(Mu et al.2016).
Setshedi et al. (2013) prepared the exfoliated polypyrrole-organically modified montmorillonite
clay nanocomposite as a potential adsorbent, via in situ polymerization of pyrrole monomer for
adsorption of toxic Cr(VI) from aqueous solution. According to their results the WAXD and
SAXS studies indicated that the clay sheets were exfoliated in the prepared nanocomposite. HR-
TEM results showed good dispersion of the clay into the polymer matrix. An improved surface
area was observed by authors for the nanocomposite compared to the montmorillonite clay.
Batch adsorption experimental studies revealed that Cr(VI) adsorption process was rapid,
spontaneous in nature and favored an increased temperature at pH 2.The kinetic experimental
data fitted well to the pseudo second order kinetic model while the equilibrium data was
described well by the Langmuir isotherm. The Langmuir maximum adsorption capacity of
Cr(VI) at pH 2 was found to be 112.3, 119.34, 176.2 and 209.6 mg/g at 19 ͦ C, 25 ͦ C, 35 ͦ C and
45 ͦ C, respectively. The selective adsorption of Cr(VI) was demonstrated in binary adsorption
systems with co-existing ions. Desorption experiments revealed that the nanocomposite can be
reused effectively for two consecutive adsorption–desorption cycles without any loss of its
original capacity.
Shyaa et al., (2015) observed that the capacity of chromium adsorption on polyaniline/zeolite
increases with initial metal concentration, the metal ion adsorption on surfactant is well
represented by the Freundlich isotherm. The maximum adsorption of Cr(VI) occurred at pH 2–6,
and decreased at higher pH values.
80
Pan et al. (2016) prepared alkaline clay which was immobilized with cationic star polymer to
yield amino groups, and the cationic star polymer-immobilized alkaline clay was applied to
remove Cr(VI) from the aqueous solution in batch experiments. Various influencing factors,
including pH, contact time and immobilization amount of cationic star polymer on adsorption
capacity of cationic star polymer-immobilized alkaline clay for Cr(VI) were also investigated.
The results obtained demonstrated that Cr(VI) adsorption was highly pH dependent. The
optimized pH value was 4.0. The adsorption isotherms of the adsorbent fit the Langmuir model
well, with the maximum adsorption capacity of 137.9 mg/g at 30 °C.
A polypyrrole-coated halloysite nanotube nanocomposite was prepared via in situ polymerization
of pyrrole in the dispersion of HNTs and assessed for the removal of toxic Cr(VI) from aqueous
solutions (Ballav et al., 2014a). The maximum adsorption capacity was found to be 149.25 mg/g
at pH 2.0 at 25 °C and the adsorption process was spontaneous and endothermic in nature. XPS
stud confirmed the adsorption of Cr(VI) onto the nanocomposite where some part of Cr(VI)
reduced to Cr (III) by electron-rich polypyrrole moiety. The desorption study suggested that the
nanocomposite can be reused three times without loss of its original removal efficiency.
2.7.Conclusion
All heavy metals treatment methods have their advantages and limitations. Adsorption can be
considered as the most effective and economic method for the treatment of wastewaters
containing heavy metals because it is low cost, and locally and naturally available material can
be utilized efficiently for the removal of heavy metals ions from environmental water. Whereas
commercial activated carbon is as an effective adsorbent for the removal of heavy metals its high
cost restricts its use. The review showed that agricultural waste, waste based chitosan and clay
81
minerals are low-cost alternative adsorbents in which surface modification of these material
surfaces provides increased capacity for adsorption and selectivity of Cr, V and Mn ions.
Although various adsorbents and nano adsorbents can be applied, selection of the most suitable
adsorbent and nanoadsorbent in removing target heavy metal pollutants depends on the
characteristics of effluents to be treated, technical applicability, discharge standards, cost-
effectiveness, regulatory requirements, and long-term environmental impacts. Due to their ability
to minimize the generation of secondary waste using less resources and capability of removing
any types of pollutants from contaminated water effectively, the use of nanomaterials could offer
alternative solution to heavy metals problem in environmental water and protecting the aquatic
environment in the future (Kurniawan et al., 2012).
Clay polymers nanocomposite adsorbents for water treatment combine the advantageous
properties of their respective components, such as mechanical stability, a particular high surface
structure, surface charge, chemical stability, significantly improved adsorption capacities for
micropollutants. What makes the magnetic clay polymer nanocomposites more favorable as
adsorbents is the fact that they can be easily removed from the solution by a simple magnetic
process after use.
82
CHAPTER 3
Adsorption of Hexavalent Chromium Onto Magnetic Natural Zeolite-Polymer Composite
Abstract
Magnetic natural zeolite-polypyrrole (MZ-PPY) composite was prepared for enhanced removal
of hexavalent chromium [Cr(VI)] from aqueous solution. The structure and morphology of the
prepared adsorbent was analyzed with the Fourier transform infrared (FTIR), field-emission
microscope (FE-SEM), energy dispersive X-ray (EDX), high resolution transmission electron
microscope (HR-TEM) and X-ray diffraction (XRD). The effects of magnetic zeolite loading
within the composite, initial pH, sorbent dosage, temperature and Cr(VI) concentration on
Cr(VI) removal efficiency were investigated. Up to 99 % removal efficiency was obtained when
the pH was 2 and the initial Cr(VI) concentration was 300 mg/L. The sorption kinetic data fitted
well to the pseudo-second order model and isotherm data fitted well to the Langmuir isotherm
model. The maximum adsorption capacity determined from the Langmuir isotherm increased
from 344.83 to 434.78 mg/g as the temperature was increased from 25 °C to 45 °C.
Thermodynamic parameters revealed that the adsorption process is spontaneous and endothermic
in nature. The results further indicated that the adsorption capacity is decreased in the presence
of SO42−
ions. The adsorption–desorption studies indicated that MZ-PPY retained its original
Cr(VI) sorption capacity up to two consecutive cycles.
3. Introduction
Mining and metallurgical industries are associated with heavy metals containing wastewaters
that are directly or indirectly discharged into the environment. Heavy metals are known to be
toxic or carcinogenic (Hu et al., 2005). Hexavalent chromium as an example of heavy metal
83
often exists in the effluents of the electroplating, tanning, mining, and fertilizer industries and
acts as carcinogen, mutagen, and teratogen in biological systems (Huang et al., 2013, Li et al.,
2013a, Bhaumik et al., 2014b). Consequently effluents containing hexavalent chromium must be
treated using appropriate techniques. Several treatment methods to remove Cr(VI) from
wastewaters have been reported (Mohan et al., 2006). These include chemical
precipitation (Golbaz et al., 2014, Golder et al., 2011, Kongsricharoern and Polprasert, 1995,
Kongsricharoern and Polprasert, 1996), ion exchange (Alvarado et al., 2013, Cavaco et al.,
2007, Fan et al., 2013), reverse osmosis (Afonso et al., 2004, Benito and Ruíz, 2002,
Bhattacharya et al., 2013, Bódalo et al., 2005), ultrafiltration (Aroua et al., 2007, Chakraborty et
al., 2014) , electrochemical (Almaguer-Busso et al., 2009, Lakshmipathiraj et al., 2008, Liu et
al., 2011, Ouejhani et al., 2008), bio-sorption (Hou et al., 2012, Dittert et al., 2014, Febrianto et
al., 2009, Khambhaty et al., 2009, Li et al., 2008) and adsorption (Anirudhan et al., 2013,
Anupam et al., 2011, Baccar et al., 2009, Baral et al., 2006, Behnajady and Bimeghdar, 2014,
Chen et al., 2014) . Many efforts have been devised to develop new technologies in which
technological, environmental and economic constraints are taken into consideration.
The purification methods have to avoid generation of secondary waste and to involve materials
that can be recycled and easily used on an industrial scale. Adsorption compared with other
methods appears to be an attractive process in view of its efficiency and capacity of removing
heavy metal ions over wide range of pH and to a much lower level, and an ability to remove
complex form of metals that is generally not possibly by other methods. It is also
environmentally friendly, cost effective and easy to operate compared to other processes (Rao et
al., 2010) . In the past, several investigations have been undertaken for the removal of heavy
84
metals from wastewater using different adsorption media. These include amongst many others
activated carbon made from other materials (Baccar et al., 2009, Bishnoi et al., 2004, Demirbas
et al., 2008, Duranoğlu et al., 2012) , treated saw dust (Baral et al., 2006) agricultural wastes
(Gao et al., 2008), alumina and natural zeolite (Ngomsik et al., 2006) .
Natural zeolite is stable over wide environmental conditions and is low cost adsorption media
that is suited for fixed bed operations. However, it has poor capacity for most contaminants.
Thus to render it effective for water treatment, surface modification is required. In recent studies
polypyrrole and polyaniline have been reported as excellent adsorbent for the removal of Cr(VI)
(Bhaumik et al., 2011d, Bhaumik et al., 2012, Bhaumik et al., 2014a). However these polymers
have very low density and therefore in real industrial applications, large volume of fix beds
would be required thus limiting their use. Both natural zeolite and polymers are bulk adsorption
materials. In adsorption, bulk materials have inherent limitations as they suffer from massive
mass transfer resistance due to large surface areas and large diffusion lengths. To overcome these
limiting factors in adsorption, adsorption media could be designed to have some nano-features in
order to maximize mass transport kinetics by providing contaminants with rapid access to high
surface area and by promoting internal mass transport (Hristovski et al., 2007). One way of
achieving this is by developing nanocomposites in which nanoparticles are imbedded in bulk
materials.
Consequently this study explores the use of magnetic natural zeolite-polypyrrole composite as a
robust adsorption media for hexavalent chromium removal from water. The effects of several
variables are considered including the composition of zeolite in the composite, solution pH,
adsorbent dose, temperature and contact time. Appropriate kinetic and equilibrium models are
85
used to interpret experimental results. It is shown that the material exhibit enhanced capacity for
hexavalent chromium.
3.1.Materials and Methods
3.1.1. Materials
Zeolite (natural clinoptilolite) was supplied by Ajax Industries CC in Cape Town, South Africa.
All the chemical reagents used in this study were of analytical reagent grade [AR Grade] and
were obtained from Sigma-Aldrich. FeCl3·6H2O, FeCl2·4H2O and NaOH were used for magnetic
zeolite preparation. Anhydrous FeCl3 was used as an oxidant for polymerization of pyrrole (Py).
A stock solution of Cr(VI) was prepared by dissolving a known amount of potassium
dichromate (K2Cr2O7) with deionised water. Hydrochloric acid (HCl), ethanol and 1,5
diphenylcarbazide were also used in the study to adjust the pH of the solution and for analysis of
Cr(VI), respectively.
3.2.Methods
3.2.1. Synthesis of magnetic zeolite
Zeolite was first conditioned with 1 M NaCl for 36 h at room temperature. Thereafter, the solids
were vacuum filtered and washed thoroughly with deionized water. Afterwards the solids were
air dried for 48 h. Magnetic zeolite was prepared through co-precipitation of Fe3+
and Fe2+
. First,
250 mL of sodium hydroxide solution (1 M) and a known amount of conditioned zeolite powder
were placed in a three-neck round bottom flask and heated to 90° C in an oil bath. The mixture
was homogenously mixed by vigorously stirring and deoxygenating by bubbling of N2 gas.
Molar ratio 2:1 of ferrous chloride and ferric chloride was prepared by adding 25 mL of 0.25 M
HCl. Then Fe3+/
Fe2+
solution was added drop wise to the vigorously stirred mixture of zeolite
86
and NaOH under N2 atmosphere. The reaction temperature was maintained at 95 °C for an
additional hour under the nitrogen environment. The solution was continuously stirred and
cooled down to room temperature. The obtained magnetic zeolite powder was washed repeatedly
with deionized water until neutral pH was obtained. Finally, obtained magnetic powder was
dried under vacuum for 12 h.
3.2.2. Synthesis of the magnetic zeolite-polypyrrole composite (MZPPY)
A known amount of magnetic zeolite (0.5 - 4g) was dispersed in 80 mL deionised water and
ultrasonicated for 20 min. Then 0.8 mL of pyrrole was syringed into the mixture and then
shaken for 5 min. Pyrrole was polymerized by 6 g FeCl3 oxidant. After 3 h of stirring, the
mixture was filtered and the residue was washed with water and acetone. The composite was
then dried at 80°C for 6 h. The prepared composite is hereafter referred to as MZPPY.
3.2.3. Characterization
The structure of magnetic zeolite-polypyrrole composite was characterized by using Fourier
transformed infrared (FTIR) spectrometer (Perkin–Elmer Spectrum100 spectrometer), equipped
with an FTIR microscopy accessory, on the attenuated total reflection (ATR) diamond crystal.
To characterize the morphology of the magnetic zeolite and the composite, field-emission
scanning electron microscopy (FE-SEM) images with energy dispersive X-ray analysis (EDX)
were obtained on a LEO, Zeiss SEM. A JEOL JEM 2100F microscopy with a LAB6 filament
operating at 200 kV was used to obtain high resolution transmission electron microscope (HR-
TEM) images. The TEM samples were dispersed on a copper grid coated with a carbon film to
reduce charging. X-ray diffraction (XRD) patterns measurements were performed on a
PANalytical X’Pert PRO-diffractometer using Cu Kα radiation (wavelength, λ = 1.5406A˚) with
variable slits at 45 kV/40mA.
87
3.2.4. Batch adsorption experiments
3.2.4.1.Effect of magnetic zeolite loading within the composite, pH and sorbent dosage
A stock solution containing 1000 mg/L Cr(VI) was diluted to prepare appropriate concentrations
to perform batch adsorption experiments. The adsorption process was studied by contacting
MZPPY adsorbent with 50 mL solution containing 300 mg/L Cr(IV) in 100 mL plastic bottles.
The samples were placed in a thermostatic shaker and agitated for 24 h at 150 rpm. The effect of
the magnetic zeolite composition of the composite was tested at pH 2 while effect of pH was
studied by varying the solution pH in the range of 2-12 by using 0.1 M of NaOH or HCl. The
mass of MZPPY used for both studies was 0.1 g. Magnetic zeolite was also tested for
comparison under the same experimental conditions. The effect of MZPPY dosage on the
removal of Cr(VI) was studied by varying the mass of the adsorbent from 0.025 to 0.15 g using
50 mL of solution (300 mg/L) at pH 2.0. At the end of all experiments, the adsorbent was
separated from solution by filtration and the filtrate was analysed for residual Cr(VI) using the
UV–Vis spectrometer at 540 nm. For analysis 1,5 diphenylcarbazide reagent used. The removal
efficiency of Cr(IV) was determined by the following equation.
% 𝑅𝑒𝑚𝑜𝑣𝑎𝑙 = (𝐶𝑜−𝐶𝑒
𝐶𝑜) × 100 (3-1)
where Co and Ce are the initial and the equilibrium concentration (mg/L) of Cr(VI), respectively.
3.2.4.2.Effect of co-existing ions
To investigate the effect of competitiveness of various co-existing ions such as Cu2+
, Ni2+
,
Al3+
,Cl-, NO3
- and SO4
2- on Cr(VI) adsorption, 50 mL of 200 mg/L Cr(VI) solutions containing
200 mg/L each of various components with 0.1 g of adsorbent were shaken at pH 2.0. After
88
equilibrium was established, the adsorbent was separated from the solution and the filtrate was
analysed for Cr(VI) concentration.
3.2.4.3.Regeneration studies
Regeneration of an adsorbent is very important therefore adsorption-desorption studies were
conducted to investigate the reusability of the MZPPY composite. First 0.1 g of MZPPY was
contacted with 50 mL solution containing 200 mg/L of Cr(VI) at pH 2. Then desorption of
Cr(VI) loaded adsorbent was performed by contacting 50 mL aqueous solution of 0.1- 1 M
NaOH and placed in a thermostatic shaker for 24 h. The best desorbing NaOH concentration was
used for further desorption experiments. The amount of Cr(VI) desorbed was measured.
Regeneration of the spent adsorbent was done by treating MZPPY with 2 M HCl for 3 h and then
reused. Three adsorption–desorption cycles were performed, using the same adsorbent to test the
reusability of the adsorbent.
3.2.4.4. Kinetics and equilibrium isotherm studies.
The kinetic experiments for adsorption of Cr(VI) were carried out at pH 2 in a 1 L batch
reactor. A 1 g of MZPPY was added to the 1000 mL of solutions with initial concentrations of
100, 175 and 300 mg/L. The reactor was stirred at a constant agitation speed of 150 rpm with an
overhead stirrer. 5 mL sample solution was withdrawn at predetermined time intervals from the
reaction mixture and filtered through a syringe filter and analysed for residual Cr(VI)
concentrations. Amount of chromium adsorbed was calculated using the following equation
𝑞𝑡 = (𝐶𝑜−𝐶𝑡
𝑚) × 𝑉 (3-2)
89
where qt is the adsorbed amount of chromium per unit mass of the adsorbent (mg/g) at any time
(t), Ct is the residual concentration (mg/L) of chromium at any time t, m is the mass of the
adsorbent added (g) and V is the sample volume (L).
Sorption isotherm were generated by contacting 0.1 g of adsorbent with 50 mL of Cr(IV)
containing solution ranging from 300 to 1100 mg/L in 100 mL plastic sample bottles. The pH of
the solution was fixed at 2. The samples were shaken for 24 h at varying temperatures (25° C,
35° C and 45° C) at 150 rpm. The equilibrium sorption capacity was calculated by following
equation
𝑞𝑒 = (𝐶𝑜−𝐶𝑒
𝑚) × 𝑉 (3-3)
where qe is the equilibrium amount of Cr(VI) adsorbed per unit mass (m) of adsorbent (mg/g).
3.3.Results and discussion
3.3.1. Characterization of the adsorbent.
The FTIR spectrum of MZPPY before and after adsorption process is shown in Figure 3-1.The
bands at 1423 cm-1
and 1513 cm-1
are related to C-N stretching and PPy ring stretching
vibration. While the C-H stretching vibration, the C-H deformation are observed at 1080 and
958-824 cm-1
, respectively (Setshedi et al., 2013, Bhaumik et al., 2011d) . The adsorption of
Cr(VI) on the adsorbent is indicated by a shift of the peaks to increasing wavelength. New
vibrations peaks of 783 and 906 cm-1
are observed on the adsorbent spectra after adsorption.
These are attributed to intrinsic vibrations of Cr-O and Cr=O bonds. This indicates that Cr(VI)
90
ions is adsorbed on the surface of the MZPPY by replacing the doping Cl¯ ions. The
diffraction patterns of the crystalline structure of the (a) magnetic zeolite (MZ) (b) MZPPY
adsorbent before and (c) MZPPY after adsorption are shown in Figure 3-2.The peaks at 35.83°,
43.14°, 57.24° and 63.01° can be assigned to (311), (400), (511) and (440) which are associated
with Fe3O4 crystal planes.
Figure 3-1. FTIR spectra of the MZPPY (a) before and (b) after adsorption with Cr(VI).
91
Figure 3-2. XRD pattern of magnetic zeolite (a) and MZPPY before (b) after (c) Cr(VI)
adsorption and (d) after regeneration process
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
<511>
<311>
<400><440>
(b)
(a)
2 theta (degree)
(c)
MZ
Inte
nsity (
a.u
)
10 20 30 40 50 60 70 80
Inte
nsity
2 thetha (degree)
(d)
92
It is observed that there is no change in the crystalline structure of MZPPY before and after
adsorption. The surface morphology of the adsorption material was evaluated using field-
emission scanning electron microscopy (FE-SEM). Figure 3-3 shows the SEM images of (a)
magnetic natural zeolite, (b) MZPPY before adsorption and (c) MZPPY after adsorption. Figure
3-3(a) shows a smooth layered surface of natural zeolite (clinoptilolite) particles. It can be seen
that the zeolite has completely been covered by PPy and has a cauliflower-like structure in
Figure 3-3 (b). Natural zeolite particles have been reported to act as growth centres for
polypyrrole chains and consequently aid in making of a dense and compact structure for zeolite-
polypyrrole composite (Rashidzadeh et al., 2013).
93
Figure 3-3.Scanning electron microscopic images of (a) magnetic zeolite and (b) MZPPY before
and (c) after adsorption of Cr(VI).
Figure 3-4 shows the TEM images of (a) magnetic zeolite, and (b)-(d) MZ- PPy composites
before adsorption at different magnifications. From the images irregular layers of zeolite are
observed in the magnetic zeolite (MZ) image. Almost spherical Fe3O4 nanoparticles are
observed within the structure of MZ and MZPPY images and exist as agglomerates due to high
surface area and magneto dipole–dipole interactions between the particles (Bhaumik et al.,
2011a) .
Figure 3-4. Transmission electron microscopic images of (a) magnetic zeolite and MZPPY ((b)–
(d)).
94
Figure 3-5. Energy dispersive X-rays (EDX) spectra of MZPPY (a) before and (b) after Cr(VI).
95
Energy dispersive X-rays (EDX) was used to analyse the elemental constituents of the MZPPY
before and after Cr(VI) adsorption. The EDS spectra in Figure 3-5 show the presence of major
elements found in zeolite (Na, Mg, Al, Si, Ca, K), and Fe and Cl peaks are due to the presence
of magnetite and oxidant. After Cr(VI) adsorption process, additional peaks of Cr are
observed in Figure 4-5b which provides a direct evidence for Cr(VI) adsorption on the surface
of the MZPPY composite.
The pH of point-of-zero (pHpzc) charge of the MZPPY was determined by measuring the zeta
potential of the composite as a function of pH. The point of zero charge of the MZPPY is at
about 7.5 (Figure 3-6) , thus indicating that when pH is less than pHpzc, the MZPPY has positive
surface charge and can attract anions, while at pH greater than (pHpzc), the surface charge of
MZPPY is negative which will attract cations.
Figure 3-6. Zeta potential of the MZPPY at different pH values.
2 4 6 8 10-50
-40
-30
-20
-10
0
10
20
30
40
Zet
a po
tent
ial (
mV
)
pH
96
3.3.2. Effect of magnetic zeolite loading within the composite, pH and sorbent dosage on
Cr(VI) removal
The amount of MZ loaded onto the polymer was investigated to establish the optimum mass that
can be loaded onto the polymer. The higher the mass of MZ in the composite the higher is the
density of MZPPY for better fixed bed operation. The results in Figure 3-7 indicate that polymer
improved the performance of magnetic zeolite which only achieved the highest chromium
removal of 35.63 %.Upon the addition of pyrrole to different masses of MZ, the Cr(VI) removal
efficiency improved from 65.64 % at 0 g of MZ and to 99.99% at a MZ loading of 1.5 g to 3 g.
An increase in performance as the loading was increased is probably due to an increase in the
number of active sites provided by the magnetite nanoparticles in the zeolite which have a high
affinity for Cr(VI) (Shen et al., 2009b, Shen et al., 2009a). Interestingly, a decrease in
performance (chromium removal of 88.2 %) is observed when the MZ mass was increased to 4g.
Thus 3 g was chosen for subsequent studies. The bulk density of the polypyrrole, zeolite and
magnetic zeolite/polyprrole was found to be 0.034; 0.876 and 0.195 g/mL. This indicates that the
density of polypyrrole was improved by the incorporation of magnetic zeolite, a feature so
desired for fixed bed operation.
The pH of the solution is an important parameter for metal ion sorption since it not only affects
the surface charge of adsorbent but also the degree of ionization and speciation of adsorbate
during reaction (Demiral et al., 2008). To investigate the effect of pH on the adsorption
efficiency of Cr(VI) onto MZPPY, the pH of the solution was varied from 2.0 to 12 at a
temperature of 25 C ± 1° C.
97
Figure 3-7. Effect of magnetic zeolite loading onto PPY. Initial concentration: 300 mg/L;
Temperature : 25 ͦ C; dose: 2 g/L; contact time : 24 hrs
Results in Figure 3-8 shows that Cr(IV) removal is pH-dependent and that maximum removal
occurs at pH 2. Hexavalent chromium exists in aqueous solution in the form of oxyanions such
as HCrO4¯, Cr2O7
2- and CrO4
2- . The dominant form of Cr(VI) between pH 1.0 and 4.0
is monovalent HCrO4¯, which is then gradually converted to the divalent form CrO4
2- as pH
increases (Bhaumik et al., 2011d, Gupta et al., 2010, Yang et al., 2014). The higher uptake at
lower pH is due to the strong attraction between predominant oxyanions HCrO4¯ and the
positively charged surface of the adsorbent. The increased removal can be further attributed to
the anion exchange property of the polypyrrole through replacement of the doped Cl¯ with either
HCrO4¯ or Cr2O7
2- (Bhaumik et al., 2011d). As the pH is increased more hydroxyl ions are
98
present in the solution and compete with chromate ions resulting in the reduction of Cr(VI)
removal.
Figure 3-8. Effect of pH on the adsorption of Cr(VI) by MZPPY and magnetic zeolite. 300 mg/L;
Temperature : 25 ͦ C; dose: 2 g/L; contact time : 24 hrs
The effect of adsorbent dosage is an important parameter as it determines the capacity of an
adsorbent for a given initial concentration, which contributes to the overall cost of water
treatment. The results in Figure 3-9 show that the removal efficiency of Cr(VI) was increased
from 68.16 % at 0.025 g to 99.99 % at 0.1 g to 0.15 g . An increase in the sorbent dosage
increases the number of active sites available for the chromium ions to interact with. As a
consequence, performance improves with mass of adsorption media. The results therefore
suggest that MZPPY is highly efficient in Cr(VI) removal as only a small quantity (2g/L) is
required to achieve excellent performance.
99
Figure 3-9. Effect of dosage of adsorbent on the adsorption of Cr(VI). 300 mg/L; Temperature :
25 ͦ C; contact time : 24 hrs
3.3.3. Effect of co-existing ions
In most cases industrial wastewaters contains various kinds of ions including chloride, sulphate,
nitrate, copper, zinc, and nickel. The adsorption of Cr(VI) onto the MZPPY was investigated in
the presence of co-existing ions. Results (see Figure 3-10) indicate that there is no decrease in
Cr(VI) removal in the presence of ions such as Cl¯, NO3
¯, Ni
2+, Cu
2+ and Zn
2+. However the
presence of SO42-
in the aqueous solution inhibited the adsorption of Cr(VI) whose capacity
reduced from 99 mg/g to 87 mg/g. This indicate that there was competition between the SO42-
and HCrO4−
ions for the adsorption sites on the adsorbent surface, and this resulted in less Cr(VI)
adsorption. SO42-
and HCrO4−
ions are both negatively charged and during adsorption, both
anions are electrostatically attracted to the positively charged sites of the adsorbent (Bhaumik et
al., 2016).
100
Figure 3-10. Effect of coexisting ions on the adsorption of Cr(VI) onto MZPPY.
3.3.4. Regeneration studies
For economic and environmental reasons, an adsorption media should be regenerable and reused.
Figure 3-11 shows cycles used to test reusability of MZPPY. In the first two cycles, the
adsorption capacity of MZPPY was maintained at 99 mg/g. However, there was a remarkable
decrease in capacity in the subsequent cycles. In particular, the adsorption capacity was reduced
to 60 mg/g in the third cycle and further to 36 mg/g. These results therefore indicate that MZPPY
can be reused twice successfully in adsorption-desorption cycles without the loss of adsorption
capacity. There were no structural changes observed in XRD pattern in Figure 3-6 (d) after
regeneration process. The decrease in adsorption capacity may be due to the rupture of polymer
101
chain by repeated oxidation reaction with highly oxidizing Cr(VI) species, thereby reducing the
mass (sorption sites) of the used adsorbent (Bhaumik et al., 2012) .
Figure 3-11. Adsorption–desorption cycles
3.3.5. Kinetic studies
The adsorption kinetic studies at three different initial concentrations are shown in Figure 3-12 .
It is observed that when the initial Cr(VI)concentration increases from 100–300 mg/L, the
adsorption capacity of the MZPPY increases too. It is also observed that a rapid uptake was
experienced in the first 10 minutes but slowed down considerably as reaction approached
equilibrium. The rapid uptake in the initial time period is due the high driving force exerted by
the large concentration gradient. For the same reason, the higher uptake at higher initial
102
concentration is a consequence of larger concentration gradient between the bulk solution and
sorbent phase.
Figure 3-12.Adsorption kinetics for adsorption of Cr(VI) adsorption onto MZPPY.
The kinetic data were further interpreted using the Lagergren pseudo–first order and the Ho
pseudo-second order kinetic models (Ho and McKay, 2000). The linearized models of pseudo-
first and pseudo-second order kinetic models are given in Eq. (3-4) and Eq. (3-5), respectively.
log(𝑞𝑒 − 𝑞𝑡) = log 𝑞𝑒 −𝑘1
2.303 (3-4)
𝑡
𝑞𝑡=
1
𝑘2𝑞𝑒2 +
𝑡
𝑞𝑒 (3-5)
103
where k1 (min-1
) and k2 (g/mg/min) are the rate constants of the pseudo-first order and pseudo-
second order adsorption models, respectively. The pseudo-first order and pseudo second order
rate parameters for chromium sorption were, respectively, calculated from the linear plots of log
(qe–qt) versus t and t/qt versus t (Figure 3-13), and are listed in Table 3-1.
Figure 3-13.The pseudo-second order kinetic plot for adsorption of Cr(VI) adsorption onto
MZPPY.
From the values of correlation coefficients (R2) and calculated equilibrium capacity values which
are consistent with experimental values, the interaction between MZPPY and chromium ions can
be said to follow the pseudo-second order mechanism.
104
Table 3-1. Kinetics parameters for Cr(VI) adsorption onto MZPPY-
Co
(mg/L)
Experi-
metal
Pseudo-first –order
model
Pseudo-second –order model Intraparticle diffusion
model
qe k1 qe R2 k2 qe R
2 Kp C R
2
(L/mi
n)
(mg/g) (g-
mg/min)
100 89.2 0.080 56.45 0.987 0.00094 92.51 0.999 8.185 4.0909 0.988
175 124.3 0.068 82.32 0.977 0.000656 128.21 0.999 10.711 20.6673 0.989
300 155.3 0.030 73.14 0.980 0.000915 157.72 0.999 6.224 85.09 0.996
The intra-particle diffusion model was used to analyse the nature of the rate controlling step in
adsorption. The model gives a relationship between the time dependant adsorption capacity qt
and t0.5
and is express in equation (3-6):
𝑞𝑡 = 𝐾𝑝𝑡0.5 + 𝐶 ( 3-6)
where Kp is the intraparticle diffusion rate constant (mg/g/min0.5
) and C is a parameter related to
the thickness of boundary layer. If the value of C is large then the boundary layer is large too.
105
Figure 3-14. Intraparticle diffusion model for adsorption of Cr(VI) adsorption onto MZPPY.
The results show in F that the adsorption of Cr(VI) by the adsorbent is not linear on the entire
adsorption process time indicating that more than one process is involved in the sorption of
Cr(VI)by MZPPY. The slope of linear plots of the initial portion of the curve is presented in
Table 3-1. This indicates that the initial rapid adsorption of Cr(VI)maybe due to the external
boundary layer diffusion and the subsequent slower uptake is attributed to intraparticle diffusion
(Bhaumik et al., 2012).
3.3.6. Equilibrium isotherm and thermodynamic studies
To determine the capacity and thermodynamics parameters of Cr(VI) adsorption by MZPPY,
adsorption isotherms were obtained at three different temperatures and are presented in
106
Figure 3-15. Results indicate that Cr(VI) adsorption onto MZPPY is enhanced with an increase
in temperature.
Figure 3-15. Nonlinear isotherm plots: (-) Langmuir model, (—) Freundlich model at different
temperatures of Cr(VI) sorption onto MZPPY.
Meanwhile the adsorption equilibrium data were fitted with linearized Langmuir (3-7) and
Freundlich (3-8) isotherm models. The Langmuir adsorption isotherm model assumes monolayer
adsorption onto a surface with a finite number of sites that are identical and equivalent, and that
no lateral interaction and steric hindrance between the adsorbed molecules exists even on
adjacent sites (Foo and Hameed, 2010). Freundlich isotherm is the earliest known relationship
describing the non-ideal, reversible adsorption that is not restricted to the formation of
monolayer.
107
𝐶𝑒
𝑞𝑒=
1
𝑞 𝑚𝑏+
𝐶𝑒
𝑞 𝑚 (3-7)
log 𝑞𝑒 = log 𝐾𝐹 +1
𝑛log 𝐶𝑒 (3-8)
where qm is the monolayer adsorption capacity (mg/g ), Ce is the equilibrium solute concentration
(mg/L), b is the free energy of adsorption, KF and 1/n are Freundlich constants related to the
adsorption capacity (mg/g) and intensity of adsorption, respectively. The plots of linearized
Langmuir and Freundlich isotherms are shown on Figure 3-16 and Figure 3-17, respectively.
Figure 3-16. Equilibrium data fit to linear Langmuir isotherm of Cr(VI) sorption onto MZPPY.
108
Figure 3-17. Equilibrium data fit to linear Freundlich isotherm (c) of Cr(VI) sorption onto
MZPPY.
The related isotherm parameters with regression coefficients R2 are given in Table 3-2. From the
regression coefficients, the equilibrium data are better described by the Langmuir isotherm. The
maximum adsorption capacity determined from the Langmuir isotherm increased from 344.83 to
434.78 mg/g as the temperature increased from 25°C to 45°C, which confirms that the adsorption
process is endothermic in nature. The capacity values obtained in this study is quite competitive
compared to those reported in literature (Setshedi et al., 2013, Salgado-Gómez et al., 2014, Hu
and Luo, 2010, Sarkar et al., 2010, Santhana Krishna Kumar et al., 2012, Leyva-Ramos et al.,
2008, Yuan et al., 2009, Kalhori et al., 2013). (See Table 3-3)
109
Table 3-2. Langmuir and Freundlich isotherms parameters for Cr(VI) adsorption onto MZPPY
Temperature °
C
Langmuir constants Freundlich constants
qo
(mg/g)
b
(L/mg)
R2 RL KF
(mg/g)
1/n R2
25 ° C 344.83 0.386 0.999 0.005107 184.43 0.122 0.903
35 ° C 370.37 0.730 0.999 0.002709 191.16 0.129 0.919
45 ° C 434.78 1.35 0.999 0.001464 221.56 0.134 0.943
Table 3-3. Comparison of maximum sorption capacity of other adsorbents for
hexavalent chromium adsorption at room temperature.
Adsorbent qo
(mg/g)
pH References
Exfoliated polypyrrole-organically modified
montmorillonite clay
119.3 2 (Setshedi et al.,
2013)
Hexadecyltrimethylammonium-modified zeolite-rich
tuff
1.0585 3 (Salgado-Gómez
et al., 2014)
Montmorillonite modified with hydroxyaluminum and
cetyltrimethylammonium bromide
11.970 4 (Hu and Luo,
2010)
Bentonite based Arquad® 2HT-75 organoclay 14.64 4.7 (Sarkar et al.,
2010)
Dodecylamine modified sodium montmorillonite 23.69 2.5 (Santhana
Krishna Kumar et
al., 2012)
Surfactant-modified zeolite 5.07 6 (Leyva-Ramos et
al., 2008)
110
Montmorillonite-supported magnetite nanoparticles 20.16 2.5 (Yuan et al.,
2009)
Surface modified lightweight expanded clay aggregate
with MgCl2
236.24 3 (Kalhori et al.,
2013)
Natural lightweight expanded clay aggregate 198.39 3 (Kalhori et al.,
2013)
Surface modified lightweight expanded clay aggregate
with with H2O2
218.29 3 (Kalhori et al.,
2013)
Magnetic natural zeolite- polypyrrole (MZPPY) 344.83 2 Present work
Meanwhile, the dimensionless separation factor, RL, which is an essential characteristic of the
Langmuir model for defining the favourability of an adsorption process, was determined from
equation (4-9):
𝑹𝑳 =𝟏
𝟏+𝒃𝑪𝒐 (3-9)
An adsorption process is favourable if 0 < RL < 1 but unfavourable when RL > 1. The average RL
values summarised in Table 3.2 at different temperatures suggest that the adsorption was
favourable.
The standard enthalpy change (∆H°) and the standard entropy (∆S°) for Cr(VI) sorption on
MZPPY were obtained using Van’t Hoff equation.
𝐥𝐧 𝒃 = −∆𝑯°
𝑹𝑻+
∆𝑺°
𝑹 (3-10)
111
where b is the adsorption coefficient from the Langmuir adsorption isotherm, ∆H° is the
enthalpy change (J/mol), ∆S° is the entropy change (J/mol/K) , R is the gas constant (8.314
J/mol/K) and T is temperature in K. The values of ln b versus 1/T are plotted according to
equation (10) and shown in Figure 3-18. The adsorption enthalpy change, entropy change and
Gibbs free energy change were calculated and are reported in Table 3-4. The Gibbs free energy
change (∆G°) of adsorption was calculated from Equation (3-11)
∆𝑮° = ∆𝑯° − 𝑻∆𝑺° (3-11)
Figure 3-18. Plot to determine thermodynamic parameters of Cr(VI) adsorption onto MZPPY.
112
Table 3-4. Thermodynamics parameters for the adsorption of Cr(VI) on MZPPY composite.
T (K) ∆H (kJ /mol) ∆S ( J/mol/K) ∆G (kJ /mol)
25° C -14.1894
35° C 46.58 206 -16.9584
45° C -19.0921
The positive value of ∆H° indicates that the adsorption is endothermic nature, which is also
supported by the increase in the value of uptake capacity of the sorbent with the rise in
temperature. The positive value of ∆S° suggests an increase in disorder at the solid–liquid
interface during the sorption of Cr(VI) on MZPPY. The magnitude of G° decreases with an
increase in temperature and the negative values indicates the spontaneous nature of sorption
process.
It has been previously reported that the mechanisms of adsorption of Cr (VI) onto polypyrrole
based materials involves ion exchange mechanism via the replacement of doped Cl ¯ by HCrO4¯
ions. The XPS studies conducted suggested that some fraction of adsorbed Cr(VI) was reduced
to Cr(III) by a reduction process which is due to the presence of electron rich polypyrrole
moieties (Bhaumik et al., 2011d).
Conclusion
MZPPY with improved density was successfully synthesized, characterized and used as an
adsorbent for the removal of Cr(VI) from aqueous solution. The results indicate that removal
efficiency is dependent on pH, initial concentration, adsorbent mass, contact time and
temperature. The removal efficiency of 99.99% was obtained when the pH was 2 and the initial
Cr(VI) concentration was 300 mg/L. The sorption kinetic data fitted to the pseudo-second order
model while the equilibrium data fitted well to the Langmuir isotherm model. The Langmuir
113
adsorption capacity increased from 344.83 to 434.78 mg/g with an increase in temperature from
25oC to 45
oC. Thermodynamic parameters confirmed that the adsorption process is spontaneous,
endothermic in nature and marked with an increased randomness at solid–liquid interface. In the
presence of SO42-
as a co-existing ion, Cr(VI) adsorption onto MZPPY was slightly decreased.
Regeneration studies indicated that MZPPY can be used in two cycles without loss of capacity.
This work confirms that the MZPPY is an effective adsorbent for the removal of Cr(VI) in
aqueous solution. However, further studies need to be conducted against industrial wastewater to
obtain real performance data.
114
CHAPTER 4
Vanadium (V) adsorption isotherms and kinetics using polypyrrole coated magnetized
natural zeolite
Abstract
Magnetized natural zeolite- polypyrrole (MZPPY) composite as a potential adsorbent for
vanadium was prepared via polymerization of pyrrole monomer using FeCl3 oxidant in aqueous
medium in which magnetized natural zeolite particles were suspended. The MZPPY composite
was characterized by attenuated total reflectance Fourier transform infrared spectroscope (ATR-
FTIR), field emission scanning electron microscope (FE-SEM) and high resolution transmission
electron microscope (HR-TEM). Batch adsorption studies were performed to test the ability of
the adsorbent to remove V(V) ions from aqueous solution. From sorption equilibrium modelling,
the equilibrium data is well described by Langmuir, Sips and Redlich–Peterson isotherms while
the adsorption kinetics is described by the pseudo-second order model. The Langmuir adsorption
capacity is 65.0 mg/g at 298K. Thermodynamic parameters confirm the spontaneous and
endothermic nature of the vanadium adsorption process. Meanwhile V(V) removal process is
found not to be affected by co-existing ions. Furthermore, the material can be used in 2
adsorption cycles without compromising its capacity.
4. Introduction
Industries such as glass, textile, ceramic, photography, metallurgy, rubber and plants producing
industrial inorganic chemicals and pigments discharge vanadium and vanadium compounds into
water bodies (Anirudhan and Radhakrishnan, 2010, Wang et al., 2012) . Vanadium is a toxic
115
metal and exists in environment both as tetravalent [V(IV)] and pentavalent [V(V)] ion (Aureli
et al., 2008). The pentavalent form is more toxic than the tetravalent one and is the most stable
species under oxidation conditions (Filik and Yanaz, 2009, Patel et al., 1990) . Vanadium ions
enter the human body through food chain and contaminated water consumption and cause
breathing disorders, paralysis and negative effects on the liver and kidney (Zhang et al., 2014). It
is therefore essential to treat vanadium-polluted streams prior to their discharge to the
environment. Vanadium in wastewater streams are commonly treated by techniques which
include but not limited to microbial fuel cell technology (Zhang et al., 2012) , photocatalytic
reduction (Sturini et al., 2013) , ion exchange and solvent extraction (Li et al., 2013b) ,
adsorption (Hu et al., 2014, Kaczala et al., 2009) and biological methods (Pennesi et al., 2013).
Amongst the aforementioned vanadium removal techniques, adsorption has been widely studied
due to its inherent robust features that makes it the preferred choice. The technique requires the
use of adsorbing materials (adsorbents). A wide variety of adsorbents have been studied and
used for the removal of V(V) ions from aqueous solutions (Namasivayam and Sangeetha, 2006,
Peacock and Sherman, 2004, Jansson-Charrier et al., 1996, Nakajima, 2002). For optimum
adsorption the choice of the adsorbent material is important and depends on its cost, availability
and suitability to remove the given pollutant.
Because of its sorption ability for numerous pollutants, its abundance, high ion exchange
capacity, relatively high surface area and relatively low cost; natural zeolite has been recognized
as an efficient adsorbent for a large number of water treatment applications including removal of
organics and heavy metals from industrial wastewaters (Shahbazi et al., 2014, Moussavi et al.,
116
2011, Wang and Peng, 2010, Motsi et al., 2011, Nguyen et al., 2015b, Egashira et al., 2012,
Motsa et al., 2011, Inglezakis et al., 2003, Shavandi et al., 2012). To improve adsorption
performance of natural zeolites, the surface needs to be modified. Our studies have shown that
conducting polymers such as polypyrrole and polyaniline offer good prospects for
functionalization due to their nontoxicity, environmentally stability, ease of preparation and high
efficiency for removal of toxic metals in water (Bhaumik et al., 2011a, Bhaumik et al., 2011b,
Bhaumik et al., 2012, Bhaumik et al., 2013a, Bhaumik et al., 2013b, Setshedi et al., 2014,
Setshedi et al., 2015).
After adsorption it can be difficult to separate polymerized powdered adsorbents from water
using conventional separation methods such as filtration and sedimentation. In an effort for
easy separation pellet adsorbents are also used. However, they are associated with decreased
adsorption capacity depending on the binder used which increases the bulk density of the
extrudate (Shams and Mirmohammadi, 2007, Sun et al., 2008). A method combining magnetic
separation as an alternative for treatment of wastewaters has for this reason received
attention in recent years (Chen et al., 2013, Chen et al., 2011b, Faghihian et al., 2013, Li et
al., 2013a, Lv et al., 2014b). Because of size and magnetic property, magnetic adsorbents
have large surface area, are highly dispersible in water and exhibit superparamagnetic properties
which make them easily separable from aqueous solution by the application of external magnetic
field (O’Handley, 2000). The main advantage of this technology is that it can dispose a mass of
wastewater in a very short period of time (Feng et al., 2010) and reduces production of large
quantity of sludge associated with sedimentation and filtration.
117
This study investigates the sorption properties of magnetized natural zeolite-polypyrrole
composite in the removal of vanadium from aqueous solution. The prepared adsorption
material is characterized and its adsorption ability explored in a batch set up. Appropriate
kinetic and equilibrium models are applied to obtained data in order to get insight into
vanadium adsorption characteristics.
4.1. Experimental
4.1.1. Chemicals and methods
Natural zeolite (clinoptilolite) was provided by Ajax Industries CC in Cape Town, South Africa.
Pyrrole (Py, 99%) Anhydrous iron (III) chloride (FeCl3), hydrochloric acid (HCl), sodium
hydroxide (NaOH), iron (II) chloride tetrahydrate (FeCl3·6H2O), iron (III) chloride hexahydrate
(FeCl2·4H2O) were purchased from Sigma-Aldrich. Synthetic vanadium stock solution was
prepared by dissolving 2.39 g of sodium metavanadate (anhydrous, 99.9% metals basis-Sigma
Aldrich) powder in 1000 mL deionized water. The mixture was placed on a hot plate where it
was magnetically stirred and heated at a temperature of 50 0C for 10 min.
4.1.2. Methods
Magnetic zeolite- polypyrrole composite (MZPPY) was prepared by the method reported in our
previous study (Mthombeni et al., 2015b). Accordingly, zeolite was magnetized by chemical co-
precipitation of Fe3+
and Fe2+
ions at a ratio of 1:1 in the presence of zeolite under N2
environment. During magnetization, the solution was continuously stirred and cooled down to
room temperature. The obtained magnetic zeolite powder was washed repeatedly with deionized
118
water until a neutral pH was attained. The resulting magnetic zeolite was dried under vacuum for
12 h. 3 g of the obtained dry magnetic zeolite powder was dispersed into 80 mL of deionized
water and ultrasonicated for 20 min. 0.8 mL of pyrrole was added drop-wise and 6 g of oxidant
(FeCl3) was also added. The mixture was shaken for 3 h at room temperature to allow
polymerization to go into completion. Finally, acetone (10 mL) was added to stop the reaction.
Afterwards, the precipitated magnetic zeolite-polypyrrole composite was vacuum filtered,
thoroughly rinsed with distilled and dried at 800C for 6 h.
4.1.2.1.Characterization
The functional groups on the surface of MZPPY were characterized using Fourier transform
infrared spectroscopy (FTIR performed on a Perkin–Elmer Spectrum100 spectrometer),
equipped with an FTIR microscopy accessory and a diamond crystal. The field emission
scanning electron microscopy (FE-SEM) was used to characterize the morphology of the
adsorbents. FE-SEM images with energy dispersive X-ray analysis (EDX) were obtained on a
LEO, Zeiss SEM. High resolution transmission electron microscope (HR-TEM) images were
obtained from a JEOL JEM 2100F microscopy with a LAB6 filament operating at 200kV. X-ray
diffractometer with a CuKα (λ= 1.5406Å) monochromated radiation source operated at 45.0 kV
and 40.0 mA.
4.1.2.2.Batch adsorption, isotherms and kinetics studies
The effect of pH was studied by varying the initial pH of the V(V) solution within the range of
2–8, and contacting 0.15 g of raw zeolite, magnetite, polypyrrole and MZPPY with 50 mL of
100 mg/L V(V) solution. Sorption isotherm studies were carried out in a temperature controlled
thermostatic shaker. 50 mL of V(V) solutions of concentration ranging from 25 to 250 mg/L
were contacted with 0.15 g of magnetic zeolite-polypyrrole composite in 100 mL plastic
119
bottles. The samples were shaken for 24 h at varying temperatures of 298, 308 and 318 K at 160
rpm. The experiments were performed at pH 4.5. At the end of the experiment, samples were
filtered using syringe filters and the residual vanadium concentration was measured by
Inductively Couple Plasma- Emission Spectroscopy (ICP-OES). The V(V) removal percentage
was determined by using the following equation:
% 𝑹𝒆𝒎𝒐𝒗𝒂𝒍 = (𝑪𝒐−𝑪𝒆
𝑪𝒐) × 𝟏𝟎𝟎 ( 4-1)
Meanwhile the equilibrium sorption capacity was determined using the following equation:
𝒒𝒆 = (𝑪𝒐−𝑪𝒆
𝒎) × 𝑽 (4-2)
where qe is the equilibrium amount of V(V) adsorbed per unit mass of adsorbent (mg/g), m is the
sorbent mass (g), V is the sample volume (L), Ce is the equilibrium concentration (mg/L) and Co
is the initial concentration (mg/L) . The adsorption kinetics experiments were carried out in a
batch reactor with initial V(V) concentrations of 50, 80 and 110 mg/L. A mass of 3 g of the
sorbent was used. The reactor was continuously stirred at a speed of 250 rpm. At predetermined
time intervals a 5 mL sample was withdrawn from the reactor. Each sample was immediately
filtered using syringe filters and the resulting filtrates were analysed using Inductively Couple
Plasma- Emission Spectroscopy (ICP-OES) at a wavelength of 292.4 nm to determine the
vanadium residual concentration. The amount of vanadium removed at any given time was
calculated using Eq. (5-3).
𝒒𝒕 =𝑪𝟎−𝑪𝒕
𝒎× 𝑽
( 4-3)
120
where Ct (mg/L) is the vanadium residual concentration at time t and qt (mg/g) is the time
dependent amount of vanadium adsorbed per unit mass of adsorbent.
4.2. Effect of co-existing ions and desorption studies
Usually industrial wastewater discharges may contain vanadium along with other ions which
may have competitive effect on V(V) adsorption. Therefore, it is important to investigate the
competitive effect of coexisting ions on V(V) removal using MZPPY. 50 mL of 100 mg/L V(V)
solutions containing 100 mg/L of various components (Ni2+
, Cu2, Cl
-, NO3
- or SO4
2- ) were
contacted with 0.15 g at pH 4.5 for 24 hrs. At the end of the experiment, the residual
concentration of V(V) was determined.
Meanwhile the reusability of the adsorbent is an important economic factor; therefore,
adsorption-desorption studies were conducted to investigate the potential of reusing the MZPPY
composite. Initially, equilibrium experiments were carried out at 25 ° C using 0.15 g of MZPPY
which was contacted with 50 mL of 100 mg/L solution containing V(V) at pH 4.5. Thereafter
desorption experiments were performed by contacting 0.15 g of V(V) loaded adsorbent with
varying concentration (0.1 M – 1 M) and volumes (10 – 40 mL) of NaOH solution for 24 h.
For regeneration, the spent MZPPY composite was treated with 2 M HCl for 3 h and then reused.
To evaluate the reusability of the adsorbent, three adsorption-desorption cycles were performed.
4. Results and Discussion
4.2.1. Characterization of the adsorbent
Fe3O4 precipitates on the external surface and internal surface of pores of zeolite and coats the
surface when the mixture of Fe2+
/Fe3+
is added to the alkaline reaction mixture. Figure 4.1a
121
shows the FTIR of magnetized zeolite spectrum which shows the characteristics bands related to
zeolites. A band at 3620 cm−1
is assigned to acidic hydroxyls Si–O(H)–Al, while a band at
Scheme 4. 1.
3430 cm−1
is due to the vibration of the bonds O–H–O. A band at 1630 cm−1
is connected to
deformation vibration of absorbed water (Korkuna et al., 2006). A band at 1020 cm−1
is due to
the asymmetric valence vibrations in tetrahedra SiO4 (Faghihian et al., 2013). Another band near
645 cm−1
is considered to arise from Si–O–Na bond (Korkuna et al., 2006) . The characteristic
band of iron oxide which is Fe–O stretching vibration bond of is observed at 578 cm−1
(Maity
and Agrawal, 2007).
122
Figure 4-1. FTIR spectra of the magnetized zeolite (a) and MZPPY (b) before and (c) after
adsorption with V(V).
123
The FTIR spectrum of the MZPPY before adsorption (Figure 4.1b) demonstrates peaks at 958 -
824 cm-1
, 1080 cm-1
, 1423 cm-1
and 1513 cm-1
that are considered to arise from C–H
deformation, C–H stretching, conjugated C–N stretching and pyrrole ring stretching,
respectively (Setshedi et al., 2013, Rezaul Karim et al., 2007) . New peaks at wavelengths of 813
cm-1
and 980 cm-1
are observed on the adsorbent spectra after adsorption (Figure 4-1c). These are
considered to arise from V–O stretching bands that are reported to appear in the region between
800 and 1000 cm-1
(Frost et al., 2003, Frost et al., 2006, Palmer et al., 2007). A peak at 1643 cm-
1 is also observed which is attributed to the stretching vibration of V=O (Hu et al., 2014), and
therefore confirms the adsorption of V(V).
The morphology of the adsorption media was evaluated by scanning electron microscope and
images are shown in Figure 4. 2 for natural zeolite (Figure 4.2a), magnetized zeolite (Figure
4.2b), magnetic zeolite- polypyrrole media before (Figure 4.2c) and after adsorption (Figure
4.2d). It can be seen that polypyrrole completely covered the surface of magnetized zeolite and
has cauliflower-like structure which is a characteristic morphology of polypyrrole deposits
(Boukerma et al., 2006) and that no change is observed in the morphology of the adsorbent after
adsorption. Natural zeolite particles act as growth centres for polypyrrole chains and
consequently aid in making the MZPPY dense and compact structure (Rashidzadeh et al., 2013).
Figure 4.3 shows the TEM images of MZPPY composites before adsorption at two different
magnifications. The TEM images of the composites indicate that the magnetic zeolite particles
are embedded in the polypyrrole matrix. The darker shaded core is magnetic Fe3O4 nanoparticles
which are within the structure of zeolite and light shaded area is polypyrrole in the composite,
due to the different electron penetrability. Energy dispersive X-rays (EDX) was used to analyse
124
the elemental constituents of the sorption media before and after V(V) adsorption. It is observed
that additional peaks of V at 4.94 and 5.41 keV appear upon V(V) adsorption (Figure 4.5). The
EDX data therefore provide a direct evidence for V(V) adsorption on the surface of the MZPPY.
Figure 4-2. Scanning electron microscopic images of natural zeolite (a), magnetized zeolite (b),
magnetic zeolite–polypyrrole media before (c) and after adsorption (d).
The magnetic property of MZPPY is as shown in Scheme 5.1 in which the MZPPY after V(V)
adsorption are attracted by magnet. This further confirms that the composites are magnetic in
nature and provide a good prospect for an effective application in magnetic adsorption for
industrial wastewater treatment. Figure 4-6 shows the XRD patterns for natural zeolite and
magnetized natural zeolite, respectively. The x-ray diffraction pattern of the natural zeolite show
125
peaks of major intensity corresponding to clinoptilolite. The x-ray diffraction pattern of modified
natural zeolite shows peaks corresponding to clinoptilolite and Fe3O4 crystal plane.
Figure 4-3. Transmission electron microscopic images of MZPPY.
Figure 4-4. Energy dispersive X-rays (EDX) spectra of MZPPY before V(V) adsorption
0 1 2 3 4 5 6 7 8
Inte
nsity a
.u.
Energy keV
C
O
FeNaMg
Al
Si
Cl
Cl
KFe
Fe
(a)
126
Figure 4-5. Energy dispersive X-rays (EDX) spectra of MZPPY after V(V) adsorption
0 1 2 3 4 5 6 7 8
Inte
nsity a
.u.
Energy keV
C
O
FeNaMg
Al
SiCl
Cl
K
V
V
Fe
Fe
(b)
127
Figure 4-6. XRD pattern of natural zeolite and magnitezed zeolite
128
4.2.2. Effect of pH
The effect of initial solution pH on V(V) percentage removal by the MZPPY composite and its
constituents zeolite, polypyrrole and magnetite is shown in Figure 5. 7. It is evident that the
highest (58%)V(V) removal efficiency was found for the MZPPY composite, whereas for
polypyrrole, magnetite and zeolite removal efficiencies recorded were 49%, 26 % and 18%,
respectively, at pH 4-5.
Figure 4-7. Effect of pH on the adsorption of V(V) by MZPPY, polypyrrole, magnetite and
zeolite.
The results indicate that V(V) removal is dependent on solution pH and that maximum removal
occurs at pH 4-5. The effectiveness of sorption at different initial solution pH of V(V) is greatly
129
related to the speciation of V(V) in aqueous solution as well as the surface characteristics of the
adsorbent. The decrease in removal percentages at below pH 3.0 might be attributed to the
existence of VO2+ ion, which experiences electrostatic repulsion from the protonated amino
groups of the adsorbent (Anirudhan and Radhakrishnan, 2010, Zhang et al., 2014). By increasing
the initial solution pH, the surface of MZPPY is deprotonated and its exchange capacity
therefore increased. A decrease in vanadium removal at pH range of 6-9 could be explained by
the polymerization ability of vanadium ions at various pH levels. This polymeric species could
limit the removal efficiency of vanadium ions even at optimum pH especially when high
concentration of vanadium is involved (Crans, 2005, Cran and Tracey, 1998). In general, the
study on the effect of pH reveals that combining different materials into a composite provides
improved performance in adsorption.
4.2.3. Adsorption kinetics of V(V) onto MZPPY
Figure 5. 8 presents the sorption kinetics data of vanadium for three different initial
concentrations (50 ppm - 110 ppm) as a function of contact time. The adsorption rate of V(V)
onto MZPPY was very rapid at the initial stage of adsorption and then progressed at a slower rate
to reach the final equilibrium stage. Such rapid uptake is indicative of readily available and
accessible sorption sites in the composite. The time dependent kinetics sorption data was
analysed using non-linear method on Origin 9.1 software by pseudo-first (Tseng et al., 2010),
pseudo-second order (Ho and McKay, 2000) and Elovich (Chien and Clayton, 1980) kinetic
models given in Eqs. (4-4)–(4-6):
𝒒𝒕 = 𝒒𝒆(𝟏 − 𝒆−𝒌𝟏𝒕) (4-4)
𝒒𝒕 =𝒒𝒆
𝟐𝒌𝟐𝒕
𝟏+𝒒𝒆𝒌𝟐𝒕 ( 4-5)
𝒒𝒕 =𝟏
𝜷𝐥𝐧(𝜶𝜷) +
𝟏
𝜷𝐥𝐧(𝒕) (4-6)
130
where k1 and k2 are the pseudo first-order and pseudo-second-order rate constants, α and β are the
Elovich equation constants.
Figure 4-8. Adsorption kinetics plot of V(V) onto MZPPY at three different concentrations and
with experimetal data fitted to kinectic models
The kinetic parameters are summarised in Table 4.1. The kinetic model that best describes the
kinetic sorption data was assessed by considering the correlation coefficient (R2) and sum of
squared errors (SSE). Based on the R 2
and the SSE values, it is suggested that the adsorption
kinetic data is best described by the pseudo-second-order equation (Eq. (4-5)). This may suggest
131
that chemisorption was part of the mechanism for the removal of V(V) by the MZPPY which
further corroborates results of EDX.
Table 4-1. Kinetics parameters for V(V) adsorption onto MZPPY
Co
(ppm)
Pseudo-first –order model
k1
(L/min)
qe
(mg/g)
R2 SSE
110 0.248 25.2820 087 10.020
80 0.615 19.3832 0.70 38.427
50 0.193 10.2070 0.75 103.777
Pseudo-second –order model
k2
(g-mg/min)
qe R2 SSE
110 0.012 27.285 0.99 2.099
80 0.042 20.524 0.97 9.28
50 0.029 10.805 0.98 18.32
Elovich model
α β R2 SSE
110 33.834 0.7434 0.89 8.163
80 1978.80 0.5707 0.90 12.502
50 120.260 0.3111 0.93 29.171
4.2.4. Adsorption isotherm
The adsorption isotherm of V(V) onto the MZPPY sorbent was investigated to evaluate the
material adsorption capacity at temperatures 298, 308 and 318 K. The results are given in Figure
132
4.9. It is observed that the adsorption capacity increased with an increase in metal concentration
and temperature. The equilibrium adsorption results were modelled using Langmuir (Langmuir,
1916), Freundlich (Freundlich, 1906), Sips (Langmuir-Freundlich), Redlich–Peterson (Redlich
and Peterson, 1959) and Dubinin–Radushkevich (Dubinin et al., 1947) adsorption isotherms (Eq
4-7 – 4-11):
𝑞𝑒 =𝑞𝑚𝑏𝐶𝑒
1+𝑏𝐶𝑒 ( 4-7)
𝑞𝑒 = 𝐾𝐹𝐶𝑒1 𝑛⁄
(4-8)
𝑞𝑒 =𝐾𝑠 𝐶𝑒
𝛽𝑠
1+ 𝑎𝑠 𝐶𝑒𝛽𝑠
4-9)
𝑞𝑒 =𝐾𝑅𝑃𝐶𝑒
1+𝑎𝑅𝑃𝐶𝑒𝑏𝑅𝑃
( 4-10)
𝑞𝑒 = (𝑞𝑚)exp (−𝐾𝐷𝑅𝜀2) (4-11)
𝜀 = 𝑅𝑇𝑙𝑛 (1 +1
𝐶𝑒) (4-12)
where qm the monolayer maximum adsorption capacity (mg/g ); KF, KS, KRP, are Freundlich,
Sips, and Redlich–Peterson constants (L/g); b, as, aRP are affinity coefficients (L/mg ); 1/n is a
heterogeneity coefficient; KDR is the adsorption energy related constant (mol2/kJ
2) and 𝜺 is the
Polanyi potential (kJ/mol) given by Eq. (4-12). Matches between experimental and model results
are shown in Figures 4.10-4.12. From these figures isotherm parameters were extracted and are
summarized in Table 4.2. The Langmuir maximum adsorption capacity (qm) and b values
increase with an increase in temperature from 65.1 to 75.0 mg/g and 0.10 to 0.17 (L/mg),
respectively. The Freundlich isotherm parameters also increase with an increase in temperature
while the Redlich-Peterson, Sips and Dubinin-Radushkevich isotherm parameters do not show
any specific trend.
133
To determine which isotherms fitted the experimental data, both the regression coefficient (R2)
and the sum of squared errors (SSE) values were considered. It can be concluded from the R2 and
SSE values summarised in Table 4-2 that the Langmuir, Redlich-Peterson and Sips isotherms
describe the experimental data fairly well.
Figure 4-9. Effect of temperature of V(V) sorption onto MZPPY
134
Figure 4-10. Equilibrium isotherms of V(V) sorption onto MZPPY and nonlinear isotherm plots:
(-) Langmuir model, (---) Freundlich model at 298 K ,(···) Langmuir Freundlich, (-..-..)
RedlichPeterson, (-.-.) Dubinin–Radushkevich
Figure 4-11. Equilibrium isotherms of V(V) sorption onto MZPPY and nonlinear isotherm plots:
(-) Langmuir model, (---) Freundlich model at 308 K,(···) Langmuir Freundlich, (-..-..)
RedlichPeterson, (-.-.) Dubinin–Radushkevich
135
Figure 4-12. Equilibrium isotherms of V(V) sorption onto MZPPY and nonlinear isotherm plots:
(-) Langmuir model, (---) Freundlich model at 318 K ,(···) Langmuir Freundlich, (-..-..)
RedlichPeterson, (-.-.) Dubinin–Radushkevich
Further, favorability of interaction between V(V) and MZPPY was explored by considering the
dimensionless constant called equilibrium parameter RL (Eq. 5-13).
𝑅𝐿 = 1
(1 + 𝑏𝐶0) (4-13)
The RL values between 0 and 1 indicate favourable adsorption. The RL values were found to be
between 0.050 and 0.031 for all the concentrations of V(V) studied and temperatures indicating
that the process was favourable.
136
Table 4-2. Isotherms parameters for vanadium adsorption onto MZPPY
Temperature K Langmuir isotherm
Qm
(mg/g)
b
(L/mg)
R2 RL SSEs
298 K 65.056 0.101 0.99 0.050 2.782
308 K 70.013 0.119 0.99 0.043 3.696
318 K 74.967 0.166 0.99 0.031 3.613
Temperature K Freundlich isotherm
KF
(L/g)
1/n R2 SSEs
298 K 18.269 0.279 0.96 13.377
308 K 20.001 0.288 0.94 27.911
318 K 21.774 0.319 0.96 7.998
Temperature K Redlich-Peterson isotherm
Kr
(L/g)
ar
(L/mg)
n R2 SSEs
298 K 6.225 0.086 1.024 0.99 2.719
308 K 6.427 0.050 1.145 0.99 0.865
318 K 17.440 0.365 0.879 0.99 1.415
Temperature K Dubinin–Radushkevich isotherm
Qm
(mg/g)
Kad
(mol2/kJ
2)
E R2 SSEs
298 K 54.326 0.028 4.217 0.95 18.714
308 K 57.962 0.018 5.200 0.96 18.038
318 K 59.865 0.007 8.170 0.88 68.428
Temperature K Sips isotherm
Ks
(L/g)
βs
(L/mg)
as R2 SSEs
298 K 5.109 1.118 0.0816 0.99 2.581
308 K 4.121 1.350 0.065 0.99 0.849
318 K 17.329 0.745 0.195 0.99 1.269
Meanwhile Table 4-3 presents a comparison of Langmuir adsorption capacity with other earlier
reported adsorbents in literature. A direct comparison with other adsorbents is difficult because
of the different experimental conditions applied. However, the V(V) uptake value obtained in
this study is highly competitive.
137
Table 4-3. Comparison of maximum sorption capacity of other adsorbents for vanadium removal
Adsorbent Qm (mg/g) References
PGTFS-NH3 + Cl
- 45.86 (Anirudhan and Radhakrishnan, 2010)
Chitosan-Zr(IV) composite 208 (Zhang et al., 2014)
Zr(IV)-SOW 56.75 (Hu et al., 2014)
ZnCl2 activated carbon 24.9 (Namasivayam and Sangeetha, 2006)
Metal hydroxide adsorbents:
TiO2
Fe(OH)3 and β-FeOOH
FeOOH
25.06
111.11
45.66
(Naeem et al., 2007)
MZPPY 74.97 Current study
The thermodynamic parameters such as Gibbs free energy, enthalpy and entropy were also
determined. The Gibb’s free energy change is obtained using following relationship:
∆𝐺 ͦ = −𝑅𝑇 𝑙𝑛 𝑏 ( 4-14)
where R is the gas constant (8.314 J/mol/K) , T is temperature in K and b is the Langmuir
constant . Enthalpy and entropy changes were determined from Eq. (4-15).
ln 𝑏 = −∆𝐻 ͦ
𝑅𝑇+
∆ 𝑆 ͦ
𝑅 (4-15)
The values of ∆ 𝑮 ͦ are negative for all temperatures (Table 4.4.), and decreases from -42.8 to -
58.8 kJ/mol when the temperature is increased from 298 to 318 K indicating that the sorption of
vanadium on MZPPY was thermodynamically spontaneous and favourable at high temperature.
138
Table 4-4. Thermodynamics parameters for the adsorption of V(V) on MZPPY composite.
T (K) ∆H (kJ /mol) ∆S ( J/mol/K) ∆G (kJ /mol)
298 -42.761
308 19.468 46.029 -48.311
318 -58.751
The positive value of ∆𝑯 ͦ indicates that the adsorption process is endothermic in nature while
the positive value of ∆𝑺 ͦ indicates an increased randomness state at the solid-solution interface
during the fixation of vanadium on the active sites of the adsorbent (Hu et al., 2014).
4.2.5. Effect of co-existing ions and desorption studies
The effects of competitive common ions, such as chloride, sulfate, nitrate, copper and nickel
(Cl¯; SO4
2- ; NO3
-; Cu
2+; Ni
2+) that are normally present in mining and industrial waters were
studied. The results in Figure 4.13 indicate that the presence of these commons ions do not affect
adsorption of V(V) onto MZPPY. These results suggest that the MZPPY can be used to remove
vanadium in effluents even in the presence of common competing ions, which are frequent in the
environment. The economics of an adsorption process is determined by how many times an
adsorbent can be used in adsorption-desorption cycle. For this reason 0.1-1.0 M NaOH was used
to desorb V(V) bound on MZPPY. 0.3 M NaOH was found to be the best performing
concentration for desorption of vanadium from MZPPY. Thus, different volumes of 0.3 M
NaOH were used (10–40 mL) in order to investigate the possibility of regenerating the MZPPY
adsorbent at minimum volumes of NaOH. It was observed that there was no significant
difference between the volumes used for regeneration.
139
Figure 4-13. Effect of coexisting ions on the adsorption of V(V) onto MZPPY
Figure 4.14 shows that the adsorbent could be successfully used for two consecutive cycles but
the adsorption capacity dropped to 53% in the third cycle. These results correspond to our
previous results that showed that the adsorbent can be successfully used in two cycles
(Mthombeni et al., 2015b).
140
Figure 4-14. Adsorption–desorption cycles of V(V) onto MZPPY
4.3. Conclusions
The removal of V(V) ion from aqueous solution was carried out in a batch adsorption mode
using MZPPY. The MZPPY adsorbent exhibited effectiveness in the removal of vanadium from
aqueous solution. The vanadium uptake was depended on the initial concentration and
temperature. Adsorption of V(V) ions increased with an increase in temperature and initial
concentration. The sorption kinetics of V(V) was described by the pseudo-second-order kinetic
model. The adsorption process was found to be spontaneous and endothermic in nature. The
equilibrium sorption data fitted well to the two- parameter Langmuir and the three-parameter
isotherms; LangmuirFreudlich and Redlich–Peterson models. The Langmuir maximum
141
adsorption capacity increased with an increase in temperature from 65.1 to 75.0 mg/g when
temperature was increased from 298 K to 318 K. Adsorption of V(V) in the presence of other
competitive ions was not affected. The adsorbent retained the original adsorption capacity after
two adsorption–desorption cycles, which confirms the reusability of the adsorbent
142
CHAPTER 5
Fixed bed Column studies for Chromium (VI) and Vanadium (V) removal from aqueous
using of Magnetic Zeolite Polypyrrole
Abstract
The removal of Cr(VI) and V(V) using magnetic zeolite polypyrrole (MZPPY) composite was
investigated in a fixed bed adsorption column. The effects of adsorbent mass, influent Cr(VI)
and V(V) concentration and flowrate on the adsorption bed performance characteristics of
adsorbent was explored. Experimental results confirmed that the breakthrough curves were
dependent on bed mass, initial Cr(VI) and V(V) concentration and flowrate. Three kinetic
models; Yoon–Nelson, Thomas, Bohart–Adams were applied to the experimental data to predict
the breakthrough curves to determine the characteristic parameters of the column that are useful
for process design. The Yoon–Nelson and Thomas models were found appropriate for
description of the breakthrough curves. Based on performance data, it can be concluded that the
MZPPY composite is a competitive sorption media for Cr(VI) and V(V) removal from aqueous
environments. The performance of the column was better at lower flow rate and initial metal ion
concentration and higher bed mass.
5. Introduction
Mining and metallurgical industries are associated with heavy metals wastewaters that are
directly or indirectly discharged into the environment increasingly, especially in developing
countries. Heavy metals are not biodegradable and tend to accumulate in living organisms and
many heavy metal ions are known to be toxic or carcinogenic (Hu et al., 2005b). Heavy metals
such chromium and vanadium are often discharge to the environment from industrial activities.
Chromium exists in two forms as non-toxic trivalent Cr(III) ions play an essential role in the
143
metabolism of plant and animals, and hexavalent toxic Cr(VI) ions which is known to be
carcinogenic (Sugashini and Begum, 2015). The toxicity of vanadium is also dependent on its
oxidation state, the pentavalent form V(V) is more toxic than the tetravalent V(IV) one (Filik and
Yanaz, 2009, Patel et al., 1990). Several treatment methods to remove heavy metals from
mining and industrial wastewater have been reported. Adsorption compared with other methods
appears to be an attractive process in view of its efficiency and capacity of removing heavy metal
ions over wide range of pH and to a much lower level, ability to remove complex form of metals
that is generally not possibly by other methods. It is also environmentally friendly, cost effective
and its ease of operation compared to other processes with which it can be applied in the
treatment of heavy metal containing wastewater (Rao et al., 2010).
Liquid-phase adsorption separation and purification process can be configured in a number of
ways such as batch, fixed-bed, and fluidized bed. Fixed bed adsorption columns have become
the most widely used due to advantages of such as: inherent production of high quality drinking
water, its simplicity, ease of operation and handling as well as the possibility of in situ
regeneration. The fixed bed mode is suitable for use as a point-of-use (POU) system and ideal for
individual household and small communities (Onyango et al., 2009, Setshedi et al., 2014). Batch
and continuous column operation are the main modes used in water treatment. Batch operations
are only limited to treatment of small quantity of water and easy to apply on laboratory studies
but difficult to apply for field studies (Bharathi and Ramesh, 2013, Kumar and Bandyopadhyay,
2006, Hasan et al., 2010). The adsorption capacity obtained from batch equilibrium experiments
is useful in providing fundamental information about the effectiveness of material in removing
metal ions. Batch experimental data are generally not applicable to most treatment system such
144
as column operations where contact time is not sufficiently long for the attainment of
equilibrium (Bharathi and Ramesh, 2013, Bhaumik et al., 2013b, Low and Lee, 1991). The
continuous adsorption systems have significant process engineering advantages, such as treating
large volume of wastewater, easy to scale-up from laboratory scale processes to industrial scale
and simple operation (Kumar and Bandyopadhyay, 2006, Long et al., 2014, Nguyen et al.,
2015a).
Therefore, it is important to evaluate the performance of magnetic zeolite polypyrrole (MZPPY)
in a fixed-bed column condition for the removal of Cr(VI) and V(V). The aim of this study is to
perform continuous column study for the removal of Cr(VI) and V(V) and to investigate the
performance of MZPPY composite under various process variables such as flow rate, bed mass
and influent metal ion concentration. Thomas, Bohart –Adams and Yoon-Nelson models were
used to analyze the experimental data.
5.1. Materials and methods
Magnetic zeolite polypyrrole composite (MZPPY), Cr(VI) and V(V) solutions were prepared by
the methods reported in sections 3.2. and 4.2.
5.2. Column studies
Column experiments were performed using a polyvinyl chloride (PVC) column of 20 mm
diameter and 30 cm length. Each column was packed with MZPPY sorbent with glass beads and
glass wool placed in the upper and bottom ends of each column to hold the adsorbent media.
Water contaminated with metal ions (Cr(VI) and V(V)) was pumped vertically upwards
continuously through the column using a peristaltic pump (Figure 5.1.). The effect of several
process variables was studied and is described in section 5.2.1. to 5.2.4.
145
Figure 5-1. Schematic diagram of a fixed-bed adsorption column set up
5.2.1. Effect of bed mass of magnetic zeolite polypyrrole
Contaminated water containing Cr(VI) and V(V) at initial concentration of 20 mg/L and 47 mg/L
respectively was pumped through the column using 1, 2.5, and 5 g of MZPPY for each run, at a
flow rate of 1.5 mL/min. The treated water samples were collected at 60 min time intervals and
analyzed for residual metal ion. Residual Cr(VI) concentration was analyzed using the UV–Vis
spectrometer at 540 nm using 1,5 diphenylcarbazide reagent. Meanwhile the residual V(V)
concentration was analyzed using inductively couple plasma-emission spectroscopy (ICP-OES)
at a wavelength of 292.4 nm.
5.2.2. Effect of flow rate
Cr(VI) and V(V) contaminated waters at initial concentrations of 20 mg/L and 47 mg/L
respectively were pumped vertically at a flow rate of 1.5, 2.5, and 4.5 mL/min through columns
packed with 2.5 g MZPPY sorbent.
146
5.2.3. Effect of initial metal concentration
The bed mass was kept constant at 2.5 g while water contaminated with Cr(VI) and V(V) was
pumped upwards at a constant flow rate of 1.5 mL/min through the column. The initial
concentrations of Cr(VI) were 20, 35 and 74 mg/L. The initial concentrations of V(V) were 47,
63 and 84 mg/L. Treated water sample collection and metal ion analysis was done described in
Section 5.2.1.
5.2.4. Environmental sample
An environmental water sample with a Cr(VI) concentration of 5 mg/L was collected from
Lanxess Chrome Mine in Rustenburg. The physical composition of the water is given in
summarized Table 5-1.The water was pumped vertically upward at a flowrate of 1.5 mL/min
through columns packed with 2.5 g MZPPY media.
Table 5-1. Selected physico-chemical property of mine water from Lanxess Chrome Mine
(Rustenburg)
Parameters Units Values SANS 241
Chromium mg/L 5.0 < 0.1
pH 6.98 5–9.5
Iron mg/L 1.43 < 0.2
Sulphate mg/L 364 < 400
Nitrate mg/L 1.28 < 10
Aluminium mg/L 0.06 < 0.3
Chloride mg/L 84.0 <200
Magnesium mg/L 4.37 <70
147
5.2.5. Breakthrough analysis
The objective of operating a fixed bed column is to reduce the concentration of a given
contaminant to a value below the maximum allowable concentration. The performance of the bed
is directly related to the number of bed volumes processed before the breakthrough point
(Bhaumik et al., 2013b, Setshedi et al., 2014, Mthombeni et al., 2012). In the study the
breakthrough point was when Cr(VI) and V(V) concentration in the treated water sample reached
0.05 mg/L and 0.1 mg/L, respectively (allowable limit for domestic use). The capacity of the bed
at breakthrough point is determined from the following equation:
𝑞𝑏 =𝐶0
𝑚∫ (1 −
𝐶𝑡
𝐶0)
𝑉𝑏
0𝑑𝑉 (5-1)
where qb is the bed capacity at breakthrough point (mg/g), m is the bed mass (g), C0 is the initial
adsorbate concentration (mg/L), Ct is the concentration of treated water of the outlet solution
(mg/L)at any time t and Vb is the volume processed at breakthrough point (L).
The total cost of the fixed bed is determined by the volume of the bed and size of the column
(Onyango et al., 2009). The performance of a fixed bed column is directly related to the number
of bed volumes (BV) processed before the breakthrough point is reached (Onyango et al., 2009,
Mthombeni et al., 2012). The number of bed volumes at breakthrough point is given by the
following equation:
𝐵𝑉 =𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑎𝑡 𝑏𝑟𝑒𝑎𝑘𝑡ℎ𝑟𝑜𝑢𝑔ℎ 𝑝𝑜𝑖𝑛𝑡(𝐿)
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑏𝑒𝑑(𝐿) (5-2)
The adsorbent exhaustion rate (AER) is defined as the mass of adsorbent material exhausted per
volume of water treated at breakthrough point and is expressed as:
148
𝐴𝐸𝑅 =𝑀𝑎𝑠𝑠 𝑜𝑓 𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 (𝑔)𝑖𝑛 𝑐𝑜𝑙𝑢𝑚𝑛
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑡𝑟𝑒𝑎𝑡𝑒𝑑(𝐿) (5-3)
The rate of at which the adsorbent becomes exhausted is an indication on how often the
adsorbent will be replaced and the operating costs involved. Low values of AER imply good
performance of the bed (Onyango et al., 2009).The empty bed contact time (EBCT), is used to
determine the optimum adsorbent usage in a fixed-bed column. The capital and operating costs
for a fixed-bed adsorption system are dependent on the EBCT and the adsorbent exhaustion rate
(Ko et al., 2000, Ma et al., 2014, McKay and Bino, 1990). The EBCT is defined as the time
required for the influent to fill the volume of the adsorbent bed and is a direct function of the
influent flow rate and volume of the adsorbent bed. It enables system designers to determine the
adsorption column size required (Ma et al., 2014).
𝐸𝐵𝐶𝑇 =𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑏𝑒𝑑 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
𝑓𝑙𝑜𝑤𝑟𝑎𝑡𝑒 (𝑚𝐿/𝑚𝑖𝑛) (5-4)
5.3. Results
5.3.1. Effect of bed mass
By varying the bed mass of the adsorbent the amount of active sites available and the contact
time between metal ions and the adsorbent are increased. The effect of varying bed mass is
shown in Figure 5-2 and 5-3 as breakthrough curves for Cr(VI) and V(V) adsorptions
respectively.
149
Figure 5-2. Breakthrough curves for adsorption of Cr(VI) from water using MZPPY at different
bed masses. Flow rate of 1.5 mL/min and initial concentration of 20 mg/L at pH 2.
It is observed that the breakthrough time increases with an increase in bed mass. The
experimental breakthrough time for removal of Cr(VI) and V(V) increased from 900 to 4440 min
and from 60 to 840 min, respectively when the bed mass was increased from 1 to 5 g. The
adsorbent is saturated faster at lower bed mass due to lesser active site, which results in an early
breakthrough point. Furthermore, the lower bed mass which have shorter bed heights may lead to
axial dispersion as the predominant mass transfer phenomenon and thus automatically reducing
the radial diffusion of metal ions.
150
Figure 5-3.Breakthrough curves for adsorption of V(V) from water using MZPPY at different
bed masses. Flow rate of 1.5 mL/min and initial concentration of 47 mg/L at pH 4.5.
In comparison with the higher bed mass which has longer bed height, the mass transfer zone gets
broadened and more sorption sites are available for adsorption (Singha et al., 2012). Treated
water volumes processed at breakthrough point for Cr(VI) removal were 1.35; 2.52 and 6.66 L
for 1; 2.5 and 5 g, respectively. The volumes processed for V(V) were 0.09; 0.63 and 1.26 L 1,
2.5; and 5 g, respectively. The empty bed contact time (EBCT) also increases from 2.51 to 10.47
min with an increase in bed mass from 1 to 5 g. (See Table 5-2 and 5-3)
151
Table 5-2. Summary of results at breakthrough point for Cr(VI) removal using MZPPY
Time at
breakthrough
point
tb (min)
Capacity
mg/g
Volumes
processed at
breakthrough
L
Bed volume
processed
L
AER
g/L
EBCT
(min)
Mass of MZPPY adsorbent (g)
1 900 28.99 1.35 287 0.74 2.51
2.5 1680 21.6 2.52 308 0.99 5.44
5 4440 28.63 6.66 424 0.75 10.47
Flow rate (mL/min)
1.5 1680 28.99 2.52 308 0.99 5.44
3 960 24.77 2.88 353 0.87 2.72
4.5 660 25.66 2.97 364 0.84 1.81
Initial Cr (VI) concentration (mg/L)
20 1680 28.99 2.52 308 0.99 5.44
35 1500 31.55 2.25 275 1.11 5.44
74 1200 53.2 1.8 220 1.39 5.44
Environmental sample
5 mg/L 6600 19.3 9.9 1212 0.25 5.44
152
Table 5-3. Summary of results at breakthrough point for V(V) removal using MZPPY
5.3.2. Effect of flowrate
The adsorption process is depended on the contact time between the adsorbent and the adsorbate
(Setshedi et al., 2014). The results in Figure 5-4 and 5-5 indicate that at a lower flow rate more
processing time was required for breakthrough point to be reached. The volume of treated water
at breakthrough point was found to be 2.52; 2.88 and 2.97 L for Cr (VI), and 0.63; 0.36 and 0.27
for V(V) with corresponding bed volumes of 308; 353 and 364 for Cr(VI) removal and 77; 44
Time at
breakthrough
point
tb (min)
Capacity
mg/g
Volumes
processed at
breakthrough
L
Bed volume
processed
L
AER
g/L
EBCT (min)
Mass of MZPPY adsorbent (g)
1 60 6.37 0.09 24 11 2.51
2.5 420 11.85 0.63 77 4 5.44
5 840 12.01 1.26 80 4 10.47
Flow rate (mL/min)
1.5 420 11.89 0.63 77 4 5.44
3 120 6.7 0.36 44 7 2.72
4.5 60 5.08 0.27 33 9 1.81
Initial V(V)concentration (mg/L)
47 420 11.89 0.63 77 4 5.44
63 360 13.57 0.54 66 5 5.44
84 60 3.02 0.09 11 28 5.44
153
and 33 for V(V) removal at 1.5; 3 and 4.5 mL/min, respectively. The AER values obtained for
Cr(VI) removal are 0.99, 0.87 and 0.84 g/L, while the AER values for V(V) removal are 3.97,
6.94 and 9.26 at the flow of 1.5, 3, 4.5 mL/min, respectively. The decrease in performance of
the bed with an increase in flow rate may be due to the increase in the movement of mass
transfer zone, which results in a decrease adsorbent–adsorbate contact time.
Figure 5-4. Breakthrough curves for the removal Cr(VI) from water using MZPPY at different
flow rates. Initial concentration of 20 mg/L, bed mass of 2.5 g.
154
Figure 5-5. Breakthrough curves for the removal V(V) from water using MZPPY at different
flow rates. Initial concentration of 47 mg/L, bed mass of 2.5 g.
5.3.3. Effect of initial concentration
The effect of initial concentration on breakthrough performance of the MZPPY adsorbent was
studied by varying the initial Cr(VI) and V(V) concentrations from 20 to 74 mg/L and 47 to 84
mg/L, respectively. The breakthrough curves obtained from the tests are shown in Figure 5-6
and 5-7. It is observed from the graphs that the breakthrough point decreases with an increase in
initial concentration Cr( VI) and V(V).
155
Figure 5-6. Effect of initial concentration of Cr(VI) on breakthrough performance MZPPY
at pH 2.
Volume of treated water at breakthrough point decreased when the initial concentration of metal
ions was increased, and are 2.52; 2.25 and 1.8 L for 20; 35 and 74 mg/L Cr(VI) concentration,
respectively. And the volumes processed for 47; 63 and 84 mg/L V(V) concentration were
recorded at 0.63; 0.54 and 0.09 L; respectively. Also at higher initial concentration binding sites
are saturated faster which results in an earlier breakthrough point. At high metal ion
concentrations, there is an increase in driving force for mass transfer which leads to faster
saturation of the adsorbent's active sites, resulting in an increase in the adsorbent exhaustion rate
156
(Chen et al., 2012). The bed volumes at breakthrough point are 308; 275 and 220 for Cr(VI)
while the bed volumes for V(V) are 77; 66 and 11; respectively. The AER values obtained for
Cr(VI) removal was 0.99; 1.11 and 1.39 and for V(V) removal was 3.97; 4.63 and 27.78;
respectively. High values of AER at higher initial metal ion concentration indicates that in
operation the adsorbent will need to be replaced at a higher frequency when processing water
containing higher initial metal ion concentration.
Figure 5-7. Effect of initial concentration of V(V) on breakthrough performance of MZPPY at
pH 4.5.
157
5.3.4. Removal of Cr(VI) from environmental water sample
The water sample at pH value 6.98 and 5 mg/L of Cr(VI) was also tested. The water sample also
contained 1.43 mg Fe/L; 364 mg SO42-
/L; 1.28 NO3-/L; 0.06 mg Al/L; 84 mg Cl
-/L and 4.37 mg
Mg/L as coexisting ions. As a result the breakthrough point was observed at 6600 min (Figure
5.8) with the volume of water treated recorded as 9.9 L. The bed volume processed was 1212
and AER value of at 0.25 g/L was achieved. The good performance of the column was due to the
low initial concentration of Cr(VI) in the sample. High bed volume processed indicates that
effects of most coexisting ions are negligible but it was indicated earlier in section 3.2.3 that
SO42-
ions reduced the adsorption capacity of MZPPY.
Figure 5-8. Breakthrough curves for the removal of Cr(VI) from environmental water sample
with initial concentration of 5 mg/L; at flow rate 1.5 mL/min; bed mass 2.5 g and pH 2.
158
5.3.5. Breakthrough curves modelling
To model the breakthrough behaviors of Cr(VI) and V(V) sorption using MZPPY composite
three mathematical models, namely; Yoon–Nelson, Thomas and Bohart–Adams were used to
match and model fixed bed column experimental data.
5.3.5.1. Thomas model
Thomas equation is one of the most widely used models often used to interpret fixed bed column
adsorption data. This model assumes that adsorption process data follows Langmuir isotherm
and also obeys second-order reversible reaction kinetics. It also assumes that there is absence of
internal and external diffusion limitation (Karimi et al., 2012). The model can be expressed as:
𝑪𝒕
𝑪𝟎=
𝟏
𝟏+𝐞𝐱𝐩 (𝒌𝑻𝒉𝒒𝟎𝒎
𝒗−𝒌𝑻𝒉𝑪𝟎𝒕)
(5-5)
where kTh is the Thomas rate constant (mL/min mg), qo is the adsorption capacity (mg/g), Co is
the initial metal ions concentration (mg/L), Ct is the effluent metal ions concentration at time t
(mg/L), m is the mass of adsorbent (g), v is the feed flow rate (mL/min), and t is time (min). The
parameters of fitted models are listed in Table 5-4. The high correlation coefficients of which
was ≥ 0.98, for all the experimental data conditions suggests that the experimental data are well
described by the Thomas model. However, adsorption capacities determined by the model did
not match the experimental adsorption capacities. Meanwhile the kTh values increased with an
increase in both the adsorbent bed mass and flow rate and decreased as the initial Cr(VI) ion
concentration was increased. The q0 values increased for all experimental Cr(VI) sorption
conditions.
159
The values of kTh and q0 calculated for V(V) sorption process listed in Table 5-5 indicate that the
values decrease as the bed mass increased. It is also observed that kTh and q0 values increased as
the influent flow rate is increased. The kTh values did not show any specific trend while the q0
values decrease with increase in initial concentration. In addition, an increase in kTh values is also
noted as the flow rate increases.
5.3.5.2. Bohart-Adams model
The Bohart- Adams model is based on assumption that equilibrium is not instant and the
adsorption rate is controlled by external mass transfer (Wang et al., 2015). The model can be
expressed as follows:
𝐶𝑡
𝐶0= 𝑒𝑥𝑝 (𝑘𝐴𝐵𝐶0𝑡 − 𝑘𝐴𝐵𝑁0
𝑍
𝑈0) (5-6)
where kBA is the kinetic constant (L/mg·min), No is the saturation concentration (mg/L), z is the
bed depth of the fixed bed column (cm) and U0 is the superficial velocity (cm/min) defined as the
ratio of the volumetric flow rate v (cm3/min) to the cross-sectional area of the bed (cm
2). The
values of kBA and No are listed in Table 5-4 and 5-5. It is observed that the experimental
breakthrough curves are not described well by the Bohart–Adams model based on low regression
values. The poor simulation by Bohart-Adams model can be explained by the fact that the
isotherms exhibited Langmuir equilibrium characteristics and cannot be approximated
sufficiently by a rectangular isotherm (Chu, 2010).
160
Table 5-4. Thomas, Adams–Bohart and Yoon–Nelson models constants for the Cr(VI) sorption
Thomas Model Parameters Bohart-Adams Model Parameters Yoon-Nelson Model Parameters
kTh
(mL/min
mg)
q0 (mg/g) R2 kBA (L/mg
min)
N0
(mg/L)
R2 kYN (min
-1)
𝝉 (min) R
2
Mass of adsorbent (g)
1 2.03×10-4
22705 0.99 9.45×10-5
20409 0.96 0.00439 1626.72 0.99
2.5 4.44×10-4
32744 0.99 1.48×10-4
13211 0.96 0.00958 2526.54 0.99
5 4.73×10-4
35111 0.99 1.58×10-4
14036 0.99 0.00892 5419.11 0.99
Flow rate (ml/min)
1.5 4.44×10-4
32744 0.99 1.48×10-4
13211 0.96 0.00958 2526.54 0.99
3 4.46×10-4
31973 0.99 1.06×10-4
12535 0.89 0.00963 1233.54 0.99
4.5 7.92×10-4
31709 0.98 1.93×10-4
14192 0.92 0.0170 815.561 0.98
Initial Cr(VI) concentration (mg/L)
20 4.44×10-4
32744 0.99 1.48×10-4
13211 0.96 0.00958 2526.54 0.99
35 2.93×10-5
43350 0.99 9.91 ×10-5
14952 0.95 0.00964 2200.21 0.99
74 1.8×10-5
67974 0.99 6.24 ×10-5
23475 0.95 0.0134 1520.99 0.99
Environmental sample
1.59×10-3
24438 0.99 7.48×10-4
7695 0.99 0.0087 7404.32 0.99
5.3.5.3. Yoon-Nelson model
This model is based on the assumption that the rate of decrease in the probability of adsorption
for each adsorbate molecule is proportional to the probability of adsorbate adsorption and the
probability of adsorbate breakthrough on the adsorbent (Bhaumik et al., 2013b, Chen et al., 2012,
Setshedi et al., 2014). Yoon–Nelson model for a single component system can be expressed as :
161
𝐶𝑡
𝐶0−𝐶𝑡= exp (𝑘𝑌𝑁𝑡 − 𝜏𝑘𝑌𝑁) (5-7)
where kYN is the rate constant in (min-1
), τ is the time required for 50% adsorbate breakthrough
(min) and t is the processing time (min). Results listed in Table 5.4 indicate that kYN values
increases with an increase in flowrate and in initial concentration for Cr(VI) adsorption whilst τ
values are observed to increase for an increase in adsorbent mass.
Table 5-5. Thomas, Adams–Bohart and Yoon–Nelson models constants for the V(V) adsorption
Thomas Model Parameters Bohart-Adams Model Parameters Yoon-Nelson Model Parameters
kTh
(mL/min
mg)
q0 (mg/g) R2 kBA (L/mg
min)
N0
(mg/L)
R2 kYN (min
-1)
𝝉 (min) R
2
Mass of adsorbent (g)
1 9.79×10-5
46818 0.99 3.52×10-5
21750 0.92 0.00463 659.89 0.99
2.5 4.23×10-5
46390 0.99 2.75×10-5
14675 0.94 0.00314 1040.64 0.99
5 4.53×10-5
31523 0.99 3.03×10-5
9154 0.95 0.00337 1414.26 0.99
Flow rate (ml/min)
1.5 4.23×10-5
46390 0.99 2.75×10-5
14675 0.94 0.00314 1040.64 0.99
3 5.37×10-5
55412 0.99 2.66×10-5
19423 0.90 0.00399 695.72 0.99
4.5 9.79×10-5
62030 0.97 4.14×10-5
21425 0.87 0.00727 414.33 0.99
Initial V(V) concentration (mg/L)
47 4.23×10-5
46390 0.99 2.75×10-5
14675 0.94 0.00314 1040.64 0.99
63 5.75×10-5
32108 0.99 2.36 ×10-5
16414 0.91 0.00363 849.23 0.99
84 4.74×10-5
27075 0.99 1.33 ×10-5
18807 0.90 0.00402 532.71 0.99
162
For V(V) results listed in Table 5.5 indicate that the kYN values increase when flowrate and
initial V(V) concentration are increased while it decrease when the adsorbent bed mass is
increased. τ values are observed to increase with an increase in adsorbent mass and decreases
when the initial V(V) and flow rate are increased . The Yoon–Nelson model fitted very well the
experimental breakthrough data as a consequence the R2 values are 0.99.
5.4. Conclusions
Removal of Cr(VI) and V(V) using magnetic zeolite polypyrrole composite in dynamic column
studies was investigated by varying the process variables such as bed mass of the adsorbent,
initial Cr(VI) and V(V) concentrations and flowrate. The performance of the column was better
at lower flow rate and initial metal ion concentration and higher bed mass. Low values of AER
and large bed volumes were observed at lower metal ion concentration and higher bed mass for
Cr(VI) and V(V) removal indicating good performance. However better performance was
observed at higher flow rate for chromium and lower flow rate for vanadium. Thomas, Bohart–
Adams and Yoon–Nelson mathematical models were used to interpret the experimental
breakthrough curves. Both Yoon–Nelson and Thomas models were found to describe
breakthrough curves well under all different experimental conditions. This study has shown that
MZPPY can be used to treat water contaminated with both Cr(VI) and V(V) in fixed bed
operation mode.
163
CHAPTER 6
Adsorptive removal of manganese from aqueous solution using batch and column studies
Abstract
Improper disposal of toxic wastes from mining and chemical process industries can yield higher
manganese concentrations well above those normally found in environmental water. Bentonite/
Natural zeolite as a potential adsorbent for manganese was prepared. Mn(II) adsorption onto
bentonite/zeolite (B/Z) from aqueous solutions was explored using batch and packed-bed
column studies. The effectiveness of B/Z as an adsorbent for Mn(II) removal was evaluated as a
function of solution pH, initial Mn(II) concentration, temperature, bed mass and time. Batch
adsorption isotherm data at solution pH of 6, was satisfactorily described by the Freudlich
isotherm model, while the kinetic data fitted well with the pseudo second order kinetic model.
Sorption of Mn(II) onto B/Z results showed that the in presence of co-existing ions there was as
significant effect on Mn(II) removal. At breakthrough point, high bed volumes and low AER
values were attained at lower flow rate, lower initial manganese concentration and higher
adsorbent bed mass. Thomas and Yoon–Nelson models fitted experimental breakthrough curves
well compared to Bohart– Adams.
6. Introduction
South Africa has the world’s largest resources of manganese which is estimated to be around
80%. Manganese is used in the manufacture of iron and steel alloys, batteries, glass and
fireworks, a significant fertilizer for plants, food additive for stocks and catalyst for organic
synthesis (Li et al., 2010). Manganese is also found in the effluents of mine waters, either
neutral or acid (AMD) (Silva et al., 2012). Mn is one of the most widely used metals in the
world and one of the important indexes of water pollutants (Ma et al., 2013).
164
Manganese occurs naturally in many surface water and groundwater sources and in soils that
may erode manganese into waters. Improper disposal of dry-cell batteries or other toxic wastes
from industries can yield higher manganese concentrations well above those normally found in
environmental water (WHO, 2011) and cause significant harm to public health (Frisbie et al.,
2012). Manganese at lower doses is an essential nutrient for humans and animals. Manganese at
elevated concentrations is a powerful neurotoxin which affects the nervous system and causes
learning disabilities and intellectual impairment in children (Bouchard et al., 2007, Bouchard et
al., 2011). Manganese intoxication is also linked to Mn induced Parkinsonism (Lucchini et al.,
2009, Aschner et al., 2009) , low fetal weight (Grazuleviciene et al., 2009, Zota et al., 2009) ,
infant mortality (Hafeman et al., 2007, Spangler and Spangler, 2009) and increased cancer rates
(Spangler and Reid, 2010).
Several treatment methods to remove Mn from mining and industrial wastewaters have been
reported. Mn in wastewater are commonly removed by precipitation (Pakarinen and Paatero,
2011, Silva et al., 2010, Silva et al., 2012, Zhang et al., 2010b, Nishimura and Umetsu, 2001) ,
ion-exchange (White and Asfar-Siddique, 1997), oxidation and filtration (Roccaro et al., 2007,
Piispanen and Sallanko, 2010), coagulation and flocculation (Fu-Wang et al., 2009), biosorption
(Vijayaraghavan et al., 2011) and adsorption (Al-Rashdi et al., 2011). A lot of efforts have been
made on the development of new technologies in which technological, environmental and
economic constraints are taken into consideration. The technologies have to avoid generation of
secondary waste and involve materials that can be recycled and reused on an industrial scale
(Ngomsik et al., 2006).
165
Adsorption compared with other methods appears to be an attractive process in view of its
efficiency and capacity of removing heavy metal ions over wide range of pH and to a much
lower level, ability to remove complex forms of metals that is generally not possibly by other
methods. It is also environmentally friendly, cost effective and easy to operate compared to
other processes with which it can be applied in the treatment of acid mine and heavy metal
containing wastewater (Rao et al., 2010). Various low-cost materials have been studied for Mn
such as agricultural wastes (Li et al., 2010, Adeogun et al., 2013, García-Mendieta et al., 2012,
Ali and Saeed, 2015) and natural materials (Taffarel and Rubio, 2010) (Ates, 2014, Belviso et
al., 2014, Shavandi et al., 2012, Dawodu and Akpomie, 2014, Cvetković et al., 2010) locally
available in certain regions. The abundance, versatility and low cost of natural clays make them
attractive as adsorbents (Vhahangwele and Mugera, 2015). Clays are widely used of clays
because of their high specific area, high chemical and mechanical stability, and variety of surface
and structural properties. The adsorption ability clay is determined by the chemical nature and
pore structure (Kubilay et al., 2007). Zeolites have been widely used in adsorption of heavy
metals due to their unique physical and chemical properties (Dimirkou and Doula, 2008).
Bentonite is known as a clay material consisting essentially of smectite mineral of the
montmorillonite group (Erdem et al., 2009) . Bentonite possess a net negative structural charge
attributed to isomorphic substitution of cations in crystal lattice which gives them the ability to
attract and hold cations such as toxic heavy metals (Kubilay et al., 2007, El-Korashy et al.,
2016). However it is well-known that Ca-bentonite has lower sorption capacity of heavy metals
and in order to enhance the capacity the use of zeolite has been proposed (Du et al., 2015, Hong
et al., 2012).
166
To reduce the hazards associated with manganese in aqueous wastes, natural zeolite and
bentonite is considered to be one of the most viable options. Consequently this study explores the
application of natural zeolite/bentonite (B/Z) clay as an adsorbent for remediation of Mn(II) ions
in aqueous solution in batch and continuous fixed bed modes.
6.1. Materials and Methods
6.1.1. Chemicals and reagents
Natural zeolite was provided by Ajax Industries (Cape Town, South Africa). Bentonite calcium
clay with particle size < 53 µm was provided by GW Mineral Resources (Wadeville South
Africa). Hydrochloric acid (HCl), sodium hydroxide (NaOH) and sodium chloride (NaCl) were
purchased from Sigma Aldrich. Manganese (II) nitrate tetrahydrate (Mn(NO3)2.4H2O) was used
to prepare a stock solution of 1000 mg/L of Mn(II). Acid mine drainage sample was collected
from a gold mine in West Rand (South Africa).
6.1.2. Methods
Zeolite was crushed to < 160 µm and then conditioned with 1 M NaCl. Calcium bentonite and
conditioned zeolite were physically mixed at a ratio 1:1 to prepare a Calcium Bentonite/Zeolite
(B/Z) adsorbent. Finally, the material was used for characterization and adsorption studies.
Fourier transform infrared spectroscopy (FTIR) was done on a Perkin Elmer Spectrum 100
spectrophotometer. X-ray diffraction (XRD) patterns measurements were performed on a
PANalytical X’Pert PRO-diffractometer using Cu Kα radiation (wavelength, λ = 1.5406
A˚ ) with variable slits at 45 kV/40 mA.
167
6.1.3. Batch adsorption experiments
6.1.3.1. Effect of pH and sorbent dosage
Batch adsorption experiments were performed by contacting B/Z adsorbent with 60 mL
Mn(II) solution in 100 mL plastic bottles. The samples were agitated for 24 h at 150 rpm in a
temperature controlled thermostatic shaker. The effect of the composition of the clay
adsorbent was tested at pH 6 with synthetic water containing 50 mg/L of Mn(II), while the
effect of pH of acid mine water containing 25 mg/L Mn(II) ions was done at different solution
pH using 1 g of the adsorbent at 25 ͦ C . The samples were then filtered, using 0.45 um
Whattsman filter paper and the filtrate analyzed using an inductively couple plasma-emission
spectroscopy (ICP-OES) for manganese residual. The effect of adsorbent dosage on the
removal of Mn (II) was explored by varying the mass of the B / Z adsorbent from 0.05 to
0.8 g using 60 mL of AMD solution (25 mg/L) at pH 6. The removal efficiency of Mn(II) was
determined by the following equation.
% 𝑟𝑒𝑚𝑜𝑣𝑎𝑙 = (𝐶𝑜−𝐶𝑒
𝐶𝑜) × 100 (6-1)
where Co and Ce are the initial and equilibrium concentrationS (mg/L) of Mn(II),
respectively.
6.1.3.2. Kinetics and equilibrium isotherm studies
Kinetic studies for adsorption of Mn(II) were carried out at pH 6 in a 1 L batch reactor. 4 g
of B/Z was added to the 1000 mL of s yn t h e t i c solutions with initial concentrations of
6 5 , 1 0 0 , and 200 mg/L. The studies were also conducted on AMD sample containing initial
168
concentrations of 25, 34, and 45 mg/L of Mn(II) ions. The reactor was stirred at a constant
stirring speed of 150 rpm with an overhead stirrer. A 5 mL sample solution was withdrawn at
predetermined time intervals from the reaction mixture and filtered through a syringe filter
and analyzed for residual Mn(II) concentrations. Amount of manganese adsorbed was
calculated using the following equation
𝒒𝒕 =𝑪𝟎−𝑪𝒕
𝒎× 𝑽
(6-2)
where Ct (mg/L) is the manganese residual concentration at time t and qt (mg/g) is the time
dependent amount of manganese adsorbed per unit mass of adsorbent.
Adsorption isotherms were generated by contacting 0.4 g of adsorbent with 60 mL of
Mn(II) containing synthetic solution ranging from 50 to 300 mg/L in 100 mL plastic
sample bottles at pH 6. The samples were shaken for 24 h at varying temperatures (298 K,
308 K and 318 K) at 150 rpm. The equilibrium sorption capacity of the adsorbent was
calculated by following equation
𝑞𝑒 =𝐶0−𝐶𝑒
𝑚× 𝑉 (6-3)
where qe (mg/g) is the equilibrium amount of Mn(II) adsorbed per unit mass (m) of
adsorbent.
6.1.3.3. Effect of co-existing ions and desorption studies
Usually industrial wastewater discharges may contain other ions which may have competitive
effect on Mn(II) adsorption. It is very important to investigate the competitive effect of
coexisting ions on Mn(II) removal. 60 mL of 50 mg/L Mn(II) solutions containing 50 mg/L of
169
various components (Mg2+
, Fe2+
and Al2+
) were contacted with 0.4 g of B/Z adsorbent at pH 6
for 24 hrs. At the end of the adsorption experiment, the residual concentration of Mn (II) was
determined.
The reusability of B/Z adsorbent was investigated by performing adsorption-desorption studies.
Initially, equilibrium experiments were carried out at 25 ° C using 0.4 g of B/Z adsorbent which
was contacted with 60 mL of 50 mg/L solution containing Mn(II) at pH 6. Thereafter desorption
experiments were performed by contacting Mn(II) loaded adsorbent with HNO3, HCl, NaCl,
NaOH and deionized H2O as potential eluents at different concentrations (0.1, 0.5 and 1 M).
Then the adsorbent was washed and reused. To investigate the reusability of the adsorbent, four
adsorption-desorption cycles were performed.
6.1.4. Column studies
Continuous fixed bed column adsorption studies were performed using a lab scale columns to
evaluate the performance of the adsorbent for Mn(II) removal from aqueous solutions. For this a
cylindrical perspex glass tube of 3.4 cm internal diameter and a height of 30 cm were used as an
adsorption column. The column was packed with bentonite/zeolite adsorbent between glass
wool and supported by inert glass beads. The influent Mn(II) solution was pumped in an upward
flow through the packed bed using a peristaltic pump. Subsequently, the effluent samples were
collected at predetermined time intervals and were analyzed for Mn(II). To evaluate the
performance of the bed three main parameters that affect the shape of the breakthrough curves
were investigated namely; the flow rate, bed mass and initial influent concentration.
170
6.1.4.1. Effect of bed mass, flow rate and initial concentration
The effect of bed mass was evaluated by varying the B/Z adsorbent media mass from 5 g to 15 g,
with 50 mg/L initial Mn(II) concentration at pH 6 and a flow rate of 5 mL/min. The effect of
initial Mn(II) concentration was evaluated by varying the initial Mn(II) concentration between 50
mg/L and 150 mg/L at pH 6, while keeping the bed mass and flow rate constant at 10 g and 5
mL/min, respectively. The effect flow rate was assessed by pumping water vertically upward at a
flow rate of 2.5, 5.0 and 7.5 mL/min, while the initial Mn(II) concentration, pH 6 and bed mass
(10 g) were kept constant. The acid mine drainage (AMD) sample with Mn(II) concentration of
25 mg/L was also treated using a packed column . The AMD sample was pumped vertically
upward at a flow rate of 5 mL/min through columns packed with 10 g bentonite/zeolite clay.
6.2. Results and discussions
6.2.1. Characterization
The x-ray diffraction pattern in Figure 6-1. (a) of the natural zeolite shows peaks of major
intensity corresponding to clinoptilolite (C) which are observed at 10, 11.4, 17.4, 23, 26, 28.2,
30.2 and 32 ͦ at 2θ (Olad and Naseri, 2010, Nezamzadeh-Ejhieh and Khorsandi, 2014). The XRD
pattern of natural Ca-bentonite is given in Figure 6.1. (b). The figure indicates that the clay is
composed mainly of montmorillonite (M) component which are seen at 5.76, 17.60, 19.84, and
34.80 ͦ (2θ) (Caglar et al., 2009). Other species present in small quantities are dolomite and
quartz. Ca-Bentonite/Zeolite x-ray diffraction pattern indicates that the clay contains both
clinoptilolite (C) and montmorillonite (M) component Figure 6.1. (c)) and that there was no
change in the crystalline structure of the adsorbent be fo re and after adsorption.
171
Figure 6-1. XRD patterns of (a) raw zeolite, (b) Ca-bentonite and (c) Ca-betonite/Zeolite before
and after adsorption
FTIR spectra of bentonite/zeolite before and after adsorption are shown Figure 6-2. A band at
3611 cm −1
is assigned to acidic hydroxyls Si–O(H)–Al group, while a band at 3402 cm −1
is due
to the vibration of the bonds O–H–O. A band at 1625 cm −1
is connected to deformation
vibration of absorbed water (Setshedi et al., 2013). A band at 1116 cm −1
is due to the
asymmetric valence vibrations in tetrahedra SiO4 (Duranoğlu et al., 2012). An intense peak is
observed at 986 cm-1
due to the Si–O–Si stretching modes (Caglar et al., 2009). New peaks and
at 1003, 930, 856 and 783 cm-1
are observed after adsorption suggesting that Mn(II) interact
172
with functional groups present in the adsorbent and it is evident that the structure of the
adsorbent was altered due chemical adsorption process.
.
Figure 6-2. FTIR spectra of the bentonite/zeolite (a) before and (b) after adsorption with
Mn(II) ions.
6.2.2. Effect of sorbent dosage and pH
A comparative study was conducted to investigate the removal percentages of zeolite, bentonite
and the mixture of zeolite and bentonite at different mixing ratios. From the results in Figure 6-3
it is evident that the highest removal efficiency of 99% was achieved when the bentonite and
zeolite was physically mixed at a ratio of 1:1. For bentonite and zeolite only, the removal
efficiencies recorded were 37% and 59%, respectively.
173
Figure 6-3. Removal efficiency of zeolite, bentonite and bentonite/zeolite mixture
The solution pH is a significant parameter because it can influence the solubility of the metal
ions and induce some chemical reactions, such as hydrolysis, complexation, and precipitation
reactions during the adsorption process (Islam et al., 2015). The pH of the solution was varied
from 2 to 8 to investigate the effect of pH on Mn(II) ion adsorption. The results in Figure 6-4
indicate that Mn(II) removal increased with an increase in initial solution pH. The removal
efficiency increased from 8.7 % to 77.9 % when the pH was increased from 2 to 8. At lower pH
values the solution creates repulsive forces between H+ and Mn(II) ions resulting in low metal
uptake. As the pH in the solution was increased, the competition from H+ ions decreased and the
removal efficiency of Mn(II) increased (Masukume et al., 2014). High pH values can cause
manganese ion to precipitate hence pH of 6 was chosen as optimum pH.
174
Figure 6-4. The effect of pH on the adsorption of Mn(II) by B/Z adsorbent .
It is important to study the effect of adsorbent dose to determine the optimum amount of B/Z
adsorbent for a given initial concentration for effective adsorption of manganese. The results in
Figure 6-5. show that the removal efficiency of Mn(II) was increased from 69 % at 0.2 g to
85% at 0.8 g. An increase in the adsorbent dosage resulted in a corresponding increase in
Mn(II) ion removal owing to an increase in the number of active sites available for the
manganese ions to interact with. As the adsorbent dosage is increased the adsorption capacity of
the adsorbent is decreased mainly because of unsaturation of adsorption sites through the
adsorption process. At 0.4 g removal percentage was 76.32 % which was subsequently used for
other tests to be conducted.
175
Figure 6-5. Effect of B/Z adsorbent dosage on the removal of Mn(II) ions at an initial Mn(II)
concentration of 50 mgL, at pH 6 and a temperature of 298 K.
6.2.3. Effect of co-existing ions and desorption studies
Most wastewaters contain various kinds of ions including magnesium, aluminum and iron and
sulphates. The adsorption of Mn (II) onto the B/Z was ex p lo red in the presence of co-
existing ions. Results in Figure 6-6 indicate that there is a decrease in Mn(II) adsorption
capacity in the presence of coexisting ions. The adsorption capacity were found to be 3.2 ; 4.8
; 5.5 and 1.35 mg/g for such as Fe2+
, Mg2+
, Al2+
and SO42-
respectively compared to 7.1 mg/g
in the absence of these ions. According to the Irving-Williams series, that is, the order of
complex stabilities order, Mn2+
must be the least adsorbed metal ion, because the Mn(II)
oxidation states are not very stable (Güzel et al., 2008).
176
The reusability of the adsorbent is very important for economic and environmental impact.
The best eluent to desorption of Mn(II) was found to be NaCl compared to other eluents used
and the results are presented in Figure 6.7 , NaCl showed that it can efficiently desorb 76.6 % of
manganese from the adsorbent.
Figure 6-6. Effect of coexisting ions on the adsorption of Mn(II) onto B/Z.
The reusability of the adsorbent is shown in Figure 6-8 and it was observed that t h e r em o v a l
e f f i c i en c y of B/Z decreased remarkably from 95% to 73 % in second cycle and further
decreased to 48.6 and 31.4 % in the third and fourth cycle, respectively. The clays are
abundantly available and based on the reusability studies desorption studies might not be cost
effective.
177
Figure 6-7. The effect of desorption on Mn(II) from the adsorbent using different eluent at three
different concentrations
Figure 6-8. Adsorption–desorption cycles on the adsorption of Mn (II) onto B/Z.
178
6.2.4. Adsorption kinetics of Mn(II) onto B/Z adsorbent
The rate at which adsorption process take place is very important when designing batch
adsorption process. The adsorption kinetic studies of synthetic water a t three different initial
Mn(II) concentrations are presented in Figure 6.-9. A d sorption kinetics data of Mn(II) was
very rapid at the initial stage of adsorption and then progressed at a slower rate to reach the final
equilibrium stage. Such rapid uptake is indication of readily available and accessible adsorption
sites in the adsorption media.
Figure 6-9. Adsorption kinetics plot of Mn (II) onto B/Z at three different initial manganese ion
concentrations.
179
The adsorption kinetic data was analyzed using non-linear method on Origin 9.1 software by
pseudo- first (Tseng et al., 2010), pseudo-second order (Ho and McKay, 2000) and Elovich
(Chien and Clayton, 1980) kinetic models. Based on the correlation coefficient the pseudo-
second-order equation was found to be the best fit for the test results. The pseudo second order
adsorption capacities predicated were consistent with the experimental data results. The pseudo-
first, pseudo second order rate parameters and Elovich rate parameters for manganese
adsorption are l isted in Table 6-1. The constants k1 and k2 indicating the adsorption rate,
were found to decrease slightly when the initial concentration of Mn (II) increased from 50 to
200 mg/L. The values of k1 are found to be 0.039 – 0.024 (1/min) while values of k2
are 0.003-
0.001 (g-mg/min) for initial concentrations of 50 to 200 mg/L. The experimental data fitted to
pseudo first and pseudo second order kinetic models as well as Elovich kinetic model.
Table 6-1. Kinetics parameters for Mn(II) adsorption onto B/Z adsorbent using synthetic water
Co
(mg/L)
Pseudo-first –order model Pseudo-second –order model Elovich model
k1
(L/min)
qe
(mg/g)
R2 k2
(g-
mg/min)
qe
(mg/g)
R2 α β R
2
50 0.038 12.58 0.98 0.0035 14.05 0.98 1.61 0.375 0.95
100 0.029 15.56 0.98 0.0022 17.54 0.99 1.71 0.300 0.98
200 0.024 18.77 0.97 0.0014 21.43 0.99 1.78 0.247 0.99
180
Table 6-2. Kinetics parameters for Mn(II) adsorption onto B/Z adsorbent using AMD water
samples
Co
(mg/L)
Pseudo-first –order model Pseudo-second –order model Elovich model
k1
(L/min)
qe
(mg/g)
R2 k2
(g-mg/min)
qe
(mg/g)
R2 α β R
2
25 0.031 3.41 0.99 0.0082 4.05 0.99 0.289 1.16 0.98
34 0.053 3.93 0.99 0.0022 4.32 0.99 0.858 1.24 0.96
45 0.069 5.74 0.99 0.0011 6.41 0.99 1.78 0.247 0.96
Figure 6-10. Adsorption kinetics plot of Mn (II) onto B/Z using AMD samples with experimental
data fitted to Pseudo first and second order kinetic models as well as Elovich kinetic model.
181
The kinetic data of AMD water samples are presented in Figure 6-10. It is also observed that the
adsorption kinetics data of Mn (II) is very rapid at the initial stage of adsorption and then
progressed at a slower rate to reach the final equilibrium stage. The kinetic model that best
describes the kinetic adsorption data was assessed by considering the correlation coefficient (R2)
and according to the on the R2 values, it is suggested that that the adsorption kinetic data of
AMD samples are best described by the both pseudo first and pseudo second-order models. The
kinetic parameters are summarized in Table 6.2.
6.2.5. Adsorption isotherm
Adsorption isotherm is used to represent how Mn(II) interacts with the adsorbent. Langmuir and
Freundlich isotherms have been applied for the analysis of equilibrium data. Langmuir isotherm
refers to homogeneous adsorption process, where all adsorption sites possess equivalent affinity
for the adsorbate and each adsorbate molecule has constant enthalpies and sorption activation
energy (Foo and Hameed, 2010, Sun et al., 2014b). Freundlich model describes the non-ideal
and reversible adsorption and is widely applied in multilayer sorption on heterogeneous surfaces
where the adsorption heat and affinities are distributed non-uniformly (Foo and Hameed, 2010,
Sun et al., 2014b). Temperature affects the adsorption rate by altering the molecular interactions
and the solubility of the adsorbate (Setshedi et al., 2013). The adsorption isotherm of Mn (II)
was studied to evaluate the material adsorption capacity at temperatures 298, 308 and 318 K. The
results are given in Figure 6-11. It is observed that the adsorption capacity of Mn (II) ions
increased with an increase in Mn (II) concentration and temperature. The manganese adsorption
capacity of the B/Z adsorbent was found to be within the range of 4.8 – 16.6 mg/g. Table 6.3
represents a comparison of adsorption capacity with some other clay adsorbents previously
reported. A direct comparison of adsorbents is difficult because of the different applied
182
experimental condition. The adsorption capacity for most natural clays for removal of
manganese have low adsorption capacity as seen in Table 6.3 and the adsorption for this study
was also very low.
Table 6-3. Maximum sorption capacities of Mn ions on clay mineral
Clay Sorbent pH Adsorption capacity
mg/g
Reference
Clinoptilolite
Fe- Clinoptilolite
7.53
8.14
7.69
27.1
(Doula, 2006,
Dimirkou and Doula,
2008)
Serbian natural zeolite 5.5 10 (Rajic et al., 2009)
Brazilian vermiculite 6.4 31.53 (da Fonseca et al.,
2006)
Kaolite N/a 0.446 (Yavuz et al., 2003)
Natural zeolite
Al-Natural zeolite
NH4-Natural zeolite
6
6
6
7.69
25.12
24.33
(Ates, 2014)
(Ates, 2014)
(Ates, 2014)
Turkey natural zeolite 2.5
3.5
0.37
0.52
(Motsi et al., 2009)
Natural zeolite – Slovakia 7 0.076 (Shavandi et al., 2012)
Lignite
6
3.4 - 18.7
(Mohan and Chander,
2006)
Natural Bentonite/Zeolite 6 4.8-16.6 This study
n/a : not available
183
The equilibrium data were further fitted to Freundlich and Langmuir isotherms. The Freundlich
model described the equilibrium data satisfactorily based on the correlation factor R2.
Figure 6-11. Equilibrium isotherms of Mn(II) adsorption nonlinear onto B/Z adsorbent.
The isotherm parameters are presented Table 6-4. The constant related to the adsorption
capacity KF, was found to be 3.31, 4.34 and 4.88 for 298 K, 308 K, and 318 K, respectively. The
KF values confirm that adsorption is favored at high temperature. The heterogeneity coefficient,
184
1/n, on the other hand is 0.324, 0.289 and 0.302 for 298 K, 308 K, and 318 K, respectively. The
1/n values suggest heterogeneous sorption sites.
Table 6-4. Langmuir and Freundlich isotherms parameters for Mn(II) adsorption onto B/Z
adsorbent
Temperature Langmuir constants Freundlich constants
qo
(mg/g)
b
(L/mg)
R2 RL KF
(mg/g)
1/n R2
298 K 16.85 0.06 0.96 0.0051 3.31 0.32 0.99
308 K 17.68 0.09 0.98 0.0027 4.34 0.29 0.99
318 K 21.51 0.1 0.98 0.0015 4.88 0.30 0.99
The thermodynamic parameters such as changes in entropy and enthalpy for the adsorption of
manganese over the sorption media were determined by
ln𝑚𝑄𝑒
𝐶𝑒=
∆𝑆0
𝑅+
−∆𝐻0
𝑅𝑇 6-4)
where m is the adsorbent dose (g/L), Qe is the amount of manganese adsorbed per unit mass of
adsorbent (mg/g), Ce is the equilibrium concentration (mg/L) and T is the temperature in K. The
ratio mQe/Ce is referred to as adsorption affinity (Bhaumik et al., 2011a). The changes in
enthalpy (∆𝑯𝟎) and the entropy (∆𝑺𝟎) for the adsorption of manganese onto bentonite/zeolite
adsorbent were obtained from the plot of ln(mQe/Ce) vs 1/T and the values are 17.05 kJ/mol
and 32.09 J/mol K, respectively. The positive value of ∆𝑯 ͦ indicate an endothermic adsorption
185
process while the positive value of ∆𝑺 ͦ suggests an increased randomness state at the solid-
solution interface (Hu et al., 2014; Bhaumik et al., 2011a ).
The change in standard Gibbs energy (∆𝑮 ͦ) was computed from the values of change
∆𝑯 aͦnd ∆𝑺 ͦ and found to decrease from 7.5 kJ/mol at 298 K to 6.8 kJ/mol at 318 K, which
presumes to a spontaneous process.
6.2.6. Column studies
When designing adsorption fixed bed columns for water treatment operating parameters such as
bed mass, flow rate and initial metal ion concentration are of high significance. In this study the
effects of these parameters for adsorption of Mn(II) by bentonite zeolite were investigated. The
main objective of operating adsorption fixed bed columns is to reduce the concentration of a
given contaminant to a level lower than maximum allowable concentration. The capacity of the
bed at breakthrough point is determined from the following equation:
𝑞𝑏 =𝐶0
𝑚∫ (1 −
𝐶𝑡
𝐶0)
𝑉𝑏
0𝑑𝑉 (6-5)
For a given bed mass the adsorption performance is directly related to the number of bed
volumes processed before the breakthrough point (point when Mn (II) concentration in the
effluent is ≤ 1 mg/L) is reached (Bhaumik et al., 2013b, Setshedi et al., 2014, Mthombeni et al.,
2012). The number of bed volumes at breakthrough is given by the following equation:
𝐵𝑉 =𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑎𝑡 𝑏𝑟𝑒𝑎𝑘𝑡ℎ𝑟𝑜𝑢𝑔ℎ 𝑝𝑜𝑖𝑛𝑡(𝐿)
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑏𝑒𝑑(𝐿) (6-6)
186
The rate of at which the adsorbent becomes exhausted during the continuous column operation is
used to indicates how often the material is replaced and the operating costs involved. The
adsorbent exhaustion rate (AER) is defined as the mass of adsorbent material exhausted per
volume of water treated at breakthrough point (Onyango et al., 2009, Bhaumik et al., 2013b,
Setshedi et al., 2014, Mthombeni et al., 2012) and is expressed as :
𝐴𝐸𝑅 =𝑀𝑎𝑠𝑠 𝑜𝑓 𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙(𝑔)𝑖𝑛 𝑐𝑜𝑙𝑢𝑚𝑛
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑡𝑟𝑒𝑎𝑡𝑒𝑑(𝐿) (6-7)
Low value of AER implies good performance of the bed and is considered the main performance
indicator for comparing different operating conditions.
6.2.7. Effect of bed mass
To evaluate the effect of bed mass on Mn(II) adsorption, the influent concentration of manganese
and flow rate were kept constant at 50 mg/L and 5mL/min, respectively, while bed masses were
5 ;10 and 15 g. By increasing the bed mass of the bentonite-zeolite adsorbent and fixing the
flow rate and initial Mn(II) concentration in the influent, the amount of active sites in the
material and the contact time between the adsorbent and manganese ions are increased. The
effect of bed mass is shown in Figure 6-12. as breakthrough curves (BTCs). From the results it
can be observed that the breakthrough time increases with increasing bed mass. The
experimental breakthrough time increases from 180 to 360 min with an increase in bed mass
from 5 to 10 g. The adsorbent is saturated faster at low bed mass due to less active sites for
manganese to adsorb; which results in an early breakthrough point. At higher bed mass there are
more active sites for adsorption which results in late breakthrough time. Meanwhile, the volumes
processed at breakthrough point were 0.9, 0.9, and 1.8 L and for 5; 10; 15 g, respectively. This
is agreement with late breakthrough time observed at increased mass increases.
187
Figure 6-12. Effect of mass on breakthrough curves for adsorption of Mn (II) onto B/Z adsorbent
at a flow rate of 5mL/min and initial concentration of 50 mg/L
6.2.8. Effect of flowrate
Water contaminated with 50 mg/L Mn(II) ions was pumped in an upward flow mode at an
average volumetric flow rate of 2.5; 5 and 7.5 mL/min while the bed mass was maintained at
10g. The results in Figure 6-13.show that more processing time was required for lower flow rate
for breakthrough to be reached. The volume processed at breakthrough point was found to be
2.25; 1.5; and 1.8 with corresponding bed volumes of 55; 37; and 44 for 2.5, 5 and 7.5 mL/min,
respectively.
188
Figure 6-13. Effect of flowrate on breakthrough curves for adsorption of Mn (II) onto B/Z
adsorbent at a bed mass of 10 g and initial concentration of 50 mg/L
The AER values obtained are 4.44; 6.67 and 5.56 g/L at the flow 2.5; 5 and 7.5 mL/min,
respectively. The decrease in bed performance with an increase in flow rate maybe due to the
increase in the movement of mass transfer zone, which results in a decrease in adsorbent–
adsorbate contact time. Therefore, adsorbate does not have enough time to penetrate and diffuse
into the pores of the adsorbent thus limiting equilibrium to occur (Setshedi et al., 2014, Bhaumik
et al., 2013b, Masukume et al., 2014). From the AER and BV values is noticed that there was no
proper trend visible.
189
6.2.9. Effect of initial concentration
The metal ion concentration in contaminated water can vary depending on the nature of the
source water. It is very important to know how an adsorbent will perform under such conditions.
Consequently, the influent contaminated water at three initial concentrations of 50; 100 and 150
mg/L were used, while a bed mass of 10 g and a flow rate of 5 mL/min was maintained
throughout the experimental run. The breakthrough curves obtained from the test are shown in
Figure 6-14. It is observed that the breakthrough point decreases with an increase in initial Mn
(II) concentration. Volume of processed water at breakthrough point decreased with an increase
in initial Mn(II) concentration and were 1.5; 0.6; and 0.3 for 50; 100; and 150 mg/L,
respectively. At higher initial concentration binding sites are saturated faster which results in an
earlier breakthrough point. Moreover at high initial metal concentrations, increased driving force
for mass transfer leads to faster saturation of the adsorbent's active sites, resulting in an increase
in the adsorbent exhaustion rate (Chen et al., 2012). The bed volumes at breakthrough point were
volumes are 37; 15; and 7 L at the initial at concentrations of 50; 100 and 150 mg/L,
respectively. The AER values obtained are 6.67; 16.67 and 33.33 for 50; 100 and 150 mg/L,
respectively.
190
Figure 6-14. Effect of initial concentration on breakthrough curves for adsorption of Mn (II) onto
B/Z adsorbent at a bed mass of 10 g and flowrate of 5 mL/min
High values of AER at higher initial manganese ion concentration indicates that in operation the
bentonite/zeolite adsorbent will need to be replaced at a higher frequency when processing water
containing higher initial manganese concentration. This indicates that the poor column
performance of B/Z adsorbent.
6.2.10.Removal of Mn (II) from Acid mine water sample
Bentonite- zeolite sample was employed for adsorption of Mn(II) from real acid mine water
sample collected from Gold mine in Randfontein (South Africa). The sample was pretreated with
191
lime at the treatment plant and contained 25 mg/L of Mn(II) at pH 6. Table 6.6 shows the
concentrations of ions in mine water used in this study.
Table 6-5. Summary of results at breakthrough point
Time at
breakthrough
point
tb (min)
Capacity
mg/g
Volumes
processed at
breakthrough
L
Bed volume
processed
L
AER
g/L
Mass of B/Z adsorbent (g)
5 180 8.82 0.9 50 5.56
10 300 7.35 1.5 37 6.67
15 360 5.88 1.8 29 8.33
Flow rate (mL/min)
2.5 900 11.03 2.25 55 4.44
5 300 7.35 1.5 37 6.67
7.5 240 8.82 1.8 44 5.56
Initial Mn (II) concentration (mg/L)
50 300 7.35 1.5 37 6.67
100 120 2.94 0.6 15 16.67
150 60 1.47 0.3 7 33.33
Acid mine drainage
180 4.41 0.9 22 11.11
192
Table 6-6. Chemical composition of mine water.
Parameters Concentration Units
Chromium 5 mg/L
pH 6.43
Manganese 25 mg/L
Iron 17 mg/L
Sulphate 985 mg/L
Aluminium 7 mg/L
Magnesium 11.45 mg/L
Figure 6-15. Breakthrough curve for Mn(II) removal from mine water using bentonite/zeolite
clay
193
Figure 6-15 presents the breakthrough behavior of Mn(II) removal from mine water. The
breakthrough point was observed to be reached after 180 min with the volume of water
processed at breakthrough found to be 0.9 L using 10 g adsorbent mass at a flowrate of
5mL/min. The breakthrough point was expected to be reached at a much later time due reduced
concentration of manganese in the mine water , but due to coexisting competing ions in the water
removal of Mn (II) was impacted and the breakthrough point was reached much faster. High
values of AER as a performance indicator shows poor performance of B/Z adsorbent for the
removal of Mn(II) under all experimental conditions.
6.3. Breakthrough curves modelling
To model the breakthrough behaviors of Mn(II) adsorption onto the bentonite-zeolite adsorbent
three mathematical equations; Yoon–Nelson, Thomas and Bohart–Adams were used to match
and model experimental data.
6.3.1. Thomas model
Thomas model is the most commonly and widely used model to describe the performance theory
of the sorption process in fixed-bed column. Thomas model is developed on the assumption that
(1) the adsorption is not limited by chemical interactions but by mass transfer at the interface and
(2) the experimental data follows Langmuir isotherms and second-order kinetics (Foo et al.,
2013, Wang et al., 2015, Nguyen et al., 2015a). The model can be described by the following
expression:
𝐶𝑡
𝐶0=
1
1+exp (𝑘𝑇ℎ𝑞0𝑚
𝑣−𝑘𝑇ℎ𝐶0𝑡)
(6-8)
194
where kTh is the Thomas rate constant (mL/min mg), qo is the adsorption capacity (mg/g), Co is
the inlet Mn(II) ions concentration (mg/L), Ct is the outlet Mn(II) ions concentration at time t
(mg/L), m is the mass of adsorbent (g), v is the feed flow rate (mL/min), and t is time (min).
The results are listed in Table 6.7. It is observed that the kTh values decrease and q0 values
increase with an increase in both the adsorbent bed mass and initial Mn (II) concentration. While
the value of kTh increased as the flow rate is increased. Based on the high correlation coefficients
the model described the experimental data well, which indicates that the external and internal
diffusions are not the limiting step (Chen et al., 2012, Setshedi et al., 2014).
6.3.2. Bohart–Adams model
This model is based on the surface reaction theory which is a fundamental equation, describing
the relationship between Ct/C0 and t in a continuous system (Bohart and Adams, 1920). The
model assumes that equilibrium is not instant and the adsorption rate is controlled by external
mass transfer (Quintelas et al., 2013, Nguyen et al., 2015a). The model can be expressed as:
𝐶𝑡
𝐶0= 𝑒𝑥𝑝 (𝑘𝐴𝐵𝐶0𝑡 − 𝑘𝐴𝐵𝑁0
𝑍
𝑈0) (6-9)
where kBA is the kinetic constant (L/mg min), No is the saturation concentration (mg/L), z is the
bed depth of the fixed bed column (cm) and U0 is the superficial velocity (cm/min) defined as the
ratio of the volumetric flow rate v (cm3/min) to the cross-sectional area of the bed (cm
2). The
values of kBA and No are listed in Table 6.7. The experimental breakthrough curves are not
described well by the Bohart–Adams model as shown by the low regression values. It is
observed that the values of kBA decreases with an increase in bed mass and increase with increase
initial concentrations no specific trend was followed with an increase in flow and flowrates.
195
Table 6-7. Thomas, Adams–Bohart and Yoon–Nelson models constants for Mn(II) adsorption
onto B/Z adsorbent.
Thomas Model Parameters Bohart-Adams Model Parameters Yoon-Nelson Model Parameters
kTh
(mL/min
mg)
q0 (mg/g) R2 kBA (L/mg
min)
N0
(mg/L)
R2 kYN (min
-1) 𝜏 (min) R
2
Mass of adsorbent (g)
5 1.65×10-4
18529.8 0.97 5.26×10-5
2290.3 0.85 0.00829 370.6 0.97
10 1.43×10-4
13979.6 0.99 3.8×10-5
1681.9 0.82 0.00713 559.2 0.99
15 1.19×10-4
10575.1 0.96 3.79×10-5
805.4 0.82 0.00593 634.5 0.96
Flow rate (ml/min)
2.5 8.53×10-5
16928.5 0.99 2.4×10-5
1708.8 0.91 0.00426 1354.29 0.99
5 1.43×10-4
13979.6 0.99 3.8×10-5
1681.9 0.82 0.00713 559.2 0.99
7.5 1.84×10-4
20199.9 0.99 3.45×10-5
2521.9 0.79 0.00922 538.665 0.99
Initial Mn(II) concentration (mg/L)
50 1.43×10-4
13979.6 0.99 3.8×10-5
1681.9 0.82 0.00713 559.2 0.99
100 8.72×10-5
17174.3 0.97 8. ×10-5
17174.3 0.96 0.00872 343.484 0.99
150 9.85×10-5
20506.2 0.99 9. ×10-5
20506.2 0.97 0.0148 273.418 0.99
AMD sample
A4 3.41×10-4
5397.63 0.99 6.82×10-5
749.123 0.78 0.00854293 431.81 0.99
6.3.3. Yoon–Nelson model
(Yoon and Nelson, 1984) developed a adsorption kinetic model for a fixed bed based on the
assumption that the rate of decrease in the probability of adsorption for each adsorbate molecule
196
is proportional to the probability of adsorbate adsorption and the probability of adsorbate
breakthrough on the adsorbent (Bhaumik et al., 2013b). Yoon–Nelson model for a single
component system can be expressed as :
𝐶𝑡
𝐶0−𝐶𝑡= exp (𝑘𝑌𝑁𝑡 − 𝜏𝑘𝑌𝑁) (6-10)
where kYN is the rate constant in (min-1
), τ is the time required for 50% adsorbate breakthrough
(min) and t is the processing time (min). The Yoon–Nelson model fitted very well to the
experimental breakthrough curves based on the regression correlation coefficient which indicate
that mass transport through a fixed bed of B/Z adsorbent follows the assumption of the Yoon–
Nelson model. The results in Table 6.7 indicate that rate constant kYN values decrease with an
increase in bed mass, whilst τ values are observed to increase. Also kYN values increase with an
increase in flowrate and in initial concentration. The time required to reach 50% Mn initial
concentration, t0.5, significantly decreases with the increase of initial Mn(II) concentration and
flow rate.
4. Conclusion
The removal of Mn(II) ion from aqueous solution was carried out in a batch and column
adsorption systems using calcium bentonite/zeolite adsorbent. The adsorption capacity was
found to be depended on the initial concentration and temperature. Adsorption of Mn(II) ions
increased with an increase in temperature and initial concentration. The sorption kinetics of Mn
(II) was described by the pseudo-second-order kinetic model. The equilibrium data fitted well to
the Freundlich model. Adsorption of manganese in the presence of other coexisting ions such as
Fe2+
, Mg2+
, Al2+
and SO42-
was significantly affected. Parameters such as bed mass, initial
197
Mn(II) concentration, and flow rate were evaluated for column studies. The performance of the
column sorption process was found to be better at lower flow rate and initial Mn(II)
concentration and higher bed mass. At breakthrough point, high bed volumes and low AER
values were attained at lower flow rate, lower initial manganese concentration and higher
adsorbent bed mass, which indicated a good bed performance. Thomas and Yoon–Nelson models
fitted experimental breakthrough curves well compared to Bohart– Adams. High values of AER
in column studies and low adsorption capacity in batch studies indicated that B/Z effectiveness in
treating water contaminated with Mn is very low. However the material can be used to polish
water in further treatments.
198
CHAPTER 7
Conclusions and Recommendations
This study was carried out to prepare and apply zeolite-bentonite and zeolite polymer based
nanocomposites for the removal of metal ions in aqueous solution. MZPPY was successfully
synthesized, characterized and used as an adsorbent for the removal of Cr(VI) and V(V) from
aqueous solution. B/Z adsorbent was prepared for this study and the performance for adsorption
of Mn(II) was evaluated. The sorption performance of MZPPY and B/Z for Cr(VI), V(V) and
Mn(II) from aqueous solutions was evaluated in both batch and continuous column sorption
modes. The results indicated that removal efficiency of Cr(VI), V(V) and Mn(II) ions are
dependent on pH, initial concentration, contact time, temperature. The highest removal
efficiency was observed at pH values of 2, 4-5 and 6 for Cr(VI), V(V) and Mn(II) ions,
respectively. The sorption kinetic data of Cr(VI), V(V) and Mn(II) ions fitted to the pseudo –
second order model. The equilibrium data of Cr(VI) and V(V) ions fitted well to the Langmuir
isotherm model. The Langmuir adsorption capacity for Cr(VI) sorption increased from 344.83 to
434.78 mg/g with an increase in temperature from 298 K to 318 K. While the Langmuir
maximum adsorption capacity for V(V) sorption increased with an increase in temperature from
65.1 to 75.0 mg/g when temperature was increased from 298 K to 318 K. The Freundlich model
described the equilibrium data of Mn(II) adsorption satisfactorily. Thermodynamic parameters
confirmed that the adsorption process is spontaneous, endothermic in nature and marked with an
increased randomness at solid–liquid interface. Removal of Cr(VI), V(V) and Mn(II) using
MZPPY and B/Z in dynamic column studies was investigated by varying the process variables
such as bed mass of the adsorbent, initial Cr(VI), V(V) and Mn(II) concentrations and flowrate.
199
Thomas, Bohart–Adams and Yoon–Nelson mathematical models were used to interpret the
experimental breakthrough curves. Both Yoon–Nelson and Thomas models were found to
describe breakthrough curves well under all different experimental conditions. The performance
of the column was better at lower flow rate and initial metal ion concentration and higher bed
mass. Low values of AER and large bed volumes were observed at lower metal ion concentration
and higher bed mass for Cr (VI) removal indicating good performance. The adsorption capacity
in batch studies of B/Z for Mn(II) sorption was found to be very low and the high values of AER
in column studies indicated that B/Z effectiveness in treating water contaminated with Mn is
very low.
Based on the excellent performance of MZPPY as an adsorbent for Cr and V it is recommended
that the adsorbent media be pelletized and the adsorption process scaled up to simulate industrial
applications. Other heavy metals that are available within the industrial water should be also be
studied using MZPPY adsorbent. Environmental water from various sources should also be
tested. A comparative economic evaluation should be conducted between treatment strategies
that are currently used in South Africa with the adsorption treatment using MZPPY. To reduce
the cost of preparing the adsorbent it is recommended that the study to conduct to determine if
the amount of pyrrole added to polymerize the media can be reduced without affecting the
adsorption capacity of the composite.
200
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APPENDIX
Equilibrium isotherms for Cr(VI)
Initial Cr (VI)
concentration
mg/L
25 ° C
Ce
mg/L
35 ° C
Ce
mg/L
45 ° C
Ce
mg/L
293 0.60 0.45 0.04
353 1.08 0.69 0.58
414 1.25 1.07 0.75
514 5.75 3.15 1.24
686 35.09 32.89 8.85
820 142.83 104.65 52.46
900 205.33 164.29 84.56
1122 431 390.23 270.73
Kinectics Cr(VI)
time 300 ppm
Ct mg/L
175 ppm
Ct mg/L
100ppm
Ct mg/L
0.0 299.3 175.1 97.6
1.0 209.2 140.2 89.4
2.0 204.8 136.9 82.5
5.0 202.0 129.8 70.2
10.0 192.1 124.8 61.2
20.0 188.1 105.3 49.6
30.0 178.6 90.3 33.1
45.0 170.8 83.6 27.0
60.0 165.4 72.5 22.4
75.0 160.8 66.7 16.8
275
90.0 156.8 60.7 12.1
120.0 153.8 56.9 11.2
150.0 151.5 55.1 9.5
180.0 149.0 54.1 8.6
210.0 145.0 53.3 8.6
240.0 144.9 53.0 8.6
270.0 144.9 51.9 8.4
300.0 144.9 51.9 8.4
330.0 144.6 50.8 8.4
360.0 144.0 50.8 8.4
390.0 144.0 50.8 8.4
420.0 144.0 50.8 8.4
Column Cr(VI) studies
Time
min
1 g
Ct
mg/L
2.5 g
Ct
mg/L
5 g
Ct
mg/L
60 0.0 0.0 0.0
120 0.0 0.0 0.0
180 0.0 0.0 0.0
240 0.0 0.0 0.0
300 0.0 0.0 0.0
360 0.0 0.0 0.0
420 0.0 0.0 0.0
480 0.0 0.0 0.0
540 0.0 0.0 0.0
600 0.0 0.0 0.0
660 0.0 0.0 0.0
720 0.0 0.0 0.0
780 0.0 0.0 0.0
276
840 0.0 0.0 0.0
900 0.1 0.0 0.0
960 0.2 0.0 0.0
1020 0.2 0.0 0.0
1080 0.8 0.0 0.0
1140 3.3 0.0 0.0
1200 3.9 0.0 0.0
1260 4.3 0.0 0.0
1320 5.3 0.0 0.0
1380 5.5 0.1 0.0
1440 6.4 0.1 0.0
1500 7.5 0.1 0.0
1560 10.0 0.1 0.0
1620 10.9 0.1 0.0
1680 12.1 0.2 0.0
1740 12.6 0.2 0.0
1800 12.9 0.2 0.0
1860 14.9 0.3 0.0
1920 19.6 0.3 0.0
1980 0.4 0.0
2040 0.5 0.0
2100 0.6 0.0
2160 0.8 0.0
2220 1.0 0.0
2280 1.8 0.0
2340 3.3 0.0
2400 3.3 0.0
2460 9.0 0.0
2520 9.5 0.0
2580 14.2 0.0
277
2640 16.8 0.0
2700 17.0 0.0
2760 19.5 0.0
2820 0.0
2880 0.0
2940 0.0
3000 0.0
3060 0.0
3120 0.0
3180 0.0
3240 0.0
3300 0.0
3360 0.0
3420 0.0
3480 0.0
3540 0.0
3600 0.0
3660 0.0
3720 0.0
3780 0.0
3840 0.0
3900 0.0
3960 0.0
4020 0.0
4080 0.0
4140 0.0
4200 0.0
4260 0.0
4320 0.1
4380 0.1
278
4440 0.2
4500 0.2
4560 0.2
4620 0.3
4680 0.4
4740 0.5
4800 0.5
4860 0.5
4920 0.5
4980 0.8
5040 0.9
5100 1.0
5160 1.4
5220 4.4
5280 4.5
5340 8.6
5400 9.5
5460 10.3
5520 15.2
5580 18.0
5640 21.4
Time
min
20 ppm
Ct
mg/L
40 ppm
Ct
mg/L
80 ppm
Ct
mg/L
0.00 0.00 0.00
60 0.00 0.00 0.00
120 0.00 0.00 0.00
180 0.00 0.00 0.00
279
240 0.00 0.00 0.00
300 0.00 0.00 0.00
360 0.00 0.00 0.00
420 0.00 0.00 0.00
480 0.00 0.00 0.00
540 0.00 0.00 0.00
600 0.00 0.00 0.00
660 0.00 0.00 0.03
720 0.00 0.00 0.04
780 0.00 0.00 0.04
840 0.00 0.00 0.06
900 0.00 0.00 0.07
960 0.00 0.01 0.08
1020 0.00 0.01 0.07
1080 0.00 0.02 0.35
1140 0.01 0.03 0.63
1200 0.02 0.04 9.20
1260 0.04 0.04 6.29
1320 0.06 0.06 18.10
1380 0.07 0.07 36.38
1440 0.07 0.08 41.45
1500 0.08 0.11 59.10
1560 0.10 0.13 69.09
1620 0.18 0.24 72.17
1680 0.22 0.37
1740 0.23 0.69
1800 0.25 0.76
1860 0.28 1.30
1920 0.37 1.72
1980 0.49 2.05
280
2040 0.57 4.83
2100 0.78 11.20
2160 1.02 13.70
2220 1.83 17.30
2280 3.34 22.10
2340 3.35 25.98
2400 8.98 28.78
2460 9.53 30.02
2520 14.23 31.99
2580 16.83
2640 16.97
2700 19.49
Time
min
1.5
mL/min
Ct
mg/L
3
mL/min
Ct
mg/L
4.5
mL/min
Ct
mg/L
0 0.00 0.00 0.00
60 0.00 0.00 0.00
120 0.00 0.00 0.00
180 0.00 0.00 0.00
240 0.00 0.00 0.00
300 0.00 0.00 0.00
360 0.00 0.00 0.00
420 0.00 0.00 0.07
480 0.00 0.00 0.01
540 0.00 0.00 0.29
600 0.00 0.00 0.56
660 0.00 0.00 2.80
281
720 0.00 0.00 5.61
780 0.00 0.06 6.96
840 0.00 0.03 9.91
900 0.00 0.04 19.99
960 0.00 0.07 21.57
1020 0.00 3.35 21.58
1080 0.00 3.94
1140 0.01 4.97
1200 0.02 8.99
1260 0.04 13.49 --
1320 0.06 14.20 --
1380 0.07 16.62 --
1440 0.07 16.83
1500 0.08 17.69
1560 0.10 19.29
1620 0.18 19.99
1680 0.22
1740 0.23
1800 0.25
1860 0.28
1920 0.37
1980 0.49
2040 0.57
2100 0.78
2160 1.02
2220 1.83
2280 3.34
2340 3.35
2400 8.98
2460 9.53
282
2520 14.23
2580 16.83
2640 16.97
2700 19.49
Vanadium equilibrium isotherm data
Co 298 K
Ce
298 K
Qe
308 K
Ce
308 K
Qe
318 K
Ce
318 K
Qe
mg/L mg/L mg/g mg/L mg/g mg/L mg/g
110 10.9 33.0 8.5 33.9 5.1 35.0
125 12.7 37.4 10.0 38.3 6.4 39.5
135 15.5 39.8 11.8 41.1 8.3 42.3
150 19.2 43.6 15.0 45.0 11.3 48.2
175 30.0 48.3 21.2 51.3 15.2 53.3
205 45.0 53.3 35.0 56.7 23.0 59.7
225 58.0 55.7 48.6 58.8 30.1 63.0
Vanadium kinectics
Time
min
50 ppm
Qt
mg/g
80 ppm
Qt
mg/g
110 ppm
Qt
mg/g
1 2.99013 10.2759 10.3713
2 5.03977 13.8103 12.0804
5 6.28117 15.3492 17.208
10 7.47853 16.1564 19.5064
15 8.31357 17.3277 20.1549
20 9.2041 18.0885 21.7208
25 9.66333 19.1136 22.7451
30 10.0657 19.5089 24.2745
283
45 10.2765 20.0535 26.0394
60 10.3787 20.0667 26.9915
90 10.5269 20.5867 27.0754
120 10.5692 20.6667 27.0754
150 10.5799 20.6667 27.0754
180 10.6593 20.6667 27.0754
210 10.7597 20.6667 27.0754
240 10.8083 20.6667 27.0754
Vanadium column studies
Time
min
1 g
Ct
mg/L
2.5 g
Ct
mg/L
5 g
Ct
mg/L
60 1.91 0.797 0.384
120 3.01 1.07 0.428
180 5.98 1.91 0.428
240 6.89 1.94 0.437
300 8.91 3.1 0.448
360 11 4.21 0.524
420 12.7 5.34 0.77
480 13.5 6.47 0.817
540 15.2 8.87 0.825
600 18.4 9.82 1.21
660 20.6 13.2 1.26
720 29.5 13.7 1.3
780 31.6 15.5 3.13
840 33 18.9 6.84
900 36.2 19.5 7.67
284
960 38.3 21 10.6
1020 39.6 23.4 11.7
1080 41.3 24.6 13.9
1140 41.8 25.8 14.5
1200 44.9 26.5 15.5
1260 28.7 19.2
1320 31.6 21.02
1380 34.3 23.7
1440 38.2 24.6
1500 39.3 25.8
1560 40.6 26.5
1620 41.8 28.7
1680 42.1 31.6
1740 45.2 33.3
1800 38.2
1860 39
1920 39.6
1980 42.3
2040 44.8
2100 46.9
Time
min
47 ppm
Ct
mg/L
63 ppm
Ct
mg/L
84 ppm
Ct
mg/L
60 0.797 0.434 1.23
120 1.07 2.53 5.5
180 1.91 5.2 6.69
240 1.94 6.3 11.5
285
300 3.1 8.98 13.6
360 4.21 10.03 21.2
420 5.34 12.4 24.7
480 6.47 15.5 27
540 8.87 17 34.1
600 9.82 19.1 36.2
660 13.2 21 40.5
720 13.7 25.2 42.9
780 15.5 28.9 45.2
840 18.9 30.4 46.4
900 19.5 31.8 46.7
960 21 35
1020 23.4 37.1
1080 24.6 38
1140 25.8 39.7
1200 26.5 40.2
1260 28.7 42.7
1320 31.6 44.1
1380 34.3 45.3
1440 38.2 46.7
1500 39.3
1560 40.6
1620 41.8
1680 42.1
1740 45.2
Time
min
1.5
mL/min
Ct
mg/L
3
mL/min
Ct
mg/L
4.5
mL/min
Ct
mg/L
286
60 0.797 0.434 1.23
120 1.07 2.53 5.5
180 1.91 5.2 6.69
240 1.94 6.3 11.5
300 3.1 8.98 13.6
360 4.21 10.03 21.2
420 5.34 12.4 24.7
480 6.47 15.5 27
540 8.87 17 34.1
600 9.82 19.1 36.2
660 13.2 21 40.5
720 13.7 25.2 42.9
780 15.5 28.9 45.2
840 18.9 30.4 46.4
900 19.5 31.8 46.7
960 21 35
1020 23.4 37.1
1080 24.6 38
1140 25.8 39.7
1200 26.5 40.2
1260 28.7 42.7
1320 31.6 44.1
1380 34.3 45.3
1440 38.2 46.7
1500 39.3
1560 40.6
1620 41.8
1680 42.1
1740 45.2
287
