Removal of direct dyes from synthetic effluents by agro-industrial wastes: Batch and column studies By Sana Sadaf (M.Phil UAF) 2006-ag-401 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILISOPHY IN CHEMISTRY DEPARTMENT OF CHEMISTRY AND BIOCHEMISTRY FACULTY OF SCIENCES UNIVERSITY OF AGRICULTURE, FAISALABAD 2014
179
Embed
Full thesis (Sana Sadaf) - Higher Education …prr.hec.gov.pk/jspui/bitstream/123456789/1143/1/1986S.pdfthesis. I offer my cordial and profound thanks to Prof. Dr. Asgher Bajwa, Chairman,
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Removal of direct dyes from synthetic effluents by agro-industrial wastes: Batch and column studies
By
Sana Sadaf
(M.Phil UAF)
2006-ag-401
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILISOPHY
IN
CHEMISTRY
DEPARTMENT OF CHEMISTRY AND BIOCHEMISTRY
FACULTY OF SCIENCES UNIVERSITY OF AGRICULTURE,
FAISALABAD 2014
To,
The Controller of Examinations,
University of Agriculture,
Faisalabad.
“We, the Supervisory Committee, certify that the contents and form of thesis
submitted by Miss Sana Sadaf, 2006-ag-401, have been found satisfactory and
recommend that it be processed for evaluation, by the External Examiner(s) for the
award of degree”.
SUPERVISORY COMMITTEE:
1. Chairman __________________________
(Prof. Dr. Haq Nawaz Bhatti)
2. Member __________________________
(Dr. Shaukat Ali)
3. Member __________________________
(Prof. Dr. Khalil-ur-Rehman)
Declaration
I hereby declare that the contents of the thesis “Removal of direct dyes from synthetic
effluents by agro-industrial wastes: Batch and column studies” are product of my own
research and no part has been copied from any published source (except the references,
standard mathematical or genetic models/equations/formulate/protocols etc). I further declare
that this work has not been submitted for award of any other diploma/degree. The University
may take action if the information provided is found inaccurate at any stage. (In case of any
default the scholar will be proceeded against as per HEC plagiarism policy).
SANA SADAF
I want to consecrate this humble effort to the gleaming tower of knowledge
Hazrat Muhammad
(May Peace and Blessings of Allah be upon Him)
&
My Affectionate Parents
Whose esteemed love enabled me to get the success and whose hearts are always beating to wish for me maximum felicity in life.
ACKNOWLEDGEMENT
All praises to Almighty ALLAH, the creator, dominant, self existing and sustainer, who
enabled me to accomplish this project and all respect is for his last Prophet MUHAMMAD
(Peace and Blessing of Allah Be Upon Him) who is forever a torch of guidance and
knowledge in our life.
I pay my humble gratitude to my worthy supervisor Prof. Dr. Haq Nawaz Bhatti, Dept. of
Chemistry and Biochemistry, University of Agriculture, Fasisalabad for his absorbing
attitude, constant guidance, timely suggestions, inspiration and encouragement throughout
my studies.
I am greatly indebted to Dr. Shaukat Ali and Prof. Dr. Khalil-ur-Rehman for their co-
operation, valuable suggestions and guidance during my research and compilation of my
thesis.
I offer my cordial and profound thanks to Prof. Dr. Asgher Bajwa, Chairman, Dept. of
Chemistry and Biochemistry, University of Agriculture, Fasisalabad and Prof. Dr. Munir
Ahmad Sheikh, Ex-Dean, Faculty of Sciences, University of Agriculture, Faisalabad for
their nice behavior and co-operation during my study.
I am lucky enough to have the support of many good friends. Special thanks are extended to
Misbah Amin, Saima Andleeb, Asma Hanif, Sana Nosheen, Saira Yasmeen and Sumreen
Anjum for their prayers, moral support and sincere suggestions. I want to express my
gratitude, deep appreciation and very special thanks to my sweet sister Tanzila Rafique,
without her help, moral support, encouragement and friendly behavior it would not be
possible for me to complete my degree in such a good way. Special thanks are due to my all
lab fellows for their friendly behaviour and co-operation during research work.
Words always seem to shallow whenever it comes to my dearest and loving parents. I am
absolutely nothing without their encouragement and especially their prayers. My appreciation
and great thanks are extended to my brothers, sister and all other family members who
prayed for me.
Last but not the least thanks are extended to Higher Education Commission of Pakistan for
their financial support during this project.
SANA SADAF
CONTENTS
Sr. No TITLE Page No.
1 Introduction 1
2 Review of Literature 6
3 Materials and Methods 25
4 Results and Discussion 36
5 Summary 147
Literature Cited 149
LIST OF TABLES
Sr. No
TITLE PAGE NO.
3.1 General characteristics of direct dyes 26
3.2 Experimental ranges and levels of independent variables
34
4.1 Kinetic modeling of data for the removal of Direct Violet 51 by sugarcane bagasse biomass
62
4.2 Kinetic modeling of data for the removal of Indosol Turquoise FBL by sugarcane bagasse biomass
63
4.3 Kinetic modeling of data for the removal of Indosol Black NF by peanut husk biomass
64
4.4 Kinetic modeling of data for the removal of Indosol Yellow BG by peanut husk biomass
65
4.5 Kinetic modeling of data for the removal of Indosol Orange RSN by peanut husk biomass
66
4.6 Equilibrium modeling of data for the removal of Direct Violet 51 by sugarcane bagasse biomass
70
4.7 Equilibrium modeling of data for the removal of Indosol Turquoise FBL by sugarcane bagasse biomass
71
4.8 Equilibrium modeling of data for the removal of Indosol Black NF by peanut husk biomass
72
4.9 Equilibrium modeling of data for the removal of Indosol Yellow BG by peanut husk biomass
73
4.10 Equilibrium modeling of data for the removal of Indosol Orange RSN by peanut husk biomass
74
4.11 Thermodynamic parameters for the removal of Direct Violet 51 by sugarcane bagasse biomass
76
4.12 Thermodynamic parameters for the removal of Indosol Turquoise FBL by sugarcane bagasse biomass
76
4.13 Thermodynamic parameters for the removal of Indosol Black NF by peanut husk biomass
77
4.14 Thermodynamic parameters for the removal of Indosol Yellow BG by peanut husk biomass
77
4.15 Thermodynamic parameters for the removal of Indosol Orange RSN by peanut husk biomass
78
4.16 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Direct Violet 51 dye
98
4.17 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Turquoise FBL dye
98
4.18 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Black NF dye
99
4.19 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Yellow BG dye
99
4.20 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Orange RSN dye
100
4.21 Thomas Model parameters for the removal of Direct Violet 51 dye 101
4.22 Thomas Model parameters for the removal of Indosol Turquoise FBL dye 102
4.23 Thomas Model parameters for the removal of Indosol Black NF dye 102
4.24 Thomas Model parameters for the removal of Indosol Yellow BG dye 103
4.25 Thomas Model parameters for the removal of Orange RSN dye 103
4.26 BDST parameters for the removal of Direct Violet 51 dye 105
4.27 BDST parameters for the removal of Indosol Turquoise FBL dye 105
4.28 BDST parameters for the removal of Indosol Black NF dye 105
4.29 BDST parameters for the removal of Indosol Yellow BG dye 106
4.30 BDST parameters for the removal of Indosol Orange RSN dye 106
4.31 ANOVA results for the removal of Direct Violet 51 dye through RSM 117
4.32 ANOVA results for the removal of Indosol Turquoise FBL dye through RSM
118
4.33 ANOVA results for the removal of Indosol Black NF dye through RSM 118
4.34 ANOVA results for the removal of Indosol Yellow BG dye through RSM 119
4.35 ANOVA results for the removal of Indosol Orange RSN dye through RSM 119
4.36 Analysis of variance (ANOVA) results for response parameters 121
4.37 Box-Behnken design matrix for the real and coded values along with experimental and predicted results for the removal of four direct dyes by selected agricultural wastes
122
4.38 Box-Behnken design matrix for the real and coded values along with experimental and predicted results for the removal of Indosol Turquoise FBL dye from aqueous solution
123
4.39 Kinetic modeling of data for the removal of COD from textile effluents using corncobs biomass
143
4.40 Equilibrium modeling of data for the removal of COD from textile effluents using corncobs biomass
143
4.41 Physico-chemical characteristics of real effluents 144
LIST OF FIGURES
Sr. No
TITLE PAGE NO.
4.1 Biosorption capacity of five different agricultural wastes for each direct dye 36
4.2 Effect of different pretreatments on the biosorption of five direct dyes 37
4.3 Point of zero charge of sugarcane bagasse biomass 39
4.4 Point of zero charge of peanut husk biomass 40
4.5 Effect of pH on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
41
4.6 Effect of pH on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
41
4.7 Effect of pH on the removal of Indosol Black NF dye by peanut husk biomass
42
4.8 Effect of pH on the removal of Indosol Yellow BG dye by peanut husk biomass
42
4.9 Effect of pH on the removal of Indosol Orange RSN dye by peanut husk biomass
43
4.10 Effect of contact time on the removal of Direct Violet 51dye by sugarcane bagasse biomass
44
4.11 Effect of contact time on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
45
4.12 Effect of contact time on the removal of Indosol Black NF dye by peanut husk biomass
45
4.13 Effect of contact time on the removal of Indosol Yellow BG dye by peanut husk biomass
46
4.14 Effect of contact time on the removal of Indosol Orange RSN dye by peanut husk biomass
46
4.15 Effect of biosorbent dose on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
48
4.16 Effect of biosorbent dose on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
49
4.17 Effect of biosorbent dose on the removal of Indosol Black NF dye by peanut husk biomass
49
4.18 Effect of biosorbent dose on the removal of Indosol Yellow BG dye by peanut husk biomass
50
4.19 Effect of biosorbent dose on the removal of Indosol Orange RSN dye by peanut husk biomass
50
4.20 Effect of initial dye concentration on the biosorption capacity of sugarcane bagasse for the removal of Direct Violet 51 dye
52
4.21 Effect of initial dye concentration on the biosorption capacity of sugarcane bagasse for the removal of Indosol Turquoise FBL dye
52
4.22 Effect of initial dye concentration on the biosorption capacity of peanut husk biomass for the removal of Indosol Black NF dye
53
4.23 Effect of initial dye concentration on the biosorption capacity of peanut husk biomass for the removal of Indosol Yellow BG dye
53
4.24 Effect of initial dye concentration on the biosorption capacity of peanut husk biomass for the removal of Indosol Orange RSN dye
54
4.25 Effect of temperature on the removal of Direct Violet 51 dye by using sugarcane bagasse biomass
56
4.26 Effect of temperature on the removal of Indosol Turquoise FBL dye by using sugarcane bagasse biomass
56
4.27 Effect of temperature on the removal of Indosol Black NF dye by using peanut husk biomass
57
4.28 Effect of temperature on the removal of Indosol Yellow BG dye by using peanut husk biomass
57
4.29 Effect of temperature on the removal of Indosol Orange RSN dye by using peanut husk biomass
58
4.30 Effect of presence of electrolytes on the biosorption potential of sugarcane bagasse for the removal of Direct Violet 51 dye
79
4.31 Effect of presence of electrolytes on the biosorption potential of sugarcane bagasse for the removal of Indosol Turquoise FBL dye
80
4.32 Effect of presence of electrolytes on the biosorption potential of peanut husk for the removal of Indosol Black NF dye
80
4.33 Effect of presence of electrolytes on the biosorption potential of peanut husk for the removal of Indosol Yellow BG dye
81
4.34 Effect of presence of electrolytes on the biosorption potential of peanut husk for the removal of Indosol Orange RSN dye
81
4.35 Effect of presence of heavy metal ions on the biosorption potential of sugarcane bagasse for the removal of Direct Violet 51 dye
82
4.36 Effect of presence of heavy metal ions on the biosorption potential of sugarcane bagasse for the removal of Indosol Turquoise FBL dye
83
4.37 Effect of presence of heavy metal ions on the biosorption potential of peanut husk for the removal of Indosol Black NF dye
83
4.38 Effect of presence of heavy metal ions on the biosorption potential of peanut husk for the removal of Indosol Yellow BG dye
84
4.39 Effect of presence of heavy metal ions on the biosorption potential of peanut husk for the removal of Indosol Orange RSN dye
84
4.40 Effect of presence of surfactants/detergents on the biosorption of direct dyes 86
4.41 Effect of bed height on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
88
4.42 Effect of bed height on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
88
4.43 Effect of bed height on the removal of Indosol Black NF dye by peanut husk biomass
89
4.44 Effect of bed height on the removal of Indosol Yellow BG dye by peanut husk biomass
89
4.45 Effect of bed height on the removal of Indosol Orange RSN dye by peanut husk biomass
90
4.46 Effect of flow rate on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
91
4.47 Effect of flow rate on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
91
4.48 Effect of flow rate on the removal of Indosol Black NF dye by peanut husk biomass
92
4.49 Effect of flow rate on the removal of Indosol Yellow BG dye by peanut husk biomass
92
4.50 Effect of flow rate on the removal of Indosol Orange RSN dye by peanut husk biomass
93
4.51 Effect of initial dye concentration on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
95
4.52 Effect of initial dye concentration on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
96
4.53 Effect of initial dye concentration on the removal of Indosol Black NF dye by peanut husk biomass
96
4.54 Effect of initial dye concentration on the removal of Indosol Yellow BG dye by peanut husk biomass
97
4.55 Effect of initial dye concentration on the removal of Indosol Orange RSN dye by peanut husk biomass
97
4.56 FT-IR spectrum of unloaded sugarcane bagasse (native) 108
4.57 FT-IR spectrum of unloaded peanut husk (native) 108
4.58 FT-IR spectrum of unloaded HCl-treated sugarcane bagasse 109
4.59 FT-IR spectrum of unloaded PEI-treated peanut husk 109
4.60 FT-IR spectrum of unloaded CH3COOH-treated peanut husk 110
4.61 FT-IR spectrum of unloaded immobilized sugarcane bagasse 110
4.62 FT-IR spectrum of unloaded immobilized peanut husk 111
4.63 FT-IR spectrum of native sugarcane bagasse loaded with Direct Violet 51 dye
111
4.64 FT-IR spectrum of native sugarcane bagasse loaded with Indosol Turquoise FBL dye
112
4.65 FT-IR spectrum of native peanut husk loaded with Indosol Black NF dye 112
4.66 FT-IR spectrum of native peanut husk loaded with Indosol Yellow BG dye 113
4.67 FT-IR spectrum of native peanut husk loaded with Indosol Orange RSN dye 113
4.68 SEM analysis of unloaded (a) sugarcane bagasse (b) peanut husk biomass 114
4.69 SEM analysis of sugarcane bagasse loaded with (a) Direct Violet 51 (b) Indosol Turquoise FBL dye
115
4.70 SEM analysis of peanut husk biomass loaded with Indosol Black NF (b) Indosol Yellow BG dye
115
4.71 SEM analysis of peanut husk biomass loaded with Indosol Orange RSN dye 116
4.72 Normal probability plot of Residuals for Direct Violet 51 dye
124
4.73 Normal probability plot of Residuals for Indosol Turquoise FBL dye
125
4.74 Normal probability plot of Residuals for Indosol Black NF dye 125
4.75 Normal probability plot of Residuals for Indosol Yellow BG dye
126
4.76 Normal probability plot of Residuals for Indosol Orange RSN dye 126
4.77 Contour plot showing interaction of initial dye concentration and biosorbent dose on the removal of Direct Violet 51 by HCl-treated sugarcane bagasse
128
4.78 Contour plot showing interaction of initial dye concentration and biosorbent dose on the removal of Indosol Turquoise FBL by HCl-treated sugarcane bagasse
129
4.79 Contour plot showing interaction of initial dye concentration and biosorbent dose on the removal of Indosol Black NF by PEI-treated peanut husk
129
4.80 Contour plot showing interaction of initial dye concentration and biosorbent dose on the removal of Indosol Yellow BG by CH3COOH-treated peanut husk
130
4.81 Contour plot showing interaction of initial dye concentration and biosorbent dose on the removal of Indosol Orange RSN by PEI-treated peanut husk
130
4.82 Contour plot showing interaction of initial dye concentration and pH on the removal of Direct Violet 51 dye by HCl-treated sugarcane bagasse
131
4.83 Contour plot showing interaction of initial dye concentration and pH on the removal of Indosol Turquoise FBL by HCl-treated sugarcane bagasse
131
4.84 Contour plot showing interaction of initial dye concentration and pH on the removal of Indosol Black NF by PEI-treated peanut husk
132
4.85 Contour plot showing interaction of initial dye concentration and pH on the removal of Indosol Yellow BG by CH3COOH-treated peanut husk
132
4.86 Contour plot showing interaction of initial dye concentration and pH on the removal of Indosol Orange RSN by PEI-treated peanut husk
133
4.87 Contour plot showing the interaction of biosorbent dose and pH on the removal of Direct Violet 51 by HCl-treated sugarcane bagasse
133
4.88 Contour plot showing interaction of biosorbent dose and pH on the removal of Indosol Turquoise FBL by HCl-treated sugarcane bagasse
134
4.89 Contour plot showing the interaction of biosorbent dose and pH on the removal of Indosol Black NF by PEI-treated peanut husk
134
4.90 Contour plot showing interaction of biosorbent dose and pH on the removal of Indosol Yellow BG by CH3COOH-treated peanut husk
135
4.91 Contour plot showing interaction of biosorbent dose and pH on the removal of Indosol Orange RSN by PEI-treated peanut husk
135
4.92 Overlay Perurbation plot of all the independent variables for biosorption of Direct Violet 51
136
4.93 Overlay Perurbation plots of all the independent variables for biosorption of (a) Indosol Turquoise FBl (b) Indosol black NF (c) Indosol Yellow BG (d)
137
Indosol Orange RSN dyes
4.94 Screening of different agricultural waste materials for the reduction of COD from real textile effluents
138
4.95 Effect of biosorbent dose on the removal of COD from real textile effluents 139
4.96 Effect of contact time on the removal of COD from real textile effluents 140
4.97 Effect of agitation speed on the removal of COD from real textile effluents 141
4.98 Effect of temperature on the removal of COD from real textile effluents 142
4.99 Desorption of direct dyes by using NaOH as eluent in different concentrations (M)
145
Abstract
The present study was designed to remove five different direct dyes (Direct Violet 51,
Indosol Turquoise FBL, Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN)
from aqueous solutions by using agro-industrial waste materials (sugarcane bagasse, peanut
husk, corn cobs, cotton sticks and sunflower) in batch and column mode. The batch mode
study was performed to compare the biosorption potential of native, pretreated and
immobilized forms of biosorbents for the removal of direct dyes. Important process
parameters like pH, contact time, biosorbent dose, initial dye concentration and temperature
were optimized during batch experiments. The results revealed that low pH, low biosorbent
dose and low temperature were the feasible conditions for maximum removal of dyes. The
pretreated form of biosorbents depicted highest biosorption capacity (39.6 mg/g for Direct
Violet 51, 65.09 mg/g for Indosol Turquoise FBL, 89.6 mg/g for Indosol Black NF, 79.5
mg/g for Indosol Yellow BG and 79.7 mg/g for Indosol Orange RSN) as compare to native
and immobilized form of biosorbents. The experimental data of all the five dyes was
subjected to different kinetic models and pseudo-second order kinetic model was found to be
best fit on the experimental results. Different equilibrium isotherms were applied on the data
to explain the mechanism of biosorption and Langmuir adsorption isotherm model fitted very
well on the experimental results for all the dyes. Thermodynamic study showed that
biosorption process was feasible at lower temperatures as indicated by lower values of ∆G.
The negative values of ∆H indicated that biosorption process was exothermic in nature. The
column mode experiments were conducted to optimize the bed height, flow rate and initial
dye concentration. Higher bed heights, lower flow rates and higher initial dye concentrations
were found to be favorable conditions for maximum dye removal in column mode study.
Box-Behnken experimental design was used to investigate the main and interaction effects of
three important parameters like initial dye concentration, biosorbent dose and pH on the
removal of direct dyes and results were analyzed by ANOVA and p-values. The biosorption
process was also applied on the real textile effluents for the efficient removal of COD.
Characterization of biosorbents was carried out by FT-IR and SEM analysis. The results
revealed that agricultural waste materials have high biosorption capacities for the removal of
dyes from wastewaters.
1
Chapter # 1
INTRODUCTION
Over the years, rapid population growth, urbanization, industrialization and increased
farming has resulted in the depletion of natural water resources worldwide. The
environmental degradation and climatic changes have worsened the global water shortage
problems. Water scarcity has also been resulted due to pollution of water resources (Lu et al.,
2010). Rapid industrialization has increased the concerns about the ongoing deterioration of
the global environment. Industrial growth has resulted in an increased water demand which is
being used in various production processes and much of the water being used is not being
reclaimed. Recycling of the wastewater is a starting point in conserving the limited water
supply (Lu and Leung, 2003).
The textile industry is playing a key role in the economy of many countries but the
textile industry is also responsible for intensifying the environmental problems by generating
the colored effluents. Dye containing textile effluents are the key source of water pollution
(Nandi et al., 2009). Various industries including paper, leather, hair, cosmetics, food and
textile use dyes in order to color their products (Arulkumar et al., 2011). The textile industry
ranks first among the different industries in usage of synthetic dyes for coloring the fiber. In
the dyeing processes, 50 % of the dye is lost to wastewater because of the low levels of dye-
fiber fixation (Mohan et al., 2007) which results in the generation of considerable amount of
colored wastewater (Saad et al., 2010).
The discharge of colored wastewater is damaging the esthetic nature of receiving
streams and has found to be a serious threat for the human health and environmental turmoil
(Akar et al., 2009). This colored wastewater is directly released to the water bodies and it
shows its negative effects on aquatic life by obstructing the sunlight penetration which
retards the photosynthetic activity of aquatic plants. People of different areas consume this
wastewater for washing, bathing and drinking (Sharma and Sobti, 2000). The carcinogenic
and mutagenic nature of synthetic dyes impart many harmful effects on human being such as
kidney dysfunction, damage to the reproductive system, central nervous system, liver and
brain (Dincer et al., 2007). Dyes can also cause allergy, dermatitis and skin irritation in
humans (Shen et al., 2009). Dye manufacturers and consumers are interested in stability and
2
fastness and so they are producing the dyestuff that is hard to degrade after use (Asgher and
Bhatti, 2012). Hence these dyes once entered in the natural aquatic environment are hardly
eliminated. Therefore it is important to remove these dyes from the wastewater before its
discharge to the environment and to validate the water quality (Saha et al., 2012a).
Synthetic dyes can be classified as anionic (direct, reactive and acid dyes), cationic
(basic dyes) and non-ionic (disperse dyes). The annual production of the synthetic dyes is
approximately 0.7 million tons worldwide. In case of anionic and nonionic dyes, the azo and
anthraquinone groups mostly act as chromophores (Srinivasan and Viraraghavan, 2010).
Approximately 70 % of the synthetic dyes belong to the azo group which contains N=N bond
in there molecular structure (Hsueh et al., 2005). The highly conjugated molecular structure
of direct dyes and presence of one or more anionic sulfonate groups make them responsible
for their water solubility (Safa and Bhatti, 2011a). The direct dyes and their metabolites
containing benzidine groups have proved to be severely toxic and carcinogenic in nature
(Bayramoglu et al., 2006).
The treatment of textile effluents for the removal of dyes in an economic and cost
effective manner remains a major problem for textile industries. Many conventional methods
have been extensively applied for the removal of dyes from wastewater. Over the years,
techniques like photocatalytic degradation (Mahmoodi et al., 2005), coagulation (Bozdogan
and Goknil, 1989) membrane filtration (Wu et al., 1998), microbiological decomposition
(Pearce et al., 2003) etc. have been utilized for the removal of pollutants from aqueous
solution but these methods have been proved to be practically infeasible with reference to the
cost and application (Singh et al., 2012). These methods also pose techno-economical
limitations for field-scale applications (Akbal, 2005). The low biodegradability of synthetic
dyes has made it more difficult for the conventional biological wastewater treatment
processes to be effective. Also some dyes are highly toxic and/or carcinogenic, and their
biodegradation can produce even more toxic aromatic amines (Dulman and Cucu-Man,
2009). These facts motivated the researchers to explore newer and cost-effective techniques
for the treatment of dye containing wastewater.
The dye molecules show the affinity to adhere on the different surfaces and this
property of dyes provided an idea about the exploitation of adsorption process for their
removal from wastewater (Nawar and Doma, 1989). Adsorption is the physical attachment of
3
different molecules/compounds onto the surface of different materials. It has now been well
established that adsorption process has an edge over other physico-chemical techniques due
to its sludge free and easy operation. Adsorption process can be used to completely remove
the dyes even from the dilute solutions (Azhar et al., 2005). Among the different techniques
being used for the removal of dyes, adsorption technique is the procedure of choice and gives
best results (Jain et al., 2003). Adsorption of dyes by using activated carbon is one of the best
technologies for decolorizing textile effluents due to its high adsorption capacity (Ahmad and
Hameed, 2010). Activated carbon has porous structure and it is prepared from the materials
having high carbon content and show very high adsorption capacity. But in spite of the high
efficiency and applicability for adsorbing a wide range of toxicants, the high cost of activated
carbon has made its use limited due to economic considerations especially in developing
countries (Jesus et al., 2011). In addition, the activated carbons also exhibit the problem of
regeneration and it is also difficult to separate the activated carbon from the treated
wastewater (Uddin et al., 2009).
Currently the agro wastes are getting stern considerations as raw materials for
wastewater treatment because of their copious availability and low-cost. The feasibility of
using agricultural waste materials could be beneficial not only to the environment in solving
the solid waste disposal problem, but also to the economy (Mittal et al., 2013). The
inadequate disposal of agricultural wastes to the environment cause aesthetic problems so it
is better to exploit these materials for the remedy of different pollutants from the
environment (Raymundo et al., 2010). The development and use of various low cost
agricultural by-products have been explored for efficient removal of coloring agents from
wastewater (Han et al., 2008). These include rice husk (Han et al., 2008; Safa and Bhatti,
2011b), wheat straw (Wu et al., 2009), wheat husk (Bulut and Aydın, 2006), cereal chaff
(Han et al., 2006), fallen leaves (Han et al., 2009), de-oiled soya and bottom ash (Gupta et
al., 2006), bittim shell (Aydın and Baysal, 2006), coir pith (Namasivayam et al., 2001),
papaya seeds (Hameed, 2009) and citrus peel (Asgher and Bhatti, 2011). Presence of
hydroxyl, carboxylic and amino groups on the surface of these biomaterials is responsible for
the removal of pollutants by adsorption.
The biosorption capacity of agricultural by-products can be enhanced by various
physical and chemical treatments. Surface functionalization technology has been proven to
4
be effective (Wu et al., 2012). These modification may result in increase in surface area of
biosorbent hence enhance the biosorption efficiency by denaturing complex lignin
compounds (Bhatti et al., 2010). Generally the biosorption process takes place on the surface
of biomaterials, so by increasing or activating the active sites on the surface of biomaterials
would result in enhancing the biosorption capacity (Vijayaraghavan and Yun, 2008).
Low mechanical strength of biosorbents due to their low density and low rigidity may
create some difficulties in solid–liquid separation, failure to recycle and reuse and
development of high pressure drop in the column mode (Vijayaraghavan and Yun, 2007).
The biosorbents can be made suitable for the process applications by using different well
known immobilization techniques like as entrapment and cross linking (Veglio and
Beolchini, 1997). The biosorbents can be immobilized using different immobilization
matrices like sodium alginate (Xiangliang et al., 2005), polyacrylamide (Bai and Abraham,
2003), polyurethane (Hu and Reeves, 1997) and polysulfone (Beolchini et al., 2003;
Vijayaraghavan et al., 2007). It can maintain the native properties of the biomass and has the
advantages of improved strength and handling capacity, reduced blockage and head-loss in a
column mode operations and better regeneration characteristics (Tobin et al., 1993).
Both batch and fixed-bed biosorption studies are necessary to find key parameters
required for the design of fixed-bed adsorber (Song et al., 2011). Batch reactors are easy to
use in the laboratory study, but show less feasibility for industrial applications. In practice,
continuous flow operations in the packed bed column are considered more useful in large-
scale wastewater treatment because of their simplicity, ease of operation, handling and
regeneration capacity. The large volumes of wastewater can be continuously treated by using
a definite amount of adsorbent in the column (Charumathi and Das, 2012).
Keeping in view the importance of agricultural wastes, the present study was designed
with following objectives:
Evaluation of the biosorption potential of locally available waste biomaterials i.e.,
sugarcane bagasse, cotton sticks, corn cobs, sunflower and peanut husk for the
removal of some selected direct dyes
Pretreatments of the selected biosorbents to enhance their biosorption capacity
Optimization of important experimental parameters during batch and column study.
5
Application of different kinetic and equilibrium models and thermodynamic study on
the experimental data
Characterization of raw and loaded biosorbents by FT-IR and SEM studies.
6
Chapter # 2
REVIEW OF LITERATURE
Dyes have complex aromatic structure and different methods have been investigated by
researchers to remove these dyes from aqueous solutions. A comprehensive review of
biosorption of dyes by using different adsorbents has been given below.
2.1 OPTIMIZATION OF IMPORTANT PHYSICO-CHEMICAL
PARAMETERS: BATCH EXPERIMENTS
2.1.1 Effect of pH
pH plays a very significant role in the biosorption process. It seems to affect the solution
chemistry of dyes and functional groups of biosorbents. Malik (2004) prepared activated
carbon from sawdust and utilized it for the removal of direct dyes from wastewaters. The
experiments were conducted to check out the effect of pH on dyes removal and it was
observed that favorable pH for enhanced dye removal was in acidic range (pH 2). At low pH,
protonation of functional groups takes place on the surface of biosorbent which results in
enhanced electrostatic attraction between anionic dye molecules and adsorbent surface and
leads to higher dye removal.
Arami et al. (2006) worked on the biosorption of direct (Direct red 80 and Direct red
81) and acidic dyes (Acid blue 92 and Acid red 14). The biosorption experiments were
conducted with soy meal hull which is an agricultural by product. Effect of pH was
investigated in the pH range of 2-11 and for both type of dyes (acidic and direct), maximum
removal was obtained at pH 2. Increase in pH resulted in decreased dyes removal. This might
be due to increase in concentration of –OH at higher pH values which compete with dye
anions to attach on the surface of biosorbent hence the biosorption of dye decreased.
Effect of pH was also investigated by Mall et al. (2006b). Bagasse fly ash was used as
biosorbent for the removal of Orange G dye from synthetic solutions. During batch study,
they checked out the effect of pH on the adsorption of dye. They concluded that pH has a
pronounced effect on dye removal. Over 90 % of the dye was removed at pH 2 because at
low pH, the adsorbent surface becomes positively charged and there exist electrostatic
attraction between dye anions and adsorbent. As pH of the medium increased, the positive
7
charge on the adsorbent surface decreased and electrostatic repulsion between dye anions and
adsorbent resulted in decreased adsorption.
Bayramoglu and Arica (2007) conducted experiments for the adsorptive removal of
Direct Blue 1 and Direct Red 128 using Trametes versicolor biomass. Highest dye removal
(24.8 mg/g) for direct blue 1 was obtained at pH 6 and for direct red 128 (73.3 mg/g) was
obtained at pH 3. By further increase in pH, the adsorption potential of Trametes versicolor
biomass decreased.
Almond shell biomass was used for the removal of direct red 80 dye from aqueous
solution (Ardejani et al., 2008). In this study, it was observed that pH had not shown a
pronounced effect on the dye removal. Only a slight variation in adsorption was observed
over a wide range of pH (2-12). Maximum removal was observed at pH 2 and at higher pH
decrease in dye removal was observed.
Mohan et al. (2008) attempted to remove direct azo dye from aqueous solution by
adsorption onto Spirogyra sp. I02. They checked out the effect of pH on the dye removal and
suggested that dye uptake was higher (almost 80 %) at pH 2 as compare to higher pH range.
Amin (2009) prepared activated carbon from pomegranate peels and utilized it for the
removal of Direct Blue 106. Wide range of pH (2-9.4) was selected to check out the effect of
pH on the removal of dye and it was observed that acidic pH was more feasible for the
maximum removal of Direct Blue 106 dye because of the electrostatic attraction between the
dye anions and positively charged activated carbon. As pH increased, the positive charge on
the surface of adsorbent decreased which resulted in decrease in dye removal.
An agricultural waste material (Loofa egyptiaca) was exploited for the preparation of
activated carbon to remove Direct Blue dye from aqueous solutions (Ashtoukhy, 2009).
Experiments were conducted to optimize the pH for maximum dye removal and maximum
dye removal was observed at pH 2.
Khaled et al. (2009) investigated the effect of pH on the removal of Direct Navy Blue
106 by using activated carbon prepared from orange peel. He observed that maximum
removal (93.5 %) of DNB 106 was at pH 2 which sharply decreased with increasing pH.
Erden et al. (2011) worked on the adsorptive removal of Sirius Blue K-CFN dye by
using Trametes versicolor biomass. During batch study experiments, it was observed that the
8
maximum dye removal was achieved at pH 3 and as the pH increased the removal of dye
decreased.
Haq et al. (2011) explored the adsorption efficiency of barley husk which is a low
cost agricultural waste for the removal of Solar Red BA dye from synthetic solution. Batch
study experiments were conducted to optimize different process parameters. Effect of pH
was investigated in the pH range of 2-10 and maximum dye removal was observed at pH 2
because of electrostatic attraction between dye anions and positively charged adsorbent
surface at low pH.
Mehmoodi et al. (2011) utilized a biocompatible composite (sodium alginate/titania
nanoparticle) (SA/n-TiO2) to check out its adsorption capacity for the removal of two textile
dyes (Direct Red 80 and Acid Green 25) from synthetic solutions. They investigated the
effect of pH on the removal of dyes and found out that maximum removal of these anionic
dyes take place at pH 2. The removal potential of adsorbent decreased as pH increased. They
concluded that maximum electrostatic interaction of anionic dye molecules with adsorbent is
favorable at acidic range of pH.
Safa and Bhatti (2011a) worked on the adsorptive removal of Direct Orange 26 and
Direct Red 31 by using rice husk as a low cost biosorbent. During batch study, effect of pH
on dyes removal was investigated and highest removal of both dyes was observed at acidic
range of pH. Highest removal of Direct Red 31(86.58 %) was found to be at pH 2 while
Direct Orange 26 showed maximum removal (68.63 %) at pH 3. Decrease in biosorption of
both dyes was observed with increase in pH.
Direct Blue 15 dye was removed from aqueous solutions by using bacterial cellulose
biomass (Ashjaran et al., 2012). To check out the effect of pH on the removal of dye, pH was
varied from 2-10. Maximum dye removal was observed at pH 3 and further increase in pH
resulted in decrease in dye removal.
Hen feathers have been proved as an efficient adsorbent material for the removal of
Congo red from synthetic solutions (Chakraborty et al., 2012). When the experiments were
conducted to explore the effect of pH, it was observed that adsorption of dye increased with
increasing pH of medium but it was up to certain limit. At pH 5 maximum removal of dye
(over 90 %) was observed and by further increase in pH decrease in dye removal was
observed.
9
Reddy et al. (2012) checked out the feasibility of using jujuba seeds as a low cost
adsorbent for the removal of Congo red from aqueous solutions. Experiments were
conducted to examine the effect of pH on dye removal. Congo red is anionic in nature and
results showed that highest removal of dye was achieved at pH 2.
Saha et al. (2012b) demonstrated their study on the utilization of egg shells as low
cost adsorbent for the removal of Direct Red 28 from aqueous solutions. They investigated
the effect of pH on dye removal by varying pH range from 4-10 and observed maximum
removal of dye at pH 6. Further increase in pH resulted in decreased dye adsorption.
2.1.2 Effect of contact time
The contact time is also a very important parameter in the biosorption process. Malik (2004)
attempted to remove Direct Blue 2B and Direct Green B dyes from aqueous solutions by
adsorbing onto activated carbon which was prepared from sawdust. He tried to optimize the
effect of contact time on adsorption of dyes and he found out that 2 h contact time was
sufficient to get maximum dyes removal.
Contact time was also optimized by Arami et al. (2006) for the removal of Direct Red
80 and Direct Red 81 dyes by using soy meal hull biomass. They investigated the effect of
contact time and found biosorption as a rapid process. They observed that 10 min contact
time was enough to get equilibrium point for maximum dyes removal.
Mall et al. (2006b) attempted to find out the effect of contact time on the removal rate
of Orange G by using bagasse fly ash as adsorbent. The experiments were conducted for 24 h
contact time. They observed the rapid adsorption rate in first 15 min and maximum dye
removal was obtained in 4 h. After that rate of dye removal remained constant. This was due
to the fact that at initial, higher number of active sites were available on the surface of
biosorbent which resulted in higher biosorption rates initially. With the progress of
biosorption process, the active sites got occupied and less active sites remained available to
the dye molecules hence the biosorption process became slow after sometime.
Ahmad et al. (2007) investigated the effect of contact time on the removal of Direct
Blue 71 by adsorption onto palm ash. Experiment was conducted to optimize the contact time
and equilibrium point was obtained in 1 h where maximum dye removal was attained.
Sureshkumar and Namasivayam (2008) demonstrated their work on the adsorption
removal of Direct Red 12B by using modified coconut coir pith as adsorbent. The effect of
10
contact time was investigated during study and it was concluded that 25 min time is enough
to attain equilibrium when initial dye concentration was 20 mg/g.
Amin (2009) investigated the effect of contact time on adsorptive removal of Direct
Blue 106 by pomegranate peels biomass and it was observed that the adsorption rate was
rapid in first 25 minutes which decreased with the passage of time and equilibrium was
attained in 2 h. Initial rapid rate of dye removal was due to adsorption of dye molecules on
external surface and when external surface get saturated the remaining dye molecules move
inside the adsorbent and attached to the internal surface. This diffusion of dye anions into
internal surface of adsorbent was a slow process.
Loofa egyptiaca biomass was exploited by Ashtoukhy (2009) for the adsorptive
removal of Direct Blue dye. Experiments were conducted to check out the effect of contact
time on the dye removal. It was concluded that rate of dye removal was fast in initial stages
and maximum dye adsorption was attained at the contact time of 2h.
Kahled et al. (2009) investigated the effect of contact time on removal of Direct N
Blue 106 by using activated carbon which was prepared from orange peels. It was observed
that adsorption is a rapid process and almost 70 % dye was removed in initial 10 minutes.
The rate of adsorption decreased with the passage of time and equilibrium was achieved after
3 h where maximum dye removal was obtained. The initial rapid rate of adsorption was due
to availability of large number of vacant sites which become occupied by dye molecules with
the passage of time resulting in reduction of adsorption rate.
Haq et al. (2011) worked on the biosorption of Solar Red by using barely husk as
adsorbent and optimized different process parameters. The effect of contact time on dye
removal was checked out and it was determined that the equilibrium time required to attain
maximum dye removal was 90 minutes with 0.1 g adsorbent dose.
Kayranli (2011) attempted to remove Direct Blue 71 from aqueous solution by
adsorbing it onto waterworks sludge. The effect of contact time was investigated and it was
found out that 100 min contact time was sufficient for the attainment of equilibrium.
Agitation time was also optimized by Safa and Bhatti (2011c) for the adsorptive
removal of Direct Orange 26 and Direct Red 31 dyes by using rice husk as low cost
biosorbent. The results showed that both dyes attained equilibrium point after 180 minutes.
11
During the initial time course, the dye removal process was rapid but it decreased with the
passage of time.
Ashjaran et al. (2012) utilized bacterial cellulose biomass for the adsorptive removal
of Direct Blue 15 dye from synthetic solutions. The effect of contact time was determined at
different temperatures and it was observed that time to reach equilibrium became short at
high temperatures. Equilibrium time at 60 °C was 5 min, at 45 °C, equilibrium time was 60
min and at 30ᵒC the equilibrium was achieved after 120 minutes.
Reddy et al. (2012) demonstrated their work on the removal of Congo red dye by
adsorption onto jujube seeds biomass. During batch study, different operational parameters
were optimized including contact time. It was seen that the rate of adsorption was rapid in
initial 60 min while it took 300 min for complete equilibrium attainment.
Saha et al. (2012b) investigated the effect of contact time on the adsorption rate of
Direct Red 28 onto egg shells. It was observed that in initial 60 minutes, there was a fast rate
of dye adsorption which became slow with the passage of time and it took 240 minutes to get
equilibrium.
2.1.3 Effect of biosorbent dose
Biosorbent dose is one of the very important factors which affect the biosorption process.
Arami et al. (2006) attempted to investigate the effect of biosorbent dose on the removal of
Direct Red 80 and Direct Red 81 by soy meal hull biomass. The range of biosorbent dose to
see the effect of adsorbent concentration on dye removal was 0.2-0.36 g for Direct Red 80
and 0.04-0.6 g for Direct Red 81 and it was concluded that increasing biosorbent
concentration resulted in increased dye removal. This might be due to the higher surface area
of biosorbent at higher higher biosorbent doses which resulted in the enhanced dye uptake by
the biomass.
Mall et al. (2006b) investigated the effect of adsorbent mass on the removal of
Orange G by using bagasse fly ash (BFA) and the results indicated that by the increase in
BFA dose, the dye adsorption increased up to certain limit, above which there was no effect
on the dye removal. The optimum adsorbent mass for maximum dye removal was found to
be 0.1 g/50 mL.
Sureshkumar and Namasivayam (2008) utilized coconut coir pith for the removal of
Direct Red 12B from aqueous solutions. The effect of biomass concentration was optimized
12
during this study and it was found out that the % removal of dye increased with increasing
dose of adsorbent. Maximum dye removal was attained by using 1 g of biosorbent/50 mL.
Ashtoukhy (2009) demonstrated his study on the removal of Direct Blue dye by
Loofa egyptiaca biomass. To check out the effect of biosorbent dose, the biomass
concentration was varied from 0.1-1.0 g and it was observed that the % age dye removal
increased with increasing concentration of biosorbent.
Khaled et al. (2009) tried to investigate the effect of adsorbent dosage on the removal
of Direct N Blue 106 by orange peel biomass. The biosorbent dose was varied from 0.2-1.0
g/100 mL and it was observed that % age removal of dye increased with increasing
concentration of adsorbent dose. 64 % dye removal was observed at 0.2 g absorbent dose
while 100 % dye removal was obtained when 1.0 g adsorbent was used.
Haq et al. (2011) utilized barely husk biomass for the removal of Solar Red BA and
investigated the effect of biosorbent dose on the dye removal. The range of biosorbent dose
selected for this purpose was 0.1-0.5 g and it was observed that with the increase in
concentration of biosorbent, the capacity of adsorbent to adsorb dye was decreased. 33.4
mg/g dye was adsorbed when 0.1 g biosorbent dose was selected and this capacity reduced to
2.29 mg/g when the biosorbent dose was further increased upto 0.5 g. This might be due to
the fact that at higher biosorbent dosage, the available dye molecules are insufficient to
completely cover the available binding sites on the biosorbent surface, which usually results
in low solute uptake.
Safa and Bhatti (2011c) studied the effect of biosorbent concentration on the removal
of two direct dyes (Direct Orange 26 and Direct Red 31) by using rice husk as adsorbent. It
was observed that % age dye removal increased from 35.5 - 68.6 % for Orange Red 26 and
50.1-90.9 % for Direct Red 31 when the biosorbent dose was increased from 0.05-0.1 g.
Ghaedi et al. (2012) investigated the effect of adsorbent dose on the removal of
Direct Yellow 12 by activated carbon. The adsorbent concentration was varied from 0.0025-
0.03 g and it was observed that maximum dye removal was attained at 0.025 g. Further
increase in adsorbent concentration had not shown any effect on dye removal.
2.1.4 Effect of initial dye concentration
Initial dye concentration acts as a controlling factor in the adsorption process. It is the main
driving force which overcomes all the mass transfer resistance. Mall et al. (2006b)
13
investigated the effect of initial dye concentration on the adsorptive removal of Orange G by
bagasse fly ash. The results described a decrease in percent removal of dye by increase in dye
concentration while sorption capacity (mg/g) was increased.
Ahmad et al. (2007) studied the effect of initial dye concentration on the removal
efficiency of fly ash biomass for Direct Blue 71 dye. The results indicated an increase in
sorption efficiency of biomass in presence of high concentration of dye due to greater mass
transfer.
Ardejani et al. (2008) tried to investigate the effect of initial dye concentration on the
removal of Direct Red 80 dye from aqueous solution and results indicated a decrease in
percent dye removal from 94 to 83.5 % by increasing the concentration from 50-150 mg/L.
This might be due to accumulation of dye ions at higher dye concentration which resulted in
decreased dye removal at higher initial dye concentrations.
Amin (2009) demonstrated his work on the optimization of different operational
parameters during batch biosorption study for removal of Direct Blue 106 by using
pomegranate peels biomass. The results of initial dye concentration showed that the percent
removal of dye was reduced at higher initial dye concentrations.
Ashtoukhy (2009) attempted to find out the effect of initial dye concentration on the
removal of Direct Blue dye by Loofa egyptiaca biomass. Their results indicated that the
sorption capacity (mg/g) increased with increasing initial dye concentration. Maximum
removal efficiency was found to be 73.53 mg/g. Higher biosorption capacity at higher initial
dye concentrations is attributed due to the fact that at higher dye concentrations, the active
sites available for biosorption become fewer compared to the moles of solute present and;
hence, the removal of solute is strongly dependent upon the initial solute concentration.
Khaled et al. (2009) carried out batch experiments for the optimization of different
operational parameters during adsorptive removal of Direct N Blue 106 onto activated carbon
which was prepared from orange peel. The effect of initial dye concentration with different
adsorbent amounts was investigated and it was suggested that with the increase in initial dye
concentration the equilibrium sorption capacity (mg/g) increased from 20.4 to 54.3 mg/g with
0.2 g adsorbent dose, 11.6 to 28.3 mg/g with 0.4 g adsorbent dose and 7.9 to 23.09 mg/g with
0.6 g adsorbent dose when initial concentrations were varied from 50 to 150 mg/L but the
14
percent sorption decreased. This showed that the adsorption was decreasing with the increase
in initial dye concentration but the amount of dye adsorbed on the adsorbent increased.
Mahmoodi et al. (2011) observed that initial dye concentration plays a very important
role in the adsorption process. They investigated the effect of initial dye concentration on the
removal of Direct Red 80 by a biocompatible composite. It was observed that as the initial
dye concentration increased, the amount of dye adsorbed on the composite increased because
the initial dye concentration is the main driving force in mass transfer but the % age dye
removal decreased with increasing initial dye concentration.
Effect of initial dye concentration was also checked by Ghaedi et al. (2012) on the
removal of Direct Yellow 12 dye by using activated carbon. The initial dye concentration
was varied from 15-60 mg/L and a decrease in the percent removal from 96 to 71 % was
observed.
Reddy et al. (2012) performed experiments to demonstrate the effect of initial dye
concentration on removal of Congo red dye and observed an increase in sorption capacity of
jujuba seeds biomass at higher concentrations of Congo red dye. When the concentration
increased from 25-100 mg/L there was an increase in sorption capacity from 10.4-34.6 mg/g.
2.1.5 Effect of temperature
Textile industries release their effluents at relatively high temperatures so temperature can be
an important factor in dye removal process. Bayramoglu and Arica (2007) observed that
temperature is one of the key process parameter which can affect the adsorption process.
They utilized fungal biomass (Trametes versicolor) for the removal of Direct Blue 1 and
Direct Red 128 dyes. To see the effect of temperature on dye removal, the temperature was
varied from 5 ᵒC to 35 ᵒC and it was observed that the dye removal was increased with
increasing temperature. This was due to surface activation of biosorbent at high temperatures.
So adsorption of Direct Blue 1 and Direct Red 128 onto Trametes versicolor biomass was
found to be endothermic in nature.
Sureshkumar and Namasivayam (2008) conducted experiments to check out the effect
of temperature on the removal of Direct Red 12B by adsorbing it onto coconut coir pith
biomass. The results showed that by increasing temperature from 32-60 °C, the dye removal
increased from 76.3 to 81.3 mg/g. this shows that adsorption of Direct Red 12B onto coconut
coir pith biomass was an endothermic process.
15
Amin (2009) investigated the effect of temperature on the removal Direct Blue 106
by using pomegranate peels biomass. The temperature was varied from 20 to 80 °C and it
was observed that the adsorptive removal of dye decreased with increase in temperature
which shows the exothermic nature of reaction.
Rodríguez et al. (2009) studied the influence of temperature on the adsorptive
removal of Orange II dye onto activated carbon by varying the temperature from 30 to 65 °C
and results of their experiments explain the endothermic nature of adsorption process.
Trametes versicolor biomass was utilized by Erden et al. (2011) for the removal of
Sirius Blue K-CFN dye from aqueous solution. Effect of temperature was checked out by
varying temperature from 7 to 45 °C. The results indicated that the adsorptive removal of dye
increased with increasing temperature up to 26 °C and by further increase in temperature, the
dye removal decreased. They explained the reason for this behavior as by initial increase in
temperature the surface activation of adsorbent increased which increased dye removal but
by further increase in temperature there might be the possibility of loss of some active sites
which resulted in decrease in dye removal efficiency of Trametes versicolor biomass.
Ashjaran et al. (2012) determined the influence of temperature on the direct blue 15
removal by utilizing bacterial cellulose biomass and found that the process was exothermic in
nature. Increase in temperature resulted in decreased dye removal.
Chakraborty et al. (2012) worked on the utilization of hen feathers for the removal
Congo red dye. The influence of temperature was investigated and it was observed that
process was endothermic in nature and was favorable at high temperatures.
Ghaedi et al. (2012) demonstrated their work to investigate the influence of
temperature for the removal of Direct Yellow 12 onto activated carbon and found the
endothermic nature of ongoing process. The experiments were performed in the temperature
range of 10 to 60 °C and an increase in adsorption of DY 12 with the increase in temperature
was observed.
Reddy et al. (2012) explained the effect of temperature on the adsorptive removal of
Congo red dye onto jujube seeds biomass. The temperature was varied from 30 to 60 °C and
there was an increase in dye removal was observed with the increase in temperature which
explains that reaction was endothermic in nature.
16
Saha et al. (2012b) investigated the effect of temperature on the removal of Congo
red by egg shells and conducted experiments in the temperature range of 20 to 40 °C. The
results showed an increase in adsorption removal of dye by increase in temperature so
ongoing process was endothermic in nature.
Taleb et al. (2012) studied the effect of temperature on the adsorptive removal of
Direct Pink 3B by a nanocomposite material (calcium alginate/organophilic
montmorillonite). When the experiment was conducted to investigate the effect of
temperature, it was observed that there was an increase in dye adsorption when temperature
was increased from 30 to 40 °C but further increase in temperature resulted in decreased dye
removal. With the initial increase in temperature, there might be swelling of biosorbent take
place so more dye molecules can penetrate into the adsorbent but as temperature was further
increased, the dye molecules get more kinetic energy and their fast collision leads to the slow
adsorption of dye molecules.
Toor and Jin (2012) attempted to explain the effect of temperature on the removal of
Congo red by surface modified bentonite. The temperature was varied from 25 to 60 °C and
it was observed that with increase in temperature, the dye removal was decreased. This
observation confirms that adsorption of Congo red onto modified bentonite is an exothermic
process.
2.2 KINETIC STUDY
Kinetic studies are necessary to optimize different operating conditions for the biosorption
process. Various kinetic models have been suggested for explaining the order of reaction.
Malik (2004) treated the experimental data with pseudo-first-order and pseudo-
second-order kinetic models in order to investigate the mechanism of adsorption for the
adsorptive removal of Direct Blue 2B and Direct Green B by using sawdust as adsorbent.
The R2 values for both dyes were higher than 0.99 when pseudo-second-order kinetic model
was applied which confirms the fitness of this model.
Mall et al. (2006b) applied four kinetic models (pseudo-first-order, pseudo-second-
order, intraparticle diffusion and Bangham model to determine the adsorption mechanism of
Orange G. It was observed that the data follows pseudo-second-order kinetic model because
of its correlation coefficient value near to 1.
17
Kinetic study was carried out by Arami et al. (2006) on the experimental data
obtained from adsorption of Direct Red 80 and Direct Red 81 onto soy meal hull. Pseudo-
first-order and pseudo-second-order kinetic models were applied and the R2 values show that
rates of reaction for both dyes followed pseudo second order kinetic model.
Ahmad et al. (2007) treated the experimental data obtained from adsorptive removal
of Direct Blue 71 onto fly ash with pseudo-first-order and pseudo-second-order kinetic
models in order to determine mechanism of adsorption and found that adsorption dynamics
followed pseudo-second-order kinetic kinetic model.
Ardejani et al. (2007) applied pseudo-first-order and pseudo-second-order kinetic
model for the determination of adsorption mechanism of Direct Red 80 and Direct Red 23
and pseudo-second-order kinetic model was found to be more suitable on the kinetic data.
Ashtoukhy (2009) attempted to determine the order of reaction by applying kinetic
models (pseudo-first-order, pseudo-second-order kinetic and intraparticle diffusion) on the
experimental data of adsorption of direct blue dye onto Loofa egyptiaca biomass and the
results indicated that pseudo first order and pseudo-second-order kinetic models have a high
correlation coefficient (R2˃ 0.97).
Khaled et al. (2009) applied different kinetic models on the experimental data
obtained from adsorption of Direct N Blue onto activated carbon which was prepared from
orange peel. Pseudo-first-order, pseudo-second-order, Elovich and intraparticle diffusion
models were applied to check out the mechanism of adsorption. High correlation coefficient
value was obtained when pseudo-second-order kinetic model was applied. It was concluded
that pseudo-second-order-kinetic model was best fit with intraparticle diffusion as one of the
rate determining step.
Mehmoodi et al. (2011) applied different kinetic models to search out the mechanism
of adsorption for the removal of Direct Red 80 onto a biocompatible composite. Pseudo-first-
order, pseudo-second-order and intraparticle diffusion models were applied on the batch
study data. The R2 value of pseudo-second-order kinetic model correspond its good fitness to
the experimental data and also the experimental and calculated qe (mg/g) values were in close
agreement with each other which confirmed the good fitness of pseudo-second-order kinetic
model.
18
Toor and Jin (2012) examined the batch study data of adsorption of Congo red dye
onto modified bentonite by applying pseudo-first-order and pseudo-second-order kinetic
models. The experimental qe (mg/g) was in accordance with predicted qe (mg/g) from
pseudo-second-order and high correlation coefficient of pseudo-second-order kinetic model
confirmed that the reaction was following chemisorption mechanism because pseudo-second-
order kinetic model assumes that chemisorption is the rate determining step in adsorption
process.
2.3 EQUILIBRIUM MODELING
Equilibrium data, mostly known as biosorption isotherms, are basic requirements to
understand the mechanism of the biosorption. Khaled et al. (2009) applied different
equilibrium models on the experimental data obtained from the adsorption of Direct N Blue
dye onto activated carbon prepared from orange peel. The equilibrium models applied were
Langmuir, Freundlich, Temkin, Redlich Peterson, Koble Corrigan and Dobinin-
Radushkevish adsosorption isotherms. The experimental data obeyed Freundlich adsorption
isotherm with high correlation coefficient values which explained that adsorption was non-
uniform, non-specific and heterogenous in nature. Erden et al. (2011) tested the experimental
data by applying Langmuir and Freundlich adsorption equations and the results indicated that
Sirius Blue K-CFN adsorption onto Trametes versicolor biomass follows Langmuir
adsorption isotherm with good correlation coefficient value (0.968).
Safa and Bhatti (2011c) worked on the biosorption of direct dyes (Everdirect Orange-
3GL and Direct Blue-67) by using rice husk biomass and applied seven different equilibrium
models which include Langmuir (Four linear expressions), Freundlich and Temkin isotherm
on the experimental data. The results indicate that Langmuir type 1 and type 2 models were
better fit on the experimental results of biosorption of Everdirect Orange-3GL dye while
Langmuir type 2 was found to be best suited equilibrium model for Direct Blue-67
biosorption onto rice husk biomass.
Langmuir and Freundlich models were applied to analyze the adsorption data
obtained from the study of Toor and Jin (2012) which was conducted for the adsorptive
removal of Congo red dye by using natural modified bentonite. Freundlich adsorption
isotherm provided a better fit on the experimental data with high R2 values.
19
Chakraborty et al. (2012) performed the adsorption experiments for the removal of
Congo red and Crystal violet dyes from aqueous solutions by using hen feathers as adsorbent
and Langmuir, Freundlich and D-R adsorption isotherm equations were applied to analyze
the experimental data. Langmuir adsorption isotherm model showed good fitness to the
isotherm data explaining the monolayer chemisorption phenomena involved in the adsorption
of both dyes.
Saha et al. (2012b) treated the equilibrium data with Langmuir, Freundlich and D-R
models. It was observed that adsorption of Congo red onto egg shells biomass follow the
both Langmuir and Freundlich adsorption isotherms.
2.4 THERMODYNAMIC STUDY
Kayranli (2011) tested the experimental data of Direct Blue 71 removal for the
thermodynamic study and negative values of Gibbs free energy indicated that reaction was
spontaneous in nature.
Safa and Bhatti (2011c) used the experimental data of adsorptive removal of Direct
Red 31 and Direct Orange 26 by rice husk to calculate different thermodynamic parameters
(∆Gᵒ, ∆Hᵒ and ∆Sᵒ). The results indicated that the process of removal of both dyes was
endothermic process. The values of Gibbs free energy indicated that reaction was
spontaneous at higher temperatures.
Chakraborty et al. (2012) estimated different thermodynamic parameters (∆Gᵒ, ∆Hᵒ
and ∆Sᵒ) from the experimental results of the adsorptive removal of Congo red by hen
feathers. The results indicated that the adsorption process under study was spontaneous and
endothermic in nature.
Ghaedi et al. (2012) calculated thermodynamic parameters (Standard free energy
change, standard enthalpy change and standard entropy change) from the experimental data
for the removal of Direct Yellow 12 dye by using activated carbon. The results indicated
thermodynamic feasibility and spontaneity of reaction and also indicated that reaction was
endothermic in nature.
Saha et al. (2012b) performed thermodynamic study on the experimental data
obtained from adsorption of Congo red dye by egg shells biomass and the thermodynamic
parameters (∆Gᵒ, ∆Hᵒ and ∆Sᵒ) showed that adsorption process was spontaneous and
endothermic in nature.
20
2.5 POINT OF ZERO CHARGE OF ADSORBENTS
The linear range of pH sensitivity can be determined by an important factor, Point of zero
charge (pHpzc). pHpzc gives us information about the type of binding sites present on the
surface of the adsorbents and hence tells us about the adsorption potential of the adsorbent.
Rodríguez et al. (2009) investigated the pHpzc of activated carbon during the adsorption study
of Methylene Blue and Orange II dyes. pHpzc for activated carbon was found to be 7.63.
Ghaedi et al. (2012) determined the point of pH at which surface of activated carbon
that was loaded with silver nanoparticles has no charge. Final pH drift method was used to
determine the point of zero charge and it was observed that pHpzc for activated carbon was
6.5.
Reddy et al. (2012) found out the point of zero charge of jujube seeds biomass by
solid addition method. The charge on the surface of jujube seeds was zero at pH 7 hence for
jujube seed biomass pHpzc was 7.
Toor and Jin (2012) investigated the pHpzc of raw bentonite, thermally activated
bentonite and acid treated bentonite during the adsorption studies conducted for the removal
of congo red dye. The point of zero charge was found to be 4.8, 5.2 and 4.5 for raw,
thermally activated and acid treated bentonite respectively.
2.6 EFFECT OF PRETREATMENTS OF BIOSORBENTS
The adsorption capacity of adsorbents can be enhanced by surface modification which can be
done by chemical and physical pretreatment of the adsorbents. Bayramoglu and Arica (2007)
performed a comparative study for the removal of Direct Blue 1and Direct Red 128 dyes by
using native and heat treated Trametes versicolor biomass. The enhanced dyes removal was
obtained with heat treated biomass as compare to native biomass.
Sureshkumar and Namasivayam (2008) used the coir pith biomass in modified form
by its treatment with Hexadecyltrimethylammonium bromide for the removal of Direct Red
12B and results indicated that treatment of biomass with surfactant enhanced its removal
efficiency as compare to the raw coir pith biomass.
Safa and Bhatti (2011d) pretreated rice husk with different acids, alkali and
surfactants in order to see the effect of pretreatments on the removal efficiency of adsorbent.
The results indicated that treatment of biomass with acids and cationic surfactants increased
the adsorption potential of rice husk due to protonation of adsorbent which increased the
21
electrostatic attraction for the anionic dyes (Everdirect Orange 3G and Direct Blue 67).
Different physical pretreatments (boiling and heating) were also carried out but they put no
effect on dye adsorption capacity of rice husk.
Asghar and Bhatti (2012) pretreated citrus waste biomass with different acids, organic
solvents and surfactants and utilized the pretreated biomass for the removal of reactive dyes.
The results of batch study showed that pretreatment with acids enhance the adsorption
potential of citrus waste biomass.
Dawood and Sen (2012) treated pine cone biomass with hydrochloric acid and
compared the adsorption potential of native and pretreated biomass for removal of Congo red
and the results indicated that pretreated biomass had better efficiency to adsorb dye due to
increased surface area of adsorbent.
Dua et al. (2012) utilized heat treated and live Pseudomonas sp. strain DY1 for the
removal of Acid Black 172 and concluded that heat treated biomass has more potential for
the removal of dye than live biomass.
2.7 EFFECT OF IMMOBILIZATION OF BIOSORBENTS
Wang et al. (2008) immobilized the Aspergillus fumigatus biomass by using
carboxylmethylcellulose (CMC) immobilization matrix for the removal of azo dye.
Maximum dye removal was achieved at pH 2 and adsorption of azo dye on immobilized
biomass was found to be an endothermic process.
Mahmoodi et al. (2011) prepared adsorbent by immobilizing titania nanoparticles
onto sodium alginate and used it for the removal of Direct Red 80 dye from aqueous solution.
The results indicated that sodium alginate immobilized titanium oxide nanoparticles can be
used as an efficient and eco-friendly adsorbent for the removal of anionic dyes.
Mona et al. (2011) attempted to remove Reactive Red 198 from aqueous solution by
Nostoc linckia HA 46 biomass that was immobilized by calcium alginate. 93.5 mg/g dye was
removed by this process which indicated that immobilized calcium alginate Nostoc linckia
HA 46 can be used as an efficient adsorbent for the removal of Reactive Red 198.
2.8 RESPONSE SURFACE METHODOLOGY STUDIES
Ravikumar et al. (2007) applied 24 full factorial central composite design for the removal of
Acid Brown 29 dye from aqueous solutions. Four factors used in study were pH,
temperature, paticle size and contact time. The results indicated that the dye was 100 %
22
removed from the solution at optimum conditions that were pH 10.8, temperature 59.25 ᵒC,
particle size 0.0525 mm and time 395 min.
Jaikumar and Ramamurthi (2009) conducted experiments for the removal of Acid
Blue and Acid Yellow dyes from aqueous solutions by brewery waste biomass. Different
parameters viz. pH, initial dye concentration, contact time and adsorbent dose were
optimized using response surface methology and it was found that response surface
methodology is a good method that can be efficiently applied for the optimization of different
parameters.
Arulkumar et al. (2011) optimized different important operational parameters viz.
initial dye concentration, contact time and adsorbent dose by response surface methodology.
Thespesia populnea was used to prepare activated carbon for the adsorption of Orange G
dye. Best removal (17.6 mg/L) was obtained at the contact time of 4.03 h by using 0.54 g
adsorbent dose.
Mona et al. (2011) used Box-Behnken design to see the interaction effect of three
parameters viz. temperature, initial dye concentration and pH by response surface
methodology. 94 % dye removal was obtained at pH 2, temperature 35 °C and initial dye
concentration 100 mg/L.
Central composite design (CCD) was applied by Singh et al. (2011) for the removal
of crystal violet dye from aqueous solution by using magnetic nanocomposite material. The
four variables selected for this study were temperature, pH, initial dye concentration and
adsorbent dose. The optimum conditions for maximum dye removal (113.31 mg/g) were
found to be initial dye concentration 240 mg/L; temperature 50 ◦C; pH 8.50 and adsorbent
dose 1 g/L.
Kousha et al. (2012) applied response surface methodology on three important
process parameters viz pH, initial dye concentration and adsorbent dose to check out their
interaction effect on the removal of Acid Black 1 dye by brown macroalgae. Over 99 % dye
was removed by these experiments which showed that brown macroalgae species can be used
for the removal of Acid Black 1 dye.
24 full factorial response surface central composite design was applied for the
removal of two acid dyes (Acid Yellow and Acid Blue) by using brewery industrial waste
The solutions of all the five dyes with all three biosorbent types (native, pretreated and
immobilized) were agitated for three hours to investigate the effect of agitation time on the
dyes removal. The experiments were conducted at pre-optimized condition of pH for all the
28
five direct dyes while keeping the other parameters constant eg., biosorbent dose: 0.1 g/50
mL dye solution of 50 mg/L concentration; particle size: 300 μm, shaking speed: 120 rpm
and temperature: 30 °C.
3.6.3 Effect of biosorbent dose
To investigate the effect of biosorbent dose, the amount of biosorbent was varied from 0.05-
0.3 g/50 mL dye solution of 50 mg/L concentration under pre-optimized conditions of pH
and contact time at 30 °C temperature with particle size of 300 μm and 120 rpm shaking
speed.
3.6.4 Effect of initial dye concentration
Effect of initial dye concentration was investigated by changing the dye concentration from
10-200 mg/L for all the dyes using pre-optimized conditions of pH, contact time and
biosorbent dose. The temperature and shaking speed for this study were 30 °C and 120 rpm
respectively.
3.6.5 Effect of temperature
Industrial effluents are usually released at higher temperatures. To explore the effect of
solution temperature on the biosorption of dyes by different biosorbents, the temperature was
varied from 303-333 K at optimum conditions of all parameters.
3.6.6 Effect of presence of electrolytes
Effect of presence of different salts (NaCl, CaCl2·2H2O, MgSO4·H2O, NH4NO3 and NaNO3)
on the biosorption of all five direct dyes was investigated at different concentrations (0.1, 0.2,
0.3, 0.4 and 0.5 M) of these salts in 50 mg/L dye solution. Control was also run having no
electrolyte to compare the amount of dye adsorbed onto the biosorbent.
3.6.7 Effect of presence of heavy metal ions
Effect of presence of heavy metals ions (Cd, Pb, Cr, Co and Cu) at different concentrations
(50,100, 150, 200 and 250 ppm) in 50 mL of each dye solution was also studied for the
adsorptive removal of direct dyes.
3.6.8 Effect of presence of surfactants
Effect of presence of surfactants on the removal of dyes was also investigated by using 1%
solution of different surfactants Triton X-100, CTAB, SDS and two detergents, Arial and
Excel.
29
3.7 Biosorption kinetics
The kinetic data was analyzed using pseudo-first-order (Lagergren, 1898), pseudo-second-
order (Ho et al., 2000) and intraparticle diffusion (Weber and Morris, 1963) kinetic models.
3.8 Biosorption equilibrium
Five different biosorption isotherm models were applied in this present investigation to
explore the mechanism of biosorption of direct dyes. The equilibrium isotherms include the
Langmuir (Langmuir, 1918), Freundlich (Freundlich, 1906), Temkin (Temkin and Pyzhev,
1940), Harkins Jura (Harkins and Jura, 1944) and Doubinin– Radushkevich isotherm model
(Doubinin and Radushkevich, 1947).
3.9 Biosorption thermodynamics
Various thermodynamic parameters such as enthalpy changes (ΔH), entropy changes (ΔS)
and Gibbs free energy changes (ΔG) were investigated by using thermal data obtained from
the biosorption of dyes at different temperatures to determine the spontaneity and feasibility
of biosorption process.
3.10 Column studies
Biosorption performance of biosorbents in continuous system is an important factor in
accessing the feasibility of biosorbents in real applications. Continuous biosorption
experiments in a fixed-bed column were conducted in a glass column (20 mm ID and 43 cm
height), packed with a known quantity of selected biosorbent (native) for each dye. At the
bottom of the column, a stainless sieve was attached followed by a layer of glass wool. The
dye solutions at the outlet of the column were collected at regular time intervals and the
concentration was measured using a double beam UV-visible spectrophotometer (Shimadzu,
Japan) at specific λmax (nm) for each dye. All the experiments were carried out at room
temperature (28 ± 1°C).
The results of biosorption of direct dyes onto biosorbents in a continuous system were
presented in the form of breakthrough curves which showed the loading behavior of dyes to
be adsorbed from the solution expressed in terms of normalized concentration defined as the
ratio of the outlet dye concentration to the inlet dye concentration as a function of time (Ct/Co
vs. t).
30
Breakthrough capacity Q0.5 (at 50 % or Ct/Co=0.5) expressed in mg of dye adsorbed
per gram of biosorbent was calculated by the following equation: Breakthroughcapacity= % . (3.2)
3.10.1 Effect of bed height
A known quantity of the biosorbent was packed in the column to yield the desired bed height
of the biosorbent for the removal of each dye. To check out the effect of bed height on the
removal of dyes, the bed height was varied from 1-3 cm for Indosol Turquoise FBL and
Indosol Orange RSN and for Direct Violet 51, Indosol Black NF and Indosol Yellow BG
dyes, the range of bed height was 2-4 cm. The pH of dye solution was 2 and initial dye
concentration was 50 mg/L. The flow rate for these experiments was 1.8 mL/min.
3.10.2 Effect of flow rate
To investigate the effect of flow rate on the removal of dyes in continuous mode
experiments, the dye solution was pumped upward through the column at a desired flow rate
(1.8, 3.6 and 5.4 mL/min) controlled by a peristaltic pump (Prominent, Heidelberg,
Germany) keeping the dye concentration and bed height constant.
3.10.3 Effect of initial dye concentration
The effect of bed height was explored by varying the initial dye concentration form 50-100
mg/L for Indosol Turquoise FBL, Indosol Black NF and Indosol Yellow BG; 25-75 mg/L for
Direct Violet 51 and 50-70 mg/L for Indosol Orange RSN dye at optimum bed height and
flow rate.
3.10.4 Application of kinetic models on the column data
Thomas model and Bed Depth Service Time (BDST) models were applied on the
experimental results of column study to investigate the kinetic behavior of biosorption
process.
31
Author working on Glass Column Assembly
32
3.11 Characterization of biosorbents
The chemical characteristics of selected agricultural wastes were analyzed and interpreted by
Bruker Tensor 27 Fourier transform infrared spectrometer with the samples prepared as KBr
discs. The surface structure of these biosorbents was analyzed by JEOL JMT 300 scanning
electron microscope (SEM).
The point of zero charge (pHpzc) was determined by solid addition method (Mall et
al., 2006a). A series of 0.1 M KNO3 solutions (50 mL each) were prepared and their pH was
adjusted in the range of 1.0 to 12.0 by addition of 0.1 N HCl and NaOH. To each solution,
0.1 g of biosorbent was added and the suspensions were shaked manually and solution was
kept for a period of 48 h with intermittent manual shaking. The final pH of the solution was
recorded and difference between initial and final pH (∆pH) (Y-axis) was plotted against
initial pH (X-axis). The point of intersection of this curve yielded point of zero charge.
3.12 Optimization by Response Surface Methodology (RSM) Classical methods of studying a process by keeping other variables involved at an
unspecified constant level does not depict the combined effect of all the variables involved.
This method is also time consuming and requires a number of experiments to determine
optimum levels (Elibol, 2002). These limitations of a classical method can be eliminated by
optimizing all the affecting variables collectively by statistical experimental design such as
response surface methodology (RSM). RSM is a collection of mathematical and statistical
techniques useful for developing, improving and optimizing the processes and can be used to
evaluate the relative significance of several affecting variables even in the presence of
complex interactions (Ravikumar et al., 2007).
3.12.1 Experimental design
For the experimental design, Box-Behnken design was employed which has been proved
appropriate for fitting the quadratic surface (Kousha et al., 2012). Three independent
influencing variables were selected as initial dye concentration (A), biosorbent dose (B) and
pH (C). Total 17 experimental runs were generated by Design Expert software (version 7.0.0)
by using the following formula
= + + (3.3)
Where K is the number of variables and CP is the number of replicate of center points.
The coded values of process variables are obtained by the following equation
33
= − /∆ (3.4)
i=1,2,3,…,k
Where
Xi = real value of independent variable
Xo = value of Xi at central point
∆X = step change and
xi = dimensionless value of process variable
The second order equation used to show the relationship between dependent and independent variables was given as = β + β A + β B + β + β AB + β AC + β + β + β + β + ε (3.5)
Where
: The response variable
β : Intercept
β , β , β : The coefficients of A, B, C
β , β ,β : Coefficients of cross products
β , β , β : Coefficients of quadratic terms
ε : ε ~N(0,σ2)
A positive sign in the equation represents a synergistic effect of the variables, while a
negative sign indicates an antagonistic effect of the variables. The optimum values were
obtained by solving the regression equation, analyzing the contour plot and also by setting
the constraints for the levels of the variables.
The upper and lower limits of process variables for direct dyes (Direct Violet 51,
Indosol Turquoise FBL, Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN) are
presented in Table 3.2.
34
Table 3.2 Experimental ranges and levels of independent variables
Factors Ranges and levels
-1 0 +1
A:Iinitial dye concentration(mg/L) 10 105 200
B: Biosorbent dose(g) 0.05 0.17 0.30
C: pH* 2 0.05 9
C: pH** 3 6 9
* pH range and level for Direct Violet 51, Indosol Black NF, Indosol Yellow BG,
Indosol Orange RSN
** pH range and level for Indosol Turquoise FBL
3.12.2 Batch experimental program
The batch experiments were conducted as designed by the RSM at 120 rpm and 30 ᵒC for 1 h
of agitation time to check out the effect of initial dye concentration, biosorbent dose and pH
on the removal of anionic dyes. The experiments were conducted with pretreated form of
biosorbent. Solution pH was adjusted by using 0.1 M NaOH and 0.1 M HCl. After 1 h of
agitation, the samples were taken out and centrifugation was performed at 5000 rpm for 20
min and concentration of remaining dye solution was determined by using UV-Vis
spectrophotometer (Schimadzu, Japan). The responses were recorded in the form of
biosorption capacity (mg/g).
3.13 Application of method to the real effluents
3.13.1 Textile wastewater
Two raw textile wastewater samples were collected from Kamal Textile Industry, Faisalabad
and Arzo Textile Dying and Printing Industry Faisalabad. Samples were collected in
sampling bottles and placed in icebox to preserve for analysis. The effluents from Kamal
Textile Industry, Faisalabad and Arzo Textile Dying and Printing Industry Faisalabad were
labeled as Effluent 1 and Effluent 2 respectively. The physico-chemical parameters such as
pH, Electrical conductivity (EC), Chemical Oxygen Demand (COD), Total Dissolved Solids
(TDS) and Total Suspended Solids (TSS) were estimated before and after the biosorption
process using standard methodologies.
3.13.2 Screening study
35
Screening test was conducted with five different agricultural waste materials (sugarcane
bagasse, peanut husk, corn cobs, cotton sticks and sunflower waste biomass) to select one
biosorbent with maximum biosorption potential for COD removal from each textile effluent.
The selected dried biomasses were ground with a food processor (Moulinex, France) and
sieved using Octagon sieve (OCT-DIGITAL 4527-01) to a 300 μm mesh size and stored in
air tight bottles.
3.13.3 Batch experimental program
The optimization of important process parameters (biosorbent dose, contact time, shaking
speed and temperature) for the maximum removal of COD from textile effluents was carried
out using classical approach. The 250-mL conical flasks containing 50 mL of dye containing
effluents with known biosorbent dose were shaken in orbital shaking incubator (PA250/25H).
Blank solutions were run under same conditions except the addition of biosorbent. All the
experiments were performed in triplicate and reported values are mean±SD. After certain
time, the samples were taken out and their COD was recorded.
The % age COD removal from each sample was calculated by using the following
relationship: % = − 100/ (3.6)
3.14 Desorption study
Direct dyes from dye loaded biomass were desorbed by using NaOH (0.2-1.0 M). Sorption
procedure was carried out by adding 0.1g of selected biosorbent in 50 mg/L of dye solution
at optimized pH and 30 oC for 3 hours. The amount of dye sorbed (mg/g) for all dyes were
calculated. Then filtered the dyes solution and dried the dyes loaded biosorbents in oven at
60 oC and studied desorption process by shaking the dried biomass with NaOH. The amount
of dye desorbed (mg/g) for all dyes were again calculated. The % age desorption can be
estimated by using following equation:-
Desorption % = Amount of dye desorbed (mg/g) / Amount of dye sorbed (mg/g) ×100 (3.7)
3.15 Data analysis
The experimental data was analyzed by applying standard deviation.
36
Chapter #4
RESULTS AND DISCUSSION
4.1. Screening of agricultural wastes
Five different agricultural wastes (sugarcane bagasse, peanut husk, corn cobs, sunflower and
cotton sticks) have been used for the removal of direct dyes in this study. Screening test was
performed for the selection of biosorbent with maximum biosorption potential for each direct
dye. The results indicated that for three dyes (Indosol Black NF, Indosol Yellow BG and
Orange RSN) peanut husk biomass showed maximum biosorption potential and for two dyes
(Direct Violet 51 and Indosol Turquoise FBL) sugarcane bagasse was found to be efficient
biosorbent among the five agricultural waste materials. Fig. 4.1 shows the results of
screening study.
Fig. 4.1 Biosorption capacity of five different agricultural wastes for each direct dye
4.2. Effect of pretreatments
The biosorption efficiency of the biosorbents can be enhanced by different pretreatments and
surface modifications. The selected biosorbents were treated physically and chemically to
check out the effect of pretreatments on their biosorption potential. Physical treatments
include boiling and autoclaving of the biosorbents while chemical treatments include the
treatment of biosorbents with different acids (HCl, H2SO4 and HNO3 and CH3COOH), alkali
The thermodynamic parameters such as Gibbs free energy change (ΔG), enthalpy change
(ΔH) and entropy change (ΔS) were calculated from the thermal data of biosorption of Direct
Violet 51, Indosol Turquoise FBL, Indosol Black NF, Indosol Yellow BG and Indosol
Orange RSN dyes and are presented in Table 4.11 to 4.15
(4.13)
(4.14)
Where Kd=qe/Ce
R is the gas constant (8.314 J/mol K) and T is the absolute temperature.
so it can also be written as
(4.15)
The biosorption of Direct Violet 51, Indosol Turquoise FBL, Indosol Black NF, Indosol
Yellow BG and Indosol Orange RSN onto native, pretreated and immobilized form of
biomasses is an exothermic reaction which is also confirmed by negative values of ΔHo -
(Table 4.11 to 4.15). The negative values of ΔSo suggest the decrease in disorder at the
solid/solution interface during the biosorption process (Mittal et al., 2010). The negative
values of ΔGo imply the spontaneous nature of the biosorption process. Deniz and
Saygideger (2011) also reported the similar results.
76
Table 4.11 Thermodynamic parameters for the removal of Direct Violet 51 by sugarcane bagasse biomass Temperature
(K)
Direct Violet 51
Native
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Pretreated
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Immobilized
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol) (Jmol-1 K-1)
303
308
313
318
323
333
-0.39 -33.84 -107
-2.88
0.169
1.35
1.50
1.81
-1.81 -19.65 -59.22
-1.44
-1.05
-0.33
-0.23
-0.26
1.46 -16.08 -58.40
1.57
2.88
2.70
2.89
3.15
Table 4.12 Thermodynamic parameters for the removal of Indosol Turquoise FBL by sugarcane bagasse biomass Temperature
(K)
Indosol Turquoise FBL
Native
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Pretreated
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Immobilized
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol) (Jmol-1 K-1)
303
308
313
318
323
333
-2.4 -50.76 -0.162
-0.53
0.90
1.69
1.95
2.36
-3.18 -55.01 -0.172
-1.53
-0.12
0.92
1.47
1.72
0.65 -58.43 -0.194
1.86
2.78
3.82
4.90
6.26
77
Table 4.13 Thermodynamic parameters for the removal of Indosol Black NF by peanut husk biomass Temperature
(K)
Indosol Black NF
Native
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol) (Jmol-1 K-1)
Pretreated
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol) (Jmol-1 K-1)
Immobilized
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol) (Jmol-1 K-1)
303
308
313
318
323
333
-6.39, -52.07, -151
-4.71
-3.99
-3.93
-2.91
-1.73
-8.77, -70.1, -203
-6.74
-5.86
-4.80
-3.76
-2.85
-3.51, -26.19, -74.96
-3.16
-2.36
-2.12
-1.85
-1.35
Table 4.14 Thermodynamic parameters for the removal of Indosol Yellow BG by peanut husk biomass Temperature
(K)
Indosol Yellow BG
Native
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Pretreated
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Immobilized
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
303
308
313
318
323
333
-5.24 -63.94 -0.197
-2.19
-1.19
-0.25
0.312
0.687
-6.88 -73.24 -0.223
-3.17
-2.22
-1.50
-0.92
0.084
0.59 -55.56 -0.19
1.74
8.37
5.39
6.03
6.63
78
Table 4.15 Thermodynamic parameters for the removal of Indosol Orange RSN by peanut husk biomass Temperature
(K)
Indosol Orange RSN
Native
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Pretreated
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol)(Jmol-1 K-1)
Immobilized
∆G° ∆H° ∆S°
(kJ/mol) (kJ/mol) (Jmol-1 K-1)
303
308
313
318
323
333
-7.41 -64.4 -0.19
-6.60
-2.51
-2.28
-2.06
-2.11
-11.8 -74.4 -0.207
-7.70
-10.2
-9.53
-6.92
-5.28
2.19 -24.89 -0.088
2.28
2.74
2.91
3.47
4.85
4.9 Effect of electrolytes on the biosorption of selected direct dyes During the dyeing process in textile industries, large amounts of salts are consumed (Aksu
and Balibek, 2010). So the concentration of salts in textile effluents is one of the important
factors that control both electrostatic and non-electrostatic interactions between the
biosorbent surface and dye molecules and therefore affects the biosorption capacity (Dogan
et al., 2008). The effect of presence of electrolytes on the biosorption ability of biosorbents
for the removal of Direct Violet 51, Indosol Turquoise FBL, Indosol Black NF, Indosol
Yellow BG and Indosol Orange RSN was investigated using different salts solutions (NaCl,
KNO3, CaCl2.2H2O, MgSO4.7H2O and AlCl3.6H2O) of concentrations ranging from 0.1 to 0.5
M (Fig. 4.30-4.34). The results indicated that presence of salts increased the biosorption
potential. This is due to the fact that increase in the ionic strength increases the positive
charge of the surface of biosorbent hence it increases the electrostatic interaction between
dye anions and biosorbent which results in increase in biosorption potential of biosorbent.
Another reason behind the this increase in biosorption of anionic dyes at higher salt
concentrations might be due to the salting out phenomena which results in reduction of dye
solubility in water which leads to the adsorption of dye molecules on the biosorbent (Li et al.,
2007).
79
Haq et al. (2011) also observed an increase in biosorption capacity of barley husk
biomass for the removal of Solar Red BA dye in presence of salt (KCl). Safa and Bhatti
(2011b) investigated the effect of presence of different salts on the adsorptive removal of
Direct Orange 26 and Direct Red 31 dyes by using rice husk biomass. Different salts (NaCl,
CaCl2·2H2O, MgSO4·H2O, NH4NO3 and NaNO3) were used in different concentrations (0.01
m to 0.3 M) and results indicated that the presence of salts enhanced the biosorption potential
of rice husk biomass for the removal of Direct Orange 26 and Direct Red 31 dyes from
aqueous solutions.
Fig. 4.30 Effect of presence of electrolytes on the biosorption potential of sugarcane bagasse for the removal of Direct Violet 51 dye
05
101520253035404550
NaCl KNO3 CaCl2.2H2O MgSO4.7H2O AlCl3.6H2O
qe (m
g/g)
Electrolytes
0.1 M
0.2 M
0.3 M
0.4 M
0.5 M
Control
80
Fig. 4.31 Effect of presence of electrolytes on the biosorption potential of sugarcane bagasse for the removal of Indosol Turquoise FBL dye
Fig. 4.32 Effect of presence of electrolytes on the biosorption potential of peanut husk for the removal of Indosol Black NF dye
0
10
20
30
40
50
60
NaCl KNO3 CaCl2.2H2O MgSO4.7H2O AlCl3.6H2O
qe(m
g/g)
Electrolytes
0.1 M
0.2 M
0.3 M
0.4 M
0.5 M
Control
4041424344454647484950
NaCl KNO3 CaCl2.2H2O MgSO4.7H2O AlCl3.6H2O
qe(m
g/g)
Electrolytes
0.1 M
0.2 M
0.3 M
0.4 M
0.5 M
Control
81
Fig. 4.33 Effect of presence of electrolytes on the biosorption potential of peanut husk for the removal of Indosol Yellow BG dye
Fig. 4.34 Effect of presence of electrolytes on the biosorption potential of peanut husk for the removal of Indosol Orange RSN dye
38
40
42
44
46
48
50
NaCl KNO3 CaCl2.2H2O MgSO4.7H2O AlCl3.6H2O
qe(m
g/g)
Electrolytes
0.1 M
0.2 M
0.3 M
0.4 M
0.5 M
Control
38
40
42
44
46
48
50
NaCl KNO3 CaCl2.2H2O MgSO4.7H2O AlCl3.6H2O
qe(m
g/g)
Electrolytes
0.1 M
0.2 M
0.3 M
0.4 M
0.5 M
Control
82
4.10 Effect of heavy metal ions on the biosorption of selected direct dyes
Presence of heavy metal ions in the dye solution also affects the biosorption capacity of
biosorbent. Different heavy metal ions (Cr, Cu, Co, Pb and Cd) were used in different
concentrations (50 to 250 mg/L) to check out the effect of their presence on the adsorptive
removal of Direct Violet 51, Indosol Turquoise FBL, Indosol Black NF, Indosol Yellow BG
and Indosol Orange RSN dyes from aqueous solutions and results are depicted in Fig. 4.35-
4.39.
Fig. 4.35 Effect of presence of heavy metal ions on the biosorption potential of sugarcane bagasse for the removal of Direct Violet 51 dye
0
10
20
30
40
50
60
Cd Pb Cr Co Cu
qe(m
g/g)
Metal ions
50 ppm
100 ppm
150 ppm
200 ppm
250 ppm
Control
83
Fig. 4.36 Effect of presence of heavy metal ions on the biosorption potential of sugarcane bagasse for the removal of Indosol Turquoise FBL dye
Fig. 4.37 Effect of presence of heavy metal ions on the biosorption potential of peanut husk for the removal of Indosol Black NF dye
0
10
20
30
40
50
60
Cd Pb Cr Co Cu
qe (m
g/g)
Metals ions
50 ppm
100 ppm
150 ppm
200 ppm
250 ppm
Control
0
10
20
30
40
50
60
Cd Pb Cr Co Cu
qe(m
g/g)
Metal ions
50 ppm
100 ppm
150 ppm
200 ppm
250 ppm
Control
84
Fig. 4.38 Effect of presence of heavy metal ions on the biosorption potential of peanut husk for the removal of Indosol Yellow BG dye
Fig. 4.39 Effect of presence of heavy metal ions on the biosorption potential of peanut husk for the removal of Indosol Orange RSN dye
The results indicate that the presence of heavy metal ions enhance the biosorption of
Direct Violet 51, Indosol Turquoise FBL, Indosol Black NF and Indosol Orange RSN dyes.
The increase in concentration of the metal ions results in further increase in dyes removal
except in case of Direct Violet 51 and Indosol Turquoise FBL dye in which the increase in
concentration of Cu and Cr resulted in decrease in biosorption potential of sugarcane
394041424344454647484950
Cd Pb Cr Co Cu
qe(m
g/g)
Metal ions
50 ppm
100 ppm
150 ppm
200 ppm
250 ppm
Control
0
10
20
30
40
50
60
Cd Pb Cr Co Cu
qe(m
g/g)
Metal ions
50 ppm
100 ppm
150 ppm
200 ppm
250 ppm
Control
85
bagasse. On the other side, the biosorption of Indosol Yellow BG dye was found to be
decreased in the presence of all the heavy metal ions and with increasing the concentration of
metal ions in aqueous solution, the biosorption of Indosol Yellow BG dye further decreased.
The increase in biosorption capacity in presence of heavy metal ions is due to the fact
that interaction between heavy metals and dye molecules result in the precipitation or
aggregation of dye molecules which lowers its solubility in the solution and enhances the
biosorption of dye onto the biosorbent (Haq et al., 2011). Zhou and Banks (1993) also
reported the similar results. The decrease in biosorption of dye in presence of some heavy
metal ions can be explained due to the fact that these ions can occupy some of the binding
sites of the biomass and ultimately biosorption capacity decreases (Asgher and Bhatti, 2010).
O’Mahony et al. (2002) explored that the presence of high levels of heavy metal ions
decrease the biosorption capacity of the biomass due to competition between metal ions and
dye molecules.
4.11 Effect of surfactants/detergents on the biosorption of selected direct dyes Surfactants are also used in the textile industries during different operations and hence their
presence in the textile effluents also affects the biosorption potential of biosorbent. Different
surfactants (SDS, CTAB and Triton X-100) and two detergents (Arial and Excel) were used
(1 %) to check out their effect on the removal of Direct Violet 51, Indosol Turquoise FBL,
Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN dyes from the solution. The
results are depicted in Fig. 4.40. The results indicated that presence of surfactants in the dye
solution significantly decreased the biosorption capacity. This might be due to the
competition between dye molecules and surfactants for the attachment to the biosorbent
surface (Haq et al., 2011). The drastic decrease in the biosorption of all the dyes was
observed in presence of anionic surfactant, SDS. The repulsion between the anionic dye
molecules and anionic surfactant molecules resulted in the drastic decrease in the biosorption
of dyes. Brahimi-Horn et al. (1992) also observed that the presence of detergent in dye
solution reduce the binding capacity of the biosorbents.
86
Fig. 4.40 Effect of presence of surfactants/detergents on the biosorption of direct dyes
0
10
20
30
40
50
60
Direct Violet51
IndosolTurquoise
FBL
IndosolBlack NF
IndosolYellow BG
IndosolOrange RSN
qe (m
g/g)
Direct dyes
SDS
CTAB
Triton X-100
Arial
Excel
Control
87
4.12 Column study
The biosorption of five direct dyes (Direct Violet 51, Indosol Turquoise FBL, Indosol Black
NF, Indosol Yellow BG and Indosol Orange RSN) onto selected biosorbents in fixed-bed
systems was investigated as a function of bed height, flow rate and initial dye concentration
and the results are presented in the form of breakthrough curves.
4.12.1 Effect of bed height
Bed height is an important process parameter for the removal of dyes in continuous mode
study. The effect of bed height was checked by varying the bed height from 1 cm to 3 cm for
Indosol Turquoise FBL and Indosol Orange RSN dyes while for Direct Violet 51, Indosol
Black NF and Indosol Yellow BG dyes, the bed height was varied from 2 cm to 4 cm. The
flow rate and initial dye concentration were kept constant (1.8 mL/min and 50 mg/L
respectively) during the optimization of bed height. The breakthrough curves at different bed
heights were presented in Fig. 4.41-4.45. The results indicated that increase in bed height
results in increase in dye removal.
Maximum dye removal was achieved at maximum bed height for all the dyes and it
was found to be 16.2 mg/g at 4 cm bed height for Direct Violet 51 (sugarcane bagasse); 27
mg/g at 3 cm bed height for Indosol Turquoise FBL (sugarcane bagasse); 34.56 mg/g at 4 cm
bed height for Indosol Black NF (peanut husk); 20.16 mg/g at 4 cm bed height for Indosol
Yellow BG (peanut husk) and 8.1 mg/g at 3 cm bed height for Indosol Orange RSN (peanut
husk). The column parameters such as breakthrough time, volume of treated dye solution and
biosorption capacity of column are presented in Table 4.16 to 4.20.
The increase in biosorption of dyes with the increase in bed height can be explained
due to the fact that more amount of biosorbent at higher bed heights provides more binding
sites for the attachment dye molecules (Al-Degs et al., 2009). The results also indicated that
by increasing the bed height, the breakthrough time increases. Breakthrough time is the
defining parameter of the biosorption process in column mode so the larger breakthrough
time indicates the better intra-particulate phenomenon which leads to the higher biosorption
capacity of column. The reason behind this can be explained as with the increase in bed
height, there is decrease in axial dispersion in the mass transfer which results in the increase
in diffusion of the dye molecules into the biosorbent. Hence at higher bed heights, the solute
88
get enough time to diffuse into the whole biosorbent resulting in staying more time into the
column and treating more volume of effluent (Li et al., 2011).
Fig. 4.41 Effect of bed height on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
Fig. 4.42 Effect of bed height on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 200 400 600 800 1000
Ct/C
o
Time (min)
4 cm
3 cm
2 cm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 200 400 600 800 1000 1200
Ct/C
o
Time (min)
3 cm
2 cm
1 cm
89
Fig. 4.43 Effect of bed height on the removal of Indosol Black NF dye by peanut husk biomass
Fig. 4.44 Effect of bed height on the removal of Indosol Yellow BG dye by peanut husk biomass
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000 1200 1400 1600
Ct/C
o
Time (min)
4 cm
3 cm
2 cm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 200 400 600 800 1000
Ct/C
o
Time (min)
4 cm
3 cm
2 cm
90
Fig. 4.45 Effect of bed height on the removal of Indosol Orange RSN dye by peanut husk biomass
Effect of bed height was also studied by Charumathi and Das (2012) during the treatment of
textile wastewater in continuous mode experiments by using immobilized C. tropicalis
biomass. With the increase in bed height from 5 cm to 15 cm the dye removal increased from
85.35 to 93.05%. Uddin et al. (2009) worked on the adsorptive removal of methylene blue
dye by using jackfruit leaf powder as adsorbent. The effect of bed height was studied by
varying bed height from 5 cm to 10 cm. The increase in bed height resulted in increase in
breakthrough time, exhaustion time and dye adsorption as more binding sites are available at
higher bed heights. A. filiculoides biomass was exploited for the removal of acid Green 3 dye
in column mode experiments and bed height was optimized for maximum dye removal
(Padmesh et al., 2005). The bed height was increased from 15 cm to 25 cm and dye
adsorption was found to be increased with the increase in bed height.
4.12.2 Effect of flow rate
Flow rate seems to be the controlling factor in the biosorption of dyes during continuous
mode study. To explore the effect of flow rate, the experiments were conducted at three
different flow rates (1.8 mL/min, 3.6 mL/min and 5.4 mL/min) keeping the initial dye
concentration constant (50 mg/L) at pre-optimized bed heights. The results are depicted in in
the form of breakthrough curves (Fig. 4.46-4.50.)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100 150 200 250 300 350
Ct/C
o
Time (min)
3 cm
2 cm
1 cm
91
Fig. 4.46 Effect of flow rate on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
Fig. 4.47 Effect of flow rate on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 200 400 600 800 1000
Ct/C
o
Time (min)
1.8 mL/min
3.6 mL/min
5.4 mL/min
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 200 400 600 800 1000 1200
Ct/C
o
Time (min)
1.8 mL/min
3.6 mL/min
5.4 mL/min
92
Fig. 4.48 Effect of flow rate on the removal of Indosol Black NF dye by peanut husk biomass
Fig. 4.49 Effect of flow rate on the removal of Indosol Yellow BG dye by peanut husk biomass
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000 1200 1400 1600
Ct/C
o
Time (min)
1.8 mL/min
3.6 mL/min
5.4 mL/min
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 200 400 600 800 1000
Ct/C
o
Time (min)
1.8 mL/min
3.6 mL/min
5.4 mL/min
93
Fig. 4.50 Effect of flow rate on the removal of Indosol Orange RSN dye by peanut husk biomass
The results indicated that by increasing the flow rate of solution the biosorption capacity of
column decreased. By increasing the flow rate from 1.8 to 5.4 mL/min, the dye removal
decreased from 16.2 to 10.8 mg/g for Direct Violet 51 dye (sugarcane bagasse); 27 to 6.75
mg/g for Indosol Turquoise FBL dye (sugarcane bagasse); 34.56 to 21.6 mg/g for Indosol
Black NF dye (peanut husk); 20.16 to 15.12 mg/g for Indosol Yellow BG dye (peanut husk)
and 8.1 to 5.4 mg/g for Indosol Orange RSN dye (peanut husk). Maximum biosorption for all
the five direct dyes was achieved at flow rate of 1.8 mL/min. This is due to the fact that at
higher flow rates, the dye solution acquires insufficient residence time in the column and in
case of packed bed columns, the residence time of the solute inside the column is an
important parameter. At high flow rates, all the solute in the solution do not get sufficient
time to penetrate and to react with the functional groups of biosorbent which usually results
in a shorter breakthrough time i.e. improper utilization of biosorption capacity (Charumathi
and Das, 2012). The results also demonstrate a decrease in breakthrough time at higher flow
rates (Table 4.16 to 4.20). The earlier breakthrough point at higher flow rates was due to
reduced contact time between dye molecules and biosorbent (Hasan et al., 2010).
The biosorption capacity of rice husk biomass was explored at different flow rates for
the removal of methylene blue dye by Han et al. (2007). The different flow rates selected for
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100 150 200 250 300 350
Ct/C
o
Time (min)
1.8 mL/min
3.6 mL/min
5.4 mL/min
94
this study were 3.4 mL/min, 5.8 mL/min and 8.2 mL/min. The breakthrough time was found
to be increased at higher flow rates. Flow rate was also optimized by Ahmad and Hameed
(2010) for the adsorptive removal of Reactive Black 5 dye by using activated carbon. The
higher flow rates resulted in earlier breakthrough time. Column study was performed by Akar
et al. (2011) for the removal of Reactive Blue 49 dye by using Capsicum annuum seeds
biomass. The effect of flow rate was explored by varying the flow rate from 1 to 9 mL/min.
The maximum dye removal was achieved at 1 mL/min flow rate.
4.12.3 Effect of initial dye concentration
Initial dye concentration is an important factor during biosorption of dyes in continuous
mode study. To investigate this effect, the experiments were conducted at different initial dye
concentrations keeping the bed height and flow rate constant and results are presented in the
form of breakthrough curves (Fig. 4.51-4.55). The results indicated that the time to attain 50
% breakthrough capacity decreased as the initial dye concentration increased. This may be
explained by the fact that a lower concentration gradient caused a slower transport due to a
decreased diffusion coefficient or decreased mass transfer coefficient (Gupta et al., 2011).
This indicates that higher initial dye concentrations can modify the rate of dye removal
through the column bed. The volume of treated dye solution for all the dyes in column also
became reduced with the increase in initial dye concentration which is due to quick saturation
of biosorbent active sites at higher initial dye concentrations (Goshadrou and Moheb, 2011).
The biosorption capacity of the biosorbents was found to be increased with the increase in
initial dye concentration. The dye removal was found to increase from 11.16 to 17.28 mg/g
by increasing initial dye concentration from 25 to 75 mg/L for Direct Violet 51 (sugarcane
bagasse); 27 to 28.8 mg/g by increasing initial dye concentration from 50 to 100 mg/L for
Indosol Turquoise FBL (sugarcane bagasse); 34.56 to 40.32 mg/g by increasing initial dye
concentration from 50 to 100 mg/L for Indosol Black NF (peanut husk); 20.16 to 25.92 mg/g
by increasing initial dye concentration from 50 to 100 mg/L (peanut husk) and 8.1 to 8.82
mg/g by increasing initial dye concentration from 50 to 70 mg/L for Indosol Orange RSN
(peanut husk). The increase in dye removal with increasing the initial dye concentration can
be explained due to the fact that the major driving force for biosorption is the concentration
difference between the dye on the biosorbent and the dye in the solution (Aksu and Gonen,
2003).
95
The biosorption capacity obtained from the column study was lower than that of
obtained from the batch study for the same initial dye concentrations for all the dyes. The
difference between the biosorption capacity of biosorbents in batch and continuous mode experiments
could also be attributed to the fact that effective surface area of the biosorbents packed in the column
become lower than that in the stirred batch vessels. This also might be due to the insufficient
contact time between the dye molecules and the biosorbent in the continuous flow columns
(Al-Qodah and Lafi, 2003).
Tan et al. (2008) explored the effect of initial dye concentration on the removal of
methylene blue dye by using activated carbon in column mode study varying the initial dye
concentration from 50 to 150 mg/L. The results presented that the saturation of active sites
take place more quickly at higher initial dye concentrations. The initial dye concentration
was varied from 50 to 200 mg/L for the removal of Reactive Black 5 dye by using activated
carbon (Ahmad and Hameed, 2010). The maximum dye removal (39.02 mg/g) was obtained
at 100 mg/L initial dye concentration. Breakthrough time was decreased at higher dye
concentration.
Fig. 4.51 Effect of initial dye concentration on the removal of Direct Violet 51 dye by sugarcane bagasse biomass
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000 1200
Ct/C
o
Time (min)
25 mg/L
50 mg/L
75 mg/L
96
Fig. 4.52 Effect of initial dye concentration on the removal of Indosol Turquoise FBL dye by sugarcane bagasse biomass
Fig. 4.53 Effect of initial dye concentration on the removal of Indosol Black NF dye by peanut husk biomass
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000 1200
Ct/C
o
Time (min)
50 mg/L
75 mg/L
100 mg/L
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000 1200 1400 1600
Ct/C
o
Time (min)
50 mg/L
75 mg/L
100 mg/L
97
Fig. 4.54 Effect of initial dye concentration on the removal of Indosol Yellow BG dye by peanut husk biomass
Fig. 4.55 Effect of initial dye concentration on the removal of Indosol Orange RSN dye by peanut husk biomass
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000
Ct/C
o
Time (min)
50 mg/L
75 mg/L
100 mg/L
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100 150 200 250 300 350
Ct/C
o
Time (min)
50 mg/L
60 mg/L
70 mg/L
98
The results for the optimization of bed height, flow rate and initial dye concentration in
continuous mode study for all the five direct dyes have been summarized in Table 4.16 to
4.20.
Table 4.16 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Direct Violet 51 dye
Inlet
concentration
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
Breakthrough
point (50%)
(min)
Biosorption
capacity
(mg/g)
50
50
50
50
50
25
75
2
3
4
4
4
4
4
1.8
1.8
1.8
3.6
5.4
1.8
1.8
220
320
450
200
100
620
320
13.2
14.4
16.2
14.4
10.8
11.16
17.28
Table 4.17 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Turquoise FBL dye
Inlet
concentration
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
Breakthrough
point(50%)(min)
Biosorption
capacity
(mg/g)
50
50
50
50
50
75
100
1
2
3
3
3
3
3
1.8
1.8
1.8
3.6
5.4
1.8
1.8
120
400
600
160
50
420
320
10.8
24
27
14.4
6.75
28.35
28.8
99
Table 4.18 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Black NF dye
Inlet
concentration
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
Breakthrough
point (50%)
(min)
Biosorption
capacity
(mg/g)
50
50
50
50
50
75
100
2
3
4
4
4
4
4
1.8
1.8
1.8
3.6
5.4
1.8
1.8
380
620
960
420
200
700
560
22.8
27.9
34.56
30.24
21.6
37.8
40.32
Table 4.19 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Yellow BG dye
Inlet
concentration
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
Breakthrough
point (50%)
(min)
Biosorption
capacity
(mg/g)
50
50
50
50
50
75
100
2
3
4
4
4
4
4
1.8
1.8
1.8
3.6
5.4
1.8
1.8
260
420
560
240
140
440
360
15.6
18.9
20.16
17.28
15.12
23.76
25.92
100
Table 4.20 Column data and parameters with different bed heights, flow rate and inlet concentration for the removal of Indosol Orange RSN dye
Inlet
concentration
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
Breakthrough
point (50%)
(min)
Biosorption
capacity
(mg/g)
50
50
50
50
50
60
70
1
2
3
3
3
3
3
1.8
1.8
1.8
3.6
5.4
1.8
1.8
83
130
180
75
40
160
140
7.47
7.8
8.1
6.75
5.4
8.64
8.82
4.13 Application of Thomas Model on the column data
Thomas model (Thomas, 1944), is derived from the assumption that the rate driving force
obeys second-order reversible reaction kinetics. It is the most commonly used model in
packed systems. It uses the Langmuir isotherm for equilibrium and second-order reversible
reaction kinetics. This model also assumes a constant separation factor but it is applicable to
either favorable or unfavorable isotherms.
The linearized form of Thomas model can be expressed as follows:
(4.16)
where kTh (mL/min mg) is the Thomas rate constant; qo (mg/g) is the equilibrium dye uptake
per g of the biosorbent; Co (mg/L) is the inlet dye concentration; Ct (mg/L) is the outlet
concentration at time t; W (g) the mass of biosorbent, Q (mL/min) the flow rate and ttotal
(min) stands for flow time. A linear plot of ln[(Co/Ct)−1] against time (t) was employed to
determine values of kTh and qo from the intercept and slope of the plot.
The column data for all the dyes were fitted to the Thomas model to determine the Thomas
rate constant (kTh) and maximum solid-phase concentration (qo). The determined coefficients
and relative constants were obtained using linear regression analysis according to Eq. (4.16)
101
and the results are listed in Table 4.21 to 4.25. The batch studies results indicated the fitness
of Langmuir adsorption isotherm and pseudo-second order kinetic model on the experimental
results for all the five direct dyes. The higher values of correlation coefficients and close
agreement between experimental and predicted biosorption capacities at different
experimental conditions indicate the best fitness of Thomas model to the experimental data
for all the five direct dyes. The rate constant kTh represents the rate of solute transfer from
liquid to solid phase. Values of kTh showed a linear increase with the increase in flow rate for
all the five direct dyes (Table 4.21 to 4.25).
Table 4.21 Thomas Model parameters for the removal of Direct Violet 51 dye
Inlet conc.
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
KTh
(mL/min
mg) × 103
qo Cal
(mg/g)
qe Exp
(mg/g)
R2
50
50
50
50
50
25
75
2
3
4
4
4
4
4
1.8
1.8
1.8
3.6
5.4
1.8
1.8
0.21
0.17
0.15
0.16
0.24
0.23
0.10
13.24
14.13
16.67
15.62
11.42
10.99
17.41
13.2
14.4
16.2
14.4
10.8
11.16
17.28
0.997
0.987
0.983
0.959
0.982
0.983
0.992
102
Table 4.22 Thomas Model parameters for the removal of Indosol Turquoise FBL dye
Inlet conc.
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
KTh (mL/min
mg) × 103
qo Cal
(mg/g)
qe Exp
(mg/g)
R2
50
50
50
50
50
75
100
1
2
3
3
3
3
3
1.8
1.8
1.8
3.6
5.4
1.8
1.8
0.15
0.07
0.068
0.142
0.154
0.060
0.052
9.47
23.22
26.49
15.15
5.25
26.85
26.01
10.8
24
27
14.4
6.75
28.35
28.8
0.955
0.995
0.996
0.958
0.950
0.968
0.982
Table 4.23 Thomas Model parameters for the removal of Indosol Black NF dye
Inlet conc.
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
KTh
(mL/min
mg) × 103
qo Cal
(mg/g)
qe Exp
(mg/g)
R2
50
50
50
50
50
75
100
2
3
4
4
4
4
4
1.8
1.8
1.8
3.6
5.4
1.8
1.8
0.16
0.13
0.10
0.16
0.19
0.067
0.04
24.08
29.4
34.94
32.01
22.66
37.76
40.15
22.8
27.9
34.56
30.24
21.6
37.8
40.32
0.971
0.979
0.975
0.974
0.99
0.972
0.984
103
Table 4.24 Thomas Model parameters for the removal of Indosol Yellow BG dye
Inlet conc.
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
KTh
(mL/min
mg) × 103
qo Cal
(mg/g)
qe Exp
(mg/g)
R2
50
50
50
50
50
75
100
2
3
4
4
4
4
4
1.8
1.8
1.8
3.6
5.4
1.8
1.8
0.184
0.18
0.14
0.212
0.246
0.084
0.073
16.46
19.48
20.64
18.97
17.19
25.05
26.53
15.6
18.9
20.16
17.28
15.12
23.76
25.92
0.978
0.994
0.996
0.991
0.995
0.987
0.991
Table 4.25 Thomas Model parameters for the removal of Orange RSN dye
Inlet conc.
(mg/L)
Bed height
(cm)
Flow rate
(mL/min)
KTh
(mL/min
mg) × 103
qo Cal
(mg/g)
qe Exp
(mg/g)
R2
50
50
50
50
50
60
70
1
2
3
3
3
3
3
1.8
1.8
1.8
3.6
5.4
1.8
1.8
0.484
0.396
0.326
0.48
0.74
0.305
0.242
8.13
8.24
8.26
7.44
6.1
8.59
8.67
7.47
7.8
8.1
6.75
5.4
8.64
8.82
0.989
0.987
0.988
0.989
0.996
0.990
0.993
104
4.14 Application of Bed Depth Service Time (BDST) Model
Bed Depth Service Time (BDST) approach is based on Bohart and Adams equation and it is
widely used model (Mukhopadhyay et al., 2008). This model is used to get an idea about the
efficiency of column under constant operating conditions for attaining a desired breakthrough
point. The prediction of time for which the biosorbent show the ability to sustain the removal
of specific amount of impurities before regeneration is the major design criteria in fixed bed
systems. This specific time period is called the service time of the bed. BDST is a simple
model for predicting the relationship between bed height (Z) and service time (t) in terms of
process concentrations and biosorption parameters. Hutchins proposed a linear relationship
between bed height and service time given by Eq. (4.17)
(4.17)
where Co is the initial dye concentration (mg/L), Cb is the breakthrough dye concentration
(mg/L), U is the linear velocity (cm/min), No is the biosorption capacity of bed (mg/L), ka is
the rate constant in BDST model (L/mg/min), t is the time (min) and Z is the bed height (cm)
of the column. Eq. (4.17) can be re written in the form of a straight line.
(4.18)
Where
(4.19)
And
(4.20)
The results of BDST model are presented in Table 4.26 to 4.30 for all the five direct dyes
which show that at different Ct/Co ratios, the values of correlation coefficient are high which
show good agreement of experimental data with BDST model for all the dyes.
105
Table 4.26 BDST parameters for the removal of Direct Violet 51 dye
Ct/Co a b Ka (Lmg-1
min-1) 104
No(×10-4) mg
L-1
R2
0.2
0.4
0.6
90
110
120
-6.667
53.33
131.67
-41.57
1.518
-0.615
25.79
31.53
34.39
0.984
0.981
0.986
Table 4.27 BDST parameters for the removal of Indosol Turquoise FBL dye
Ct/Co a b Ka (Lmg-1
min-1) 104
No(×10-4) mg
L-1
R2
0.2
0.4
0.6
105
220
265
83.33
156.6
60
3.32
0.517
-0.135
30.09
63.05
75.95
0.981
0.983
0.990
Table 4.28 BDST parameters for the removal of Indosol Black NF dye
Ct/Co a b Ka (Lmg-1
min-1) 104
No(×10-4) mg
L-1
R2
0.2
0.4
0.6
232.5
270
305
-275.83
-231.67
-185
1.005
0.35
0.44
66.64
77.39
87.42
0.998
0.994
0.993
106
Table 4.29 BDST parameters for the removal of Indosol Yellow BG dye
Ct/Co a b Ka (Lmg-1
min-1) 104
No(×10-4)
mg L-1
R2
0.2
0.4
0.6
122.5
140
150
-122.55
-53.33
23.33
2.26
1.52
-3.47
35.1
40.12
42.99
0.989
0.993
0.998
Table 4.30 BDST parameters for the removal of Indosol Orange RSN dye
Ct/Co a b Ka (Lmg-1
min-1) 104
No(×10-4) mg
L-1
R2
0.2
0.4
0.6
26
44
49
6.667
21.33
60.33
41.57
3.79
1.34
7.45
12.6
14.04
0.999
0.999
0.996
107
4.15 Characterization of biosorbents
4.15.1 FT-IR Study
The FT-IR spectra of sugarcane bagasse and peanut husk biomass before and after the
biosorption of selected direct dyes were studied in the range of 400–4000 cm−1. The results
of the FT-IR spectrum of unloaded native form of biosorbents revealed the presence of peak
in the region of 2900 cm−1 which is due to the C–H stretching and indicates the presence of –
CH and CH groups in the structure of sugarcane bagasse and peanut husk biomass (Fig.4.56
and 4.57 respectively). The band at 1730 cm−1 allocates the C=O stretching vibrations. A
broad band in the region of 3300 cm-1 indicates the presence of O-H group (carboxylic acids,
phenols and alcohols) on the surface of both biosorbents as in cellulose, pectin and lignin.
The peak at 1421.5 cm−1 was caused by the CH2 bending. The peak at 1259.5 cm−1 is
indicative of the OH in-plane bending cellulose. The –OH stretching peaks in dye loaded
biosorbent disappeared or absorbed at lower frequency which confirmed the involvement of
hydroxyl groups in the biosorption mechanism. The FT-IR spectra indicate the exchanging
sites and functional groups on which biosorption takes place (Akar et al., 2009).
The FT-IR spectra of pretreated form of biosorbents revealed the appearance of some
new peaks on the surface of biosorbents. The FT-IR spectra of unloaded HCl-treated
sugarcane bagasse, PEI-treated peanut husk and CH3COOH-treated peanut husk biomasses
are presented in Fig. 4.58 to 4.60 respectively. The peak appeared in the region of 3750 cm -1
indicates the presence of N–H group. This shows that the treatment of biosorbents with acids
and chelating agent resulted in the exposure of buried amino groups on the surface of
biosorbents which leads to the higher adsorption capacity of the treated biomasses. The
presence of peak in the region of 2370 cm-1 might be due to presence of C≡C bonds on the
surface of treated form of sugarcane bagasse and peanut husk biomass. The appearance of
these new peaks results in the higher adsorption capacities of treated biosorbents as compare
to native form of biosorbents due to involvement of new functional groups. FT-IR Spectra of
immobilized form of sugarcane bagasse and peanut husk biomass is presented in Fig. 4.61
and 4.62 respectively. These two peaks also appeared in the immobilized form of peanut
husk biomass due to interaction of alginate and biosorbent. Due to specific interaction
between biosorbent and dye molecules, change in the spectra was observed due to vanishing
108
and broadening of some peaks. The FT-IR spectra of dye loaded biosorbents are shown in
Fig. 4.63 to 4.67.
Fig. 4.56 FT-IR spectrum of unloaded sugarcane bagasse (native)
Fig. 4.57 FT-IR spectrum of unloaded peanut husk (native)
109
Fig. 4.58 FT-IR spectrum of unloaded HCl-treated sugarcane bagasse
Fig. 4.59 FT-IR spectrum of unloaded PEI-treated peanut husk
110
Fig. 4.60 FT-IR spectrum of unloaded CH3COOH-treated peanut husk
Fig. 4.61 FT-IR spectrum of unloaded immobilized sugarcane bagasse
111
Fig. 4.62 FT-IR spectrum of unloaded immobilized peanut husk
Fig 4.63 FT-IR spectrum of native sugarcane bagasse loaded with Direct Violet 51 dye
112
Fig 4.64 FT-IR spectrum of native sugarcane bagasse loaded with Indosol Turquoise FBL dye
Fig 4.65 FT-IR spectrum of native peanut husk loaded with Indosol Black NF dye
113
Fig 4.66 FT-IR spectrum of native peanut husk loaded with Indosol Yellow BG dye
Fig 4.67 FT-IR spectrum of native peanut husk loaded with Indosol Orange RSN dye
114
4.15.2 Scanning Electron Microscopic (SEM) Studies
The surface features and morphological characteristics of the biosorbent can be studied by
using scanning electron microscope (SEM). It is used to determine the particle shape and
porous structure of biomass (Bulut et al., 2007). Greater the number of pores, greater will be
the biosorption of dye onto the biosorbent surface. Typical SEM photographs of free and
dyes loaded sugarcane bagasse and peanut husk biomass are presented in Fig. 4.68 to 4.71.
These photographs indicated the porous and fibrous texture of the biosorbents with high
heterogeneity that could contribute to the biosorption of the dyes.
Fig 4.68 SEM analysis of unloaded (a) sugarcane bagasse (b) peanut husk biomass
115
Fig 4.69 SEM analysis of sugarcane bagasse loaded with (a) Direct Violet 51 (b) Indosol Turquoise FBL dye
Fig. 4.70 SEM analysis of peanut husk biomass loaded with Indosol Black NF (b) Indosol Yellow BG dye
116
Fig. 4.71 SEM analysis of peanut husk biomass loaded with Indosol Orange RSN dye
117
4.16 Response surface methodology
Optimization and interaction of three important independent variables (initial dye
concentration (A), biosorbent dose (B) and pH (C)) was investigated by using Box-Behnken
Experimental Design for the removal of Direct Violet 51, Indosol Turquoise FBL, Indosol
Black NF, Indosol Yellow BG and Indosol Orange RSN dyes. The experiments were
conducted with pretreated form of selected biosorbents for each dye.
The results were analyzed by analysis of variance (ANOVA). The application of
ANOVA is found to be the most reliable way for the evaluation of the quality of the fitted
model (Kousha et al., 2012). ANOVA is used to compare the variation due to the treatment
(change in the combination of variable levels) with the variation due to random errors
inherent to the measurements of the generated responses. Linear, coefficient of quadratic and
interaction effects and p-values for the biosorption of all five direct dyes are shown in
ANOVA tables (Table 4.31 to 4.35). The p-values clearly confirm the significance of each of
the model term.
Table 4.31 ANOVA results for the removal of Direct Violet 51 dye through RSM
Source Sum of square df Mean square F value p-value
A close agreement between actual and predicted biosorption capacities (mg/g) was observed
for the removal of all five direct dyes by using pretreated form of selected biosorbents. Table
4.37 presents the actual and predicted biosorption capacities for the removal of Direct Violet
51, Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN dyes while the actual
and predicted biosorption capacity of HCl-treated biomass for the removal of Indosol
Turquoise FBL dye was presented in Table 4.38. These results indicate good fitness of the
model to the response data.
122
Table 4.37 Box-Behnken design matrix for the real and coded values along with experimental and predicted results for the removal of four direct dyes by selected agricultural wastes
Run order
Real (coded) values Biosorption capacity (mg/g)
Direct Violet 51 Indosol Black NF
Indosol Yellow BG
Indosol Orange RSN
A B C Exp Predictd Exp Predictd Exp Predictd Exp Predictd
Table 4.38 Box-Behnken design matrix for the real and coded values along with experimental and predicted results for the removal of Indosol Turquoise FBL dye from aqueous solution
The difference between the predicted and the actual value is termed as residual and it plays
an important role in judging model adequacy. The visual inspection of the residual graphs
can also generate valuable information about the model suitability. Thus, if the mathematical
model is well fitted, its graph of residuals presents a behavior that suggests a normal
distribution (Bezerra et al., 2008). The plots of normal % probability versus studentized
residuals are presented in Fig. 4.72 to 4.76 for Direct Violet 51, Indosol Turquoise FBL,
Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN dyes respectively. The
normality assumption was satisfied as the residual plot approximated along a straight line.
Fig. 4.72 Normal probability plot of Residuals for Direct Violet 51 dye
Internally Studentized Residuals
Norm
al %
Pro
bability
Normal Plot of Residuals
-2.27 -1.14 0.00 1.14 2.27
1
5
10
20
30
50
70
80
90
95
99
125
Fig. 4.73 Normal probability plot of Residuals for Indosol Turquoise FBL dye
Fig. 4.74 Normal probability plot of Residuals for Indosol Black NF dye
Internally Studentized Residuals
Norm
al %
Pro
bability
Normal Plot of Residuals
-2.61 -1.30 0.00 1.30 2.61
1
5
10
20
30
50
70
80
90
95
99
Internally Studentized Residuals
Norm
al %
Pro
babili
ty
Normal Plot of Residuals
-2.31 -1.15 0.00 1.15 2.31
1
5
10
20
30
50
70
80
90
95
99
126
Fig. 4.75 Normal probability plot of Residuals for Indosol Yellow BG dye
Fig. 4.76 Normal probability plot of Residuals for Indosol Orange RSN dye
Internally Studentized Residuals
Norm
al %
Pro
bability
Normal Plot of Residuals
-2.39 -1.19 0.00 1.19 2.39
1
5
10
20
30
50
70
80
90
95
99
Internally Studentized Residuals
Norm
al %
Pro
babili
ty
Normal Plot of Residuals
-2.50 -1.25 0.00 1.25 2.50
1
5
10
20
30
50
70
80
90
95
99
127
4.16.2 Effect of Independent variables
The effect of initial dye concentration and biosorbent dose on the removal of Direct Violet
51, Indosol Turquoise FBL, Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN
dyes was investigated by varying the initial dye concentration from 10 to 200 mg/L and
biosorbent dose from 0.05 to 0.3 g keeping the pH constant at 5.5 for four dyes (Direct Violet
51, Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN) and pH was adjusted 6
for Indosol Turquoise FBL. The results are presented in Fig. 4.77 to 4.81. Both the
independent variables exerted a significant effect on the biosorption of all five dyes. Initial
dye concentration is a strong controlling parameter in the biosorption process. It acts as a
driving force to overcome the mass transfer resistance between solid and aqueous phase. Fig.
4.72 to 4.76 indicated that with the increase in initial dye concentration from 10 to 200 mg/L,
the amount of dye adsorbed per unit mass of biosorbent also increased. Maximum dye
removal (mg/g) was achieved at higher initial dye concentrations. This might be due to the
fact that at higher initial dye concentrations, there is a decrease in resistance to the uptake of
solute from dye solution (Mall et al., 2006). Ahmad et al. (2007) also found an increase in
dye adsorption with the increase in initial concentration of Direct Blue 71 dye by using palm
ash biomass. The biosorbent dose also plays a significant role in the biosorption process. The
results indicated that with the increase in biosorbent dose from 0.05 to 0.3 g/50mL dye
solution, there is a remarkable decrease in biosorption capacity (mg/g) of the biosorbent for
all the dyes. It can be attributed to the overlapping or aggregation of active sites resulting in
decrease in the total biosorbent surface area available for the attachment of dye molecules
and an increase in diffusion path length (Senturk et al., 2010). Highest dye removal was
obtained by using lower amounts of biosorbents.
The combined effect of initial dye concentration and pH on the removal of all five
direct dyes was investigated by varying the pH from 2-9 for Direct Violet 51, Indosol Black
NF, Indosol Yellow BG and Indosol Orange RSN and 3-9 for Indosol Turquoise FBL, while
initial dye concentration was varied from 10-200 mg/L for all the dyes keeping biosorbent
dose constant at 0.17 g/50 mL dye solution. The results are presented in the form of contour
plots in Fig. 4.82 to 4.86. Solution pH exerts a very pronounce effect on the dyes removal.
pH strongly effects the solution chemistry of adsorbate and adsorbent. Maximum dyes
removal was observed at lower pH range. At basic range of pH, the dyes removal decreased
128
significantly for all the dyes. Anionic dyes are favorably adsorbed on the biosorbent surface
at acidic range of pH due to protonation of functional groups which leads to the electrostatic
attraction between dye anions and positively charged biosorbent surface (Safa and Bhatti,
2011a). Higher initial dye concentration and lower pH are found to be the favorable
conditions for the removal of direct dyes.
The results regarding the combine effect of pH and biosorbent dose on the removal of
Direct Violet 51, Indosol Turquoise FBL, Indosol Black NF, Indosol Yellow BG and Indosol
Orange RSN dyes by using selected agricultural wastes are presented in the form of contour
plots (Fig. 4.87 to 4.91). The experiments were conducted at constant initial dye
concentration of 105 mg/L. The results indicated that maximum dyes removal was achieved
at low pH and low biosorbent dose for all the dyes. With the increase in solution pH and
amount of biosorbent, a remarkable decrease in biosorption of dyes was observed.
Fig. 4.77 Contour plot showing the interaction of initial dye concentration and biosorbent dose on the removal of Direct Violet 51 dye by HCl-treated sugarcane bagasse
10.00 57.50 105.00 152.50 200.00
0.05
0.11
0.17
0.24
0.30
A: Initi Dy e Conc
B: B
ioso
rbent d
ose
10.333
19.647128.9613
38.2754
47.5896
55555
129
Fig. 4.78 Contour plot showing the interaction of initial dye concentration and biosorbent dose on the removal of Indosol Turquoise FBL dye by HCl-treated sugarcane bagasse
Fig. 4.79 Contour plot showing the interaction of initial dye concentration and biosorbent dose on the removal of Indosol Black NF dye by PEI-treated peanut husk biomass
10.00 57.50 105.00 152.50 200.00
0.05
0.11
0.17
0.24
0.30
A: Initi Dy e Conc.
B: B
ioso
rbent D
ose
6.42765
14.2142
14.2142
22.0007
29.7872
37.5737
42.5271
55555
10.00 57.50 105.00 152.50 200.00
0.05
0.11
0.17
0.24
0.30
A: Initi Dy e Conc.
B: B
ioso
rbent D
ose
24.775
48.1125
71.45
94.7875
118.125
55555
130
Fig. 4.80 Contour plot showing the interaction of initial dye concentration and biosorbent dose on the removal of Indosol Yellow BG dye by CH3COOH-treated peanut husk biomass
Fig. 4.81 Effect of interaction of initial dye concentration and biosorbent dose on the removal of Indosol Orange RSN dye by PEI-treated peanut husk biomass
10.00 57.50 105.00 152.50 200.00
0.05
0.11
0.17
0.24
0.30
A: Initi Dy e Conc
B: B
ioso
rbent D
ose
5.9741713.7146 21.455
29.1954
36.9358
33.4403
40.9727
55555
10.00 57.50 105.00 152.50 200.00
0.05
0.11
0.17
0.24
0.30
A: Initi Dy e Conc.
B: B
ioso
rbent D
ose
10.226
27.7883
45.3505
62.9128
80.475
55555
131
Fig. 4.82 Contour plot showing the interaction of initial dye concentration and pH on the removal of Direct Violet 51 dye by HCl-treated sugarcane bagasse
Fig. 4.83 Contour plot showing the interaction of initial dye concentration and pH on the removal of Indosol Turquoise FBL dye by HCl-treated sugarcane bagasse
10.00 57.50 105.00 152.50 200.00
2.00
3.75
5.50
7.25
9.00
A: Initi Dy e Conc
C: p
H10.4883
20.0374 29.5865
39.1356
48.6847
55555
10.00 57.50 105.00 152.50 200.00
3.00
4.50
6.00
7.50
9.00
A: Initi Dy e Conc.
C: p
H 6.42765
14.2142
14.2142
22.0007
29.7872
37.5737
42.5271
55555
132
Fig. 4.84 Contour plot showing the interaction of initial dye concentration and pH on the removal of Indosol Black NF dye by PEI-treated peanut husk biomass
Fig. 4.85 Contour plot showing the interaction of initial dye concentration and pH on the removal of Indosol Yellow BG dye by CH3COOH-treated peanut husk biomass
10.00 57.50 105.00 152.50 200.00
2.00
3.75
5.50
7.25
9.00
A: Initi Dy e Conc.
C: p
H 20.2833
37.9917 55.773.4083
91.1167
55555
10.00 57.50 105.00 152.50 200.00
2.00
3.75
5.50
7.25
9.00
A: Initi Dy e Conc
C: p
H 5.97417
13.714621.455
29.1954
40.9727
34.227
47.2189
55555
133
Fig. 4.86 Contour plot showing the interaction of initial dye concentration and pH on the removal of Indosol Orange RSN dye by PEI-treated peanut husk biomass
Fig. 4.87 Contour plot showing the interaction of biosorbent dose and pH on the removal of Direct Violet 51 dye by HCl-treated sugarcane bagasse
10.00 57.50 105.00 152.50 200.00
2.00
3.75
5.50
7.25
9.00
A: Initi Dy e Conc.
C: p
H5.28704 17.5672
29.8474
42.1276
54.4078
61.8698
55555
0.05 0.11 0.17 0.24 0.30
2.00
3.75
5.50
7.25
9.00
B: Biosorbent dose
C: p
H
25.5928
32.8267
40.0607
47.2946
54.5286
55555
134
Fig. 4.88 Contour plot showing the interaction of biosorbent dose and pH on the removal of Indosol Turquoise FBL dye by HCl-treated sugarcane bagasse
Fig. 4.89 Contour plot showing the interaction of biosorbent dose and pH on the removal of Indosol Black NF dye by PEI-treated peanut husk biomass
0.05 0.11 0.17 0.24 0.30
3.00
4.50
6.00
7.50
9.00
B: Biosorbent Dose
C: p
H
22.0007
29.7872
37.5737
42.5271
17.3077
51.2216
55555
0.05 0.11 0.17 0.24 0.30
2.00
3.75
5.50
7.25
9.00
B: Biosorbent Dose
C: p
H
29.075
41.662554.2566.8375
79.425
89.2855
73.584759.856
84.3572
55555
135
Fig. 4.90 Contour plot showing the interaction of biosorbent dose and pH on the removal of Indosol Yellow BG dye by CH3COOH-treated peanut husk biomass
Fig. 4.91 Contour plot showing the interaction of biosorbent dose and pH on the removal of Indosol Orange RSN dye by PEI-treated peanut husk biomass
0.05 0.11 0.17 0.24 0.30
2.00
3.75
5.50
7.25
9.00
B: Biosorbent Dose
C: p
H
13.7146
21.455
29.1954
40.9727
34.227
47.2189
55555
0.05 0.11 0.17 0.24 0.30
2.00
3.75
5.50
7.25
9.00
B: Biosorbent Dose
C: p
H
29.8474
42.1276
61.8698
20.74
24.1535
24.1535
77.7156
51.453755555
136
4.16.3 Interaction effect of three independent variables
The results regarding the simultaneous effect of three independent variables on the
biosorption of dyes are presented in the form of perturbation plots (Fig. 4.92 to 4.93). The
perturbation plots are used to show the adsorptive removal of dyes where one variable moves
from the preferred reference keeping all other factors constant at the coded zero level. Hence,
the perturbation plots show the deviation of the factorial level from the adjusted reference
point of all the variables. It can be seen from the perturbation plots that all the three
as the controlling factors for the maximum biosorption of dyes.
Fig 4.92 Overlay Perurbation plot of all the independent variables for biosorption of Direct Violet 51
Perturbation
Dev iation f rom Ref erence Point (Coded Units)
Direct V
iole
t 51
-1.000 -0.500 0.000 0.500 1.000
1
16.5
32
47.5
63
A
A
B
B
C
C
137
Fig. 4.93 Overlay Perurbation plots of all the independent variables for biosorption of (a) Indosol Turquoise FBl (b) Indosol black NF (c) Indosol Yellow BG (d) Indosol Orange RSN dyes
Perturbation
Dev iation f rom Ref erence Point (Coded Units)
Ind
oso
l Tu
rqu
ois
e F
BL
-1.000 -0.500 0.000 0.500 1.000
0
15
30
45
60
A
A
B
B
C
C
Perturbation
Dev iation f rom Ref erence Point (Coded Units)In
do
sol B
lack
NF
-1.000 -0.500 0.000 0.500 1.000
0
37.5
75
112.5
150
A
AB
B
C
C
Perturbation
Dev iation f rom Ref erence Point (Coded Units)
Ind
oso
l Ye
llow
BG
-1.000 -0.500 0.000 0.500 1.000
0
14.75
29.5
44.25
59
A
A
B
B
C
C
Perturbation
Dev iation f rom Ref erence Point (Coded Units)
Ind
oso
l Ora
ng
e R
SN
-1.000 -0.500 0.000 0.500 1.000
-10
17.5
45
72.5
100
A
A
B
B
C
C
(a) (b)
(c) (d)
138
4.17 Application of biosorption process on real textile effluents
The real textile effluents were collected from two different local textile industries.
Biosorption process was applied to reduce COD from the textile effluents. The
characterization of textile effluents was carried out by determining the pH, EC, COD, TDS
and TSS before and after the application of biosorption process.
4.17.1 Screening study
Five different agricultural waste materials (sugarcane bagasse, peanut husk, corn cobs, cotton
sticks and sunflower) were used for the screening study to select one biosorbent having
maximum capacity for the reduction of COD from the textile effluents. The results of
screening study are presented in Fig. 4.94. The results indicated that corncobs showed
maximum capacity to reduce COD for both effluents. Initial COD of Effluent 1 was recorded
as 287 mg/L while for Effluent 2 initial COD was found to be 189 mg/L. By using corn cobs,
18.64 % and 15.56 % reduction in COD was observed for effluent 1 and 2 respectively. Corn
cobs biomass was selected as biosorbent for further study.
Fig. 4.94 Screening of different agricultural waste materials for the reduction of COD from real textile effluents
0
2
4
6
8
10
12
14
16
18
20
Effluent 1 Effluent 2
% R
emov
al
Textile effluents
sugarcanebagassePeanut husk
Corncobs
Cotton sticks
Sunflower
139
4.17.2 Effect of biosorbent dose
Effect of biosorbent dose was determined by varying the amount of corncobs biomass from
0.1 to 0.6 g/50 mL effluent solution and results are presented in Fig. 4.95. The results
indicated that with the increase in biosorbent dose, there is increase in % removal of COD
from textile effluents. A sharp increase in the % removal of COD was observed with the
increase in biosorbent dose up to 0.5 g while further increase in biosorbent dose has not
shown any remarkable change in the COD reduction. So 0.5 g biosorbent dose / 50 mL
effluent solution was selected for further study. Almost 75 and 69 % reduction in COD was
observed for Effluent 1 and Effluent 2 respectively. Higher COD reduction at higher
biosorbent doses was due to the availability of more surface area which facilitates the
adsorption of COD from the effluent (Ahmad and Hameed, 2009). Patel and Vashi, (2010)
also worked on the treatment of real textile effluents through adsorption and while
investigating the effect of biosorbent dose, they found similar trend of increasing COD
reduction by increasing adsorbent dose.
Fig. 4.95 Effect of biosorbent dose on the removal of COD from real textile effluents
4.17.3 Effect of contact time
The effect of contact time on the removal of COD from real textile effluents was explored by
varying the contact time from 0-120 min and results are presented in Fig. 4.96. The results
clearly indicated that in the initial 30 minutes, the % removal of COD was very high which
become slow down with the passage of time. No remarkable change in the reduction of COD
01020304050607080
0.1 0.2 0.3 0.4 0.5 0.6
% R
emov
al
Biosorbent dose (g)
Effluent 1
Effluent 2
140
was observed after 60 minutes for Effluent 1 and Effluent 2. Contact time of 60 minutes was
found to be sufficient to attain equilibrium. El-Naas et al. (2010) worked on the treatment of
refinery effluent by using date pit waste biomass for the reduction in COD and found similar
results for the effect of contact time. They varied the contact time from 0-120 minutes and
observed increase in % COD reduction with the increase in contact time up to 30 minutes.
Fig. 4.96 Effect of contact time on the removal of COD from real textile effluents
4.17.4 Effect of agitation speed
To check out the effect of agitation speed on the removal of COD from textile effluents, the
agitation speed was varied from 60 to 140 rpm by using 0.5 g biosorbent dose and results are
depicted in Fig. 4.97. The results indicated that with the increase in agitation speed, the %
removal of COD also increased. With increasing the agitation speed, the rate of diffusion of
solute molecules from bulk liquid to the liquid boundary layer surrounding the particle
become higher because of an enhancement of turbulence and a decrease of thickness of the
liquid boundary layer (Patil et al., 2012). Almost 80.4 % and 72.4 % reduction in COD was
observed at the agitation speed of 140 rpm for Effluent 1 and Effluent 2 respectively.
0
10
20
30
40
50
60
70
80
0 5 10 15 30 45 60 90 120
% R
emov
al
Time (min)
Effluent 1
Effluent 2
141
Fig. 4.97 Effect of agitation speed on the removal of COD from real textile effluents
4.17.5 Effect of temperature
Mostly the textile effluents are released at higher temperatures so temperature can be an
important process parameter which affects the biosorption process. To investigate the effect
of temperature on the removal of COD from textile effluents, the temperature range was
selected from 30 ° C to 70 °C and results are shown in Fig. 4.98. The results showed that by
increasing the temperature from 30 to 70 °C, there was a pronounced decrease in the %
removal of COD. The removal of COD decreased from 80.8 to 34.8 % for Effluent 1 and
from 67.7 to 30.1 % for Effluent 2 with the increase in temperature from 30 to 70 ͦ C. This
can be explained due to the fact that in the process of biosorption, weak interaction forces
(Van der Waals forces and hydrogen bonding) are involved and increase in temperature
results in breakdown of adsorptive forces which result in decrease in sorbate removal at
higher temperatures (Chatterjee et al., 2009).
0102030405060708090
60 80 100 120 140
% R
emov
al
Agitation speed (rpm)
Effluent 1
Effluent 2
142
Fig.4.98 Effect of temperature on the removal of COD from real textile effluents
4.17.6 Kinetic studies
The biosorption mechanism and potential rate controlling steps are important to study for
design purposes during the wastewater treatment. Different kinetic models have been
suggested to explain the kinetic behavior of biosorption process. Mostly used kinetic models
including the pseudo-first-order and pseudo-second-order were applied to the experimental
data to evaluate the kinetic behavior of adsorption of COD from textile effluents onto
corncobs biomass. The applicability of these kinetic models was determined by measuring
the correlation coefficients (R2). The theoretical description of these kinetic models has
already been presented in section 4.6. The results regarding the application of these two
kinetic models on the experimental data for the removal of COD from real textile effluents
are presented in Table 4.39.
0
10
20
30
40
50
60
70
80
90
30 40 50 60 70
% R
emov
al
Temp (ᵒC)
Effluent 1
Effluent 2
143
Table 4.39 Kinetic modeling of data for the removal of COD from textile effluents using corncobs biomass
Textile
effluent
Pseudo first order kinetic model Pseudo-second order kinetic model
The results presented in Table 4.40 indicated that among the three isotherm models used to
study the mechanism of adsorption process, Langmuir adsorption isotherm model suggested
best fitness to the experimental data with high correlation coefficient values (R2) for both the
textile effluents as compare to the other models.
144
4.17.8 Characterization of real textile effluents
The physico-chemical characteristics of textile effluents were carried out before and after the
treatment of textile effluents and results are presented in Table 4.41. The results indicated a
decrease in COD, TDS and TSS after the treatment of textile effluents through biosorption
which indicate that biosorption process is effective for the treatment of textile effluents.
Table 4.41 Physico-chemical characteristics of real effluents
Parameters Units Effluent 1 Effluent 2
Before treatment
After treatment
Before treatment
After treatment
pH 6.94 6.80 6.86 7.12
Electrical conductivity
mS/cm 3.65 1.49 4.69 1.38
COD mg/L 287 55 189 61
TDS mg/L 1923 977 1834 909
TSS mg/L 376 189 119 59
145
4.18 Desorption study
The recovery of adsorbent and adsorbate can be achieved by desorption study. For the
desorption studies, the dyes loaded native biosorbents were first separated from the dye
solution by centrifugation and then dried. As the adsorption of direct dyes was achieved at
low pH, so for desorption, a strong base was used in different concentrations (0.2 to 1 M).
The dried dye loaded biosorbents were agitated in NaOH solution of different concentrations
for the specific time intervals and results are depicted in Fig. 4.99. The results indicated that
with the increase in concentration of NaOH, desorption of sorbed dyes also increased. This
trend was observed for all the five dyes (Direct Violet 51, Indosol Turquoise FBL, Indosol
Black NF, Indosol Yellow BG and Indosol Orange RSN dye). Maximum desorption for
Direct Violet 51, Indosol Turquoise FBL, Indosol Black NF, Indosol Yellow BG and Indosol
Orange RSN (35.74, 45.33, 26.58, 48.14 and 16.6 % respectively) was achieved by using 1
M solution of NaOH.
Fig. 4.99 Desorption of direct dyes by using NaOH as eluent in different concentrations (M) The desorption of direct dyes by using NaOH as eluent is due to the fact that in
presence of NaOH, the biosorbent surface acquires negative charge and electrostatic
repulsion between sorbed dye molecules and negatively charged biosorbent surface leads to
the detachment of dye molecules (Vijayaraghavan et al., 2008). As these results are opposite
0
10
20
30
40
50
60
Direct Violet51
IndosolTurquoise FBL
Indosol BlackNF
IndosolYellow BG
IndosolOrange RSN
% D
esor
ptio
n
Dyes
0.2 M 0.4 M 0.6 M 0.8 M 1 M
146
to the effect of pH so these indicates that the major mechanism of biosorption of direct dyes
was ion exchange.
Desorption study was also conducted by Patel and Suresh (2008). They investigated
desorption efficiency of reactive black 5 dye by using Aspergillus foetidus biomass. NaOH
was used as eluent in different concentration (0.1 to 1M). Maximum desorption of reactive
dye was found to be 90 % by using NaOH. Reddy (2006) also worked on the desorption of
congo red dye by using tamarind fruit shell biomass and reported similar results.
147
Chapter # 5
SUMMARY
The textile industries are responsible for intensifying the environmental problems by
generating the colored effluents. The present study was designed to investigate the
biosorption potential of locally available agro-industrial wastes for the removal of direct dyes
from synthetic effluents. The results of the present investigation revealed that the direct dyes
can be efficiently removed from the aqueous solutions by using agro-industrial waste
materials. Screening test was conducted to select one biosorbent with maximum biosorption
capacity (mg/g) among the five agricultural waste materials (sugarcane bagasse, peanut husk,
corn cobs, cotton sticks and sunflower) for the removal of five direct dyes. Two dyes (Direct
Violet 51 and Indosol Turquoise FBL) showed maximum removal with sugarcane bagasse
while three dyes (Indosol Black NF, Indosol Yellow BG and Indosol Orange RSN) depicted
maximum removal with peanut husk biomass.
Different physical and chemical treatments were given to the selected biosorbents to
enhance their biosorption potential. The batch experimental results revealed that low pH, low
biosorbent dose and low temperature were favorable condition for dyes removal. Among the
three different forms of biosorbents (native, pretreated and immobilized), the pretreated form
of biosorbent give maximum dye removal (39.6 mg/g by HCl treated sugarcane bagasse;
65.09 mg/g by HCl treated sugarcane bagasse; 89.6 mg/g by using PEI treated peanut husk
biomass, 79.5 mg/g by using acetic acid treated peanut husk biomass and 79.7 mg/g by PEI
treated peanut husk biomass for Direct Violet 51, Indosol Turquoise FBL, Indosol Black NF,
Indosol Yellow BG and Indosol Orange RSN respectively).
Pseudo-second order kinetic model and Langmuir adsorption isotherm models were
best fitted to the experimental data for all the five dyes. Thermodynamic study results
depicted that biosorption of selected dyes onto selected biosorbents was feasible at low
temperatures and negative values of ∆H showed the exothermic nature of biosorption
process. Negative values of ∆S showed that randomness of system decrease with the progress
of biosorption process.
Effect of presence of electrolytes, heavy metal ions and surfactants/detergents on the
biosorption capacity of selected biosorbents was investigated and results revealed that
148
presence of all the five electrolytes (NaCl, KNO3, CaCl2.2H2O, MgSO4.7H2O and
AlCl3.6H2O) enhanced the biosorption potential of biosrbents. Presence of different heavy
metal ions (Cr, Cu, Co, Pb and Cd) resulted in enhanced removal of direct dyes except
Indosol Yellow BG for which presence of heavy metal ions decreased the biosorption
capacity of peanut husk biomass. Presence of surfactants and detergents in the dyes solution
resulted in decreased biosorption of dyes.
Column study results revealed that higher bed heights, lower flow rates and higher
initial dye concentrations are feasible conditions for the maximum dye removal. The
maximum dye removal in column mode experiments was 17.28 mg/g for Direct Violet 51,
28.8 mg/g for Indosol Turquoise FBL, 40.32 mg/g for Indosol Black NF, 25.92 mg/g for
Indosol Yellow BG and 8.82 mg/g for Indosol Orange RSN dye. Thomas and BDST models
were successfully applied on the column data.
Characterization of unloaded and dye loaded biosorbents was carried out by FT-IR
and SEM analysis. The FT-IR results indicated the involvement of hydroxyl, carboxylic and
carbonyl groups in the biosorption process.
Three level Box-Behnken experimental design was used to investigate the main and
interaction effects of three important independent variables (Initial dye conc. biosorbent dose
and pH). The results revealed that low pH, low biosorbent dose and higher initial dye
concentration gave maximum dyes removal. ANOVA results indicated that all the main and
interaction effects were significant. The biosorption process was also successfully applied to
the real textile effluents to remove COD.
Desorption studies were conducted for the recovery of adsorbate and adsorbent. The
results indicated that 1.0 M NaOH can be used to desorb the dyes from the adsorbent.
149
LITERATURE CITED
Ahmad, A.A. and B.H. Hameed. 2009. Reduction of COD and color of dyeing effluent from
a cotton textile mill by adsorption onto bamboo-based activated carbon. J. Hazard.
Mater., 172: 1538–1543
Ahmad, A.A. and B.H. Hameed. 2010. Fixed-bed adsorption of reactive azo dye onto
granular activated carbon prepared from waste. J. Hazard. Mater., 175: 298–303.
Ahmad, A.A., B.H. Hameed and N. Aziz. 2007. Adsorption of direct dye on palm ash:
Kinetic and equilibrium modeling. J. Hazard. Mater., 141: 70–76
Akar, S.T., A. Gorgulu, T. Akar and S. Celik. 2011. Decolorization of Reactive Blue 49
contaminated solutions by Capsicum annuum seeds: Batch and continuous mode