Chapter-2
Literature Review
2.1 Introduction
This chapter contains the various treatment technologies available and tested by industries for
removal of dye from waste water. A comprehensive comparison of all the technology was
divided into four broad treatment methods viz. chemical treatment, physical treatment,
biological treatment and emerging treatment, with principle advantages and limitations are
presented in Table-1 .The adsorption and biosorption treatment methods used in the research
work is discussed along with its merits and demerits. Also the mechanism of adsorption along
with the various isotherm and kinetic model equations used is covered.
The low cost adsorbent from untreated agriculture and plant waste along with activated carbon
prepared from the above precusors is consolidated in Table -2 and Table -3 respectively. The
information presented in these tables include the experimental conditions used by the authors for
conducting batch adsorption studies and the maximum adsorption capacity of the adsorbents.
The Fixed bed models for evaluating the experimental data and the mathematical model was also
discussed.
2.2 Treatment technologies for removal of dye from waste water
Many treatment methods have been tested for the removal of dyes from wastewater such as:
photocatalytic degradation(Soharabi & Ghavami, 2008), sonochemical degradation (Abbasi
&Asi, 2008), micellar enhanced ultrafiltration(Zaghbani et al. ,2008) cation exchange
membranes (Wu et al.,2008), electrochemical degradation (Fan et al.,), adsorption/precipitation
processes (Zhu et al.,2007), integrated chemical–biological degradation (Sudarjanto et al.,2006),
integrated iron(III) photo assisted-biological treatment (Sarria et al., 2003), solar photo-Fenton
and biological processes (Garcia et al., 2008), Fenton-biological treatment scheme (Lodha &
Chaudhari, 2007) and adsorption on activated carbon (Wu et al.,2008). However, there is no
single specific treatment process capable of successfully removing the color completely from
waste water as most of the above methods suffer from one or more drawbacks. The
conventional biological wastewater treatment process is not very efficient because of low
biodegradability of dyes(Mohd.Rafatullah et al.,2010)
The various treatment technologies available for dye removal can be broadly classified into (i)
chemical treatment (ii) physical treatment (iii) biological treatment and (iv) emerging treatment.
The treatment methodology , treatment stage with its advantages and limitations are tabulated
below.
Table-2.1: Various current and emerging dye separation and elimination treatments applied for
textile effluents with their principal advantages and limitations (Robinson et al.,2001, Huseyin
Pekkuz et al., 20008,Salleh et al.,2011, Zaharia & Suteu,2010, S.A.Saad et al.,2010, S. Rodríguez
Couto., 2009). Treatment
methodology Treatment
stage
Advantages Limitations
Chemical treatments
1 Precipitation,
Coagulation-
flocculation
Pre/main
treatment
Short detention time and low capital costs.
Relatively good removal efficiencies
Agglomerates separation
and treatment. Selected
operating condition
2 Electrochemical
destruction
Pre treatment Break down compounds are non-
hazardous, no sludge build up.
High electricity cost
3 Fenton process
(H2O2+Fe(II) salts)
Pre/main
treatment
Effective decolorization of both soluble
and insoluble dyes. No change in
volume
Sludge generation and
disposal. Prohibitively
expensive
4 Ozonation Main
treatment
Effective for azo dye removal. Applied in
gaseous state: no alteration of volume
Not suitable for dispersed
dyes. Releases aromatic
dyes. Short half-life of
ozone (20 min)
5 Oxidation with
NaOCl
Post treatment Low temperature requirement. Initiates and
accelerates azo bond cleavage
Cost intensive process.
Release of aromatic amines
6 Cucurbituril Post treatment Complete decolorization for all class of
dyes
Expensive.
7 Photochemical
process
Post treatment No sludge production, minimum
consumption of chemicals, efficient for
recalcitrant dye, foul odors are greatly
reduced.
Expensive, formation of
byproducts, technical
constraints
8 Electrochemical
oxidation
Pre treatment No additional chemicals required and the
end products are non- dangerous/
hazardous
Cost intensive process;
mainly high cost of
electricity
Physical Treatment
1 Adsorption with solid adsorbents such as:
A Activated carbon Pre/post
treatment
Economically attractive. Good removal
efficiency of wide variety of dyes
Very expensive; cost
intensive regeneration
process
B Peat Pre treatment Effective adsorbent due to cellular
structure. No activation required
Surface area is lower than
activated carbon
C Coal ashes
Pre treatment Economically attractive. Good removal
efficiency
Larger contact times and
huge quantities are
required. Specific surface
area for adsorption are
lower than activated carbon
D Wood chips/
Wood sawdust
Pre treatment Effective adsorbent due to cellular
structure. Economically attractive. Good
adsorption capacity for acid dyes
Requires long retention
times and huge quantities
F Silica gel Pre-treatment Good for basic dyes, regenerated easily. Side reactions prevent
commercial application
G Clay Pre-treatment Low cost Limited availability.
H Molecular Sieve Pre-treatment Regenerated and reused. Possible to
modify the pore and/or constituent of a
molecular sieve to remove selected
impurities from a liquid.
Requires high temperature
for regeneration.
I Activated Alumina Pre-treatment High BET surface area, may be
regenerated to its original adsorption
efficiency.
Commercial alumina has
slow internal diffusion in
the particle.
J Chitin/chitosan Good adsorbent for chemisorption Low BET surface area,
costly.
2 Irradiation Post treatment Effective oxidation at lab scale Requires a lot of dissolved
oxygen
3 Ion exchange Main
treatment
Regeneration with low loss of
adsorbents
Specific application; not
effective for all dyes
4 Electro-kinetic
coagulation
Pre/main
treatment
Economically feasible High sludge production
5 Membrane filtration
Removes all types of dyes Concentrated sludge
production
Biological treatments
1 Aerobic process Post treatment Partial or complete decolorization for all
classes of dyes
Expensive treatment
2 Anaerobic process Main
treatment
Resistant to wide variety of dyes, bio- gas
produced is used for steam generation
Longer acclimatization
phase
3 Single cell (Fungal,
Algal & Bacterial)
Post treatment Good removal efficiency for low volumes
and concentrations. Very effective for
specific color removal
Culture maintenance is cost
intensive. Cannot cope with
large volumes of waste
water
4 Decolorization by
white-rot fungi
Pre treatment White-rot fungi are able to degrade dyes
using enzymes
Enzyme production is
unreliable
5 Adsorption by living/
dead microbial biomass
Pre treatment Some dyes have a particular affinity for
binding with microbial species
Not effective for all dyes
6 Mixed bacterial culture Pretreatment Decolorized in a day. Azo dyes are not readily
metabolized under aerobic
condition
Emerging treatments
1 Advanced oxidation
process
Main
treatment
Complete mineralization ensured. Growing
number of commercial applications.
Effective pre-treatment methodology in
integrated systems and enhances
biodegradability
Cost intensive process
2 Membrane filtration Main
treatment
Removes all types of dyes; recovered
chemicals and water is reused
High operational cost.
Concentrated sludge
production. Dissolved
solids cannot be separated
3 Photolysis Post treatment Process carried out at ambient conditions.
Inputs are no toxic and inexpensive.
Complete mineralization with shorter
detention times
Effective for small amount
of colored compounds.
Expensive process
4 Sonication Pre treatment Simplicity in use. Very effective in
integrated systems
Relatively new method and
awaiting full scale
application
5 Enzymatic Treatment Post treatment Effective for specifically selected
compounds. Unaffected by shock loadings
and shorter contact times
required
Enzyme isolation and
purification is tedious.
Efficiency curtailed due to
the presence of
interferences
6 Redox mediators Pre/supportive
Treatment
Easily available and enhances the process
by increasing electron transfer efficiency
Concentration of redox
mediator may give
antagonistic effect. Also
depends on biological
activity of the system
7 Engineered wetland
systems
Pre/post
treatment
Cost effective technology and can be
operated with huge volumes of wastewater
High initial installation
cost. Difficult to manage
during monsoon
2.3 Adsorption
Amongst the several techniques of dye removal listed in Table-1, adsorption is considered as
preferred method due to its capability to remove different type of coloring material yielding
good results. Adsorption has been found to be a successful technique for controlling the extent
of water pollution due to dyes, metallic species, surfactants and other organic pollutants ( Pekkuz
et al., 20008).
2.3.1 Mechanism of adsorption
A solid surface in contact with a solution has the tendency to accumulate a layer of solute
molecules at the interface due to imbalance of surface forces. This accumulation of molecules is
a vectorial sum of the forces of attraction and repulsion between the solution and the adsorbent .
Majority of the solute ions or molecules, accumulated at the interface, is adsorbed onto the large
surface area within the pores of adsorbent and relatively a few are adsorbed on the outside
surface of the particles. Adsorption from solutions is generally limited to a mono-layer coverage
of the adsorbent surface. The adsorptive forces are weak beyond the first mono-layer. The
mutual attraction of solutes in the first mono-layer for unadsorbed solute molecules can be
assumed to be equal to the attraction of a surface of pure liquid solute for dissolved solute
molecules. However, the pure liquid solute will be dissolved spontaneously at any concentration
below the saturation concentration. Therefore, adsorption from solution beyond the first mono-
layer occurs rarely. The equilibrium distribution of solute between the liquid and solid phases is
an important property of adsorption systems that helps in defining the capacity of a particular
system. The rate of adsorption defined as the rate at which the equilibrium is reached determines
the contacting systems.
Adsorption from an aqueous solution is influenced largely by the competition between the solute
and the solvent molecules for adsorption sites. The tendency of a particular solute to get
adsorbed is determined by the difference in the adsorption potential between the solute and the
solvent when the solute-solvent affinity is large. In general, the lower the affinity of the
adsorbent for the solvent, the higher will be the adsorption capacity for solutes. Activated carbon
and polymeric adsorbents have high adsorption capacities in water primarily because of a low
adsorption potential.
The solute affinity may be predominantly : 1) exchange adsorption due electrical attraction of
the solute to the adsorbent :2) physisorption or physical adsorption due to van der Waals
attraction : or, 3) chemisorption or chemical adsorption due to chemical reaction. The
comparision between physical adsorption and chemical adsorption are listed in Table-2.2
Table-2.2 : Comparision between physisorption and chemisorption (Treybal,1980)
2.3.2 Advantages of adsorption
Adsorption has many advantages over several other conventional methods for waste water
treatment. These include (i) less initial cost ; (ii) greater flexibility and simplicity of design ; (iii)
ease of operation ;(iv) insensitivity to toxic pollutants ; (v) less land area (half to quarter of what
is required in a biological system); (vi) lower sensitivity to diurnal variation; (vii) unaffected by
Sl.no Physisorption Chemisorption
1. Low heat of adsorption usually in the range of
20-40 kJ mol-1
High heat of adsorption in the range
of 40-400 kJ mol-1
2. Force of attraction are Van der Waal's forces
Forces of attraction are chemical
bond forces
3. It usually takes place at low temperature and
decreases with increasing temperature It takes place at high temperature
4. It is rapid and reversible It is slow and irreversible
5. It is related to the ease of liquefaction of the
gas
The extent of adsorption is
generally not related to liquefaction
of the gas
6. It is not very specific It is highly specific
7. It forms multi-molecular layers It forms monomolecular layers
8. It does not require any activation energy It requires activation energy
9. It is spontaneous It is not spontaneous
toxic chemicals;(viii) superior removal of organic contaminants (ix) it does not result in
formation of harmful substances; (x) applicability on a large scale to remove non-biodegradable
dyes from aqueous streams.(xi) low energy requirement. (xi) sludge free operation. (xii) high
quality product(Rafatullaha et al.,2010, El-Latif et al., 2010,Song et al.,2011, Mane &
Babu,2011).
2.3.3 Adsorption isotherms
Adsorption isotherm will describe the equilibrium distribution of solute between the solid and
liquid phases. The results are usually expressed as a plot of the concentration of chemical
adsorbed (mg g-1
) versus the concentration remaining in solution (mg L-1
) at constant
temperature. Adsorption isotherm is characterized by certain constant values which express the
surface properties and affinity of the adsorbent and can also be used to compare the adsorptive
capacities of the adsorbent for different pollutants. The analysis of the isotherm data by fitting
them to different isotherm models is an important step to find the suitable model that can be used
for design purposes . Adsorption isotherm is basically important to describe how solutes interact
with adsorbents, and is critical in optimizing the use of adsorbents(Tan et al,2008).
2.3.3.1 Langmuir isotherm model
In 1916, Irving Langmuir published a new model isotherm for gases adsorbed to solids, which
retained his name. It is a semi-empirical isotherm derived from a proposed kinetic mechanism.
This isotherm was based on different assumptions one of which is that dynamic equilibrium
exists between adsorbed gaseous molecules and the free gaseous molecules.
It is based on four assumptions:
1.The surface of the adsorbent is uniform, that is, all the adsorption sites are equivalent.
2. Adsorbed molecules do not interact.
3.All adsorption occurs through the same mechanism.
4.At the maximum adsorption, only a monolayer is formed: molecules of adsorbate do not
deposit on other, already adsorbed, molecules of adsorbate, only on the free surface of the
adsorbent( Dogan et al., 2008)
Basic forms of Langmuir isotherm have reasonable agreement with a large number of
experimental systems including those, which have different interfaces between the two phases.
The rate of sorption to the surface should be proportional to a driving force times an area. The
driving force is the concentration of the solution and the area is the amount of bare surface. If the
fraction of covered surface is Φ, the rate per unit of surface is:
ra = ka C (1-Φ) (2.1)
The desorption from the surface is proportional to the amounts of surface covered:
rd = kdΦ (2.2)
where ka and kd are the rate constants, ra is the sorption rate, rd is the desorption rate, C is the
concentration in the solution and Φ is the fraction of the surface covered.
The two rates are equal at equilibrium and we find that:
(2.3)
And
(2.4)
Since qe is proportional to Φ:
(2.5)
The saturated monolayer sorption capacity, qm, can be obtained.
When approaches 1, then qe = qm (Ofomaja & Ho, 2007).
The saturated monolayer isotherm can be represented as;
(2.6)
The linearized equation is of the form.
(2.7)
The essential characteristics of the Langmuir isotherm can be expressed in terms of a
dimensionless constant separation factor RL that is given by ,.
( ) (2.8)
Where Co (mg/L) is the highest initial concentration of adsorbate. The value of RL indicates the
shape of the isotherm to be either unfavorable (RL>1), linear (RL = 1), favorable (0< RL<1) or
irreversible (RL =0) ( Dogan et al., 2008 ).
2.3.3.2 Fruendlich isotherm model
Freundlich(Foo & Hameed,2010 ) studied the sorption of a material onto animal charcoal and
demonstrated that the ratio of the amount of solute adsorbed onto a given mass of adsorbent to
the concentration of the solute in the solution was not a constant at different solution
concentrations. The Freundlich isotherm is derived by assuming a heterogeneous surface with a
non-uniform distribution of heat of adsorption over the surface. The Freundlich isotherms has
been observed for a wide range of heterogeneous surface including activated carbon, silica,
clays, and polymers ( 42) and is represented by the following equation;
qe = KFCe1/n
F (2.9)
KF is the Freundlich constant indicative of the relative adsorption capacity of the adsorbent. The
constant nF is the Freundlich equation exponent that represents the parameter characterizing
qausi-Gaussian energetic heterogeneity of the adsorption surface. The Fruendlich exponent nF,
should have values lying in the range of 1-10 for classification as favorable adsorption. The slope
ranges between 0 and 1 is a measure of adsorption intensity or surface heterogeneity, becoming
more heterogeneous as its value gets closer to zero (Dogan et al.,2008). The nF parameter, can
be used to indicate whether the adsorption is linear (nF = 1), whether it is a chemical process (nF
< 1), or whether a physical process is favorable (nF > 1). On the other hand, the values of 1/nF <
1 and 1/nF > 1 indicate a normal Langmuir isotherm and cooperative adsorption,
respectively(Vargas et al.,2011). Its linearized equations is
(2.10 )
2.3.3.3 Dubinin and Radushkevich isotherm.
Dubinin and Radushkevich(Foo & Hameed,2010, Samarghandi et al.,2009) is an empirical
model initially conceived for the adsorption of subcritical vapors onto microporous solids which
follows a pore filling mechanism. It is generally applied to express the adsorption mechanism
with a Gaussian energy distribution over the heterogeneous surface. The isotherm shows the
relation between the characteristics sorption curve and the porous structure of the sorbent. The
isotherm is generally expressed as follows;
( ) (2.11)
where qs is D-R constant ε can be correlated;
(
) (2.12 )
The constant BD gives the mean free energy E of sorption per molecule of sorbate when it is
transferred to the surface of the solid from infinity in the solution and can be computed using the
following relationship;
( 2.13 )
One of the unique features of the Dubinin–Radushkevich isotherm model lies on the fact that it is
temperature-dependent, which when adsorption data at different temperatures are plotted as a
function of logarithm of amount adsorbed vs the square of potential energy, all suitable data will
lie on the same curve, named as the characteristic curve.
2.3.3.4 Tempkin isotherm
Tempkin(Foo & Hameed, 2010 ) contains a factor that explicitly takes into account adsorbing
species-adsorbate interactions. The isotherm assumes that (i) the heat of adsorption of all the
molecules in the layer decreases linearly with the coverage due to adsorbate-adsorbate
interactions, and (ii) adsorption is characterized by a uniform distribution of binding energies,
up to some maximum binding energy. Tempkin isotherm is represented by following equation;
(2.14 )
Or (2.15 )
A linearized isotherm is given by;
(2.16 )
where BT =
, T(K) is the absolute temperature, R is the universal gas constant(8.314
KJ/KmolK), AT is the equilibrium binding constant(L/mg) and b is variation of adsorption
energy(KJ/mol).
2.3.3.5 Redlich–Peterson isotherm model
Redlich–Peterson isotherm (Foo & Hameed, 2010) is a hybrid isotherm featuring both Langmuir
and Freundlich isotherms, which incorporate three parameters into an empirical equation . The
model has a linear dependence on concentration in the numerator and an exponential function in
the denominator to represent adsorption equilibria over a wide concentration range, that can be
applied either in homogeneous or heterogeneous systems due to its versatility.
( 2.17 )
In the limit, it approaches Freundlich isotherm model at high concentration (as the exponent
tends to zero) and is in accordance with the low concentration limit of the ideal Langmuir
condition (as the g values are all close to one). When g=0, above equation becomes Henry’s
law(Mall et al.,2006).
The linear form of the above equation is given by;
(
) ( 2.18 )
where AR,BR and g are Redlich-Peterson constants.
2.3.3.6 Generalized isotherm
Generalized adsorption (Crini & Peindy,2006) isotherm in has been used in the following form:
(2.19)
A linear form of this equation is given by:
((
) ) (2.20)
where K (mg/L) is saturation constant, n is the co-operative binding constant, qmax (mg/g) is the
maximum adsorption capacity of adsorbent, qe (mg/g) and Ce (mg/L) are the equilibrium dye
concentration in the solid and liquid phase respectively. The qmax values were taken from
Langmuir isotherm.
2.3.3.6 Harkin-Jura isotherm
Harkin-Jura (Basar, 2006) accounts for multilayer adsorption and can be explained by existence
of a heterogeneous pore distribution. The isotherm is represented by following equation;
(
( ))
(2.21)
where AH and BH are isotherm parameter and constant.
2.3.3.7 Halseys isotherm
Halseys adsorption( Hadi et al.,2010) isotherm is represented by the following equation;
(
) (2.22)
where KH and nH are the Halseys isotherm constant and exponent, respectively. This equation is
suitable for multilayer adsorption and the fitting of the experimental data to this equation attest
to the heterogeneous nature of the adsorbent.
2.3.3.8 Jovanovic isotherm
The model of an adsorption surface considered by Jovanovic (Hadi et al.,2010) is essentially the
same as that considered by Langmuir. The isotherm is represented by the following equation;
( ( ( ))) (2.23)
where KJ is Jovanovic constant.
2.3.4 Adsorption kinetics (Ho & Chiang, 2001)
Adsorption is a physiochemical process that involves the mass transfer of a solute (adsorbate)
from the liquid phase to the adsorbent surface. A study of kinetics of adsorption is desirable as it
provides information about the mechanism of adsorption, which is important for efficiency of the
process. Predicting the rate at which sorption takes place for a given system is probably the most
important factor for sorber design, with sorbate residence time and the reactor dimensions
controlled by the system’s kinetics. However, sorption kinetics show a large dependence on the
physical and/or chemical characteristics of the sorbent material which also influences the
sorption mechanism.
2.3.4.1 Psuedo-first order kinetics
The pseudo-first order equation (Ho & Chiang,2001,Ofomaja,2007) assumes that the rate of
change of the adsorption of solute with time may lead to changes in the uptake capacity of the
adsorbent. This phenomenon was directly proportional to the saturation of the concentration
difference and the amount of solid uptake with time. In the case of sorption preceded by
diffusion through a boundary, the kinetics most likely follows the pseudo-firstorder equation of
Lagergren :
( ) (2.24)
where qt and qe are the amount sorbed at time t and at equilibrium and k1 the rate constant of the
pseudo-first-order sorption process. The integrated rate law, after applying the initial conditions
of qt = 0 at t = 0 is:
( )
(2.25)
Plots of log (qe - qt) versus t gives a straight line for pseudo first- order kinetics, which allows
computation of the sorption rate constant, k1. If the experimental results do not follow Eq.(2.24)
and ( 2.25) they differ in two important aspects: (i) k1(qe - qt) then dose not represent the number
of available sites, and (ii) log (qe) is not equal to the intercept of the plot of log (qe - qt) against t.
2.3.4.2 Psuedo-second order kinetics
The pseudo-second order chemisorption kinetics may be expressed as (Ho & Chiang,2001):
( )
(2.26)
where k2 is the rate constant of sorption, qe and qt have the same definition as above. Separating
the variables in Eq. (2.26)
( ) (2.27)
and integrating this for the boundary conditions t = 0 to t = t and qt = 0 to qt = qt gives:
( )
t (2.28)
which is the integrated rate law for a pseudo-second order reaction. Eq. (2.28 ) can be rearranged
to obtain:
(2.29)
2.3.4.3 Intra particle diffusion
The models mentioned above in adsorption kinetics cannot identify a diffusion mechanism. The
adsorbate species are most probably transported from the bulk of the solution into the solid phase
through an intraparticle diffusion process, which is often the rate limiting step in many
adsorption processes. The possibility of intraparticle diffusion model based on Weber and
Morris, 1963, was also tested. It is an empirically found functional relationship common to
most adsorption processes, where uptake varies almost proportional with t1/2
rather than with the
contact time t. According to this theory,
qt = Ki t1/2
+ C (2.30)
where Ki (mg/g.min1/2
), the intraparticle diffusion rate constant, is obtained from the slope of
straight line of qt versus t ½
. The intercept C, gives an idea about the thickness of boundary
layer i.e., the larger the intercept, the greater the boundary layer effect. If intraparticle diffusion
occurs then plot of qt vs t 1/2
will be linear and if the plot passes through the origin then the rate
limiting process is only due to intraparticle diffusion. Otherwise some other mechanism along
with intraparticle diffusion is also involved (Dogan et al.,2008).
2.3.4.4 Rate Mechanism(Vadivelan & Kumar, 2005, Ibrahim & Hassan,2008)
It is essential to understand mass transfer mechanisms in order to design a cost effective and
efficient adsorption system. Adsorption, whether physical or chemical, involves the mass transfer
of a soluble species (adsorbate) from bulk solution to the surface of a solid phase (adsorbent).
When the adsorbent is a porous media, the transport of adsorbate to adsorbent will occur through
four main steps .
Step 1: Bulk solution transport where the adsorbate is first transported from the bulk solution
to the hydrodynamic boundary layer surrounding the adsorbent.
Step2: External (film) resistance to transport (external diffusion) where the adsorbate must
then pass through the hydrodynamic layer to the surface of the adsorbent. Transportation through
the boundary layer is due to molecular diffusion, and the distance the adsorbate must travel, or
the thickness of the boundary layer, will depend on the velocity of the bulk solution. The size of
the boundary layer will affect the rate of transportation. The thinner the boundary layer, the
higher the rate of the transportation.
Step3:Internal (pore) transport (intraparticle diffusion) occurs after the adsorbate has passed
through the boundary layer and must be transported through the pores to adsorption sites. This
intraparticle transportation may occur by molecular diffusion through the solution in the pores
(pore diffusion) or by diffusion along the adsorbent surface (surface diffusion) after adsorption
takes place
Step 4:Adsorption where the attachment of the adsorbate onto the adsorbent surface at
available sites.
Adsorption step is very rapid and therefore either external or internal diffusion steps will
control the rate of mass transfer. Bulk transportation and adsorption are rarely, if ever, rate
limiting steps. For the steps 2 and 3 in the overall transport, three distinct cases occur: case I,
external transport > internal transport; case II, external transport < internal transport; case III,
external transport = internal transport. In cases I and II, the rate is governed by film and particle
diffusion, respectively. In case III, the transport of ions to the boundary may not be possible at a
significant rate, thereby leading to the formation of a liquid film with a concentration gradient
surrounding the sorbent particles.
Usually, external transport is the rate-limiting step in systems, which have (a) poor mixing, (b)
dilute concentration of adsorbate, (c) small particle size, and (d) high affinity of the adsorbate for
adsorbent. In contrast, the intraparticle step limits the overall transfer for those systems that have
(a) high concentration of adsorbate, (b) good mixing, (c) large particle size of adsorbent, and (d)
low affinity of the adsorbate for adsorbent.
2.4 Low cost alternative adsorbents for MB adsorption.
2.4.1 Agricultural waste as low cost adsorbent
In recent times, there has been increased interest in the use of agricultural and plant waste
products for dye removal by adsorption from aqueous solution because of their natural
availability and higher removal efficiency. Agricultural materials particularly those containing
cellulose shows potential sorption capacity for various pollutants. The basic components of the
agricultural waste materials include hemicellulose, lignin, lipids, proteins, simple sugars, water,
hydrocarbons, and starch, containing variety of functional groups. Agricultural waste materials
being economic and eco-friendly due to their unique chemical composition, availability in
abundance, renewable nature and low cost are viable option for water and wastewater
remediation. Agricultural waste is a rich source for activated carbon production due to its low
ash content and reasonable hardness , therefore, conversion of agricultural wastes into low-cost
adsorbents is a promising alternative to solve environmental problems and also to reduce the
preparation costs(Crini,2007).
Attention has been focused on various natural solid supports, which are able to remove
pollutants from contaminated water at low cost. Cost is actually an important parameter for
comparing the adsorbent materials. An economic sorbent is defined as one which is abundant in
nature, or is a by-product or waste from industry and requires little processing . The use of
unconventional adsorbents has the following features: (1) it can be obtained abundant locally and
cheaply. Most of them are readily utilized; (2) regeneration of these low-cost substitutes is not
necessary whereas regeneration of activated carbon is essential. Such regeneration may result in
additional effluent and the adsorbent may suffer a considerable loss; (3) less operation cost in
terms of maintenance and supervision are required for the unconventional adsorption systems;
(4) utilization of industrial solid waste for the treatment of industrial wastewater is helpful not
only to the environment, but also to reduce the disposal cost (Hameed & El-Khaiary, 2008)
Certain waste products from industrial and agricultural operations, natural materials and
biosorbents represent potentially economical alternative sorbents. Many of them have been tested
and proposed for dye removal(Crini,2007).
Han et al., 2011, investigated the potential of lotus leaf for the removal of MB from aqueous
solution.The experiments were performed under various conditions including contact time,
adsorbent dose, initial MB concentration, solution pH, salt ionic strength and temperature. The
Langmuir, Freundlich and Koble–Corrigan isotherm models were employed to discuss the
adsorption behavior. The results of analysis indicated that the equilibrium data were perfectly
represented by Koble–Corrigan isotherm. The maximum monolayer adsorption capacity of lotus
leaf was found to be 221.7 mg g−1 at 293 K. The kinetic studies indicated that adsorption
process followed the pseudo second-order mode, suggesting that the adsorption might be a
chemisorption process.
Song et al.,2011, described adsorption of methylene blue (MB) by peanut husk in batch and
fixed-bed column modes at 293 K. The kinetic and equilibrium of adsorption in batch mode were
studied. Nonlinear regressive method was used to obtain relative parameters of adsorption
models. The kinetic process was better described by a pseudo-second-order kinetic model. The
equilibrium adsorption was effectively described by Temkin adsorption isotherm. The value of
qm from the Langmuir model was 72.13 ± 3.03 mg g−1
and the diffusion coefficient value was in
the order of 10−8
cm2 s
−1. In fixed-bed column adsorption, the effects of bed height, feed flow
rate, and inlet MB concentration were studied by assessing breakthrough curve. The column data
were fitted by the Thomas, Clark and modified dose–response models. The modified dose–
response model was best to fit the breakthrough curves at experimental conditions.
Bio-polymer treated oak dust.
Latif et al,2010, investigated oak sawdust treated with NaOH immobilized on alginate bio-
polymer for removal of MB from aqueous solution. The adsorption were modeled according to
Langmuir, Freundlich and Tempkin isotherms, with Freundlich isotherm being more suitable for
the experimental data. Batch studies were performed to evaluate the effect of various parameter
such as pH, agitation speed, initial dye concentration , contact time, adsorbent dosage and
temperature of the solution. The effect of binding polymer on dye removal revealed that the
alginate matrix had low efficiency towards MB removal.
Pua et al.,2013, studied the cocoa pod husk (CPH) treated with NaOH as adsorbent for removing
MB from aqueous solutions. The findings showed that lignin was removed after the NaOH
treatment, leaving higher accessible surface for adsorption. Among the isotherms tested,
Freundlich isotherm fitted the adsorption data very well and the maximum adsorption capacity of
the CPH was 263.9 mg/g.
Many researchers have studied the adsorption of methylene blue dye using agricultural and plant
waste such as given in Table-2.3
Table 2.3 Adsorption capacity and experimental conditions for different agricultural solid and
plant wastes used for MB removal. Sl.
No.
Adsorbent qm
(mg/g)
Experimental Condition Eq.
Time
(min)
T (oC) Ph Adsorbent
Dosage
(g)
MB conc.
(Mg/L)
Agit. Speed
(RPM)
Sample
Taken (mL)
1 Teak wood bark 914.59 20 -- -- 10-1000 -- 50 --
2 Palm kernel fibre 671.78 24 7.1 0.1 200-500 90 150 60
3 Papaya seeds 555.55 30 3-10 0.05-1.0 50-360 -- -- 30-120
4 Teak tree bark powder 333.33 30 7 1.0 g /L 10 230 25 30
5 Wheat straw( Ester treated) 312.5 R.T >4 2g/L 50-350 150 100 360
6 Rice husk 312.26 20 10-1000 50
7 Guava leaves 295 30 7.5 2 g/L 100-800 200 100 120
8 Jackfruit peel 285.71 30 2-11 0.05-1.2 35-400 130 200 180
9 Cotton waste 277.77 20 -- -- 10-1000 -- 50 --
Cocoa pod husk (NaoH
treated
263.9 20-60 -- 0.1 100-350 100 50 6 h
10 Modified rice straw (oxalic
acid treated)
256.4 20±2 6 2 g/L 250 50-200 100 1500
11 Banana stalk waste 243.90 30 4-12 50-500 100 200 390
12 Lotus leaf 222.7 20-40 7 0.02 30-200 100 20 240
13 Palm kernel fibre 217.95 26-59 7.1 0.1 100-550 80 200 120
14 Modified rice straw 208.33 20±2 6 2g/L 50-500 150 100 120
15 Broad been peels 192.72 30 5 0.3 30-325 130 200 320
16 Gulmohar (Delonix regia)
plant leaf powder
186.22 30-50 2-9 0.5-2.5
g/L
50-200 50-200 100 120
17 Castor seed shell 158.73 36 -- 2 g/L 25-300 200 -- 120
18 Moringa oleifera seeds 143.3 -- -- 0.2 5-30 130 -- 120
19 Cedar saw dust 142.36 20 -- -- 40 350 10 300
20 Pumpkin seed hull 141.92 30 7 0.3 25-300 120 200 110
21 Meranti saw dust 120.48 30 9 0.5 50-200 150 100 180
22 Pineapple stem 119.05 30 0.3 25-300 120 200 330
23 Dehydrated peanut hull 108.6 25-50 3.5 0.5-1.0 100-400 150 500 24 h
24 Palm kernel fibre 95.4 24 7.1 0.1 200-500 90 150 120
25 Wood apple shell 95.2 32 0.1 50-250 200 100 360
26 Parthenium hysterphorus
(H3PO4)
88.49 26±1 -- 0.4 50-250 180 100 60-90
27 Garlic peel 82.64 30-50 4-12 0.3 25-200 100 100 210
28 Tomato plant root 83.3 30-70 0.25 100-600 50 60
29 Fallen phoenix tree’s leaves 80.9 22 7 2 g/L 130 100 10 180
30 Ground hazelnut shells 76.9 20 2.5-4 0.5 50-1000 60 50 180
31 Peanut husk 72.13 20 0.035 80 100 20 480
32 Coconut bunch waste 70.92 30 6.5-7.5 0.2 50-500 100 200 315
33 Peanut hull 68.03 20 5 2 g/L 100 150 100 12 h
34 Carrot leaves powder 66.6 30 7 2 g/L 10 200 50 30
35 Walnut sawdust 59.17 20 2.5-4 0.5 50-750 60 50 180
36 Carrot stem powder 55.5 30 7 2 g/L 10 200 50 30
37 Yellow passion fruit 44.70 25 8 0.1-1.0 5-600 60 50 48 h
38 Olive pomace 42.3 25 0.3-0.45 10 30 240
39 Rice husk 40.59 32 8 0.03 100 95 50 48 h
40 Cherry saw dust 39.84 20 2.5-4 0.5 50-750 60 50 180
41 Parthenium hysterphorus
(H2SO4)
39.68 26±1 0.4 50-250 180 100 60-90
Oak sawdust (NaOH treated) 38.46 22±2 12 0.5 g/L 200 250 100 270
42 Hazelnut shell 38.22 25-55 9 0.25 200 50 24 h
43 Paspalum notatum (garden
grass)
30.4 30 8 0.04 100 95 30 360
44 Oak sawdust 29.94 20 2.5-4 0.5 50-500 60 50 180
45 Pitch -pine sawdust 27.78 20 2.5-4 0.5 50-500 60 50 180
46 Banana peel 20.8 30 7.2 0.1 20 180 100 24 h
47 Cereal chaff 20.3 25 0.4 20-200 100 50 180
48 Orange peel 18.6 30 7.2 0.1 20 180 100 24 h
49 Wheat shells 16.56 30-50 0.1-0.5 200 150 50 60
50 Beech sawdust
(20 % CaCl2(100oC) )
16.05 -- 1.5-13 -- 14 600 -- --
51 Beech Sawdust
20% CaCl2(23oC)
13.02 -- 1.5-13 -- 14 600 -- --
52 Indian Rosewood sawdust 11.8 26±1 7 0.4 50-500 160 100 180
53 Neem (Azadirachta Indica)
leaf powder
3.67 27 2-10g/L 25-70 -- 50 240
2.4.2 Activated carbon derived from agricultural and plant waste
Activated carbon is perhaps the most widely used adsorbent for the removal of many organic
contaminants which are biologically resistant because of their structural, textural and sorption
peculiarities, but activated carbon is prohibitively expensive. The technology to manufacture
activated carbon of good quality is not fully developed in developing countries. Moreover, there
are many problems connected with the regeneration used activated carbon. Consequently, the
high cost of the activated carbon, coupled with the problems associated with regeneration, has
necessitated the search for alternate adsorbents(Crini,2007). Biomass and other waste materials
may also offer an inexpensive and renewable additional source of activated carbon. These waste
materials have little or no economic value and often present a disposal problem. Therefore, there
is a need to valorize these low-cost by-products. So, their conversion into activated carbon
would add economic value, help reduce the cost of waste disposal and most importantly provide
a potentially inexpensive alternative to the existing commercial activated carbons(Rafatullah et
al.,2010).
Foo and Hameed ,2012, prepared high quality mesoporous activated carbon from wood sawdust
via microwave induced K2CO3 activation. The operational variables studied included chemical
impregnation ratio, microwave power and irradiation time on the carbon yield and MB
adsorption efficiency. The findings of the study was that microwave heating shortened the
processing period and produced high quality activated carbon by opening the previously
inaccessible pores and creation of new pores due to interior and volumetric heating of microwave
irradiation. The above process also resulted in saving the cost and energy.
Kumar and Tamilarasan, 2013, investigated the activated carbon prepared from Acacia fumosa
seed shell using hydrochloric acid. Batch adsorption studies were conducted in varying the
parameters such as concentration of dye, quantity of adsorbent, time, temperature and pH.
Among the isotherms tested , Tempkin isotherm showed the linearity of the plot showing the
binding energy interaction between the sorbate and sorbent was predominant during the process.
Fierro et al.,2010, used rice straw as precursor of activated carbons by activation with ortho-
phosphoric acid and suggested that the production of ortho-phoshoric acid derived ACs is far less
expensive than that of better materials obtained by 2-steps KOH activation. The process based
on H3PO4 indeed comprises only one single step process achieved at a temperature as low as 450
◦C, and which could probably be also carried out under self-generated atmosphere.
Yang and Qui,2010, prepared activated carbons from walnut shells by vacuum chemical
activation with zinc chloride as the activation agent. To optimize the preparation method, the
effects of the main process parameters such as system pressure, activation temperature, and
impregnation ratio on the properties expressed in terms of specific surface area and pore volume
of the obtained activated carbons were studied. It was found that the optimum activated carbon
obtained with system pressure of 30 kPa, activation temperature of 450 ◦C, and impregnation
ratio of 2.0 has a BET surface area of 1800 m2/g and total pore volume of 1.176 cm
3/g. The
results indicated that the MB adsorption capacity was positively correlated to the BET surface
area. The highest MB adsorption capacity was 315 mg/g and the removal percentage of MB
was 99% when the activated carbon dose was 0.75 g/L.
Deng et al.,2009, prepared activated carbon from cotton stalk with ZnCl2 as activation under
microwave radiation. Effects on the yield and adsorption capacities of activated carbon such as,
microwave power, microwave radiation time and the impregnation ratio of ZnCl2 were
evaluated. The outcome of the study was that the optimum conditions were as follows:
microwave power of 560W, microwave radiation time of 9 min and the impregnation ratio of
ZnCl2 was 1.6 g/g. Iodine number, amount of MB adsorption and the yield of activated carbon
prepared under optimum conditions were 972.92 mg/g, 193.50 mg/g and 37.92%, respectively.
The prepared adsorbent was used for the removal of MB from aqueous solutions under varying
conditions of initial concentration, carbon dosage and pH. It was found that Langmuir isotherm
was fitter than Freundlich isotherm and Temkin isotherm. The qm and KL determined from the
Langmuir isotherm were 315.04mg/g and 0.060 L/mg, respectively.
Table 2.4: Adsorption capacity and experimental conditions for different activated carbon
prepared from agricultural solid wastes for MB removal. Sl.
No.
Adsorbent qm
(mg/g)
Experimental Condition Eq.
Time
(min)
T (oC) Ph Adsorbent Dosage
(g)
MB conc. (Mg/L)
Agit. Speed (RPM)
Sample Taken
(mL)
1 Straw activated carbon 472.1 30±1 7.2 8-11.6g/L 100-400 200 50 120
2 Bamboo based activated
carbon
454.2 30±1 7 0.2 100-500 -- 200 48 h
3 Activated Carbon (molasses/
H2SO4)
435 -- -- -- 50-180 -- 250 20 h
4 Activated carbon from
coconut husk
434.78 30 -- 0.1 50-500 120 100 30 h
5 Vetiver roots activated
carbon
423 25 4-5 0.04 50-300 200 100 --
6 Oil palm fiber based
activated carbon
400 30 -- 0.1 200 120 100 --
7 Rice husk activated carbon 343.5 30±1 7.2 8-11.6g/L 100-400 200 50 120
8 Cotton stock activated carbon 315.45 9-10 4 g/L 1500 160 50 120
9 Walnut shell activated carbon 315 25 7 0.1 500-2000 270 25 24 h
10 Rattan sawdust-activated
carbon
294.12 30±1 -- 0.1 100-500 -- 100 24 h
11 Activated carbon prepared
from durian shell
289.26 30-50 -- 0.1-1.1 250 120 100 96 h
12 Coconut shell activated
carbon
277.9 30±1 7.2 8-11.6g/L 100-400 200 50 120
13 Oil palm fibre based activated
carbon
277.78 30 6.5 0.1 50-500 -- 100 24 h
14 Olive seed activated carbon 190-
263
-- -- 0.01-0.03 500 100 200 --
15 Jute fiber carbon 225.64 28 6 0.05 50-200 120 50 240
16 Activated carbon prepared
from cotton stalk
193.5 25 9 0.1 1500 160 25 120
17 Groundnut shell activated
carbon
164.9 30±1 7.2 8-11.6g/L 100-400 200 50 120
18 Bamboo dust activated
carbon
143.2 30±1 7.2 8-11.6g/L 100-400 200 50 120
19 Chemically treated Salsola
vermiculata plant
130 24 6.94 0.1 100-1000 250 25 90
20 Waste apricot based activated
carbon
102 30-50 -- 0.1 50-400 400 50 --
21 Oil palm wood activated
carbon
90.9 30 -- 0.05 10-250 125 50 --
22 Babul seed carbon 72.46 30 7 0.1 20-80 100 100
23 Bamboo charcoal 58.48-
69.93
30-50 5.3 0.1 100-500 150 50 360
24 Delonix regia pods activated
carbon
21.7-
25.1
25 7±0.1 0.1 20-100 300 50 120
25 Wood apple shell activated
carbon
36.9 32 -- 0.1 100 200 100 360
26 Sunflower oil cake activated
carbon
16.4 15-45 6 0.02 25 150 10 1440
2.5 Biosorption
Biosorption can be defined as selective uptake of organic and inorganic species including metals
and dyes using live or dead biomass or their derivatives. This biomass may be bacteria, fungi,
algae, sludge from biological wastewater treatment plants, byproducts from fermentation
industries or seaweeds. In this process, adsorbents are biological materials and are called
biosorbents. Biosorption takes place in the cell wall due to a number of metabolism-independent
processes like physical and chemical adsorption, electrostatic interaction, ion exchange,
complexation, chelation and micro precipitation(Crini,2007). The bacteria, algae and fungi as
well as their cell components: alginic acid, chitin, cellulose, etc. have special surface properties
enabling them to adsorb different kinds of dye from solutions.
Biosorption, as compared with other treatment methods such as precipitation, ion exchange,
reverse-osmosis and adsorption, gives a good performance at a very low cost. Apart from cost
effectiveness and competitive performance, other advantages are possible regeneration at low
cost, availability of known process equipment, sludge free operation and recovery of the sorbate.
The biosorption capacity of a biomass depends on several factors. It includes type of biomass
(species, age), type of sorbates, presence of other competing ions and method of biomass
preparation (culture condition for live biomass), along with several physico-chemical factors
(temperature, pH, ionic concentration)(Dogan et al.,2008)
In applying biosorption as a process in an industrial environment, a choice has to be made
between live and dead biomass. Living cells have a wide variety of decolorization mechanism
but they have serious disadvantages. The dead and dried cells have the following advantages (i)
the performance is not sensitive with operating conditions like the concentration in the effluent,
pH and temperature. (ii) nutrient supply as well as culture maintenance is not required.(iii) it
may be stored or used for extended periods without the risk of putrefaction. This makes it easier
to use and transport. (iv) biosorption capacities may be greater than, equal to, or less than those
living cells. (v) the operation is easy and their regeneration is simple. (vi) not affected by toxic
wastes and chemicals (vii) do not pollute the environment by releasing toxins and/or propagules
(Aksu,2005). (vii) dead biomass is also generated as a waste product from established industrial
processes (Fu & Viraraghavan,2001). Apart from these factors, researchers have also reported
that dead biomass is more effective in adsorbing organic pollutants than live biomass Other
studies with some organic compounds have also suggested that the fluidity of the membrane that
can be affected by temperature could be one of the factors determining the adsorption ratio of the
organic pollutants(Maurya et al.,2006)
2.5.1 Advantages and disadvantages of biosorption
The major advantages of biosorption technology are its high selectivity, effectiveness in
reducing the concentration of dyes to very low levels, efficient removal from large volumes , the
use of inexpensive biosorbent material, possible regeneration at low cost, availability of known
process equipment, sludge free operation . Apart from these the recovery of the sorbate and raw
materials which are either abundant (sea weeds) or wastes from other industrial operations
(fermentation wastes, activated sludge process wastes) can be used as biosorbents. Fungal
biomass can be produced cheaply using relatively simple fermentation techniques and
inexpensive growth media (Aksu et al.,2007: Aksu et al.,2008).
The disadvantages of the biosorption process is (i) that it is slow (ii) strongly influenced by the
initial pH of the dye solution (iii) influenced by the functional groups in the fungal biomass and
its specific surface properties (iv) the performance depends on some external factors such as
salts and ions in solution which may be in competition. (v) tested for limited practical
applications, since biomass is not appropriate for the treatment of effluents using column
systems, due to the clogging effect. (vi) the immobilized biomass used in the column reactor
adds to the cost factor of the process(Couto,2009: Fu & Viraraghavan,2001).
The use of dead biomass in powdered form in continuous operation has some problems, such as
difficulty in the separation of biomass after biosorption, loss of mass after regeneration, low
strength and small particle size. To overcome these problems, dead biomass can be
immobilized in a biopolymeric or polymeric matrix used as a supporting material. Immobilized
cells have several advantages over dispersed cells such as simple reuse of the biomass, easier
liquid–solid separation and minimal clogging in continuous-flow systems. In addition,
immobilised cultures tend to have a higher level of activity and are more resilient to
environmental perturbations such as pH, or exposure to toxic chemical concentrations than
suspension cultures and immobilisation protects the cells from shear damage(Couto,2009)
Basically, there are two types of cell immobilisation: entrapment and attachment. In the former,
the micro-organisms are entrapped in the interstices of fibrous or porous materials or are
physically restrained within or by a solid or porous matrix such as a stabilized gel or a
membrane. In the latter, the micro-organisms adhere to surfaces or other organisms by self-
adhesion or chemical bonding. A variety of matrices have been used for cell immobilisation via
the entrapment technique, such as natural polymeric gels (agar, carrageenan, alginate, chitosan
and cellulose derivatives) and synthetic polymers (polyacrylamide, polyurethane, polyvinyl) .
Entrapment in natural polymeric gels has become a preferred technique for cell immobilisation
because of the toxicity problems associated with synthetic polymeric materials. The use of
natural gels is, however, limited by their mechanical strength and the lack of open spaces to
accommodate active cell growth resulting in their rupture and cell release into the growth
medium.
The fungus mycelia has an added advantage over single cell organism in treatment of textile
effluent and dye removal as they solubilize the insoluble substrates by producing extracellular
enzymes. The fungi have a greater physical and enzymatic contact with the environment due to
an increased cell-to-surface ratio and the extra-cellular nature of the fungal enzymes is also
advantageous in tolerating high concentrations of the toxicants. Many genera of fungi have been
employed for the dye decolourization either in living or dead form(Couto,2009)
Among the fungi, Aspergillus niger is the most widespread saprophytic fungus in the terrestrial
environment and recent studies show that both active and inactive A. niger exhibit excellent
adsorption capacities in removing heavy metal ions and also dyes.
Xiong et al.(2010) studied the capacity and mechanism with which nonviable Aspergillus niger
removed the textile dye, C.I. Direct Blue 199, from aqueous solution using different parameters,
such as initial dye concentration, pH and temperature. In batch experiments, the biosorption
capacity increased with decrease in pH, and the maximum dye uptake capacity of the biosorbent
was 29.96 mg g−1
at 400 mgL−1
dye concentration and 45◦C. Since dye adsorption followed
pseudo-second order kinetics, the findings suggested that boundary layer resistance was not the
rate limiting step . The rate of dye adsorption may be controlled largely by a chemisorption
process, in conjunction with the chemical characteristics of the biomass and dye. The isotherm of
dye adsorption was well described by Langmuir and Freundlich isotherm models, which implied
that either homogeneous or heterogeneous surface conditions existed under different
experimental conditions.
Kumari and Abraham (2007) used the nonviable biomass of Aspergillus niger, and other fungus
and yeast for biosorption of textile dyes. The selected anionic reactive dyes were C.I. Reactive
Black 8, C.I. Reactive Brown 9, C.I.Reactive Green 19, C.I. Reactive Blue 38, and C.I. Reactive
Blue 3. Experiments were conducted at initial dye concentration of 50, 100, 150 and 200 mg/L.
The effect of initial dye concentration, dose of biosorbent loading, temperature, and pH on
adsorption kinetics was studied. The maximum uptake capacity for the selected dyes was in the
range 112–204 mg/g biomass.
Nanthakumar et al.(2009) investigated the biosorption equilibria and kinetics of Reactive blue
140 using dead fungal biomass of Aspergillus niger HM11. The results obtained from this study
were described by Langmuir isotherm model better than Freundlich isotherm models to the
iosorption equilibrium data. The second-order kinetic model by Ho and Mckay described well
the experimental data. Studies on pH effect and desorption show that chemisorption seems to
play a major role in the adsorption process. The maximum adsorption capacity was calculated for
dead biomass indicating that dead biomass can be considered as a good sorbent material for
Reactive blue 140 solution since autoclaved biomass is much safer as it does not pose any threat
to environment. Also efficacy of dead biomass was found to be higher due to upper adsorption
strength, change in surface property and increase in surface area due to cell rupture after death
which was found in autoclaved biomass .
2.6 Adsorption Thermodynamics
Thermodynamic parameters are evaluated to confirm the nature of the adsorption process. The
thermodynamic constants, free energy change, enthalpy change and entropy change are
calculated to evaluate the thermodynamic feasibility and the spontaneous nature of the process.
The free energy of adsorption (ΔG0) can be related with the equilibrium constant K (L/mol)
corresponding to the reciprocal of the Langmuir constant b. The standard free energy change is
calculated using the following equations:
ΔGo = -RT ln b (2.31)
Where R (8.314 J/mol K) is the universal gas constant and T (K) is absolute temperature. Also
the enthalpy (ΔHo) and entropy (ΔS
o) changes can be estimated by the following equation:
ln b = ( ΔSo/ R) – ( ΔH
o/ RT) (2.32)
Thus a plot of ln b vs 1/T should be a straight line. ΔHo and ΔS
o values is obtained from the
slope and intercept of the plot respectively (Kini et al.,2013).
2.7Activation energy
The activation energy of dye adsorption onto the adsorbent can be calculated by Arrhenius
relationship where k2 is the pseudo-second-order constant (g mol−1
min−1
), k0 is the rate constant
of adsorption (g mol−1
min−1
), Ea is activation energy of adsorption (J mol−1
), R is the gas
constant (8.314 Jmol−1
K−1
), T is the solution temperature (K). Plotting of ln K2 against the
reciprocal temperature gives a reasonably straight line, the gradient of which is −Ea/R. The
magnitude of activation energy gives an idea about the type of adsorption which is mainly
physical or chemical. Low activation energies (5–50 kJ mol−1
) are characteristics for physical
adsorption, while higher activation energies (60–800 kJmol−1
) suggest chemical adsorption. This
is because the temperature dependence of the pore diffusivity is relatively weak. Here, the
diffusion process refers to the movement of the solute to an external surface of adsorbent and not
diffusivity of material along micropore wall surfaces in a particle (Dogan et al.,2009) .
2.8 Fixed bed Theory
Numerous studies on adsorption of dyes in batch systems have been reported in the literature.
However, in practice the column type continuous flow operations which are more useful in large-
scale wastewater treatment have distinct advantages over batch treatment. It is simple to operate,
attains a high yield and it can be easily scaled up from a laboratory-scale procedure. A packed
bed is also an effective process for cyclic sorption/desorption, as it makes the best use of the
concentration difference known to be a driving force for adsorption and allows more efficient
utilization of the sorbent capacity and results in a better quality of the effluent. A large volume of
wastewater can be continuously treated using a defined quantity of sorbent in the column.
2.8.1 Breakthrough Curves
Breakthrough curves represent the time profile for saturation of a given amount of an adsorbent
structured as a fixed bed with a given solution of an adsorbate forced through this bed at a
constant rate and fixed temperature. The breakthrough curves serve two purposes: (a) to decide
whether the adsorbent is efficient for the required separation and (b) to establish the break point
(process interruption), based technical criterion.
Breakthrough curves can be developed based on the basis of transient mass balance for the
adsorbate in an infinitesimal volume of adsorbent fixed bed continuously percolated by a fluid
carrying the adsorbate. The following hypotheses are adopted:
1) temperature is constant: the adsorption heat, which increases the system temperature, is
neglected. The lower the concentration of the adsorbate in the fluid in the feed stream, the
smaller the temperature rise will be;
2) flow rate is constant: the lower the concentration of the adsorbate in the fluid in the feed
stream, the smaller the effect of adsorption on the flow rate will be.
3) interstitial velocity profile is plug flow.
4) adsorbate is not involved in chemical reactions: this is equivalent to saying that the
adsorbate fed is either adsorbed or leaves the system in the effluent;
5) adsorbate is not dispersed: all types of mixing effects (convective, diffusive and eddy) are
prohibited within the fixed bed(Correa et al.,2007)
2.8.2 Models for dye adsorption in a fixed-bed column
Successful design of a dynamic adsorption process requires the prediction of concentration–time
profile or breakthrough curve which describes the specific relation or mobility of solute
substances onto a solid adsorbent . Typically, the mathematical correlation provides an insight
into the adsorption mechanisms, surface properties as well as the degree of affinity of the
adsorbents.
2.8.2.1 Thomas Model
The Thomas model is one of the widely used model. The assumptions used in derivation of the
model are (i) It is based on Langmuir kinetics of adsorption-desorption.(ii) plug flow with no
axial dispersion.(iii) adsorption is rate driving force.(iv) It obeys second order reversible kinetics
(v) constant separation factor( Aksu & Gonen, 2004)
[
[ ]]
(2.33)
The linearization of the above equation yields
(
)
(2.34)
The model constants KTh and q can be determined from a plot of (
) Vs t .
This model is suitable for (i) adsorption processes where external and internal diffusion
limitations are absent.(ii) applicable to either favorable or unfavorable isotherm(Song et al.,2011)
The primary weakness of the Thomas solution is that its derivation is based on second order
reaction kinetics. Adsorption is usually not limited by chemical reaction kinetics but is often
controlled by interphase mass transfer. This discrepancy can lead to some error when this
method is used to model adsorption process.
2.8.2.2 Adam-Bohart model
The Adam-Bohart model (Karim et al.,2011) was derived for Cl2-Charcoal adsorption system but
its overall approach can be successfully extended in quantitative description of other systems.
The model assumes that adsorption rate is proportional to both residual capacity of the solid and
concentration of adsorbed species, which is suitable for describing the initial portion of the
breakthrough curve.
The mass transfer rate obeys the following equations;
(2.35)
(2.36)
The assumptions made for the solution of these differential equation systems are (i) the
concentration field is considered to be low C<<Co . (ii) for t∞, qNo .(iii) The speed of
adsorption is limited by external mass transfer(Ghribi & Chlendi, 2011).
When the differential equations (2.35 ) and (2.36 ) are solved the following equation ( 2.37) is
obtained
(
)
(2.37)
The parameter KAB and No are found by non-linear regression analysis( Hamdaoui ,2006).
2.8.2.3 Yoon- Nelson model
A relatively simple model addressing the breakthrough behavior was developed by Yoon-
Nelson in 1981.The model is based on the assumption that (i) the rate of decrease in the
probability of adsorption for each adsorbate molecule is proportional to the probability of
adsorbate adsorption and adsorbate breakthrough on the adsorbent. (ii) neglects effects of axial
dispersion( Foo & Hameed,2012b).
The model does not require detailed data concerning the characteristics of the adsorbate, the type
of adsorbent and the physical properties of the adsorption bed( Han et al.,2009).
(
) ( ) (2.38)
or
( )
( ) (2.39)
The approach involves a plot of
vs. sampling time (t) according to Eq. (2.38). The
parameters of KYN and τ can be obtained using the nonlinear regressive method.
2.9 MATHEMATICAL MODEL
The kinetic behavior of a fixed-bed adsorber can be explained and the characteristic
breakthrough curve of the adsorption phenomena can be obtained through mathematical models.
Inside the particle, molecules of adsorbate diffuse into the inner portions of particle via surface
diffusion, pore diffusion, or both. This study focuses on understanding the mechanism of pore
diffusion. To formulate a generalized model for diffusion mechanism, following assumptions
are made:
[1] The system operates under isothermal condition.
[2] The adsorption equilibrium relationship is non linear described by Langmuir isotherm.
[3] Intra particle mass transport is due to Fickian diffusion and it is characterized by the pore
diffusion coefficient, Dp.
[4] Mass transfer across the boundary layer surrounding the solid particles is characterized by the
external-film mass transfer coefficient, Kf. (Babu & Gupta, 2010)
[5] The adsorbent particles are spherical and homogeneous in size and density.
[6] The flow pattern in the bed can be described by an axial dispersion plug flow model.
[7] The axial velocity does not change from place to place.
[8] A pseudo one – component adsorption is assumed(Yussuf et al.,2013).
Applying the principle of conservation of mass to fluid in the column, we have:
Rate of material in + Rate of material out = Rate of accumulation of material + Rate of loss by adsorption
[ ] - [ ] + [
] - [
]
=
+ ( )
(2.40)
Dividing by A dz and taking limits ( here V = Q / A)
+
=
+ ( )
(2.41)
The following initial conditions are considered
C = Co z = 0, t = 0
C= 0 0<z≤ L , t = 0
The contour conditions at both ends of the column are given by
( ) z=0, t>0 (2.42)
z =L, t ≥ 0 (2.43)
The interphase mass transfer rate may be expressed as
( ) (2.44)
The adsorption isotherm is non-linear and is described by Langmuir isotherm
(2.6 )
2.10 Error analysis (Mall et al.,2006; Ncibi,2008)
Though linear regression analysis has been popular and frequently used in assessing the quality
of fits and adsorption performance, the interest in utilization of non-linear optimization modeling
has been on the rise. Due to the inherent error resulting from linearization, five different error
functions of non-linear regression were employed in this study to evaluate the isotherm
constants.
2.10.1. The sum of the squares of the errors (SSE)
Despite its wide applicability, the sum of the squares of error has a major limitation. The
calculated isotherm parameters from such error function will provide a better fit at the higher end
of the liquid-phase concentration range. This is because the magnitude of the errors and hence
the square of the errors will increase as concentration increases.
( ) (2.45)
qe,cal (mg/g) is the theoretically calculated adsorption capacity at equilibrium and qe,exp (mg/g) is
the experimental adsorption capacity at equilibrium.
2.10.2. The sum of the absolute errors (SAE)
Isotherm parameters determined using this method would provide a better fit as the magnitude of
the errors increase, biasing the fit towards the high concentration data.
( ) (2.46)
2.10.3 The average relative error (ARE)
The main advantage of this error function is the minimization of the fractional error distribution
across the entire studied concentration range
|
| (2.47)
where n is the number of experimental data points.
2.10.4 The hybrid fractional error function (HYBRID)
This error function was developed in order to improve the fit of the SSE method at low
concentration values by dividing by the measured value. In addition a divisor was included as a
term for the number of degrees of freedom for the system - the number of data points n minus the
number of parameters p within the isotherm equation.
|
| (2.48)
2.10.5. Marquardt’s percent standard deviation (MPSD)
This error function was used previously by a number of researchers in this field. It is similar in
some aspects to a geometric mean error distribution modified according to the number of degrees
of freedom of the system.
√[
(
)
] (2.49)
2.11 Methylene blue dye
Methylene blue (MB) is a heterocyclic aromatic chemical compound. It has many uses in a range
of different fields, such as biology and chemistry. At room temperature, it appears as a solid,
odorless, dark green powder, which yields a blue solution when dissolved in water .Methylene
blue (MB), a basic dye, was used initially for dyeing of silk, leather, plastics, paper, and cotton
mordant with tannin, as well as for the production of ink and copying paper in the office supplies
industry.
MB can cause eye burns in humans and animals, methemoglobinemia, cyanosis, convulsions,
tachycardia, dyspnea, irritation to the skin, and if ingested, irritation to the gastrointestinal tract,
nausea, vomiting, and diarrhea(Rafatullah,2010).
The adsorption of Methylene Blue (MB), is of interest for a number of reasons: (i) the adsorption
capacity of a solid adsorbent towards MB provides a measure of the decolorizing power of the
adsorbent. (ii) Since MB is a bulky molecule, it is only adsorbs on the external surface of a solid
and in any mesopores which may be present. (iii) MB contain more than one adsorption center
which may exhibit different modes of interaction with the solid surface(Ashour,2005). (iv) its
known strong adsorption onto solids, and (v) it often serves as a model compound for removing
organic contaminants and colored bodies from aqueous solutions. Also MB was chosen as the
target compound because it has a net positive charge which would be favorably adsorbed by
electrostatic force onto a negatively charged adsorbent surface. The aromatic moiety of MB
contains nitrogen and sulfur atoms. In the aromatic unit, dimethylamino groups attach to it. The
aromatic moiety is planar and the molecule is positively charged. The dimensions of MB
molecule are16.9 A for the length, 7.4A for the breadth, and 3.8A as thickness (Dogan et
al.,2008).