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).