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CHAPTER-II

LITERATURE REVIEW

This chapter deals with literature review on the removal of chromium by using conventional

methods like reduction, precipitation, ion exchange, reverse osmosis, evaporation, electro

dialysis, adsorption and removal of chromium by using various conventional and non-

conventional adsorbents. Review of literature on modeling and optimization of adsorption

parameters using response surface methodology and artificial neural network coupled with

genetic algorithm for the removal of chromium (VI) metal using non-conventional adsorbents

is also given in this chapter.

2.1 HEAVY METAL CONTAMINATION AND TOXICITY

Heavy metals are defined as metals with a specific weight usually more than 5.0 g/cm3,

which is five times higher than water. The toxicity of heavy metals occurs even in low

concentrations of about 1.0-10 mg/L. Of the 90 naturally occurring elements, 21 are non-

metals, 16 are light metals and the remaining 53 are heavy metals. Most heavy metals are

transition elements with incompletely filled d-orbitals. These d-orbitals provide heavy metal

cations with the ability to form complex compounds, which may or may not be redox-active.

Out of 53 heavy metals, 30 elements are now believed to be essential to life. They can be

divided into the 6 structural elements, 5 macro minerals and 19 trace elements (Florence,

1989). Virtually, all metals can exhibit toxicity above certain threshold concentrations, which

for highly toxic metal species may be extremely low. The toxicity caused by heavy metals is

generally a result of strong coordinating abilities (Gadd, 1992). Certain metals are known to

be toxic for centuries. Based on the physiological effect and toxicity, heavy metals are

classified as shown in Table 2.1.

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Table 2.1. Classification of heavy metals based on toxicity (Thakur, 2006).

Fe, Mo, Mn Low toxicity

Zn, Ni, Cu, V, Co, W, Cr Average toxicity

As, Ag, Sb, Cd, Hg, Pb, U High toxicity

Heavy metal contamination is a general term to describe a condition having abnormally high

levels of toxic metals in the environment. Heavy metals are subtle, silent, stalking killers. It is

realized that sometimes the natural cycles can pose a hazard to human health because the

level of heavy metals exceed the body‘s ability to cope with them. Metal toxicity is divided

into three categories i.e. blocking the essential biological functional groups of molecules,

displacing the essential metal ion in biomolecules and modifying the active conformation of

biomolecules (Florence, 1989). The health hazards presented by heavy metals depend on the

level of exposure and the length of exposure. In general, exposures are divided into two

classes: acute exposure and chronic exposure. Acute exposure refers to contact with a large

amount of the heavy metal in a short period. In some cases, the health effects are immediately

apparent; in others, the effects are delayed. Chronic exposure refers to contact with low levels

of heavy metal over a long period of time.

2.2 CONVENTIONAL METHODS FOR THE REMOVAL OF CHROMIUM

Most environmental regulations on industrial wastewater treatment establish quality

requirements of water. To meet the water quality standards consistent with environmental

protection laws, industrial wastewater needs the simultaneous removal of many contaminants.

Most of the effluents containing chromium from any industry are treated by the following

methods either to meet the pollution board standards or to recover chromium along with it.

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These methods are used individually or in combination. Some of the important methods are

reduction and precipitation, ion exchange, reverse osmosis, evaporation, electro dialysis and

adsorption. Each one of these methods has its own advantages and disadvantages. Their

applications depend on the quality of effluent and the amount of metal to be removed/

recovered.

2.2.1 Reduction and Precipitation (Mahajan, 1985)

Traditionally precipitation is used for the removal of water hardness. This method finds wide

application in the treatment of chromium. It is economical and the removal efficiency is high

(98-99%). There are three steps involved in this process. 1. pH adjustment, 2. Reduction, 3.

Precipitation. Adjustment of pH is achieved with the use of H2SO4 whereby pH is reduced to

2-3. At this pH level, the reduction of Cr+6

to Cr+3

can be achieved very efficiently. For

reduction, various reducing agents such as sulfur dioxide, sodium bisulphate or sodium

dithionite, sodium metabisulfate, ferricyanide and ferrous sulfate etc are used. SO2 in the

waste gases is a commonly used reducing agent.

2H2CrO4 + 3SO2 Cr2 (SO4)3 +2H2O

4H2CrO4 + 3Na2S2O5 + 6H2SO4 2Cr2 (SO4)3 + 6NaHSO4 + 7H2O

K2Cr2O7 + 6FeSO4 + 8H2SO4 2KHSO4 + Cr2 (SO4)3 +3Fe (SO4)3 + 7H2O

The efficiency of reduction depends upon the factors such as pH, time and nature of reducing

agent. After reduction of Cr+6

to Cr+3

, neutralization is followed with lime to precipitate the

heavy metal. Neutralization is carried out by using NaOH or Ca(OH)2. The sludge formed

can be minimized by using NaOH. However it is a costly process. On the other hand, lime

happens to be the cheaper option with a large quantity of sludge for disposal.

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Cr2 (SO4)3 + 6NaOH 2Cr (OH)3 + 3Na2SO4

Cr2 (SO4)3 + 3Ca (OH)2 2Cr (OH)3 + 3CaSO4

Precipitation of dissolved metals to form the hydroxides by pH adjustment through the

addition of alkalis is the major technique for removing chromium from aqueous wastes. For

precipitation, most favorable pH is between 8 and 9. Pretreatment and separation prior to

precipitation is often essential for effective metal removal. For instance, Cr (VI) should be

reduced to Cr (III) in order to form the poorly soluble chromium (III) hydroxide.

2.2.2 Ion Exchange (Mahajan, 1985)

Ion exchange involves the reversible exchange of ions between a solution and a solid phase

that are in direct contact. Both anionic and cationic exchange resins are employed for the

treatment of liquid wastes containing chromium. By ion exchange, it is possible to recover

chromium in the form of sodium chromate or chromic acid. In addition, the treated water can

be reused in the process. Formerly this process suffered from the limitation of high cost of

ion exchange resins and higher operational costs. However it is highly effective for trace

metal removal depending on the chemical form of the given trace metals.

2.2.3 Reverse Osmosis (Mahajan, 1985)

In this process, the concentrated solution is subjected to high pressure (in excess of the

osmotic pressure of the solution) as a result of which the solvent is forced out through a semi-

permeable membrane to the dilute solution region. The concentrated solution becomes more

concentrated and the chromium can be recovered from the concentrated solution. Initially

reverse osmosis was used for the treatment of brackish water and desalination, but with the

development of cheaper and more efficient membranes, it was also possible to use in

wastewater treatment.The three membranes most commonly used are cellulose acetate,

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aromatic polyamide, and NS 100. Cellulose acetate is used in wastewater treatment. The

factors that affect the membrane performance are membrane leakage, membrane fouling and

concentration polarization. The reverse osmosis process is expensive, i.e., both capital and

operating costs are high.

2.2.4 Evaporation

The process consists of evaporating water from wastewater by supplying heat. The

concentrated solution left is subjected for chromium recovery and reuse. Evaporative

recovery and reuse are appropriate for almost all process rinse water systems, with the

exception of those, which chemically deteriorate at high temperature. In this method, all non-

volatile constituents of the wastewater are retained in the concentrated product. In practice,

this has been a major disadvantage because of the buildup of impurities on inside the tubes of

evaporator.

2.2.5 Electro Dialysis

This is a membrane separation process in which instead of pressure an electric field is applied

across a series of membranes, which are inorganic in nature. Two types of membranes are

placed alternatively in the electro dialysis cell. They are cation exchange membrane and

anion exchange membrane. The cathode and anode are placed at the two ends of the cell.

Raw wastewater is fed continuously into the concentrating compartments and treated

wastewater withdrawn continuously from the alternate compartments. Like reverse osmosis,

fouling of membrane and concentration polarization are the common problems which affect

the performance of electro dialysis unit. Availability of power at cheaper rates however,

decides the economics of this method.

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2.2.6 Adsorption with Conventional Adsorbents

Watonabe and Ogawa (1929) first presented the use of activated carbon for the adsorption of

chromium metal ions. A number of researchers have extensively studied the mechanism of

removal of hexavalent and trivalent chromium from synthetic solutions and electroplating

effluents. According to some investigators, the removal of Cr (VI) occurs through these steps

of interfacial reactions (Huang and Bowers, 1979).

(a) The direct adsorption of Cr6+

onto carbon surface.

(b) The reduction of Cr6+

species to Cr3+

by carbon on the surface.

(c) The adsorption of the Cr3+

species produced, which occurs to a much lesser extent

than the adsorption of the Cr6+

species.

Adsorption is a process that occurs when a liquid or gas (called adsorbate) accumulates on

the surface of a solid or liquid (adsorbent), forming a molecular or atomic film. Some of the

commonly used conventional adsorbents are Silica gel (Albino et al., 2007), Activated carbon

(Atieh, 2011; Barkat et al., 2009; Candela et al., 1995; Choma et al., 1999; Lameiras et al.,

2008; Mc Kay et al., 1985; Park et al., 2002; Yue et al., 2008), Activated alumina, Activated

clay (Maria et al., 2006), Fullers earth (Khan et al., 1995), Bauxite, Zeolites (Figueiredo et

al., 2008; Pandey et al., 2010; Paola et al., 2006; Santiago et al., 1992; Erdem et al., 2004),

Bone char (Natale et al., 2007) etc.

Disadvantages of conventional methods for treatment of wastewater containing

chromium metal ions

Chromium metal ions are a class of pollutants, often toxic and dangerous, widely present in

industrial and household wastewaters. Electroplating and metal finishing operations,

electronic circuit production, steel and aluminum processes to name but a few industries,

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produce large quantities of wastewater containing chromium metal ions. Although metal

precipitation using a cheap alkali such as lime (calcium hydroxide) has been the most

favoured option, other separation technologies are favoured in recent times. Consequently,

precipitation produces large quantities of solid sludge for disposal. The technologies available

for the removal/recovery of chromium metal ions and the operating conditions are listed in

Table 2.2. The versatility, simplicity and other technology characteristics will contribute to

the overall process costs, both capital and operational. At present, many of these technologies

such as ion exchange represent significant capital investments by industry.

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Table 2.2 The technologies available for the removal/recovery of chromium metal ions and

the operating conditions.

Technology pH change Metal

selectivity

Influence of

suspended

solids

Tolerance of

organic

molecules

Working level

for

appropriate

metal

concentration

(mg/L)

Electro

chemical

Tolerant Moderate Engineered to

tolerate

Can be

accommodated

>10

Ion exchange Limited

tolerance

Chelate -

resins can be

selective

moderate

Fouled Can be

poisoned

<100

Membrane Limited

tolerance

Moderate Fouled Intolerant >10

Precipitation Limited

tolerant

Limited pH

selective

dependent

Tolerant Tolerant >10

Adsorption Tolerant Moderate Tolerant Tolerant <10

As seen from the Table 2.2, conventional methods are ineffective in the removal of low

concentrations of chromium metal ions and they are non-selective.

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2.3 Adsorption with nonconventional adsorbents

The renewable character of biomass that grows, fuelled directly or indirectly by sunshine,

makes it an inexhaustible pool of chemicals of all kinds. Some of the low-cost sorbents

reported so far include: Bark/tannin-rich materials, lignin, chitin/chitosan,

seaweed/algae/alginate, xanthate, fly ash, peat moss, modified wool and modified cotton, tea

waste, maize corn cob etc., The various nonconventional adsorbents found in the literature

are classified as below.

1. Living microorganisms

2. Non living microorganisms

3. Industrial wastes

4. Agricultural wastes

5. Miscellaneous waste materials

2.3.1 Biosorption of chromium by microorganisms

Living microorganisms and non-living microorganisms as adsorbents

Both living and non-living microorganisms have the ability to remove the heavy metals and

thereby making water contaminant free. The biomass of filamentous fungi of the order

Mucorales represents a good adsorbent material for a wide range of heavy metals. The metal

binding sites are predominantly associated with the cell wall structure of these molds

[Remacle et al., 1990]. Both Rhizopus arrhizus and Rhizopus nigricans contain chitin and

chitosan in their cell walls, which have been reported to play a role in the sequestration

chromium metal ions from solution. However due to certain inherent disadvantages in the use

of living microorganisms for metal removal, recovery is not generally feasible in all

situations like industrial effluents contain high concentrations of toxic metals under widely

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varying pH conditions. These conditions are not always suitable to the growth and

maintenance of an active microbial population.

The published literatures on the removal of chromium (VI) from aqueous solution/waste

water using different living microorganisms are given below in Table 2.3.

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Table 2.3 Review of literature for biosorption of chromium (VI) for living microorganisms

Biomass type Biomass class Metal up take, mg/g pH Isotherms Kinetics Thermodynamics References

Phormdium sp. Bacteria 22.8 2 Langmuir 2nd

order Exothermic Aksu et al.,

2009

Biofilm of E. coli

supported on

carbon

Bacteria 97.7 2 Langmuir,

Freundlich

--- --- Gobar et al.,

2009

Oedogonium hatei Algae 31.0 2 Langmuir,

Freundlich

1st order Feasibility,

Spontaneous,

Endothermic

Gupta et al.,

2009

Pseudomonas

fluorescens

TEM08

Algae 40.8 2 Langmuir --- --- Uzel et al., 2009

Spent/fresh

Spirulinaplatensis

Bacteria 213 1.5 Langmuir,

Freundlich

Initial

zero

Gokhale et al.,

2008

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order,

1st order

Spent/fresh

Chlorella vulgaris

Bacteria 189 1.5 Langmuir,

Freundlich

Initial

zero

order,

1st order

Nostoc muscorum Cyanobacteriu

m

22.92 3 Langmuir,

Freundlich

1st order,

2nd

order

Endothermic Gupta et al.,

2008

S. equisimilis. Bacteria 5.82 7.4 Freundlich. Quintelas et al.,

2008 B. coagulans. Bacteria 5.35 7.2 Freundlich.

E. coli. Bacteria 4.12 7.2 Freundlich,

RP-Model

Sargassum sp Algae 19.06 2 Langmuir,

Freundlich

--- --- Viera et al.,

2008

L. japonica Algae 59.35 1 Freundlich 2nd

order Feasibility,

Spontaneous,

Wang et al.,

2008

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Endothermic

Rhizopus arrhizus Fungus 23.92 1.3 Freundlich,

RP-Model

2nd

order --- Preetha et al.,

2007

Pseudomonas sp Bacteria 95 2 Langmuir,

Freundlich

--- --- Ziagova et al.,

2007

Staphylococcus

xylosus

Bacteria 143 2 Langmuir,

Freundlich

--- --- Ziagova et al.,

2007

Free Lentinus

sajor-caju

Algae 18.9 2 Langmuir,

Freundlich

2nd

order Arica et al.,

2005

Immobilized

Lentinus sajor-caju


Freundlich

2nd

order Arica et al.,

2005

Aeromonas caviae Bacteria 284.4 2.5 Langmuir,

Freundlich

Pseudo

2nd

order

--- Loukidou et al.,

2005

Bacillus

thuringiensis


Freundlich

--- --- Sahin et al.,

2005

Type-I, type-II Bacteria 53.5 2 Langmuir 2nd

order Endothermic Tewari et al.,

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Mucor hiemalis 2005

Rhizopus nigricans Fungus 47 2 Langmuir,

Freundlich

1st order,

2nd

order

--- Bai et al., 2003

Ochrobactrum

anthropi


Freundlich

2nd

order --- Ozdemir et al.,

2003

Dunaliella 1 Bacteria 58.3 2 Langmuir,

Freundlich

2nd

order Donmez et al.,

2002

Dunaliella 2 Bacteria 45.5 2 Langmuir,

Freundlich

2nd

order

Chlorella vulgaris Algae 23.6 2 Freundlich --- --- Aksu et al.,

1999

Chlorella vulgaris Algae 79.3 2 Langmuir,

Freundlich

--- --- Donmez et al.,

1999

Scenedesmus

obliquus


Freundlich

--- Donmez et al.,

1999

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Non-living microorganisms (Dead biomass)

There are several advantages by nonliving biomass as adsorbents and they are as

follows:

Nonliving biomass is not subject to toxicity limitation by cells.

The biomass from an existing fermentation industry, which essentially is a waste after

fermentation, can be a cheap source of biomass.

The process is not governed by physiological constraints of microbial cells.

Because nonliving biomass behaves as an ion exchange, the process is very rapid,

requiring anywhere between few minutes to few hours. Metal loading is very high on

the surface of the biomass leading to very efficient metal uptake.

Because cells are non-living, a wider range of operating conditions such as pH,

temperature, and metal concentrations are possible. In addition, aseptic operating

conditions are not essential.

Metals can be desorbed readily and then recovered. If the value and the amount of

metal recovered are insignificant and if the biomass is plentiful, the metal loaded

biomass can be incinerated, eliminating further treatment.

Adsorption isotherms, adsorption kinetics, pH and metal uptake and thermodynamics of

various non-living micro-organisms are given in Table 2.4

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Table 2.4 Review of literature for biosorption of chromium (VI) for non- living microorganisms

Biomass Type Biomass class Metal up take, mg/g pH Isotherms Kinetics Thermodynamics Reference

Aspergillus niger Fungus 117.33 1 Langmuir 2nd

order Feasible,

Spontaneous,

Endothermic

Yamin et al., 2009

Aspergillus

sydoni

Fungus 9.07 2 Langmuir,

Freundlich

--- --- Kumar et al.,

2008

Aspergillus niger Fungus 17.61 2 Langmuir,

Freundlich

--- --- Kumar et al.,

2008

Penicillium

janthinellum

Fungus 9.35 2 Langmuir,

Freundlich

---- --- Kumar et al.,

2008

Ceramium

virgatum

Fungus 26.5 1.5 Langmuir,

Freundlich,

DR-Model

2nd

order Feasible,

Spontaneous,

Endothermic

Sari et al., 2008

Dried Rhizopus Fungus 78 2 Langmuir, 2nd

order Aksu et al., 2007

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arrhizus Freundlich,

Langmuir-

Freundlich

model,

RP-Model

Dried Ulva

lactuca


Freundlich,

RP-Model,

Koble-

Corrigan

2nd

order --- Amany et al.,

2007

Aspergillus niger Fungus 11.6 2 Freundlich 2nd

order Spontaneous

Exothermic

Mungasavalli et

al., 2007

Bacillus

lichiniformis

Bacteria 300 2.5 Langmuir 2nd

order --- Zhou et al., 2007

Rhizopus oryzae Fungus 23.5 2 ---- 1st order --- Park et al., 2005

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Fusarium sp Fungus 47.5 2 Langmuir,

Freundlich

--- --- Sen et al., 2005

Spirogyra sp Algae 14.7 2 Langmuir --- ---- Gupta et al.,

2001

Rhizopus arrhizus Fungus 23.88 2 Freundlich --- --- Prakasham et al.,

1999

DR-Model-Dubinin Radushkevich Model: RP-Model-Redlich Peterson Model; IPD-Model- Intra particle diffusion-Model.

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Among microorganisms, fungal biomass offers the advantages of having high percentage of

wall material, which shows excellent metal binding properties (Gadd, 1990; Rosenberger,

1975; Paknikar, Palnitkar and Puranik, 1993). Many fungi and yeast have shown an excellent

potential of metal adsorption, particularly the general Rhizopus, Aspergillus,

Streptoverticullum and Sacchromyces (Volesky and Tsezos, 1982; siegel et al., 1984; Siegel

et al., 1986; Luef et al., 1991, Brady and Duncan, 1993 Puranik and Paknikar, 1997).

2.3.2 Adsorption of chromium (VI) by industrial waste

Wide spread industrial activities are producing large amount of solid waste materials. Some

of these materials are being put to use while others find no proper utilization and are dumped

elsewhere. The industrial waste material is available almost free of cost and causes major

disposal problem. If the solid wastes could be used as low cost adsorbents, it will provide a

two-fold advantage in reducing the pollution. Firstly, the volume of waste materials could be

partly reduced and secondly the developed low cost adsorbent can reduce the pollution of

wastewaters at a reasonable cost. With this view, a number of industrial wastes have been

investigated with or without treatment as adsorbents for the removal pollutants from

wastewaters. Important industrial wastes with their metal uptake, pH, adsorption kinetics,

isotherms and thermodynamics for various industrial wastes are shown in Table 2.5. A

number of studies have been conducted using industrial waste are given below.

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Table 2.5 Review of literature for adsorption of chromium (VI) for various industrial wastes.

Material Metal uptake, mg/g pH Isotherms Kinetics Thermodynamics References

Dolochar from sponge

industry

5.21 1 Langmuir 2nd

order Feasible,

Spontaneous,

Endothermic

Das et al., 2011

Tannery waste 177 2 Langmuir 2nd

order Feasible,

Spontaneous,

Endothermic

Mandal et al., 2011

Carbon slurry from

fertilizers

15.24 2 Langmuir,

Freundlich

2nd

order Feasible,

Spontaneous,

Endothermic

Gupta et al., 2010

Clarified sludge 26.31 3 Langmuir ,

Freundlich

2nd

order

IPD-Model

Feasible,

Spontaneous,

Endothermic

Bhattacharya et al.,

2008

Solid waste from

leather industry

133 1 langmuir --- --- Oliveira et al., 2008

Mg(OH)2 (MF5-1)

in cement industry

7.2 9.4 Langmuir,

Freundlich

1st order Feasible,

Spontaneous,

Exothermic

Gasser et al., 2007

Mg(OH)2 (MF5-2) 10.0 9.8 Langmuir, 1st order Feasible,

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in cement industry Freundlich Spontaneous,

Exothermic

Tea factory waste 54.65 2 Langmuir,

Freundlich

1st order,

IPD-Model

Feasible,

Spontaneous,

Endothermic

Malkoc et al., 2007

Waste pomace from

olive oil factory

12.15 2 Langmuir,

Freundlich

--- Feasible,

Spontaneous,

Endothermic

Malkoc et al., 2006

Cupressus female cone 119.4 0.5 Langmuir 1st order Endothermic Murugan et al.,

2003

Bagasse fly ash

from sugar industry

260 1 Langmuir,

Freundlich

--- --- Gupta et al., 1999

Activated red mud 1.6 2 Langmuir,

Freundlich

--- --- Pradan et al., 1999

Blast furnace slag 7.5 1 Langmuir,

Freundlich

1st order Endothermic Srivastava et al.,

1997

Iron (III)/Cr (III) hydroxide 0.47 5.6 Langmuir,

Freundlich

--- Endothermic Namasivayam et

al., 1993

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2.3.3 Adsorption of chromium (VI) by agricultural waste materials.

Various naturally occurring materials having characteristics of an adsorbent are available in

large quantities. The abundance of these materials in most continents of the world and their

low cost make them suitable as adsorbents for the removal of chromium from wastewaters.

Wood is the most widely spread natural material and its use as adsorbent for the removal of

chromium. The drawback of this adsorbent was the long equilibration time required for

adsorption and low adsorption capacity.Timber industry generates bark as by-product that is

effective because of its high tannin content. The polyhydroxy polyphenol groups of tannin are

thought the active species in the adsorption process. Ion exchange takes place as metal

cations displace adjacent phenolic hydroxyl groups, forming a chelate (Randall et al., 1974).

The recent literature review on the removal of chromium (VI) by adsorption using

agricultural waste material is given below in detail.

Ali et al. (2012) have investigated the removal of hexavalent chromium from aqueous

medium by activated carbon prepared from Peanut shell by chemical activation with KOH.

Unoxidized activated carbon was prepared in nitrogen atmosphere, which was then heated in

air at a desired temperature to get oxidized activated carbon. The prepared carbons were

characterized for surface area and pore volume and utilized for the removal of Cr (VI) from

aqueous solution. The effects of pH, contact time, initial concentration of adsorbate and

temperature on adsorption of Cr (VI) were investigated. Adsorption kinetics of Cr (VI) was

analyzed by pseudo first order, pseudo second order and intra particle diffusion kinetic

models. Results showed that Cr (VI) adsorption on both oxidized and unoxidized samples

followed the first and second order kinetics models most appropriately. Isotherm data were

treated according to Langmuir and Freundlich models. The results showed that both

Langmuir and Freundlich models fitted the data reasonably but the Langmuir adsorption

35

isotherm model fitted better in the temperature range studied. The adsorption capacity was

found to increase with temperature, showed endothermic nature of Cr (VI) adsorption. The

thermodynamic parameters, such as Gibb‘s free energy change, standard enthalpy change,

and standard entropy change were evaluated. The value of change in Gibb‘s free energy was

found negative for the adsorption of Cr (VI), which confirmed the feasibility, and spontaneity

of the adsorption process.

Palvannan et al. (2012) have established the rapid removal of chromium from aqueous

solution using prawn shell activated carbon (PSAC). Scanning electron microscopy (SEM),

Fourier Transform Infra Red spectroscopy (FTIR) and X-Ray Diffraction (XRD) analysis

were taken to know the complex nature of adsorbents. The adsorption isotherms obeyed the

both Langmuir and Freundlich model and followed pseudo second order kinetic reaction. The

maximum metal uptake was 100 mg/g at optimum pH of 2. Response surface methodology

was also applied to optimize the process variables for the maximum chromium removal. The

optimization parameters are 0.04 gram of PSAC dosage, 100 mg/L of chromium and 31.4

minute time. These results indicated that the PSAC was effective adsorbent for the rapid

removal of chromium from tannery effluent.

Albadarin et al. (2011) have studied the removal of Cr (VI) from aqueous solution by lignin.

The effect of various process parameters such as system pH, ionic strength, initial

concentration, adsorbent dosage, presence of other metals (Zn and Cu), and presence of salts

were studied. The optimum pH for the removal of Cr (VI) was found to be 2. The adsorption

isotherms fitted well with the Freundlich model followed by pseudo second order kinetic

reaction model. The maximum metal uptake obtained using Dubinin-Radushkevich and Khan

isotherms for Cr (VI) removal are 31.6 and 29.1 mg/g respectively. It was concluded that the

36

adsorption mechanism involves the attraction of both hexavalent chromium (anionic) and

trivalent chromium (cationic) onto the surface of lignin.

Mohan et al. (2011) have studied the removal of chromium from waste water using Oak

wood and Oak bark chars obtained from fast pyrolysis in an auger reactor at 673-723 K.

Batch sorption studies were performed at different temperatures, pH values and solid to liquid

ratios. Maximum chromium was removed at pH of 2.0. An optimum equilibrium time with an

adsorbent dosage of 10 g/L is 48 h. Adsorption studies were conducted over a concentration

range of 1-100 mg/L. Adsorption of Cr (VI) was increased with an increase in temperature

(QOak wood:298 K =3.03 mg/g; 308 K=4.08 mg/g; 318 K=4.93 mg/g and QOak bark: 298 K=4.62

mg/g; 308 K=7.43 mg/g; 318=7.51 mg/g ). More chromium was removed with oak bark than

oak wood. The Oak bark char performances were evaluated using the Freundlich, Langmuir,

Redlich-Peterson, Toth, Radke and Sips adsorption isotherm models. The Sips isotherm

models best fits the experimental data [high regression (R2) coefficients]. The overall kinetic

data was satisfactorily explained by a pseudo second order rate expression. Water penetrated

into the char walls exposing Cr (VI) to additional adsorption sites that were not on the

surfaces of dry char pores. It is remarkable that oak chars (SBET=1-3 m2/g) can remove similar

amounts of Cr (VI) as activated carbon (SBET: ~1000 m2/g). Thus, byproduct chars from bio-

oil production might be used as inexpensive adsorbents for water purification. Char samples

have been successfully used for the chromium remediation from contaminated surface water

with dissolved interfering ions.

Muthukumaran et al. (2011) have studied the removal of chromium (VI) from wastewater

using chemically activated carbon prepared from Syzygium jambolanum nut, an agricultural

waste, after activation with ammonium persulphate in the presence of sulphuric acid and then

subjected to thermal activation by dolomite process in batch studies. Effect of pH, carbon

37

dosage and equilibrium time were determined. Desorption of Cr (VI) was done with 1 M

NaOH and 10% H2O2 mixture followed by 2M HCl. Adsorption followed Freundlich,

Langmuir and Tempkin isotherms but was fitted well to Freundlich adsorption isotherm

predominantly. Kinetic studies showed that the removal process followed pseudo second

order predominantly rather than pseudo first and reversible first order reaction. Removal of

Cr (VI) followed the film diffusion process. Negative Gibb‘s free energy values indicated the

feasibility and the spontaneous nature of the adsorption process. The performance of

chemically activated Syzygium jambolanum nut carbon was compared with a commercial

activated carbon.

Tahiri et al. (2011) have studied the removal of hexavalent chromium from contaminated

water using Chestnut (C) and Mimosa (M) tannins immobilized on chrome shavings (CS).

The adsorption of hexavalent chromium onto chrome shavings-tannin (CS-T) adsorbents was

performed using batch equilibrium technique at 298±2 K. The effect of pH is highly

important especially in the case of high concentrations of hexavalent chromium. The

maximum chromium metal uptake was reported at pH 4. Two hours of contact time are

enough to reach equilibrium time. Sorption of chromium on CS-T was found to follow a

pseudo-second order kinetic model (with correlation coefficient (R2) greater than 0.999). The

adsorption equilibrium data fitted well the Langmuir model. The maximum adsorption

capacity of dry immobilized tannin adsorbent with 11.6% polyphenol, reached 42 mg /g and

38 mg/g in the case of Chestnut and Mimosa tannins, respectively.

Hong et al. (2010) have studied the removal of hexavalent chromium from aqueous solution

using activated carbon derived from blue-green algal bloom residue. For this algal bloom

residue derived activated carbon, the physical characters regarding adsorption capability were

analyzed by Scanning Electron Microscope (SEM), Energy Dispersive Spectra (EDS) and

38

Fourier Transform Infra Red (FTIR) spectroscopy. Batch studies showed that initial pH,

adsorbent dosage, and initial concentration of chromium (VI) were important parameters for

Cr (VI) adsorption. The maximum Cr (VI) removal is found at pH value of 1. The adsorption

process followed the pseudo-second order rate equation and Freundlich isotherm. The

maximum adsorption capacity for Cr (VI) was 155.52 mg/g in an acidic medium, which is

comparable to best result from activated carbons derived from biomass. Therefore, this work

put forward a nearly perfect solution, which on one hand gets rid of environment –unfriendly

algae residue while on the other hand produces high quality activated carbon that is in return

advantageous to environment protection.

Moussav et al. (2010) have investigated the removal of hexavalent chromium (VI) from

wastewater on pistachio hull powder (PHP). The effects of pH (2–8), PHP concentration

(0.5–8 g/L), Cr (VI) concentration (50–200 mg/L), temperature (278–323 K), and contact

time (1–60 min) were studied on the removal of Cr (VI) from aqueous solution. The results

revealed that PHP adsorbs over 99% of chromium from solutions containing 50–200 mg/L of

Cr (VI) at a pH of 2 and an adsorbent concentration of 5 g/L after 60 min of equilibrium time.

The percentage chromium adsorbed from solution increased with an increase in temperature

from 278 to 313 K. Kinetic and isotherm-modeling studies demonstrated that the

experimental data is fitted a pseudo-second order and Langmuir model, respectively. The

maximum Langmuir adsorption capacity was 116.3 mg/g. In the second part of the study, the

adsorption capacity of PHP was examined by analyzing the removal of Cr (VI) from

industrial wastewater. Results revealed that 2 g/L of PHP decreased the Cr (VI) concentration

from 25 mg/L to less than 0.05 mg/L after 30 min of equilibrium time.

Babu et al. (2009) have studied the removal of chromium from aqueous solutions and from

the synthetically prepared industrial effluent of electroplating and tannery industries using

39

sawdust as adsorbent. The batch experiments have been carried out to investigate the effect of

the significant process parameters such as initial pH, change in pH during the adsorption,

contact time, adsorbent amount and the initial Cr (VI) concentration. The maximum

adsorption of Cr (VI) on sawdust is found at an initial pH value of 1. The equilibrium data for

the adsorption of Cr (VI) on sawdust was tested with various adsorption isotherm models

such as Langmuir, Freundlich, Redlich-Peterson, Koble-Corrigan, Tempkin, Dubinin-

Radushkevich and generalized equation. The Langmuir isotherm model was found to be most

suitable one for the Cr (VI) adsorption using sawdust and the maximum adsorption capacity

obtained is 41.5 mg/g at a pH value of 1. The adsorption process follows the second order

kinetics. Desorption of Cr (VI) from sawdust using acid and base treatment exhibited a higher

desorption efficiency by more than 95%. A feasible solution is proposed for the disposal of

the contaminant (acid and base solutions) containing high concentration of Cr (VI) obtained

during the desorption process. The interference of other ions, which are generally present in

the electroplating and tannery industrial effluent streams on the Cr (VI) removal, was

investigated.

Das et al. (2009) have studied the removal of hexavalent chromium from aqueous solution

using activated cow dung carbon. Cow dung was carbonized and activated by treating with

concentrated H2SO4 followed by heating for 24 h at 393 K. The extent of adsorption was

studied as a function of pH, contact time, amount of adsorbent, concentration of adsorbate

and temperature. At lower pH (<3.5), the prepared sorbent was capable of removing

approximately 90% Cr (VI) at 5 mg/L concentration from aqueous synthetic solution. The

dynamics of migration of the adsorbate ions from the bulk onto the adsorbent surface was

studied and the results obtained under different experimental conditions were found to follow

standard adsorption isotherms. The reaction kinetics was found to follow of first order.

40

Garg et al., (2009) have investigated the removal of Cr (VI) from synthetic waste water

using pre-consume processing agricultural waste such as rice husk under different

experimental conditions. For this, rice husk has been used after pretreatments (boiling and

formaldehyde treatment). Effect of various parameters such as pH, adsorbent dosage, contact

time and initial concentration of Cr (VI) were stidied in batch systems. FTIR and SEM were

recorded before and after adsorption to find out Cr (VI) binding on to studied adsorbents and

changes in adsorbent surface morphology. Maximum metal uptakes for boiled and treated

rice husk were 8.5 and 10.4 respectively at pH value of 2. The experimental data were

analyzed using Freundlich, Langmuir and DR-isotherm models. It was found that Freundlich

and DR-models fitted well. The results indicated that the hexavalent chromium was

considerably adsorbed on rice husk and it could be an economical method for the removal of

hexavalent chromium.

Garg et al. (2009) have investigated the removal of chromium from aqueous solution using

Helianthus annuus (sunflower) stem waste under different conditions. Two adsorbents were

prepared by pre-treating the sunflower waste. One adsorbent were prepared by boiling it and

second adsorbent was prepared by treating it with formaldehyde. Batch mode experiments

were carried out as a function of solution pH, adsorbent dosage, Cr (VI) concentration and

contact time. FTIR spectra and Scanning electron micrographs of the adsorbents have taken

to explore the number and position of functional groups available for the binding of Cr (VI)

ions and morphology of adsorbents. The removal of chromium was dependent on the

physico-chemical characteristics of the adsorbent, adsorbate concentration, and other studied

process parameters. Maximum metal removal was found to be at pH 2. The efficiencies of

boiled sunflower stem adsorbent and formaldehyde-treated sunflower stem adsorbent for the

removal of Cr (VI) were 81.7 and 76.5%, respectively for dilute solutions at 4.0 g/L

adsorbent dosage. The applicability of Langmuir, Freundlich, and Dubinin-Radiushkevich

41

isotherms was also tested. The results revealed that the hexavalent chromium is considerably

adsorbed on sunflower stem and it could be an economical method for the removal of

hexavalent chromium from aqueous systems.

Garg et al. (2009) have investigated the removal of chromium from aqueous solution using

agricultural waste and timber industry waste carbons under different conditions. For this, rice

husk carbon (RHC) and saw dust carbon (SDC) were used as adsorbent after sulphuric acid

treatment. Effect of various process parameters such as pH, adsorbent dosage, initial

chromium concentration and contact time were studied in batch experimental systems.

Fourier Transform Infra Red (FTIR) spectroscopy and Scanning Electron Micrographs

(SEM) of the adsorbents have taken to investigate the number and position of various

functional groups available for Cr (VI) binding onto studied adsorbents and changes in

adsorbent surface morphology. Maximum metal removal was found at pH of 2.0. The

efficiencies of both RHC and SDC for the Cr (VI) removal were 91.75% and 94.33%

respectively for aqueous solutions (250 mg/L) at 20 g/L adsorbent dosage. The experimental

data was analyzed using Freundlich, Langmuir, Dubinin-Radushkevich (D-R) isotherm and

Tempkin isotherm models. It was found that Langmuir, DR-isotherm and Tempkin models

fitted well. The results indicated that the hexavalent chromium was reasonably adsorbed on

RHC and SDC and it could be an economical method for the removal of hexavalent

chromium from aqueous systems. The surface area of RHC and SDC was 1.12 and 1.16 m2/g,

respectively.

Gupta et al. (2009) have studied for the removal of Cr (VI) from aqueous solutions using an

adsorbent prepared from tamarind seeds, used after activation. The tamarind seeds were

activated by treating them with concentrated sulfuric acid (98% w/w) at a temperature of 423

K. The adsorption of Cr (VI) was observed to be maximum at low values of initial pH in the

42

range of 1–3. The adsorption process of Cr (VI) was tested with Langmuir, Freundlich,

Redlich–Peterson, Koble–Corrigan, Tempkin and Dubinin–Radushkevich models.

Applications of the Langmuir isotherm to the system have yielded a maximum adsorption

capacity of 29.7 mg/g at an equilibrium pH value ranging from 1.12 to 1.46. The adsorption

process followed the second-order kinetics. The regenerated activated tamarind seeds showed

more than 95% Cr (VI) removal of that obtained using the fresh activated tamarind seeds. A

feasible solution is proposed for the disposal of the contaminants (acid and base solutions)

containing high concentrations of Cr (VI) obtained during the regeneration (desorption)

process.

Levankumar et al. (2009) have investigated the batch removal of chromium from aqueous

solution by Ocimum americanum L.Seed pods. The speed in the shaker and pH were found to

be 121 rpm and 1.5 at optimum conditions. The equilibrium adsorption data was best fitted

with Langmuir isotherm. The maximum metal uptake calculated from Langmuir isotherm

was 83.33 mg/g dry weight of seed pods at optimum pH and the speed in the shaker. The

adsorption kinetics was studied for the concentration of 100 mg/L, 150 mg/L and 200 mg/L

chromium concentration solutions. The adsorbent dosage was 8 g/L. The adsorption

efficiency for all three Cr (VI) concentrations was 100%. The equilibrium was achieved less

than 120 min for all the three concentrations. The adsorption kinetics data was fitted with first

order and second order kinetic models. Finally it was concluded that the chromium

adsorption kinetics O.americanum L.Seed pods was well described by second order kinetic

model rather than first order model.

Qaiser et al. (2009) have investigated the adsorption of lead (II) and chromium (VI) on

groundnut hull. Batch biosorption experiments were conducted to find the equilibrium time

and metal uptake. Effect of various parameters like pH, temperature and initial metal

43

concentration was studied. The maximum metal uptake of lead (II) and chromium (VI) was

found to be 31.54 ± 0.63 and 30.21 ± 0.74 mg g,-1

respectively. The optimum pH for lead (II)

and chromium (VI) removal was 5 ± 0.1 and 2 ± 0.1, respectively. The temperature change,

in the range of 293–318 K affected the metal uptake. The maximum removal of lead (II) was

achieved at 293 ± 2 K; where as maximum uptake of chromium (VI) was observed at 313 ± 2

K. The equilibrium adsorption data was fitted to both the Langmuir and the Freundlich

isotherm models. The Langmuir model indicated better representation of data, with

correlation coefficient (R2) greater than 0.98. The kinetics of adsorption was fitted well with

the pseudo second order kinetics model. The thermodynamics parameters were calculated

from the experimental data.

Mandal et al. (2009) have investigated the removal of chromium from aqueous solution

using a new activated carbon (AC) prepared from non-usable Bael fruit shell (BS). Batch

mode experiments were performed as a function of initial pH of solution, agitation time,

adsorbate concentration and adsorbent dosage. The progressive changes on surface texture

and the confirmation of chromium binding on the adsorbent surface at different stages were

analyzed by the scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy

(EDS) and Fourier Transform Infra Red spectrometer (FTIR). Maximum chromium metal

removal was found at pH of 2.0 in an equilibrium time of 240 min by adsorption-coupled

reduction. The adsorption data was found to follow satisfactorily with Langmuir as well as

Freundlich adsorption isotherm model. Evaluation using Langmuir equation gave the

monolayer sorption capacity as 17.27 mg/g. The adsorption behavior was best described by

pseudo second order chemisorption model. Phosphoric acid activation played a significant

role to develop the well-defined pores on adsorbent surface. The results obtained in this study

illustrate that the BSAC was expected to be an effective and economically viable adsorbent

for Cr (VI) removal from aqueous system.

44

Meikap et al. (2009) have studied the removal of chromium (VI) using activated carbon

prepared from tamarind wood with zinc chloride activation. Adsorption studies were

conducted in the range of 10-50 mg/L initial chromium (VI) concentration and at temperature

in the range of 283-323 K. The experimental data were analyzed by both Freundlich isotherm

and Langmuir isotherm. Equilibrium data fitted well with both Langmuir model and

Freundlich model with maximum adsorption capacity of 28.019 mg/g. The adsorption

behavior was described by pseudo second order kinetics with good correlation and the overall

rate of chromium (VI) uptake was found to be controlled by pore diffusion, film diffusion and

particle diffusion, throughout the entire adsorption period. The plot confirmed that external

mass transfer was the rate-limiting step in the adsorption process. Different thermodynamic

parameters, change in enthalpy (∆H), change in entropy (∆S) and change in Gibbs free

energy (∆G) have also been evaluated and it has been found that the adsorption was feasible,

spontaneous and endothermic in nature. The results indicate that the activated tamarind wood

could be used to effectively adsorb chromium (VI) from aqueous solution.

Wan et al. (2009) have carried out a bench scale experiment for the removal of chromium

(VI) from aqueous solution using untreated rubber wood sawdust (RWS) by varying

parameters such as initial Cr (VI) concentrations, adsorbent dosage, pH and temperature and

eluating agent. Complete Cr (VI) removal was achieved at pH less than 2, initial Cr (VI) of

100 mg/L and RWS dosage of greater than 1.5% (w/v). The point of zero charge (pHpZC) of

4.90 explained the decrease in Cr (VI) removal capacity by RWS when pH (3-9) and initial

Cr (VI) concentrations (200-500 mg/L) were increased. Shorter time was needed for

desorption when 1 M HCl was used to recover chromium. Fourier Transform Infra Red

(FTIR) analysis suggests the importance of functional groups such as amino, hydroxyl and

carboxyl during Cr (VI) removal. Results suggest that the Cr (VI) removal by RWS is an

45

endothermic process with positive entropy and occurs non-spontaneously with positive

Gibb‘s free energy.

Wang et al. (2009) have studied the removal of chromium (VI) from aqueous solution using

walnut hull. The optimum pH for removal has found to be one at which chromium (VI)

removal was 97.3%. The removal increased with the increase in the adsorbent concentration

and the decrease in adsorbate concentration. An increasing equilibrium adsorption capacity

with the rise in temperature indicated that the nature of adsorption process is endothermic,

which is further supported by the thermodynamic parameters calculated at various

temperatures. The adsorption process was found to follow the first-order, modified

Freundlich, intra particle diffusion and Elovich models. Compared to the various other

adsorbents reported in the literature, the walnut hull in this study shows very good promise

for practical applicability. However, more studies are needed to optimize the system from the

regeneration point of view and to investigate the economic aspects.

Wang et al. (2009) have evaluated for the removal of Cr (VI) from water by carbon derived

from burning of rice straw. Rice straw was burned in the air to obtain rice carbon (RC) and

then removal of Cr (VI) by RC was investigated under various pHs and ionic strengths. After

the experiments, the oxidation state of Cr bound to RC was analyzed using X-Ray

photoelectron spectroscopy, which revealed that Cr bound to RC was predominantly in the

trivalent form. The results showed that Cr (VI) was reduced to Cr (III) after adsorption,

which was either adsorbed on RC or released back into solution. The extent and rate of Cr

(VI) removal increased with decreasing solution pH because the Cr (VI) adsorption and the

subsequent reduction of adsorbed Cr (VI) to Cr (III) both occur preferentially at low pH

values. The minimal effect of ionic strength on the rates of Cr (VI) removal and Cr (III)

adsorption indicated specific interactions between Cr (VI)/Cr (III) and their surface binding

46

sites on RC. These results suggest that rice straw-based carbon may be effectively used at low

pH as a substitute for activated carbon for the treatment of Cr (VI) contaminated water.

Wang et al. (2009) have investigated the removal of Cr (VI) from aqueous solution using

Alligator weed, a fresh water macrophyte. The removal of Cr (VI) is function of initial pH,

contact time, reaction temperature and adsorbent concentration in batch studies. The Cr (VI)

removal was found to be maximum at pH value of 1. The kinetic experimental data was

described by the pseudo second order equation. Elovich equation and Langmuir-Hinshelwood

equation were fitted very well. The adsorption of Cr (VI) onto Alligator weed conformed to

the linear forms of the Langmuir, Freundlich and Tempkin equations. The removal

efficiencies increased with the increased adsorbent dosage from 1 to 8 g/L and were 86.6,

97.6 and 99.7 % at the adsorbent dosage of 8 g/L, solution pH of 1.0 and temperature of 303,

313 and 323 K respectively. Thermodynamic parameters, change in enthalpy (∆H), change in

entropy (∆S) and change in Gibb‘s free energy (∆G) revealed that the adsorption of Cr (VI)

onto Alligator weed is endothermic, non-spontaneous, with a decreased a randomness in

nature.

Baral et al. (2008) have investigated the ability of the sawdust to remove chromium (VI)

from aqueous solutions. They have conducted experiments for the removal chromium (VI) by

varying various parameters such as contact time, pH, amount of adsorbent, concentration of

adsorbate and temperature. The kinetics of adsorption of Cr (VI) was described by pseudo

second order. Langmuir adsorption isotherm was employed in order to evaluate the optimum

metal uptake of the adsorbent. The metal uptake was found to be pH dependant. Sawdust was

found to be very effective and reached equilibrium in 3 h (adsorbate concentration 30 mg

L−1

). The rate constant has been calculated at 303, 308, 313 and 318 K and the activation

energy (Ea) was calculated using the Arrhenius equation. Thermodynamic parameters such as

47

standard Gibbs energy (ΔG) and heat of adsorption (ΔH) were calculated. The ΔG and ΔH

values for Cr (VI) adsorption on the sawdust showed the adsorption process was exothermic

in nature. The percentage of adsorption increased with decrease in pH and showed maximum

removal of Cr (VI) in the pH range 4.5–6.5 for an initial concentration of 5 mg L−1

.

Bhattacharya et al. (2008) have investigated the removal of Cr (VI) from aqueous solution

by batch adsorption technique using various low cost adsorbents. Adsorbents such as clarified

sludge from a steel industry, rice husk ash, activated alumina, fuller‘s earth, fly ash, saw dust

and neem bark were used to evaluate adsorption efficiency. The effects of pH, adsorbent

type, initial Cr (VI) concentration and contact time on the selectivity and sensitivity of the

removal process were studied. The optimum pH range for adsorption of Cr (VI) was found to

be between 2 and 3. The adsorption isotherms obeyed both Langmuir and Freundlich model

followed by pseudo second order kinetic reaction model. The thermodynamic parameters

such as change in Gibb‘s free energy and equilibrium constant were evaluated. Among

selected adsorbents, clarified sludge was most effective for the removal of chromium (VI).

The adsorption efficiencies of rice husk and activated alumina were also equally compatible

with that of clarified sludge.

Demirbas et al. (2008) have investigated the removal of toxic hexavalent chromium from

aqueous solution using activated carbon from almond shell with H2SO4 activation. The

influences of several operating parameters such as pH, particle size and temperature on the

adsorption capacity were investigated. Adsorption of Cr (VI) was found to be highly pH,

particle size and temperature dependent. Four adsorption isotherm models namely, Langmuir,

Freundlich, Tempkin, and Dubinin-Radushkevich were used to analyze the equilibrium data.

The Langmuir isotherm provided the best correlation for Cr (VI) onto the almond shell

activated carbon (ASAC). Metal uptake was calculated from the Langmuir isotherm as 190.3

48

mg/g at 323 K. Thermodynamic parameters were evaluated and the adsorption was

endothermic showing monolayer adsorption of Cr (VI). Five error functions were used to

treat the equilibrium data using non-linear optimization techniques for evaluating the fit of

the isotherm equations. ASC was found to be inexpensive and effective adsorbent for

removal of Cr (VI) from aqueous solutions.

Demiral et al. (2008) have studied the removal of chromium (VI) from aqueous solution

using activated carbon derived from olive bagasse by physical activation of steam. The pore

properties including the BET surface area, pore volume, pore size distribution and average

pore diameter were characterized. BET surface area of the activated carbon was determined

as 718m2/g. The removal of Cr (VI) from aqueous solutions by adsorption has been studied.

The effects of pH, contact time and temperature on adsorption of Cr (VI) were investigated.

The maximum chromium (VI) removal was found at pH of 2. The adsorption kinetics was

best described by pseudo second order model. Equilibrium isotherms were measured

experimentally. Results were analyzed by the Langmuir, Freundlich, Dubinin-Radushkevich,

Tempkin and Frumkin equations using linearised correlation coefficient at various

temperatures. The characteristics parameters for each isotherm have been determined. Models

and isotherm constants were evaluated depending on temperature. Langmuir equation was

found to be best fit for the equilibrium data for Cr (VI) adsorption.

Hasan et al. (2008) have studied the removal of Cr (VI) from aqueous solutions using

agricultural waste ‗maize bran‘. The effect of different parameters such as contact time,

adsorbate concentration, pH of the aqueous solution and temperature were investigated.

Maximum metal uptake of Cr (VI) was 312.52 (mg g−1

) at pH of 2.0, initial Cr (VI)

concentration of 200 mg L−1

and temperature of 313 K. Effect of pH showed that maize bran

was not only removing Cr (VI) from aqueous solution but also reducing toxic Cr (VI) into

49

less toxic Cr (III). The adsorption kinetics was tested with first order reversible, pseudo-first

order and pseudo-second order reaction and it was found that Cr (VI) uptake process

followed the pseudo-second order rate expression. Mass transfer of Cr (VI) from bulk to the

solid phase (maize bran) was studied at different temperatures. Different thermodynamic

parameters, change in Gibb‘s free energy (ΔG), change in enthalpy (ΔH) and change in

entropy (ΔS) have also been evaluated and it has been found that the sorption was feasible,

spontaneous and endothermic in nature. The Langmuir and Freundlich equations for

describing sorption equilibrium were applied and it was found that the process was well

described by Langmuir isotherm. Desorption studies was also carried out and found that

complete desorption of Cr (VI) took place at pH of 9.5.

Isa et al. (2008) have studied the removal of chromium (VI) from aqueous solution using

treated oil palm fibre. Adsorption of Cr (VI) by sulphuric acid and heat-treated oil palm fibre

was conducted using batch tests. The influences of pH, contact time, initial chromium

concentration and adsorbent dosage on the removal of Cr (VI) from the solutions were

investigated. The optimum initial pH for maximum uptake of Cr (VI) from aqueous solution

was found to be 1.5. The Cr (VI) removal efficiency was found to correlate with the initial Cr

(VI) concentration, adsorbent dosage as well as the contact time between Cr (VI) and the

adsorbent. The adsorption kinetics tested with pseudo first order and pseudo second order

models yielded high R2 values from 0.9254 to 0.9870 and from 0.9936 to 0.9998,

respectively. The Freundlich isotherm (R2=0.8778) described Cr (VI) adsorption slightly

better than the Langmuir isotherm (R2=0.8715).

Philip et al. (2008) have investigated the removal of hexavalent and trivalent chromium from

aqueous solution by palm flower (Borassus aethiopum). Batch kinetic and equilibrium

experiments were conducted to determine the adsorption kinetic rate constants and maximum

50

adsorption capacities. Both Cr (III) and Cr (VI) adsorption followed a second order kinetics.

For Cr (III) and Cr (VI), maximum adsorption capacity was 6.24 mg/g by raw adsorbent and

1.41 mg/g by acid treated adsorbent. In case of Cr (VI), raw adsorbent exhibited a maximum

adsorption capacity of 4.9 mg/g, whereas the maximum adsorption capacity for acid treated

adsorbent was 7.13 mg/g. There was a significant difference in the concentrations of Cr (VI)

and total chromium removed by palm flower. In case of Cr (VI) adsorption, first it was

reduced to Cr (III) with the help of tannin and then phenolic compounds and subsequently

adsorbed by the biosorbent. Acid treatment significantly increased Cr (VI) adsorption

capacity of biosorbent, whereas alkali treatment reduced the adsorption capacities for Cr (VI).

However, in case of Cr (III), acid treatment significantly reduced the adsorption capacity

whereas the adsorption capacity of alkali treated biosorbent was slightly less than that of raw

adsorbent. Fourier Transform Infra Red (FTIR) spectrum showed the changes in functional

groups during acid treatment and biosorption of Cr (VI) and Cr (III). Column studies were

conducted for Cr (III) to obtain the design parameters required for scale up.

Namasivayam et al. (2008) have studied the removal of chromium (VI) from water and

wastewater using surfactant modified coconut coir pith as a adsorbent. Optimum pH for Cr

(VI) adsorption was found to be 2.0. Reduction of Cr (VI) to Cr (III) occurred to a slight

extent during the removal. Langmuir, Freundlich and Dubinin Radushkevich (D–R)

isotherms were used to model the adsorption equilibrium data and the system followed all the

three isotherms. The metal uptake of adsorbent was found to be 76.3 mg g-1

, which is higher

or comparable to the metal uptake of various adsorbents reported in literature. Kinetic studies

showed that the adsorption obeyed second order and Elovich model. Thermodynamic

parameters such as change in Gibb‘s free energy (ΔG), change in enthalpy (ΔH) and change

in entropy (ΔS) were evaluated, indicating that the overall adsorption process was

51

endothermic and spontaneous. Effects of foreign anions were also examined. The adsorbent

was also tested for the removal of Cr (VI) from electroplating effluent.

Pehlivan et al. (2008) have studied on adsorption of chromium (VI) ion from aqueous

solutions using walnut Shell (WNS), Hazelnut Shell (HNS) and Almond Shell (AS). The

potential to remove Cr (VI) ion from aqueous solutions through adsorption was investigated

in batch experiments. Kinetic experiments revealed that the dilute chromium solutions

reached equilibrium in 100 min. The effect of pH on adsorption of Cr (VI) was studied at

room temperature by varying the pH of metal solution–shell suspension from 2.0 to 9.0. The

metal uptake of the shells was dependent on the pH of the chromium solution, with pH 3.5

being optimal. Adsorption of Cr (VI) ion uptake is in all the cases pH-dependent showing a

maximum at equilibrium pH values between 2.0 and 3.5, depending on the biomaterial, that

correspond to equilibrium pH values of 3.5 for (WNS), 3.5 for (HNS) and 3.2 for (AS). The

adsorption data fit well with the Langmuir isotherm model. The sorption process conformed

to the Langmuir isotherm with maximum Cr (VI) ion sorption capacities of 8.01, 8.28, and

3.40 mg/g for WNS, HNS and AS, respectively. Percentage removal by WNS, HNS and AS

was 85.32, 88.46 and 55.00%, respectively at a concentration of 0.5 mg/L.

Vaghetti et al. (2008) have investigated the removal of chromium (VI) from aqueous

solution using Brazilian-Pine fruit coat named pinon wastes (PW) as adsorbent. The

characteristics of pinon wastes were done by N2 adsorption-desorption isotherms, Fourier

Transform Infra Red spectroscopy, scanning electron microscopy, elemental analysis, mineral

composition determination and fractional group‘s detection. The batch process parameters

such as pH, contact time and adsorbent dosages have been studied. The adsorption kinetics

followed the Elovich chemisorptions kinetic model, obtaining the following initial adsorption

rate, 284.9, 396.9 and 461.5 mg g-1

h-1

using a 500, 700 and 1000 mg/L initial concentrations

52

of Cr (VI) respectively. The maximum adsorption metal uptake of PW was 240 mg/g for Cr

(VI) using Sips isotherm model.

Gopal et al. (2007) have studied the removal of chromium (VI) using low cost adsorbents

derived from groundnut husk. All the experiments were conducted in batch process with

chromium-spiked samples of drinking water. Silver impregnated groundnut husk carbon and

groundnut husk carbon were tested for the removal of chromium (VI). Effects of adsorbent

quantity, pH, and contact time and agitation rate were investigated on removal of chromium.

The adsorption data was fitted well by Freundlich adsorption isotherm. Approximately, 97%

of hexavalent chromium was removed at pH of 3 within 5 h. It was found that adsorbents

chemically modified with an oxidizing agent demonstrated better chromium removal

capabilities as compared to pure adsorbents in terms of their adsorption rate. Based on

present studies, it was concluded that groundnut husk carbon oxidized with silver treatment,

has a higher chromium adsorption capacities.

Basu et al. (2006) have studied the removal of chromium using an adsorbent developed from

waste tamarind hull. Experiments were conducted in batch mode to observe the influence of

different parameters such as initial concentration of metal ions, adsorbent dosage, adsorbent

particle size, stirrer speed, temperature and pH of the solution. Acidic pH strongly favored

the adsorption. With decreasing the pH of the solution from 5.0 to 1.0, the removal of

chromium was enhanced from 33% to 99%. The adsorption process was found to follow a

pseudo-first-order rate mechanism and the rate constant was evaluated at temperature of 303

K. The Freundlich and Redlich-Peterson isotherm fit the equilibrium data satisfactorily.

Adsorption of chromium was found to increase with increase in the process temperature.

Using an adsorbent dosage of 1.0 g/L, and acidic pH of 2, the equilibrium adsorption capacity

of the prepared adsorbent was found to be about 70 mg/g at 303 K, which increased to about

53

81 mg/g at 323 K. The entropy change, free energy change and heats of adsorption were

determined for the process.

Suksabye et al. (2006) have investigated the removal of chromium (VI) from electroplating

wastewater using coir pith. Various parameters such as pH, contact time, adsorbent dosage

and temperature. The maximum removal (99.99%) was obtained at 2% (w/v) dosage, particle

size<75µm at initial Cr (VI) 1647 mg/L, system pH 2 and an equilibrium time of 18 h. The

adsorption isotherm of coir pith fitted reasonably with the Langmuir model. The maximum

Cr (VI) adsorption metal uptakes of coir pith at 288, 303, 318 and 333 K were 138.04,

197.23, 262.89 and 317.65 mg/g respectively. Thermodynamic parameters revealed that the

process was endothermic and temperature dependent. Desorption studies of Cr (VI) on coir

pith and X-ray absorption near edge structure suggested that most of chromium bound on the

coir pith was in the Cr (III) form due to the fact that the toxic Cr (VI) adsorbed on the coir

pith by electrostatic attraction was easily reduced to less toxic Cr (III). Fourier Transform

Infra Red (FTIR) spectroscopy analysis revealed that the carbonyl (C=O) groups and

methoxy (O-CH3) groups from the lignin structure in coir pith may be involved in the

mechanism of chromium adsorption.

Lotfi et al. (2005) have studied the removal of hexavalent chromium from aqueous solutions

onto activated carbons (AC) produced from wood. Two activated carbons are tested, a KOH

activated carbon and a commercial H3PO4

activated carbon (Acticarbone CXV). The

maximum Cr (VI) adsorption is found at pH of 3, and increases with increase in temperature

for both adsorbents. The KOH activated carbon shows higher adsorption capacity for

adsorption of Cr (VI) than Acticarbone. The sorption isotherms fit the Langmuir model

accurately. The adsorption behavior was best described by pseudo second-order rate. The

activation energy and the pre-exponential factor as well as the thermodynamic functions

54

related to adsorption reaction, the change in entropy (ΔS), change in enthalpy (ΔH), change

in entropy (ΔG), were determined. Nevertheless, the global reaction rate controlled by the

intra-particular diffusion of Cr (VI) and the mass diffusivity of Cr (VI) is evaluated.

Singh et al. (2005) have studied the removal of Cr (VI) from wastewater by rice bran as

adsorbent. The maximum removal of Cr (VI) was found to be 99.4% at pH of 2, initial Cr

(VI) concentration of 200 mg/L and temperature of 293 K. The effect of process parameters

such as contact time, initial concentration, pH and temperature were investigated. The

adsorption kinetics was tested for first order reversible, pseudo first order and pseudo second

order and rate constants were calculated. Mass transfer of Cr (VI) from bulk to the solid

phase (rice bran) was studied at various temperatures. Thermodynamic parameters such as

changes in standard free energy, enthalpy and entropy were also evaluated and it was found

that reaction was spontaneous and endothermic in nature. The Langmuir and Freundlich

equations for describing adsorption equilibrium were studied. Desorption studies was also

carried out and found that complete desorption of Cr (VI) took place at pH of 9.5. The

adsorption data was also subjected to multiple regression analysis and a model was developed

to predict the removal of Cr (VI) from wastewater.

Kobya et al. (2004) have carried out batch experimentation for the adsorption Cr (VI) from

aqueous solutions onto hazelnut shell activated carbon by varying the parameters such as pH,

initial Cr (VI) concentration and temperature. The experimental data fitted well to the pseudo

first-order kinetic model and then the rate constants were evaluated. The Langmuir isotherm

provided the best correlation for Cr (VI) onto the activated carbon. Metal uptake was

calculated from the Langmuir isotherm as 170 mg/g at an initial pH of 1.0 for the 1000 mg/L

Cr (VI) solution. Thermodynamic parameters were evaluated and the adsorption is

endothermic showing monolayer adsorption of Cr (VI).

55

Tsuda et al. (2001) have investigated the ability of larch (Larix leptolepis Gold) bark to

remove Cr (VI) from dilute aqueous solutions. The research parameters included the solution

pH, contact time, temperature and initial concentration of Cr (VI) in solution. Of the

parameters studied, the solution pH was found to be the most crucial. The Cr (VI) removal

decreased steadily throughout the pH range studied (pH 2-6), while the Cr (VI) adsorption

peaked at pH of 3. Because the chemical reduction of Cr (VI) to trivalent state occurred to

lesser extent even in strong acidic media, the Cr (VI) removal was mainly governed by

physico-chemical adsorption. The positive value of the heat of adsorption has indicated that it

was endothermic nature of the Cr (VI) adsorption. The relatively slow rate and irreversible

nature of the adsorption as well as the order of the magnitude of the heat of adsorption value

suggested that the adsorption was of a chemical type. The adsorption data obtained from

equilibrium experiments were well fitted to both Langmuir and Freundlich isotherms.

Climino et al. (2000) have investigated the removal of ions such as Cd2+

, Zn2+

, trivalent and

hexavalent chromium from aqueous solutions using hazelnut shell as adsorbent. Batch

equilibrium tests showed that the metal sorption was dependent on both pH and surface

loading. For Cd2+

, Cr3+

and Zn2+

ions, the maximum removal was observed only into a

specific pH range. The metal ion sorption obeyed both the Langmuir and Freundlich

isotherms. Experiments by mixed solutions showed that more Cr3+

ions were removed than

both Cd2+

and Zn2+

ions. The Cr (VI) removal was pH dependent and fitted with the

Langmuir isotherm model. It was proceeding effectively into a short acid pH interval (2.5–

3.5) where both processes of Cr (VI) reduction and Cr (III) sorption are maximized. The

observed sorption data showed similarity with that of other fresh cellulosic materials found in

literature.

The review of literature for adsorption of chromium (VI) using agricultural waste materials is

given in Table 2.6..

56

Table 2.6 Review of literature for the adsorption of chromium (VI) for agricultural materials as adsorbents

Material Metal uptake, mg/g pH Isotherms Kinetics Thermodynamics Reference

Activated carbon

from peanut shell

16.26 2 Langmuir

Freundlich

1st order

2nd

order

Feasible,

Spontaneous,

Endothermic

Ali et al., 2012

Activated carbon

from prawn shell

100 2 Langmuir

Freundlich

2nd

order,

IPD-Model

--- Palvannan et al.,

2012

Lignin 31.6 2 Freundlich 2nd

order --- Albadarin et al.,

2011

Oak wood char

4.93 2 Langmuir,

Freundlich,

RP-Model

Sips model,

Toth model,

Radke Model

2nd

order Endothermic Mohan et al.,

2011

Oak bark char 7.51 2 Langmuir, 2nd

order Endothermic Mohan et al.,

57

Freundlich,

RP-Model

Sips model,

Toth model,

Radke Model

2011

Activated carbon

from syzygium

jambolanum nut

100 2 Freundlich 2nd

order Feasible,

Spontaneous,

Endothermic

Muthukumaran

et al., 2011

Chestnut tannins 42 4 Langmuir 2nd

order --- Tahiri et al.,

2011

Mimosa tannins 38 4 Langmuir 2nd

order --- Tahiri et al.,

2011

Activated carbon

from unwanted

algae residue

155.52 1 Freundlich 2nd

order --- Hong et al., 2010

Pistachio hull 116.3 2 Langmuir 2nd

order --- Moussavi et al.,

58

powder 2010

Saw dust 41.5 1 Langmuir 2nd

order --- Babu et al., 2009

Activated carbon

from cow dung

3.5 1st order Endothermic Das et al., 2009

Boiled Rice husk 8.5 2 Freundlich,

DR-Model

--- --- Garg et al., 2009

Treated Rice husk 10.4 2 Freundlich,

DR-Model

--- ---

Activated carbon

from rice husk

48.31 2 Langmuir,

Freundlich,

DR-Model,

Tempkin

2nd

order --- Garg et al., 2009

Activated carbon

from saw dust

53.48 2 Langmuir,

Freundlich,

DR-Model,

Tempkin

2nd

order --- Garg et al., 2009

59

Boiled Sunflower

stem waste

4.9 2 Langmuir,

Freundlich,

DR-Model

--- --- Garg et al., 2009

Treated Sunflower

stem waste

3.6 2 Langmuir,

Freundlich,

DR-Model

--- ---

Activated tamarind

seeds

29.7 1.4 Langmuir,

Freundlich

Tempkin

DR-Model

RP-Model

2nd

order --- Gupta et al.,

2009

Ocimum

americanum L.Seed

pods

83.33 1.5 Langmuir 2nd

order --- Levankumar et

al., 2009

Activated carbon

from bael fruit shell

17.27 2 Langmuir,

Freundlich

2nd

order --- Mandal et al.,

2009

60

Activated carbon

from tamarind

wood

28.019 1 Langmuir,

Freundlich

2nd

order Feasible,

Spontaneous,

Endothermic

Meikap et al.,

2009

Ground nut hull 30.95 2 Langmuir,

Freundlich

2nd

order Endothermic Qaiser et al.,

2009

Walnut hull 98.13 1 Langmuir,

Freundlich

1st order Feasible,

Spontaneous,

Endothermic

Wang et al.,

2009

Alligator weed 82.57 1 Langmuir ,

Freundlich,

Tempkin

2nd

order

Elovich

Non-spantaneous,

Endothermic

Wang et al.,

2009

Rice straw carbon 96 1 --- --- --- Wang et al.,

2009

Rubber wood saw

dust

4.87 2 -- --- Non-Spontaneous,

Endothermic

Wan et al., 2009

Saw dust 4.5 Langmuir 2nd

order Exothermic Baral et al., 2006

61

Saw dust 20.70 3 Langmuir ,

Freundlich

2nd

order

IPD-Model

Feasible,

Spontaneous,

Endothermic

Bhattacharya et

al., 2008

Neem bark 19.60 2 Langmuir ,

Freundlich

2nd

order

IPD-Model

Feasible,

Spontaneous,

Endothermic

Bhattacharya et

al., 2008

Activated carbon

from almond shell

190.3 1 Langmuir --- Feasible,

Spontaneous,

Endothermic

Demirbas et al.,

2008

Activated carbon

from olive bagasse

109.89 2 Langmuir 2nd

order Feasible,

Spontaneous,

Endothermic

Demiral et al.,

2008

Maize bran 312.52 2 Langmuir 2nd

order Feasible,

Spontaneous,

Endothermic

Hasan et al.,

2008

Treated oil palm 22.73 1.5 Langmuir, 2nd

order --- Isa et al., 2008

62

fibre Freundlich

Surfactant modified

coconut coir pith

76.3 2 Langmuir,

Freundlich

DR.Model

2nd

order,

Elovich

Feasible,

Spontaneous,

Endothermic

Namasivayam et

al., 2008

Wall nut 8.01 3.5 Langmuir,

Freundlich

---- --- Pehlivan et al.,

2008

Hazel nut 8.28 3.5 Langmuir,

Freundlich


2008

Almond shell 3.40 3.2 Langmuir,

Freundlich


2008

Palm flower 4.9 2 Langmuir

Freundlich

RP.Model,

Sips model

2nd

order

--- Philip et al.,

2008

Reed mat 1.662 4.5 Langmuir,

Fruendlich

2n order --- Philip et al.,

2008

63

Water lily 8.44 4.5 Langmuir,

Fruendlich


2008

Mangrove leaves 8.87 4.5 Langmuir,

Fruendlich


2008

Brazilian pine fruit

wastes

240 2 Sips model 2nd

order,

Elovich

--- Vaghetti et al.,

2008

Silver impregnated

groundnut husk

carbon

11.399 3 Freundlich --- ---- Gopal et al.,

2007

Tamarind hull 70 2 Freundlich,

RP-Model

1st order Basu et al., 2006

Coir pith 317.65 2 Langmuir ---- Endothermic Suksabye et al.,

2006

Acid activated

carbon from wood


order,

IPD-Model

Feasible,

Spontaneous,

Endothermic

Lotfi et al., 2005

64

KOH activated

carbon from wood


order,

IPD-Model

Feasible,

Spontaneous,

Endothermic

Lotfi et al., 2005

Activated carbon

from terminalia

arjuna nuts

28.43 1 Langmuir,

Freundlich

1st order --- Mohanty et al.,

2005

Rice bran 312.5 2 Langmuir,

Frendlich

1st order Feasible,

Spontaneous,

Endothermic

Singh et al.,

2005

Activated carbon

from hazelnut

170 1 Langmuir 1st order Endothermic Kobya et al.,

2004

Larch bark ---- 3 Langmuir,

Freundlich

--- Endothermic Tsuda et al.,

2001

Hazelnut shell 17.7 2.5 Langmuir ,

Freundlich

--- --- Climino et al.,

2000

65

2.3.4 Adsorption of chromium (VI) by miscellaneous Adsorbents

Several miscellaneous adsorbents have been used for the adsorption of chromium metal. The

published literatures on the removal of chromium (VI) from aqueous solution by different

miscellaneous adsorbents are summarized in Table 2.7.

66

Table 2.7. Review of literature for adsorption of chromium (VI) for several miscellaneous adsorbents

Material Metal

uptake, mg/g

pH Isotherms Kinetics Thermodynamics Reference

Dolomite 10.01 2 Freundlich 1st order Feasible,

Exothermic

Albadarin et al.,

2012

Iron particles embedded in orange

peel pith

5.37 1 Langmuir,

Freundlich

--- -- Patrica et al., 2011

(3-Mercaptopropyl)Trimethoxysilane

functionalized natural sepiolite

2.68 3 DR-Model --- Feasible,

Spontaneous,

Exothermic

Petrovic et al., 2011

(3-Mercaptopropyl)Trimethoxysilane

functionalized acid activated sepiolite

7.73 2.5 DR-Model --- Spontaneous,

Exothermic

Petrovic et al., 2011

Bamboo charcoal based iron 14.08 2 Langmuir 2nd

order Spontaneous,

Endothermic

Wang et al., 2011

Titanium oxide-Ag composite 25.7 2 Langmuir 2nd

order --- Zhang et al., 2011

Poly aniline-1,8-diaminonaphthalene 150 4 Freundlich 2nd

order --- Zhai et al., 2011

67

Chitosan coated with poly3-methyl

thiophene

127.62 2 Langmuir ,

Tempkin

2nd

order Endothermic Hena et al., 2010

Sulfonated lignite 27.12 2 Langmuir,

Freundlich,

Tempkin

1st order,

2nd

order,

Elovich

Model

Feasible,

Spontaneous,

Endothermic

Zhung et al., 2010

Chitosan 22.09 3 Langmuir 2nd

order Feasible,

Spontaneous,

Exothermic

Aydin et al., 2009

Boehmite 3 Langmuir,

Fruendlich,

DR-Isotherms

1st order Spontaneous,

Endothermic

Granados-Correa,

2009

Puresorbe 76.92 2 Langmuir,

Freundlich,

RP-Model

2nd

order Endothermic Nityanandi et al.,

2009

Multi walled carbon nanotubes 60.975 2.4 Langmuir, -- --- Pillay et al., 2009

68

Freundlich

Radiation grafting silica based

acidic/alkaline

22/30 Langmuir --- Qui et al., 2009

Guar grafting gum

poly(methylacrylate)

29.67 Langmuir

Freundlich

2nd

order Singh et al., 2009

Miscellar compounds 17.89 3.38 Langmuir,

Freundlich

--- Feasible,

Spontaneous,

Exothermic

Sadaoui et al., 2009

Riverbed sand 0.15 2 --- --- Exothermic Sharma et al., 2008

Silica gel coated with aniline

formaldehyde 65

3

Freundlich

2nd

order --- Albino Kumar et

al., 2007

Akaganeite 80 Freundlich,

Frumkin

I-P-

Diffusion

model

Lazaridis et al.,

2005

Mg-Al-CO3 hydrotalcite 120 6 Freundlich 1st order --- Lazaridis et al.,

2003

69

The results of many adsorption studies vary widely because of the different criteria used by

the investigators in searching for suitable materials. Some researchers have used easily

available biomass types, others specially isolated strains, and some processed the raw

biomass to different extents to improve its adsorption properties.

Certain waste products, natural materials and adsorbents have been tested and proposed for

metal removal. It is evident from the discussion so far that each low-cost adsorbent has its

specific physical and chemical characteristics such as porosity, surface area and physical

strength, as well as inherent advantages and disadvantages in wastewater treatment. In

addition, adsorption capacities of various adsorbents also vary, depending on the

experimental conditions. Therefore, comparison of adsorption performance is difficult to

make. However, it is clear from the literature survey that non-conventional adsorbents may

have potential, as they are readily available, inexpensive and effective adsorbents for both

chromium metal ions. They also possess several other advantages that make them excellent

materials for environmental purposes, such as high capacity and rate of adsorption with high

selectivity for different concentrations and rapid kinetics. There is a need to look for viable

non-conventional low-cost adsorbents to meet the growing demand due to the enhanced

quantum of chromium metal ions in the environment, despite the number of published

laboratory data.

In the present work, it is intended to check the suitability of various agricultural waste

materials like custard apple peel powder, ragi husk powder and elephant apple hull powder as

adsorbents to remove chromium (VI) from aqueous solution. It is also proposed to study the

effect of parameters such as agitation time, pH of aqueous solution, initial concentration of

chromium solution, adsorbent dosage, adsorbent size and temperature of the aqueous solution

for the adsorption of chromium (VI). It is also proposed to check the suitability of various

70

adsorption isotherms and to establish the kinetics of adsorption of chromium (VI) on custard

apple peel powder, ragi husk powder and elephant apple hull powder. From the batch

adsorption data, it is also proposed to calculate the thermodynamic parameters like free

energy change, enthalpy change and entropy change to establish spontaneity, non-

spontaneity, exothermic, endothermic, reversible, irreversible nature of adsorption process.

2.4 Response surface methodology

RSM is a collection of mathematical and statistical techniques useful for developing,

improving and optimizing processes and can be used to evaluate the relative significance of

several affecting factors even in the presence of complex interactions.

Before applying the RSM methodology, it is necessary to choose an experimental design that

will define the number of experimental runs that should be carried out in the experimental

feasible region. Several experimental designs exist that reduce the number of experiments.

Thus, if it is desired to detect the significant variables which influence the response, first-

order experimental design like Factorial design, or Plackett-Burman design can be used. On

the other hand, to maximize a response function or to optimize a process, second-order

experimental designs such as three level factorial designs, Box-Behnken Design (BBD) and

Central Composite Design (CCD) can be used.

Basically, this optimization process involves three major steps, which are, performing the

statistically designed experiments, estimating the coefficients in a mathematical model and

predicting the response and checking the adequacy of the model.

).....( ,4,3,2,1 nXXXXXfY

Where Y is the response of the system and Xi is the variables of action called factors. The

goal is to optimize the response variable (Y). It is assumed that the independent variables are

71

continuous and controllable by experiments with negligible errors. It is required to find a

suitable approximation for the true functional relationship between independent variables and

the response surfaces.

The quadratic equation model for predicting the optimal point was expressed in the following

way

k

i

k

i

k

ij

jiij

k

i

iiiii xxxxY1 1 1

1

1

2

0

Where ii represents the coefficients of the quadratic terms and ε is the error.

The optimum values of the selected variables were obtained by solving the regression

equation and by analyzing the response surface contour plots. The variability in dependent

variables was explained by the multiple coefficient of determination, R2 and the model

equation was used to predict the optimum value and subsequently to elucidate the interaction

between the factors within the specified range.

Application of RSM using Box-Behnken design for second order models for optimizing the

operational parameters in separation technology have been discussed by various authors. The

Box-Behnken experimental design in response surface methodology was used for designing

the experiments as well as for full response surface estimation. Recently many statistical

experimental design methods have been employed in chemical process optimization.

Experimental design technique is a very useful tool for this purpose as it provides statistical

models, helps in understanding the interactions among parameters. The experimental data

points were used to obtain the empirical model from Box-Behnken design.

72

Box-Behnken Design (BBD)

Box and Behnken (1960) suggested to select some

of the points from the three-level factorial

arrangement, which allow the efficient estimation of

the first and second-order coefficients of the

mathematical model. These designs in this way are,

more efficient and economical than their

corresponding 3k designs, which require a large

number of experiments when the number of

variables is more than two.

In Box-Behnken designs (Otto, 1999), the experimental points are located on a hyper sphere

equidistant from the central point, as exemplified for a three variable design in Fig. 1.1. Its

principal characteristics are:

(1) The number of experiments required is given by N= 2k (k−1) + cp, where k is the

number of variables and (cp) is the number of the central points;

(2) All variable levels have to be adjusted only at three levels (−1, 0, +1), with equal

intervals between these levels.

In the present work, BBD method is used for obtaining optimum values for the input

variables of chromium (VI) adsorption.

The recent literature review on the removal of chromium (VI) using BBD in respone surface

methodology as tool is given below

Park et al. (2011) have investigated for the removal of Cr (VI) or total Cr using dried leaves

of Pinus densiflora. Analytical results for different chromium species in aqueous and solid

Figure .2.1. BBD for three variables

73

phases indicated that the mechanism of Cr (VI) removal by the biomass was ‗adsorption-

coupled reduction‘. Among various parameters of batch operation, pH, temperature and

contact time significantly affected Cr (VI) removal and total Cr removal individually. As a

result, optimal condition for the Cr (VI) removal was not equal to that for the total Cr

removal. To examine the individual effects of these parameters on the Cr (VI) and total Cr

removals statistically, therefore Box-Behnken model for the experiment design and second

order polynomial model for fitting experimental data were used in this study. The removal

efficiency of Cr (VI) increased with a decrease in pH or with increase of temperature and

contact time until equilibrium was attained. Meanwhile, an optimum pH existed for the total

Cr removal efficiency, but increased with the increases of temperature and contact time. With

60 h of contact time, 100% of Cr (VI) removal and 95% of total Cr removal could be

obtained at pH of 4 and a temperature of 313 K.

Jain et al. (2011) have studied to optimize the removal efficiency for Cr (VI) by applying

response surface methodological approach with the chemically treated Helianthus annuus

flowers (SHC). The surface structure of SHC was analyzed by Scanning Electron Microscopy

(SEM) coupled with energy dispersive X-ray analysis (EDX). Batch mode experiments were

also carried out to assess the adsorption equilibrium in aqueous solution. The adsorption

capacity was found to be 7.2 mg/g. The effect of three parameters, that is pH of the solution

(2.0-7.0), initial concentration (10-70 mg/L) and adsorbent dosage (0.05-0.5 g/100 mL) was

studied for the removal of Cr (VI) by SHC. Box-Behnken model was used as an experimental

design. The optimum pH, adsorbent dosage and initial Cr (VI) concentration were found to be

2.0, 5.0 g/L and 40 mg/L, respectively. Under these conditions, removal efficiency of Cr (VI)

was found to be 90.8%.

74

Bhatti et al. (2011) have investigated to optimize process variables, electrolysis voltage and

treatment time for the electrocoagulation removal of hexavalent chromium. Response surface

methodology (RSM) with central composite design (CCD) was used to achieve energy

efficient removal of Cr (VI). Predictive models using ANOVA and multiple response

optimizations revealed that optimal Cr (VI) removal efficiency occurred at 11 V and 18.6 min

treatment time to give 50% Cr (VI) removal efficiency with a consumption of 15.46KWh/m3

energy. Multiple response optimizations through desirability function saves 32.3% energy

consumption. The models explained 97.5% variability for Cr (VI) reduction efficiency and

99% variability for energy consumption. Artificial neural network (ANN) model was

developed to validate the RSM predictions.

Rajasimman et al. (2010) have studied to optimize the process parameters for the extraction

of chromium from aqueous solution of waste sodium dichromate recovered from the

pharmaceutical industry wastewater using emulsion liquid membrane technique. The liquid

membrane used was composed of kerosene oil as the solvent. SPAN-80 as the surfactant and

potassium hydroxide as internal reagent and trioctylamine as carrier were used. The process

parameter namely, feed concentration; pH, internal reagent concentration and surfactant

concentration on the extraction of chromium were optimized using Box-Behnken design. The

optimum conditions for the extraction of chromium (VI) were: feed concentration (224.04

mg/L), pH (2.76), internal reagent concentration (0.71 N) and surfactant concentration

(1.92% w/w). At the optimized condition, the maximum chromium extraction was found to

be 92.5%.

Aydin et al. (2009) have studied the optimization of Cr (VI) adsorption upon chitosan flakes

against the process parameters pH, adsorbent dosage, and initial Cr (VI) concentration. The

effects of these factors were studied in the ranges 1.5-9.5, 1.8-24.2 g/L and 15-95 mg/L,

75

respectively. A predictive quadratic model was constructed by variance analysis of data

obtained from 20 experimental runs with three replicates each. Maximum removal was

attained from a solution as concentrated as 30 mg/L at pH of 3 with an adsorbent dosage of

13 g/L. The adsorption capacity of chitosan flakes was determined as 22.09 mg/g at these

specified conditions. However, the adsorption capacity was recorded as high as 102 mg/g for

100 mg/L initial concentration. Out of Langmuir, Freundlich, and Dubinin-Radushkevich

isotherm models, adsorption data was best described by Langmuir isotherm with 0.99

consistencies. The process kinetics was evaluated by pseudo-first, pseudo-second order and

intra particle diffusion models. Pseudo-second order kinetic model exhibited the highest

correlation with the experimental data. The results showed that both monolayer adsorption

and intra particle diffusion mechanisms limited the rate of Cr (VI) adsorption.

Thermodynamic parameters revealed the feasibility, spontaneity, and exothermic nature of

adsorption.

Kumar et al. (2009) have studied to design the experiments by Box-Behnken design matrix

and response surface methodology to evaluate the interactive effects of three most important

operating variables: pH of (2.0-6.0), temperature of (298-313 K) and initial concentration of

metal ions (10.0-60.0 mg/L) on biosorption of Cr (VI), Ni (II) and Zn (II) ions with

immobilized bacterial strain Bacillus brevis. The total 17 experiments were conducted in the

present study towards the construction of a quadratic model. Independent variables have

significant value 0.0001, which indicates the importance of these variables in the biosorption

process. If values of P less than 0.05, it indicate that model terms are significant for the

biosorption of Cr (VI), Ni (II) and Zn (II) ions. The regression equation coefficients were

calculated and the data fitted to a second-order polynomial equation for removal of Cr (VI),

Ni (II) and Zn (II) ions with immobilized bacterial strains B. brevis.

76

Tarangani et al. (2009) have studied to investigate the effects of different operating

conditions on the removal of hexavalent chromium on to mixed cultures of Pseudomonas

aeruginosa and Bacillus subtilis using biosorption process by response surface methodology

(RSM). Box-Behnken Design (BBD) was used for optimization of biosorption process and to

evaluate the effects and interactions of the process variables, i.e., biomass concentration, pH,

temperature and contact time on the removal of Cr (VI). A synthetic aqueous solution with a

Cr (VI) concentration of 10 mg/L was used in the experimental study as a fixed input

variable. The optimum conditions for maximum uptake (1.44 mg/g) of Cr (VI) onto the

biosorbent were established as 0.5 g/L biosorbent dosage, pH of 2 for the aqueous solution,

305 K temperature and 23 min of contact time.

Bhatti et al. (2009) have studied for the removal of Cr (VI) from 100 mg/L solution using

Al-Al electrodes with an effective surface area of 100 cm2, and placed 15 mm apart. The

interaction between voltage × time, and amperage × time best explained the Cr (VI) reduction

efficiency with the coefficient of determination (R2) being 0.8873 and 0.9270 respectively.

Similarly, the square root of energy consumption in Cr (VI) reduction had a linear correlation

with voltage × time (R2=0.8949), whereas, amperage × time better explained energy

consumption (R2=0.9400). Response surface methodology was used for the optimization of

process variables (pH, voltage and treatment time), response modeling and predictions.

Maximum Cr (VI) reduction efficiency of 90.4% was achieved at pH of 5, 24 V and 24 min

treatment time, and the treatment consumed 137.2 KWh/m3 of electrical energy. Multiple

energy consumption showed 49.6% Cr (VI) removal at pH of 5, 12.8 V and 24 min treatment

time. The response models developed explained 95.2% variability for Cr (VI) reduction

efficiency and 99.4% variability for energy consumption. Results of prediction models were

validated through laboratory scale batch experiments.

77

Rajasimman et al. (2009) have studied to optimize the process parameters for the extraction

of chromium from aqueous solution of waste sodium dichromate recovered from the

pharmaceutical industry wastewater using emulsion liquid membrane technique. The liquid

membrane used was composed of kerosene oil as the solvent, Span-80 as the surfactant and

potassium hydroxide as internal reagent and aliquat as carrier. The process parameters,

namely feed concentration, pH, internal reagent concentration and surfactant concentration on

the extraction of chromium were optimized using Box-Behnken design. The optimum

conditions for the extraction of chromium were feed concentration (223.55 mg/L), pH (2.73),

internal reagent concentration (0.72 N) and surfactant concentration (1.91% w/w). At the

optimized condition, the maximum chromium extraction was found to be 97.57%.

Kaushik et al. (2007) have investigated the removal of chromium from aqueous solution by

potential use of alginate immobilized algal beads under optimized conditions using a novel

Cynobacterium, Lyngbya putealis isolated from metal contaminated soil. Batch mode

experiments were performed to determine the adsorption equilibrium and kinetic behavior of

chromium in aqueous solution allowing the computation of kinetic parameters and maximum

metal adsorption capacity. Influences of other parameters like initial metal ion concentration

(10-100 mg/L), pH (2-6) and temperature (298-318 K) on chromium adsorption were also

examined using Box-Behnken design. Very high regression coefficient between the variables

and the response (R2=0.9984) indicates excellent evaluation of experimental data by second

order polynomial regression model. The response surface method indicated that 50-60 mg/L

initial chromium concentration, 2-3 pH and a temperature of 318 K were optimal for

biosorption of chromium by immobilized L.putealis, when 82% of the metal is removed from

the solution.

78

Aksu et al. (2006) have studied for binary biosorption of phenol and chromium (VI) onto

immobilized activated sludge in a continuous packed bed column. The phenol and chromium

(VI) binding capacity of biosorbent was shown as a function of single and dual pollutant

concentrations at a flow rate of 0.8 ml/min and at a pH value of 1.0. The equilibrium uptake

of each pollutant was determined by evaluating the breakthrough curves obtained at different

inlet concentrations changing 50-500 mg/L in single and binary systems. The maximum

column biosorption capacity of dried activated sludge was 9.0 mg/g for phenol and 18.5 mg/g

for chromium (VI) at single ion situation. The column sorption capacity of immobilized dried

activated sludge for phenol (or for chromium (VI)) decreased notably due to the presence of

other component. The mono and multi-component sorption‘s in packed bed were expressed

by the Yoon and Nelson model to determine the kinetic constants and to predict the

breakthrough curves of each component. The functional relationship between Yoon and

Nelson kinetic constant of each component and concentrations of phenol and chromium (VI)

in binary mixture was determined by using Response surface methodology.

Quintelas et al. (2006) have studied for the removal of chromium and organic compounds

(chlorophenol, phenol, and o-cresol) from aqueous solution by a bacterial biofilm supported

on granulated activated carbon. The compounds were studied in single solutes and in

different combinations between them and Cr (VI). Optimum Cr (VI) adsorption was observed

at a phenol concentration of 100 mg/L and at an initial concentration of the metal of 60 mg/L.

The maximum values of biosorption of organic compounds were 9.94 mg/g for phenol, 9.70

mg/g for chlorophenol and 13.99 mg/g for o-cresol. In terms of removal percentage, after 15

h of experiment, the affinity order was as follows: phenol>chlorophenol>o-cresol>chromium

(VI).

79

Nagarajan et al. (2005) have studied the removal of chromium from aqueous solutions by

treatment with carbon aerogel electrodes using response surface methodology. In this, an

electrochemical process using carbon aerogel electrodes was developed to treat chromium-

contaminated waters. The operational conditions viz. pH (2-7), initial metal ion concentration

(2-8 mg/L) and charge (0.3-1.3 Ah) were optimized to achieve maximum removal efficiency.

The dimensions of the cell and electrode area were 1.8 dm (length) × 0.75 dm (breadth) ×

0.95 dm (height) and 0.54 dm2 respectively. In the experiments, chromium concentration

dropped from 2 mg/L to 0.008 mg/L (99.6% removal) under optimized conditions of pH 2

and 0.8 A h. To optimize the flow rate, experiments were carried out at different flow rates

(60-600 L/h) in the electrochemical reactor. Batch experiments were conducted by response

surface methodology using Box-Behnken design, which can be used to optimize the key

parameters for maximizing the removal percentage. An R2 value of 0.9736 was obtained from

the regression analysis of the performed experiments, which exhibited a close fit between the

experimental results and model predictions.

It is noted that Box-Behnken design is more economical and efficient. This experimental

design has been applied for the optimization of several chemical and physical processes;

however, its application in separation technology is still much smaller in comparison with

central composite design and full factorial design. Some of the applications of the Box–

Behnken design in separation technology are given in Table 2.8

80

Table 2.8. Some applications of Box-Behnken Design in separation technology.

Metal Separation

technique/Adsorben

t

Objective of the study References

Cr(VI) Electro coagulation To optimize the various process parameters such as pH, temperature and

contact time for the maximum removal of chromium (VI)

Bhatti et al. 2011

Cr(VI) Treated Helianthus

annuus

To study the effect of pH, initial concentration and dosage on chromium

adsorption.

Jain et al., 2011.

Cr(VI) Pinus densiflora To optimize the process variable such as electrolysis voltage, treatment time

and energy consumption for the maximum removal of Cr (VI)

Park et al. 2011.

Cr(VI) Liquid membrane To optimize the process parameters such as feed concentration, pH, internal

reagent concentration and surfactant concentration for optimum removal of

Cr (VI).

Rajasimman et al.

2010

Pb(II)

Cd(II)

Cu(II)

Trichoderma viride To optimize the various environmental conditions like initial metal ion

concentration, temperature, biosorbent loading and pH.for biosorption of Pb

(II), Cd(II) and Cu(II).

Singh et al., 2010.

81

Cr(VI) Chitosan To optimize the process parameters such as pH, adsorbent dosage and initial

concentration of Cr (VI)

Aydin et al. 2009.

Cu(II) Ascophyllum

nodosum

To evaluate the effects of temperature, pH and initial concentration in the

Cu(II) sorption process on the adsorption

Freital et al., 2009.

Cadmium(II)

Cynobacterium

Synechocystis

pevalekii

To study the optimization of the parameters of pH, biomass and metal

concentration for cadmium removal.

Khattar et al., 2009.

Cr(VI)

Ni(II)

Zn(II)

Bacillus brevis Sp. To evaluate the interactive effects of three most important parameters pH,

temperature and metal ion concentration.

Kumar et al., 2009.

Cr(VI) Mixed culture of

Pseudomonas

aeruginosa & Bacillus

subtillis

To evaluate the effects and interactions of the process variables, biomass

concentration, pH, temperature and contact time.

Tarangini et al.,

2009.

Zn(II) Magnetic

nanoparticle

To optimize three variables pH of solution, amount of extract and amount of

nanoparticles for extraction of zinc samples

Khajeh et al., 2009.

82

Pb(II) Pistacia Vera L To study the influence the three parameters, initial concentration, pH and

contact time for the maximum removal of Pb (II) from aqueous solution.

Yetilmezsoy et al.,

2009.

Cr(VI) Cynobacterium and

Lyngbya putealis

To optimize the process parameters such as initial metal ion concentration,

pH and temperature for the maximum removal of chromium (VI)

Kaushik et al., 2007.

Cr(VI) Activated sludge To optimize the process parameters such as pH, inlet concentration and flow

rate for the maximum removal of chromium (VI).

Aksu et al., 2006

Cadmium(II) Carbon aerogel To study the optimimum pH, adsorbent dosage and temperature for cadmium

removal.

Goel et al., 2006.

Pb(II) Carbon aerogel To study the influence of three parameters, adsorbent concentration, pH and

temperature on the percentage removal of Pb (II).

Goel et al., 2005.

Cr(VI) Carbon aerogel To achieve maximum removal efficiency, pH, initial metal ion concentration

and charge were optimized.

Madaria et al. 2005.

Cadmium(II) HCl solution To study the effect of sampling flow rate, reagent concentration, pH and

buffer concentration on biosorption of Cd(II)

Souza et al., 2005.

Mercury Carbon aerogel To study the influence of three parameters i.e. initial concentration, pH and

charge on the percentage removal of mercury.

Madaria et al., 2004.

83

2.5 Artificial neural network and genetic algorithm

Artificial neural network

Artificial Neural Networks (ANN) commonly referred to as ―neural networks‖ have been

motivated by the recognition of the fact that the brain computes in an entirely different way

from the conventional digital computer. The brain is a highly complex, nonlinear and parallel

computer (information processing system). At the most basic level, neurons are considered to

be the structural constituents of the brain. The brain has the capability of organizing neurons

so as to perform certain computations many times faster than the fastest digital computer in

existence today.

The main unit of any ANN is an artificial neuron, which has two parts, one is the summing

part and the other is multiplied with a weight parameter and all weighted inputs are summed

up. The output of summed value is passed on to the second part, which is the logic part. This

logic part is a non-linear function and the power of the ANN lies in this logic part. The

intelligent way in which the neurons are prearranged and in which the interconnections are

made, results in better problem-solving capability (Turan et al., 2011). Once the network had

been trained for the satisfactory error goal, the weights and biases were fixed for model. At

this stage, the model had been trained and ready for validation using the unseen dataset, i.e.,

the test data that had not been used for training was used for this purpose. The simulated

network was used to recall the learned pattern on the test data. For this purpose, curve fitted

data of the input variables were used (Turan et al., 2011).

The most widely used neural network is backpropagation (BP). BP attempts to minimize

error by adjusting each value of a network proportional to the derivative of the error with

respect to that value. This is called the gradient descent. In the BP learning, the actual outputs

84

are compared with the target values to derive the error signals, which are propagated

backward, layer by layer, for the updating of the synaptic weights in all the lower layers

(Turan et al., 2011). ANN is known for their superior ability to learn and classify data.

Application of artificial neural network (ANN) in separation technology for neural network

model for the removal heavy metals from aqueous solutions/ industrial wastewater have been

discussed by various investigators. The ANN model was developed using some experimental

data points for training and remaining data points for testing by a single layer feed forward

backpropagation network/multilayer feed forward neural network. Different types of ANN

architecture have been tested by varying the neuron number of entrance and the hidden

layers, resulting into an excellent agreement between the experimental data and the predicted

values.

The recent literature review on the removal of chromium (VI) using ANN model as tool is

given below

Bingol et al. (2012) have employed Response Surface Methodology (RSM) and Artificial

Neural Network (ANN) to develop an approach for the evaluation of heavy metal biosorption

process. A batch sorption process was performed using Nigella sativa seeds (black cumin), a

novel and natural biosorbent, to remove lead ions from aqueous solutions. The effects of

process variables which are pH, biosorbent mass, and temperature on the sorbed amount of

lead were investigated through two-levels, three factors central composite design (CCD).

Same design was also utilized to obtain a training set for ANN. The results of two

methodologies were compared for their predictive capabilities in terms of the coefficient of

determination-R2 and root mean square error-RMSE based on the validation data set. The

results showed that the ANN model is much more accurate in prediction as compared to

CCD.

85

Abhishek et al. (2011) have used the various low cost sorbents for removal of toxic metals

from aqueous solution for the treatment of Cr (III) and Cr (VI) containing wastewater using

agricultural wastes. Artificial neural network (ANN) was applied to sorption batch studies to

develop and validate a model that can predict Cr (III) and Cr (VI) removal efficiency. Earlier

investigations correlated the experimental data with available models or some modified

empirical equations but these results were unable to predict the values of parameters from a

single equation. ANN is effective in modeling and simulation of highly non linear

multivariable relationships. A well-designed network can converge even on multiple numbers

of variables at a time without any complex modeling and empirical calculations. The

prediction of removal of Cr (III) and Cr (VI) from wastewater has been made using variables

of metal concentration, biomass dosage, contact time and initial volume. Different types of

ANN architecture have been tested by varying the neuron number of entrance and the hidden

layers, resulting into an excellent agreement between the experimental data and the predicted

values. The data of one hundred eighty laboratory experimental sets were used for structuring

single layer ANN model. Series of experiments resulted into the performance evaluation

based on considering 20% data for testing and 20% data for cross validation at 3000 epoch

with 0.7 momentums. The Levenberg-Marquardt algorithm (LMA) was applied giving a

minimum mean squared error (MSE) for training and cross validation.

Tomczak et al. (2011) have studied multicomponent sorption equilibria calculation, with

application of artificial neural network (ANN) and identification of adsorption dynamics

model using evolutionary algorithm (EA). Batch experiments have been carried out to

estimate sorptivity of a new form of a chitosan foamed structure and its selectivity towards

Cu (II), Zn (II) and Cr (VI) ions. In the case of single ions, it was found that experimental

data were well described by the Langmuir-Freundlich equation. The application of a neural

multilayer perceptron (MLP) network was proposed in the case of multicomponent mixture.

86

The universal mathematical model for adsorption in packed column includes mass balances

for fluid and adsorbent as well as adsorption kinetics were proposed. The leads to a system of

two partial differential equations. Additionally, the distance and time are composed in one

relevantly defined variable. The proposed transformations convert the system of partial

differential equation to a system of ordinary equations, which enables analytical solution of

equations system. Also, calculation of a concentration distribution within the solution and

adsorbent, dependent on the distance from inlet and process duration is achieved. The data

obtained in the measurements for Cu (II), Ni (II) and Zn (II) ions were then compared with

those obtained from the model using EA for identification of model coefficients.

Aber et al. (2009) have studied the removal of Cr (VI) from synthetic and real wastewater

using electrocoagulation (EC) process. The influence of anode material, initial Cr (VI)

concentration, initial pH of solution, type of electrolyte, current density and time of

electrolysis was investigated. During 30 min of electrocoagulation, maximum removal

efficiencies achieved by Al and Fe anodes were 15 and 98 % respectively. High removal

efficiency was achieved over pH range of 5-8. NaCl, Na2SO4 and NaNO3 were used as

supporting electrolyte during the electrolysis. NaCl was more effective than Na2SO4 and

NaNO3 in removal of hexavalent chromium. Also in this work, a real electroplating

wastewater containing 17.1 mg/L Cr (VI) was treated successfully using EC process.

Artificial neural network (ANN) was utilized for modeling of experimental results. The

model was developed using a 3-layer feed forward backpropagation network with 4, 10 and 1

neurons in first, second and third layers, respectively. A comparison between the model

results and experimental data gave high correlation coefficient (R2=0.976) shows that the

model is able to predict the concentration of residual Cr (VI) in the solution.

87

Park et al. (2006) have investigated the potential use of the brown seaweed, Ecklonia,

biomass as a bioreductant for reducing Cr (VI) in a continuous packed bed column. The

effects of operating parameters, such as influent Cr (VI) concentration, influent pH, biomass

concentration, flow rate and temperature, on the Cr (VI) reduction were investigated. Increase

in the influent Cr (VI) concentration and flow rate or a decrease in biomass concentration

inside the column led to a higher breakthrough of the Cr (VI) ions in the effluent.

Particularly, the influent pH and temperature most significantly affected on the breakthrough

curve of Cr (VI); a decrease in the influent pH or an increase in the temperature enhanced the

Cr (VI) reduction in the column. For process application, a non-parametric model using

neural network was used to predict the breakthrough curves of the column.

ANN is an effective tool in modeling and simulation of highly non-linear multivariable

relationships. A well-designed network can converge on multiple numbers of variables at a

time without any complex modeling and empirical calculations. The neural network proves to

be a very promising method in comparison with response surface methodology for analysis of

the conventional biosorption of heavy metals. Nevertheless, in comparison with artificial

neural network models, response surface methodology requires very less experimental data

points. These mathematical models are found to be reliable and predictive tools with an

excellent accuracy. The ANN applications for the removal of heavy metals in separation

technology are listed in Table 2.9.

In the present investigations, it is proposed to develop the ANN models and the RSM models

(BBD techniques) using the batch experimental data of three low cost adsorbents: RHP,

CAPP and EAHP. The output of the model is percentage removal of chromium (VI) and

input parameters are adsorbent dosage, pH and initial concentration of Cr (VI). The ANN

model/RSM model can predict the percentage removal of chromium by adsorption even if the

88

new set of input conditions other than the experiments conducted is given. The optimal

conditions for the removal of chromium can be obtained from ANN model coupled with

genetic algorithm and from RSM model. Genetic algorithms are the directed random search

techniques used to look for parameters that provide a good solution to a global optimization

problem. The genetic algorithm can be applied to solve a variety of optimization problems

that are not well suited for standard optimization algorithms, including problems in which the

objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear.

89

Table 2.9. ANN applications for the removal of heavy metals in separation technology.

Type of

metal

Adsorbent Objective of the work ANN Type References

Pb(II) Black cumin To develop an approach for the evaluation of lead biosorption in batch

process using black cumin as adsorbent

FFNN-MLP-SL-BP Bingol et al.,

2012

Pb(II) Red mud To develop the prediction models for lead removal from industrial

sludge leachate.

FFNN-single layer-

SL-BP-LMT

Geyikci et al.,

2012

Cr(VI)

Cr(III)

Zea mays To develop and validate a ANN model that can predict Cr (III) and Cr

(VI) removal efficiency in batch sorption process

FFNN- single layer

SL-BP-LMTe

Abhishek et

al., 2011

Gallium-

67

Waste acorns

quercus

ithaburensis

To estimate the adsorption efficiency against temperature, adsorbent

dosage and processing time.

FFNN-MLP-SL-BP Eroglu et al.,

2011

Zn(II) Miscellar-

enhanced

ultrafiltration

To study the removal of zinc ions from waste water efficiently. FFNN-Single layer-

SL-BP-LMT

Rahmanian et

al., 2011

90

Cu(II),

Zn(II)

Cr(VI)

Chitosan To estimate sorptivity of new form of a chitosan foamed structure and

its selectivity towards Cu (II), Zn (II) and Cr (VI) ions in the packed

bed column using ANN and evolutionary algorithm

FFNN-MLP-SL-BP Tomczak,

2011

Zn(II) Hazelnut shell To predict the percentage adsorption efficiency using ANN model FFNN-MLP-SL-BP-

LMT

Turan et al.,

2011

Cd(II) Shelled

moringa

oleifera seed

(SMOS)

To predict the removal efficiency of Cd (II) ions from aqueous

solution using SMOS powder by a two layer artificial neural network

model.

FFNN-MLP-SL-BP-

LMT

Abhishek et

al., 2010

Cd(II) Saraca indica

leaf powder

(SILF)

To predict the removal efficiency of Cd(II) ions from aqueous solution

using SILP powder by a single layer ANN model

FFNN-Single layer-

SL-BP-LMT

Srivastava et

al., 2010

Cu(II) Sunflower

shells

To study the effects of inlet concentration of Cu (II), feed flow rate,

bed height, and solution pH and particle size on break through

characteristics of adsorption systems.

FFNN-single layer-

SL-BP

Oguz et al.

2010

Ni(II) Shelled To predict the removal of efficiency of Ni (II) ions from aqueous FNN-Single layer- Raj et al.,

91

moringa

oleifera seed

(SMOS)

solution using SMOS powder by a single layer ANN model. SL-BP-LMT 2010

Cr(VI) Electrocoagul

ation

To study the removal of Cr (VI) from synthetic & real waste water

using electrocoagulation using ANN modeling

FFNN-MLP-SL-BP Aber et al.,

2009

Cu(II) Saw dust To apply ANN based on multilayered partial recurrent back-

propagation algorithm for the prediction of percentage adsorption

efficiency for the Cu (II) ion from aqueous solution by saw dust.

RNf-EN

g-SL-BP Prakash et al.,

2008

Pb(II) Electrodialysi

s

To predict separation of Pb2+

ions as a function of concentration,

temperature, flow rate and voltage.

FFNN-MLP-SL-BP-

LMT

Sadrzadeh et

al., 2008

Pb(II) Antep

pistachio

shells

To predict the removal efficiency of Pb (II) ions from aqueous

solution by Antep pistachio shells in a batch study using a three layer

artificial neural network model.

FFNN-MLP-SL-BP-

LMT

Yetilmezsoy

et al. 2008

Cd(II)

Zn(II)

Sargassum

filipendula

To study the use of ANN model to fit the equilibrium experimental

data.

Simplex

optimization-method

Klen et al.,

2007

Cr(VI) Brown To study the reduction of Cr (VI) by the brown seaweed, Ecklonia FFNNa-MLP

b+-SL

c- Park et al.,

92

seaweed,

Ecklonia

biomass

biomass as a bioreductant in a continuous packed bed column. For

process application, a non-parametric model using neural network was

used to predict the breakthrough curves of the column.

BPd 2006

Pb(II),

Cu(II)

Cd(II)

Pseudomonas

aeruginosa

To predict the lead, copper and cadmium from the contaminated water

with the fixed bed columns packed with calcium alginate (CA)-

immobilized biomass of Pseudomonas aeruginosa PU21.

FFNN-MLP-SL-BP Huang et al.,

1998

Pb(II) Granular

activated

carbon

To develop a virtual adsorber system based ANN technology from 67

bench scale experiments to optimize the granular activated carbon.

FFNN-MLP-SL-BP Carriere., et

al., 1996

a Feed forward neural network,

b Multilayer perceptron,

c Supervised learning,

d Back propagation,

e Levenberg-Marquardt technique,

f Recurrent network,

g Elman network

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