introduction - shodhganga : a reservoir of indian...

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CHAPTER 1

INTRODUCTION

HEAVY METALS: ENVIRONMENTAL THREAT

The pollution of the aquatic environment with heavy metals has become a worldwide

problem during recent years, because they are indestructible and most of them have

toxic effects on organisms (Sen et al., 2011). The term heavy metal is usually referred

to elements having densities greater than 5.0 g/cm-3

commonly adopted as a group

name for the metal and metalloids which are associated with pollution and toxicity

(Lentech, 2004). They are present in trace levels in the earth’s crust, anthropogenic

activities such as industrial processing and use of metals, alloys and metallic

compounds largely adding up their natural background levels and finally find their way

into the human system by two prominent ways. First, through inhalation and

consumption of water, secondly indirect uptake of toxic metals through consumption of

food, ultimately depending on water components of the ecosystem.

Some heavy metals are of great concern because of their toxic effects on plants and

animals and their increasing abundance in the environment. The bio toxic effects refer

to the harmful effects of heavy metals to the body when consumed above the

recommended limits. Although individual metals exhibit specific signs of their toxicity,

the following have been reported as general signs associated with cadmium, lead,

arsenic, mercury, zinc, copper and aluminium poisoning: gastrointestinal (GI)

disorders, diarrhoea, stomatitis, tremor, hemoglobinuria causing a rust–red colour to

stool, ataxia, paralysis, vomiting and convulsion, depression, and pneumonia when

volatile vapours and fumes are inhaled. The nature of effects could be toxic (acute,

chronic or sub-chronic), neurotoxic, carcinogenic, mutagenic or teratogenic (Duruibe et

al., 2007). To avoid health hazards it is essential to remove these toxic heavy metals

from waste water before its disposal.

‘HEAVY METAL POLLUTION: A PRIZE TAG OF MODERN SOCIETY’

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HEAVY METALS: AN OVERVIEW

Abundant resource of water is available in our country. Unfortunately, rapid

industrialization, fast growth in population and non-judicious use of natural resources

has resulted into many fold increase in water pollution problem. Most of the 14 major

rivers of India are victims of heavy metal water pollution; Ganga and Yamuna ranking

top among them (Singh, 2001; Jain and Sharma, 2001; Kaushik et al., 2003; Sharma,

2003 and Kumar et al., 2005, 2009). Heavy metals (lead, Cadmium, Chromium and

Nickel) being important from local environmental point of view are considered for the

study. Heavy metals have been reviewed thoroughly in the voluminous manner in the

literature. Therefore, important features of target metals like their physical and

chemical properties, environmental sources, environmental concentrations and

toxicity along with permissible limit has been presented in a concise manner.

Figure 1.1: Heavy Metals

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LEAD

Lead is a main-group element in the carbon group with the symbol Pb (from Latin:

plumbum) and atomic number 82. Lead is a soft, malleable poor metal. Lead accounts

for most of the cases of paediatric heavy metal poisoning. It is a very soft metal and

was used in pipes, drains, and soldering materials for many years. Millions of homes

built before 1940 still contain lead (e.g., in painted surfaces), leading to chronic

exposure from weathering, flaking, chalking, and dust. Every year, industry produces

about 2.5 million tons of lead throughout the world. Most of this lead is used for

batteries. The remainder is used for cable coverings, plumbing, ammunition, and fuel

additives. Other uses are as paint pigments and in PVC plastics, X-ray shielding, crystal

glass production, and pesticides. Target organs are the bones, brain, blood, kidneys, and

thyroid gland.

General Properties

Symbol Pb

Atomic number 82

Group, period, block 14, 6, p

Electronic configuration [Xe] 4f14

5d10

6s2 6p

2

Appearance Metallic grey

Atomic Properties

Crystal structure Face-cantered cubic

Electro negativity 2.33

Atomic radius 0.175 nm

Vander Waal radius 0.202 nm

Covalent radius 0.146 nm

Physical Properties

Characteristics Soft, malleable & poor

metal

Phase Solid

Density 11.34 g cm-3

Chemical Properties

Atomic mass 207.19 g/mol-1

Melting Point 327.460C

Boiling Point 17490C

Electrical resistivity (20 °C) 208 nΩ·m

Lead has been ranked as 2nd in the Environmental Protection

Agency “Top Hazardous Substance Priority List”.

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L

E

A

D

Environmental Sources Natural

Weathering,

Flaking,

Chalking, and dust.

Anthropogenic Pb batteries Paint Pigments Coatings Pesticides PVC Plastics

Environmental Concentrations

Air

(0.10- 0.30 µg/m3)

Water

(5 µg/L)

Soil

(300 µg/m3 – 1.1 mg/Kg)

Government Standards and Guidelines

The Environmental Protection Agency (EPA) allows 15 parts of cadmium per billion parts of drinking water (5 ppb).

The World Health Organization (WHO) limits 10 parts of cadmium per million parts of food color (15 ppm). The Occupational Safety and Health Administration (OSHA) limits workplace air to 50 micrograms (µg) cadmium per cubic meter (50 µg/m3). .

Health Effects

· Birth defects,

· Mental retardation,

· Autism,

· Psychosis,

· Allergies,

· Dyslexia,

· Hyperactivity,

· Weight loss,

· Shaky hands,

· Muscular weakness,

· Paralysis (beginning in the

forearms)

Source: Agency for Toxic Substances and Disease Registry (ATSDR), 2011

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CADMIUM

Cadmium was discovered in Germany in 1817 by Friedric Strohmeyer. Cadmium is

odorless and tasteless and chemical analysis is most often required to detect its

presence. Remarkable characteristics of cadmium involve its great resistance to

corrosion, low melting point and excellent electrical conductivity because of which it

plays a critical role in several cutting edge technologies such as solar cells. Cadmium is

one of the few elements that have no constructive purpose in the human body.

General Properties

Symbol Cd

Atomic number 48

Group, period, block 12,5,d

Electronic configuration [Kr] 4d10

5s2

Appearance Silvery grey metallic

Atomic Properties

Crystal structure Hexagonal



Ionic radius 0.097 nm


Physical Properties

Characteristics Malleable and ductile

Phase Solid

Density 8.65 g cm-3

Chemical Properties


Melting Point 3210C

Boiling Point 7670C

Standard Potential -0.402 V

Cadmium has been ranked as 7th in the Environmental

Protection Agency “Top Hazardous Substance Priority List”.

27

Itai-Itai disease


C

A

D

M

I

U

M

Environmental Sources Natural

Weathering of rocks Volcanic eruptions Fall out

Anthropogenic Ni-Cd batteries Pigments Coatings Stabilizers Fertilizers


Air

Rural area : 0.1-5 ng/m3 Urban area : 2-15 ng/m3 Industrial area : 15-50 ng/m3

Water Sea water : ~0.1 mg/L Fresh water : 1-10 mg/L Industrial water : 10-1000 mg/L

Soil : 3 - 750 ppm


The Environmental Protection Agency (EPA) allows 5 parts of cadmium per billion parts of drinking water (5 ppb).

The Food and Drug Administration (FDA) limits 15 parts of cadmium per million parts of food color (15 ppm). The Occupational Safety and Health Administration (OSHA) limits workplace air to 100 micrograms (µg) cadmium per cubic meter (100 µg/m3). .

Health Effects

· Itai-Itai

· Muscle cramps

· Fragile bones

· Lungs and Kidney malfunctioning

· Liver injury

· Sensory disturbance

· Carcinogenicity

· Mutagenicity

· Affect cardiovascular system

· High blood pressure

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CHROMIUM

Chromium was discovered by French chemist Nicholas Louis Vanquelin in 1797. The

most common oxidation states of Chromium are +2, +3 and +6 with +3 being the most

stable. The oxidation states +4 and +5 are relatively rare. Chromium compounds of +6

oxidation states are powerful oxidizing agents. Chrome metal (Chromium 0) is the

element that makes steel “stainless”.

General Properties

Symbol Cr

Atomic number 24


Electronic configuration [Ar] 3d4 4s

2

Appearance Silvery metallic

Atomic properties

Crystal structure cubic body centered


Atomic radius .166 nm



Physical properties

Characteristics Lustrous and brittle

Phase Solid

Density 7.19 g cm-3

Chemical properties


Melting Point 19070C



Chromium has been ranked as 17th in the Environmental


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C

H

R

O

M

I

U

M

Environmental Sources

Natural Weathering of rocks Volcanic eruptions Fall out, Meteorites

Anthropogenic

Chrome plating Dyes and paints As a catalyst Leather tanning Glasswares Magnetic tapes


Air Electrolysis sector: 500 µg/m3 Urban area : 120 µg/m3 Industrial area : 52 µg/m3

Water

Waste water : 5 mg/L Cr (III) : 0.05 mg/L Cr (VI) Drinking water : 100 µg/L

Soils : 10-1000 ppm


Environmental Protection Agency (EPA) has set a limit of 100 µg chromium (III) and chromium (VI) per liter of drinking water (100 µg/L). The Occupational Safety and Health Administration (OSHA) has set limits of 500 µg water soluble chromium(III) compounds per cubic meter of workplace air (500 µg/m³)

Environmental Toxicity · Carcinogenic in nature

· Kidney and Liver damage

· Lung Cancer

· Respiratory Problems

· Ulcers in nasal septum

· Redness and swelling of skin

· Convulsions

· Skin ulcers

· Nose irritation


Lung Cancer

Cancer

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NICKEL

Nickel is world 24th

most abundant transition metal. The element was discovered

unintentionally in 1751 by Baron Axel Frederick Cronstedt, who extracted it from a

mineral called Niccolite. Nickel can be combined with other elements such as iron,

copper, chromium and zinc to form alloys. These alloys are used to make coins,

jewellery and items such as valves and heat exchangers. Many nickel compounds

dissolve fairly easy in water and have a green colour. The most important oxidation

state of Nickel is +2.

General Properties

Symbol Ni

Atomic number 28


Electronic configuration [Ar] 3d8 4s

2

Appearance silvery metallic

Atomic properties

Crystal structure Face centered cubic





Physical properties

Characteristics Lustrous and hard

Phase Solid

Density 8.9 g cm-3

Chemical properties


Melting Point 14530C



Nickel has been ranked as 57rd in the Environmental


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Air

Electrolysis sector: 0.4 mg/m3 Urban area : 17-25 mg/m3 Industrial area : 120-170 mg/m3

Water

Sea water : 0.1-20 mg/L Drinking water : 0.7 mg/L

Soils : 3-1000 ppm

Environmental Sources

Natural Weathering of rocks Volcanic eruptions Fall out, Meteorites

Anthropogenic Roasting plants Smelting plants Electroplating Steel industry Batteries Oil Hydrogenation

N

I

C

K

E

L


The Environmental Protection Agency (EPA) recommends that drinking water should not contain more than 0.7 milligrams of nickel per liter of water (0.7 mg/L). The Occupational Safety and Health Administration (OSHA) limits workplace air to 1 µg of nickel per cubic meter (1 µg/m3).

Environmental Toxicity

§ Dental Prostheses § Acute poisoning § Dermatitis § Asthma § Respiratory cancer § Malignant neoplasm § Lung embolism § Asthma and bronchitis § Heart disorders


Dermatitis

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METAL DECONTAMINATION: TECHNIQUES USED SO FAR

Present age of rapid increase in metal concentration as well as increase in awareness of

the toxicological effects of metals released into environment, a number of studies for

metal recovery and removal for metal solution have been done (Ashraf et al. 2011).

With the enactment of several water legislations and guidelines worldwide (South

Africa Water Act, US Clean Water Act, Australian Water Quality Guidelines, etc)

coupled with the need for environmental sustainability (Goal 7 of the Millennium

Development Goals), several stringent levels of water quality in domestic and industrial

water and wastewater are required. Because heavy metal pollution affects the quality

of drinking water supply and wastewater discharge, great efforts have been made in the

last two decades to reduce pollution sources and remedy polluted water resources. The

commonly used procedures for removing metal ions from aqueous streams include

distillation, ion exchange, reverse osmosis, electro dialysis, precipitation,

coagulation, flocculation and ultra filtration. The basic principle, procedural details

and commercially available instrumentation based on above phenomenon have been

described in brief as below:

DISTILLATION

Distillation is probably the oldest method of

water purification. Water is first heated to

boiling. The water vapour rises to a condenser

where cooling lowers the temperature so the

vapour is condensed, collected and stored. It

removes a broad range of contaminants.

EVAPORATION

Evaporation is the most common and cost effective

technique for recovery of heavy metals. Evaporation can

provide total as well as partial recovery of metals. The

application of atmosphere evaporation is used on a wide

variety of process effluents from industries.

Figure 1.2: Distillation

Figure 1.3: Evaporators

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CHEMICAL PRECIPITATION

Chemical treatment process prior to biological process is widely applicable in the

treatment of raw wastewaters. Moreover, various existing conventional primary waste

water plants are shifting towards chemically enhanced treatment to improve the quality

of the treated effluent and reduce cost. Chemical

precipitation is incorporated by raising the pH of

waste water by addition of alkaline chemicals, viz.

lime, limestone, caustic soda, soda, ash,

magnesium hydroxide. At alkaline pH most heavy

metals size range between 0.1-100m precipitated as

metal hydroxides or metal carbonates and

separated by gravity clarification or field

separation method.

FLOCCULATION AND COAGULATION

Flocculation is a process which clarifies the water. Clarification is done by causing a

precipitate to form in the water which can be removed using simple physical methods.

For water treatment plants using surface water as the source water, coagulation-

flocculation is the most commonly used

physicochemical process for particle removal

and is an essential pre-treatment process for

sedimentation and filtration [Figure 1.5]. The

three main types of chemical coagulants are

(i) Inorganic electrolyte e.g. Alum, lime,

ferric chloride and ferrous sulfate (ii) Organic

polymers (iii) Synthetic polyelectrolytes with

anionic and cationic functional groups which

are often used for precipitation. On addition

of coagulant, the flocculation occurs and

the size of particle in floc increases by aggregation and settle at the faster rate. Soluble

impurities in water can also be partially removed by coagulation. Because the complete

removal of impurities requires the separation of aggregates from water treatments,

Figure 1.5: Coagulation and Flocculation

Figure 1.4: Precipitation

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colloidal systems of extremely slow settling velocity are viewed as stabilized systems.

The coagulation process in water treatment includes three sequential steps: (A)

addition of coagulant; (B) destabilization of particles (C) aggregation of destabilized

particles.

ELECTRO COAGULATION

Electro coagulation is based on the formation of the coagulant as the sacrificial anode

corrodes due to an applied current while the simultaneous evolution of hydrogen at the

cathode allow the pollutant removal by floatation. Chemical reaction occurring in the

electro coagulation process shows that the main reaction occurs at the electrodes which

are as follows:

In addition, Al+3

and OH- ions generated at electrode surfaces react in the bulk media

waste water to form aluminium hydroxide [Al(OH)3] flocs which act as adsorbents for

metal ions and eliminate them from the

waste water. The hydroxyl ions which are

produced at the cathode increase the pH in

the electrolyte bulk liquid and may include

co precipitation of metals in the form of

their corresponding hydroxides [Figure 1.6].

This acts synergistically to remove

pollutants from waste water. The efficiency

of electro coagulation is dependent on pH,

current density and metal ion concentration.

ION EXCHANGE

Ion exchange processes in water treatment have

been used primarily for softening. Some of them

are used to deionize, disinfect or scavenge

macromolecules from water. Typical ion

Al Al+3 + 3e-

2H2O + 3e- 3/2 H2 + 3 OH -

Figure 1.7: Ion exchange process

Figure 1.6: Electro Coagulation process

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exchangers are ion exchange resins (functionalized porous or gel polymer), zeolites,

montmorillonite, clay, soil humus and synthetically produced organic resins. The

synthetic organic resins are frequently used today because their characteristics can be

tailored to specific applications.

Ion exchange is an exchange of ions between two electrolytes or between an electrolyte

solution and a complex [Figure 1.7]. In most cases the term is used to denote the

processes of purification, separation, decontamination of aqueous and other ion-

containing solutions with solid polymeric or mineralic ion exchangers. Ion exchangers

are either cation exchangers that exchange positively charged ions (cations) or anion

exchangers that exchange negatively charged ions (anions). There are also amphoteric

exchangers that are able to exchange both cations and anions simultaneously. However,

the simultaneous exchange of cations and anions can be more efficiently performed in

mixed beds that contain a mixture of anion and cation exchange resins, or passing the

treated solution through several different ion exchange materials.

Ion exchangers can be unselective or have binding preferences for certain ions or

classes of ions, depending on their chemical structure. This can be dependent on the

size of the ions, their charge, or their structure.

ULTRA FILTRATION

Ultra Filtration (UF) is a variety of membrane filtration in which hydrostatic pressure

forces a liquid against a semi permeable

membrane. Suspended solids and solutes of

high molecular weight are retained while

water and low molecular weight solutes pass

through the membrane [Figure 1.8]. This

separation process is used in industry and

research for purifying and concentrating

macromolecular (103-10

6 Da) solutions,

especially protein solutions. Ultra filtration is

not fundamentally different from reverse osmosis

microfiltration or nanofiltration, except in terms of the size of the molecules it retains.

Figure 1.8: Ultra Filtration

36

The UF process is applicable for particles in the molecular range of 0.1-0.01µm. Ultra

Filtration (UF) is a pressure-driven, membrane filtration process that is used to separate

and concentrate macromolecules and colloids from wastewater. A fluid is placed under

pressure on one side of a perforated membrane of a measured pore size. All materials

smaller than the measured pore size pass through the membrane, leaving large

contaminants concentrated on the feed side of the membrane. UF is used as a

pretreatment step to Reverse Osmosis (RO) or as a stand-alone process. The UF process

cannot separate constituents from water as effectively as RO. However, the two

technologies can be used in tandem, with UF removing most of the relatively large

constituents of a process stream before RO application selectively removes water from

the remaining mixture.

REVERSE OSMOSIS

Reverse Osmosis (RO) fills a unique position in the area of water and wastewater

treatment. Reverse osmosis (RO) is the most economical method of removing 90% to

99% of all contaminants. The pore

structure of RO membranes is much tighter

than UF membranes. RO membranes are

capable of rejecting practically all particles,

bacteria and organics >300 Daltons

molecular weight.

Natural osmosis occurs when solutions

with two different concentrations are separated

by a semi-permeable membrane [Figure 1.9]. In water purification systems, hydraulic

pressure is applied to the concentrated solution to counteract the osmotic pressure. Pure

water is driven from the concentrated solution and collected downstream of the

membrane. Because RO membranes are very restrictive, they yield very slow flow

rates. RO also involves an ionic exclusion process. Only solvent is allowed to pass

through the semi-permeable RO membrane, while virtually all ions and dissolved

molecules are retained (including salts and sugars). The semi-permeable membrane

rejects salts (ions) by a charge phenomena action: the greater the charge, the greater the

rejection. Therefore, the membrane rejects nearly all (>99%) strongly ionized

polyvalent ions but only 95% of the weakly ionized mono valent ions like sodium.

Figure 1.9: Reverse Osmosis

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Table 1.1: List of commercially available Reverse Osmosis membranes

ELECTRO DIALYSIS

Electro Dialysis (ED) is used to transport salt ions from one solution through ion-

exchange membranes to another solution under the influence of an applied electric

potential difference. This is done in a configuration called an electro dialysis cell. The

cell consists of a feed (diluate) compartment and a concentrate (brine) compartment

formed by an anion exchange membrane

and a cation exchange membrane placed

between two electrodes [Figure 1.10]. In

almost all practical electro dialysis

processes, multiple electro dialysis cells

are arranged into a configuration called

an electro dialysis stack, with alternating

anion and cation exchange membranes

forming the multiple electro dialysis

cells. Electro dialysis processes are unique

compared to distillation techniques and other membrane based processes in that

dissolved species are moved away from the feed stream rather than the reverse.

Because the quantity of dissolved species in the feed stream is far less than that of the

fluid, electro dialysis offers the practical advantage of much higher feed recovery in

many applications.

COMMERCIALLY AVAILABLE ACTIVITY COMPANY

REVERSE OSMOSIS MEMBRANES

Cellulose acetate membranes for salt removal Biosour. Inc.,Worcester, U.K

Cellulose tri acetate membranes for bacterial removal Miox Corp., Albuquerque

Figure 1.10: Electro Dialysis

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EXISTING METAL REMOVAL TECHNOLOGIES: DEMERITS

The conventional processes for removing heavy metals from wastewater include many

processes such as chemical precipitation, flotation, adsorption, ion exchange, and

electrochemical deposition. Chemical precipitation is the most widely used method for

heavy metal removal from inorganic effluent. Adjustment of pH to the basic conditions

(pH 9–11) is the major parameter that significantly improves heavy metal removal by

chemical precipitation. Lime and limestone are the most commonly employed

precipitating agents due to their availability and low-cost in most countries (Mirbagherp

and Hosseini, 2004; Aziz et al., 2008). Lime precipitation can be employed to

effectively treat inorganic effluent with a metal concentration of higher than 1000

mg/L. Other advantages of using lime precipitation include the simplicity of the

process, inexpensive equipment requirement, and convenient and safe operations.

However, chemical precipitation requires a large amount of chemicals to reduce metals

to an acceptable level for discharge. Other drawbacks are its excessive sludge

production that requires further treatment, slow metal precipitation, poor settling, the

aggregation of metal precipitates, and the long-term environmental impacts of sludge

disposal (Aziz et al., 2008). Ion exchange is another method used successfully in the

industry for the removal of heavy metals from effluent. The disadvantage of this

method is that it cannot handle concentrated metal solution as the matrix gets easily

fouled by organics and other solids in the wastewater. Moreover ion exchange is non

selective and is highly sensitive to the pH of the solution. Electrolytic recovery or

electro-winning is one of the many technologies used to remove metals from process

water streams. This process uses electricity to pass a current through an aqueous metal-

bearing solution containing a cathode plate and an insoluble anode. Positively charged

metallic ions cling to the negatively charged cathodes leaving behind a metal deposit

that is strippable and recoverable. A noticeable disadvantage was that corrosion could

become a significant limiting factor, where electrodes would frequently have to be

replaced (Kurniawan et al., 2006).

Although many techniques can be employed for the treatment of wastewater laden with

heavy metals, it is important to note that the selection of the most suitable treatment for

metal contaminated wastewater depends on some basic parameters such as pH, initial

metal concentration, the overall treatment performance compared to other technologies,

39

environmental impact as well as economics parameter such as the capital investment

and operational costs. Finally, technical applicability, plant simplicity and cost

effectiveness are the key factors that play major roles in the selection of the most

suitable treatment system for inorganic effluent. All the factors mentioned above should

be taken into consideration in selecting the most effective and inexpensive treatment in

order to protect the environment.

Table 1.2: Advantages and disadvantages of treatment technologies for the

removal of heavy metals from waste water

Technology Advantages Disadvantages References

Chemical

precipitation

Process simplicity,

Not metal selective,

Inexpensive capital cost

Large amounts of sludge

containing metals

Sludge disposal cost

High maintenance cost

Aderhold et al.,

1996

Ion exchange Metal selective,

Limited pH tolerance

High regeneration

High initial capital cost

High maintenance cost

Aderhold et al.,

1996

Coagulation &

flocculation.

Bacterial inactivation

capability

Good sludge settling and

dewatering characteristic

Chemical consumption

Increased sludge volume

generation

Aderhold et al.,

1996

Flotation Metal selective

Low retention times

Removal of small particles


High maintenance and

operation costs

Rubio et al., 2002

Membrane filtration Low solid waste generation

Low chemical consumption

Small space requirement

Possible to be metal

selective


High maintenance and

operation costs

Membrane fouling

Limited flow-rates

Mandaeni &

Mansourpanah,

2003

Electrochemical

treatment

No chemical required can

be engineered to tolerate

suspended solids

Moderately metal selective

treat effluent


Production of H2 (with

some process)

Filtration process for flocs

Kongsricharoen &

Polprasert, 1995

Kongsricharoen &

Polprasert, 1996

40

BIOSORPTION

The search for new technologies for the removal of toxic metals has directed attention

to biosorption phenomenon which is based on the metal binding capacity of agricultural

wastes (Kumar and Bandyopadhyay, 2006). Biosorption is a relatively new technique

that emerged in the 1980s and gained a considerable amount of attention since it has

shown to be very promising in the removal of contaminants from effluents in an

environmentally friendly manner (Gardea-Torresdey et al., 1996, Volesky 1999).

Unlike typical synthetic ion exchange resins, plant based biomaterials do not require the

use of toxic chemicals in their preparation. Therefore, plant based biomaterial systems

are considered more effective for removal of heavy metals. In recent perspectives,

biomaterials have gained much importance for decontamination of water. Biosorption

To combine technology with environmental safety is one of the key

challenges of the new millennium. There is a global trend of bringing

technology into harmony with natural environment, thus aiming to

achieve the goals of protection of ecosystem from the potentially

deleterious effects of human activity and finally improving its quality. The

challenges of safe and various treating and diagnosing environmental

problems require discovery of newer, more potent, specific, safe and cost

effective synthetic or biomolecules. The magic plants are around and

waiting to be discovered and commercialized. They are now recognized

and accepted as storehouses of infinite and limitless benefits to human

beings. These natural systems are often referred to as ‘Green

Technologies’, as they involve naturally occurring plant materials.

Biosorption is one such important phenomenon, which is based on one of

the twelve principles of Green Chemistry “Use of renewable resources”. It

has garnered a great deal of attention in recent years due to rise in

environmental awareness and consequent severity of legislation regarding

the removal of toxic metal ions from wastewater.

41

Major advantages of biosorption process

involves processes that reduce overall treatment cost through the application of

indigenous agricultural wastes which are particularly attractive as they lessen reliance

on expensive water treatment chemicals, negligible transportation requirements and

offer genuine, local resources as appropriate solutions to tackle local issues of water

quality problems. Regeneration of the biosorbent increases the cost effectiveness of the

process thus warrants its future success.

Biosorption clearly shows that from most perspectives, plants are ideal for

environmental clean up: capital cost is low, ongoing operational costs are minimal,

implementation is easy and non-invasive and public acceptance is high (Veglio and

Belochini, 2001; Volesky, 1999). All this shows that biosorption is a new and vibrant

technology having great potential. To realize this, it will be necessary to understand the

various processes that are involved in it. This may require a multidisciplinary approach

and diverse fields of plant biology.

Æ cost-effectiveness

Æ High efficiency

Æ Minimization of chemical and or biological sludge

Æ Regeneration of biosorbent

Æ Possibility of metal recovery

Æ competitive performance

Æ heavy metal selectivity

(Volesky, 2003)

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BIOSORPTION: MECHANISTIC ASPECTS

The biosorption process involves a solid phase (biosorbent) and a liquid phase (solvent,

normally water) containing dissolved species to be sorbed (sorbate, metal ions). Due to

higher affinity of the sorbent for the sorbate species, the latter is attracted and bound

thereby different mechanisms. The process continues till equilibrium is established

between the amount of solid bound to the sorbate species and its portion remaining in

the solution. The degree of sorbent affinity for the sorbate determines its distribution

between solid and liquid phases. In Biosorption, the use of non living biomaterials

containing metal binding compounds would have the advantage of not requiring

tremendous care and maintenance as well as being useful in remediating toxic high

levels of contaminants that would otherwise kill live system (Basso et al., 2002).

The complex structure of plant materials and microorganisms implies that there are many

ways for the metal to be taken by the biosorbent. Numerous chemical groups have been

suggested to contribute to biosorption metal binding by either whole organisms or by

molecules. These groups comprises hydroxyl, carbonyl, carboxyl, sulphaydryl, thioether,

sulphonate, amine, amino, imidazole, phosphonate and phosphodiester etc. The importance

of any given group for biosorption of certain metals by plant biomass depends on factors such

as number of sites in the biosorbent material, the accessibility of the sites, the chemical state

of the sites (availability) and affinity between site and metal (Volesky et al., 1999). Various

metal-binding mechanisms have been postulated to be active in biosorption process. Due to

the complexity of the biomaterials used, it is possible that at least some of these mechanisms

are acting simultaneously to varying degrees, depending on the biosorbent and the solution

environment.

CHEMISORPTION

It is the adsorption in which the forces involved are valence forces of the same kind as

those operating in the formation of chemical compounds. Some features, which are

useful in recognizing chemisorption, include:

§ The phenomenon is characterized by chemical specificity.

43

§ Since the adsorbed molecules are linked to the surface by valence bonds, they

will usually occupy certain adsorption sites on the surface and only one layer of

chemisorbed molecules is formed (monolayer adsorption).

§ The energy of chemisorption is of the same order of magnitude as the energy

change in a chemical reaction between a solid and a fluid.

§ Chemisorption is irreversible.

Chemisorption is of four types: ion exchange, complexation, coordination and/or

chelation.

a. ION EXCHANGE

Ion exchange is a reversible chemical reaction wherein an ion in a solution is

exchanged for a similarly charged ion attached to an immobile solid particle. These

solid ion-exchange particles are either naturally occurring inorganic zeolites or

synthetically produced organic resins. Synthetic organic resins are the predominant type

used today because their characteristics can be tailored to specific applications.

Ion exchange reactions are stoichiometric, reversible and as such they are similar to

other solution-phase reactions. For example, in the reaction

NiSO4 + Ca (OH)2 ® Ni(OH)2 + CaSO4

the nickel ions of the nickel sulfate (NiSO4) are exchanged for the calcium ions of the

calcium hydroxide Ca (OH)2 molecule.

b. CHELATION

The word chelation is derived from the Greek word chele, which means claw, and is

defined as the firm binding of a metal ion with an organic molecule (ligand) to form a

ring structure. The resulting ring structure protects the mineral from entering into

unwanted chemical reactions. Examples include the carbonate (CO32–

) and oxalate

(C2O42–

) ions:

44

c. COORDINATION (COMPLEX FORMATION)

A coordination complex is any combination of cations with molecules or anions

containing free pairs of electrons. Bonding may be electrostatic, covalent or a

combination of both; the metal ion is coordinately bonded to organic molecules.

Example of the formation of a coordination compound is:

Cu2+

+ 4H2O ® [Cu (H2O)] 42+

Cu2+

+ 4Cl– ® [CuCl4]

2–

Where coordinate covalent bonds are formed by donation of a pair of electrons from

H2O and Cl– (Lewis bases) to Cu

2+ (Lewis acid).

In general, biosorption of toxic metals and radionuclide is based on non-enzymatic

processes such as adsorption. Adsorption is due to the non-specific binding of ionic

species to polysaccharides and proteins on the cell surface or outside the cell. Bacterial

cell walls and envelopes, and the walls of fungi, yeasts and algae, are efficient metal

biosorbents that bind charged groups. The cell walls of gram-positive bacteria bind

larger quantities of toxic metals and radionuclide than the envelopes of gram-negative

bacteria.

Bacterial sorption of some metals can be described by the linearized Freundlich

adsorption equation:

log S = log K + n log C

Where: S is the amount of metal absorbed in µmol/g, C is the equilibrium solution

concentration in µmol/L, K and n are the Freundlich constants.

Biomass deriving from several industrial fermentations may provide an economical

source of biosorptive materials. Many species have cell walls with high concentrations

of chitin, a polymer of N-acetyl-glucosamine which is an effective biosorbent.

Biosorption uses biomass raw materials that are either abundant (e.g., seaweeds) or

wastes from other industrial operations (e.g., fermentation wastes). The metal-sorbing

performance of certain types of biomass can be more or less selective for heavy metals,

depending on the type of biomass, the mixture in the solution, the type of biomass

preparation, and the chemical-physical environment.

45

It is important to note that the concentration of a specific metal in solution can be

reduced either during the sorption uptake by manipulating the properties of the

biosorbent or upon desorption during the regeneration cycle of the biosorbent.

PHYSIOSORPTION

In physiosorption, physiosorbed molecules are fairly free to move around the sample.

As more molecules are introduced into the system, the adsorbate molecules tend to

form a thin layer that covers the entire adsorbent surface. Some features, which are

useful in recognizing physiosorption, include:

§ The adsorbate molecules are held by comparatively weaker Vander Waal’s

forces, thus resulting into lower activation energy.

§ The process is, however, reversible as the substance adsorbed can be recovered

from the adsorbent easily on lowering the pressure of the system at the same

temperature.

§ Physiosorption may extend beyond a monolayer also, since the physical forces

can operate at any given distances.

§ Physical adsorption is not specific in nature because it involves Vander Waal’s

forces, which exist among the molecules of every two substances.

Physiosorption takes place with the help of Vander Waal’s forces. Knyucak and

Volesky (1987) hypothesized that Uranium, Cadmium, Zinc, Copper and Cobalt

biosorption by certain plant materials takes place through electrostatic attraction

between the metal ions in solution and functional groups present on the cell surface.

46

BIOSORBENTS USED SO FAR

Biosorption promises to fulfill the requirements, which are competitive, effective and

economically viable. Efforts have been made to use different forms of inexpensive plant

materials for the abatement of toxic metals from the aqueous media. Biosorbents, explored so

far in removing toxic metals from water bodies have been listed below:

Table 1.3: List of biosorbents used for metal removal

BIOSORBENT METALS REMOVED REFERENCES

Wood Cu (II), Cr (III), As (III) Clausen, 2000

Fruit peel of Orange Ni (II) Ajmal et al., 2000

Crab shell Pb (II) Park et al., 2001

Cone biomass Cr (VI) Ucun et al., 2002

Portulaca oleracea Cd (II), Cr (II), Pb (II),

As (III), Ni (II)

Vankar and Tiwari, 2002

Petiolar Palm sheath Cd (II), Ni (II), Pb (II), Zn (II) Iqbal et al., 2002

Orange juice residue As (III), As (V) Ghimire et al., 2002

Olive mill solid residue Cd (II), Ni (II), Pb (II), Cr (III) Pagnanelli et al., 2002

Olive pomace Cd (II), Cu (II), Pb (II) Pagnanelli et al., 2003

Lemna Minor Pb (II), Ni (II) Nicholas et al., 2003

Chitosan Cr (VI) Boddu et al., 2003

Cassava waste Cu (II), Zn (II) Horsfall et al., 2003

Fly ash As (III) Nagarnaik et al., 2003

Waste Crab shells Au (II, Cr (VI), Se (II) Niu and Volesky, 2003

Sphagnum peat moss Cd (II), Cu (II), Pb (II), Ni (II) Rosa et al., 2003

Rice husk Cd (II) Ajmal et al., 2003

Water Lettuce As (III), As (V) Basu et al., 2003

Sugarcane Bagasse pith Cd (II) Krishnan and Anirudhan, 2003

Hazelnut shell Cr (VI) Kobya, 2004

Aquatic moss Cd (II), Zn (II) Martins et al., 2004

Polysaccharides Pb (II), Hg (II), Cu (II) Son et al., 2004

Grape stalk wastes Ni (II), Cu (II) Isabel et al., 2004

The major challenge for the biosorption field was to select the most promising

types of biomass from an extremely large pool of readily available and

inexpensive biomaterials.

47

S.natans Au (II), Co (II) Kuyucak et al., 2005

S.cerevisiae Cu (II), Zn (II), Cd (II) Kuyucak et al., 2005

Jute fibers Cu (II), Ni (II), Zn (II) Shukla and Roshan, 2005

Chitosan Cu (II), Cr (III), As (III) Kartal and Imamura, 2005

Lechuguilla Cd (II), Gonzalez et al., 2006

Maize leaf Pb (II) Babarinde et al., 2006

C. baccata Cd (II), Pb (II) Lodeiro et al., 2006

Granular biomass Pb (II), Cd (II), Cu (II), Ni (II) Hawari et al., 2006

Fruits waste Cd (II) Schiewer et al., 2007

Saw dust, rice husk Pb (II) Abdel-Ghani et al., 2007

Saltbush plant Cr (III), Cr (VI), Cd (II) Sawalha et al., 2007

Citrus peels Cu (II) Schiewer et al., 2008

Opuntia Pb (II) Martnez et al., 2008

Sugarcane bagasse and maize cob

Cd (II) Garg U et al., 2008

Cassia fistula Cr (III) and Cr (VI) Abbas et al., 2008

Waste olive cake Zn (II) Fernando et al., 2009

Neem oil cake Cu (II), Cd (II) Rao et al.,2009

Tamarind Bark Fe (II) Prasad et al.,2009

potato peel Fe (II) Prasad et al.,2009

Mango biomass Pb (II), Zn (II), Cu (II), Ni (II) Ashraf et al.,2010

Eichhornia Crassipes Fe (III) , Cu (II),

Zn (II), Pb (II), Cr (III) and Cd (II)

Shama et al., 2010

Rice husk Cr (VI) Wongjunda and Saueprasearsit, 2010

Orange waste Zn (II) Marin et al., 2010

Spirogyra and Cladophora Pb (II) , Cu (II) Lee and Chang, 2011

Natural spider silk Pb (II) , Cu (II) Pelit et al., 2011

Broussonetia papyrifera)

leaf powder

Cu (II), Pb (II), and Cd (II) Nagpal et al., 2011

peanut shell Cu (II), Cr (III) Krowiak et al., 2011

Marine Algae Cu (II), Co (II), Ni (II), Cd (II), Hg

(II), Ag (II), Pb (II)

Elrefaii et al. 2012

Spirogyra hyalina Cd (II), Hg (II), Pb (II), As (III),Co

(II)

Kumar and oomen, 2012

Algae Scenedesmus

quadricauda and

Neochloris pseudoalveolaris

Co (II), Cr (III), Pb (II), Cd (II), Ni

(II), and Mn (II)

Kızılkaya et al. 2012

Water Hyacinth Cu (II), Zn (II) Buasri et al., 2012

Pseudomonas Oleovorans

and Brevundimonas

Vesicularis

Pb (II) Singh and Gadi, 2012

Sargassum wightii and

Caulerpa racemosa

Cr (VI), Cr (III), Pb (II) and Cd (II) Tamilselvan et al., 2012

48

NANOTECHNOLOGY AND WASTE WATER TREATMENT

As part of a major report commissioned by the UK Government from the Royal Society

and the Royal Academy of Engineering in the UK, entitled ‘‘Nanoscience and

nanotechnologies: opportunities and uncertainties’’, the following definitions were

used:

Nanoscience is the study of phenomena and manipulation of materials at atomic,

molecular and macromolecular scales, where properties differ significantly from those

at a larger scale.

OR

Nanotechnologies are the design, characterisation, production and application of

structures, devices and systems by controlling shape and size at nanometre scale.

OR

The creation of functional materials, devices and systems through control of matter on

the nanometre scale (1–100 nm) and exploitation of novel phenomena and properties

(physical, chemical, biological) at that length scale.

Figure 1.11: Comparison of nanoparticles with macro scale particles

Physicist Richard P. Feynman first described the concept of nanoscience in

December, 1959 in a lecture entitled “There's Plenty of Room at the Bottom” to the

American Physical Society and the term nanotechnology was coined in 1974 by

the Japanese researcher Norio Taniguchi to describe precision engineering with

tolerances of a micron or less. In the mid 1980s, Eric Drexler brought

nanotechnology into the public domain with his book Engines of Creation.

49

Recent advances in nano scale science and engineering suggest that many of the current

problems involving water quality could be resolved or greatly ameliorated using

nanosorbents, nanocatalysts, bioactive nanoparticles, nanostructured catalytic

membranes and nano particle enhanced filtration among other products and processes

resulting from the development of nanotechnology. Utilization of specific nanoparticles

either embedded in membranes or on other structural media that can effectively,

inexpensively and rapidly render unusable water potable is being explored at a variety

of institutions. In addition to obvious advantages for industrialized nations, the benefits

for developing countries would also be enormous. Innovative use of nanoparticles for

treatment of industrial wastewater is another potentially useful application. Many

factories generate large amounts of wastewater. Removal of contaminants and recycling

of the purified water would provide significant reductions in cost, time, and labor to

industry and result in improved environmental stewardship. Aquifer and groundwater

remediation are also critical issues, becoming more important as water supplies steadily

decrease and demand continues to increase. Most of the remediation technologies

available today, while effective, very often are costly and time consuming, particularly

pump-and-treat methods. The ability to remove toxic compounds from subsurface and

other environments that are very difficult to access in situ, and doing so rapidly,

efficiently and within reasonable costs is the ultimate goal. Recent selected studies on

the use of nanomaterials as separation and reactive media for water purification.

NANOSORBENTS

Sorbents are widely used as separation media in water purification to remove inorganic

and organic pollutants from contaminated water. Nanoparticles have two key properties

that make them particularly attractive as sorbents. On a mass basis, they have much

larger surface areas than bulk particles. Nanoparticles can also be functionalized with

various chemical groups to increase their affinity towards target compounds. Several

research groups are exploiting the unique properties of nanoparticles to develop high

capacity and selective sorbents for metal ions and anions. Li et al. (2003) have

investigated the sorption of Pb (II), Cu (II) and Cd (II) onto multiwalled carbon

nanotubes (MWCNTs). They reported maximum sorption capacities of 97.08 mg/g for

Pb (II), 24.49 mg/g for Cu (II) and 10.86 mg/g for Cd (II) at room temperature, pH 5.0

and metal ion equilibrium concentration of 10 mg/l. Li et al. (2003) also found that the

50

metal–ion sorption capacities of the MWCNTs were 3–4 times larger than those of

powder activated carbon and granular activated carbon, two commonly used sorbents in

water purification. Qi & Su (2004) have evaluated the sorption of Pb (II) onto chitosan

nanoparticles (40–100 nm) prepared by ionic gelation of chitosan and tripolyphosphate.

The phosphate-functionalized chitosan nanoparticles have a maximum Pb(II) sorption

capacity of 398 mg/g. Peng et al. (2005) have recently developed a novel sorbent with

high surface area (189 m2/g) consisting of cerium oxide supported on carbon nanotubes

(CeO2-CNTs). They showed that the CeO2-CNT particles are effective sorbents for As

(V). Interestingly, Peng et al. (2005) found that the addition (from 0 to 10 mg/l) of two

divalent cations [Ca (II) and Mg (II)] resulted in a substantial increase of the amount of

sorbed As (V) (from 10 to 82 mg/g). Deliyanni et al. (2003) have also synthesized and

characterized a novel As (V) sorbent consisting of akaganeite [b-FeO (OH)]

nanocrystals. In addition, Lazaridis et al. (2005) have shown that nanocrystalline

akaganeite is also an effective sorbent for Cr (VI).

Zeolites are effective sorbents and ion-exchange media for metal ions. NaP1 zeolites

(Na6Al6 Si10O32.12H2O) have a high density of Na ion exchange sites. They can be

inexpensively synthesized by hydrothermal activation of fly ash with low Si/Al ratio at

150 OC in 1.0–2.0 M NaOH solutions (Moreno et al., 2001). NaP1 zeolites have been

evaluated as ion exchange media for the removal of heavy metals from acid mine

wastewaters (Moreno et al., 2001). Alvarez-Ayuso et al. (2003) reported the successful

use of synthetic NaP1 zeolites to remove Cr (III), Ni (II), Zn (II), Cu (II) and Cd (II)

from metal electroplating wastewaters. Self-assembled monolayers on mesoporous

supports (SAMMS) are providing novel opportunities to develop more effective

sorbents for toxic metal ions (Yantasee et al., 2003), anions (Kelly et al., 2001) and

radionuclides (Fryxell et al., 2005; Lin et al., 2005). These sorbents are made via

surfactant templated synthesis of mesoporous ceramics. This produces nano porous

ceramic oxides with very large surface areas (1000 m2/g) and high density of sorption

sites that can be functionalized to increase their selectivity toward target pollutants.

Carbonaceous nanomaterials can serve as high capacity and selective sorbents for

organic solutes in aqueous solutions. Mangun et al. (2001) have synthesized nano

porous activated carbon fibers (ACFs) with an average pore-size of 1.16 nm and

surface areas ranging from 171 to 483 m2/g. They measured the sorption of benzene,

51

toluene, p-xylene and ethyl benzene onto the ACFs at 20 OC. Mangun et al. (2001)

found that the sorption isotherms are adequately described by a Freundlich equation. In

all cases, the ACFs had much higher organic sorption equilibrium constants than

granular activated carbon. Peng et al. (2003) have evaluated the sorption of 1, 2-

dichlorobenzene (DCB) onto CNTs. They found that it takes only 40 min for DCB

sorption onto the CNTs to reach equilibrium with a maximum sorption capacity of 30.8

mg/g. Li et al. (2004) reported that MWCNTs were better sorbents of volatile organic

compounds than carbon black in aqueous solutions. Fugetsu et al. (2004) have

successfully encapsulated MWCNTs inside cross-linked alginate vesicles. The caged

MWCNTs showed high sorption capacity and selectivity for four water soluble dyes

(acridine orange, ethidium bromide, eosin bluish and orange G). Zhao & Nagy (2004)

have synthesized hybrid inorganic–organic nanosorbents by incorporation of sodium

dodecyl sulphate (SDS) into magnesium–aluminum layered double hydroxides (LDHs).

They reported that the SDS functionalized Mg/Al LDHs had higher sorption capacity

for chlorinated alkenes [trichloroethylene (TCE)] in aqueous solutions than organo

clays. Fullerenes can also serve as sorbents for polycyclic aromatic compounds (PAHs)

such as naphthalene (Cheng et al., 2004). Nanocatalysts and redox active nanoparticles

have great potential as water-purification catalysts and redox active media due their

large surface areas and their size and shape dependent optical, electronic and catalytic

properties (Obare & Meyer, 2004). During the last decade, titanium dioxide (TiO2)

nanoparticles have emerged as promising photocatalysts for water purification

(Adesina, 2004). TiO2 nanoparticles are very versatile; they can serve both as oxidative

and reductive catalysts for organic and inorganic pollutants. The removal of total

organic carbon from waters contaminated with organic wastes was greatly enhanced by

the addition of TiO2 nanoparticles in the presence of ultraviolet light as shown by

Chitose et al., (2003). Kabra et al. (2004) have recently reviewed the utilization of

photocatalysts in the treatment of water contaminated by organic and inorganic

pollutants. They documented the successful use of TiO2 nanoparticles to (i) degrade

organic compounds (e.g. chlorinated alkanes and benzenes, dioxins, furans, PCBs, etc.)

and (ii) reduce toxic metal ions [e.g., Cr(VI), Ag(I) and Pt(II)] in aqueous solutions

under UV light. The synthesis of visible light-activated TiO2 nanoparticles has

attracted considerable interest (Asahi et al., 2001; Bae & Choi, 2003; Adesina, 2004;

Obare & Meyer, 2004). One of the most cited studies in the field is that published by

52

Ashasi et al. (2001). They synthesized N-doped TiO2 nanoparticles that were capable

of photo degrading methylene blue under visible light. Bae & Choi (2003) have

synthesized visible light-activated TiO2 nanoparticles based on TiO2 modified by

ruthenium-complex sensitizers and Pt deposits. The Pt/TiO2/RuIIL3 nanoparticles

drastically enhanced the rate of reductive dehalogenation of trichloroacetate and carbon

tetrachloride in aqueous solutions under visible light. Nanoscale zero valent iron (Fe0)

and bimetallic Fe0 particles have emerged as effective redox media for the

detoxification of organic and inorganic pollutants in aqueous solutions. These

nanomaterials (10–100 nm) have larger surface areas and reactivity than bulk Fe0

particles (Schrick et al., 2002; Zhang, 2003; Nurmi et al., 2005). Zhang (2003) has

given an overview of the synthesis, characterization and use of nanoscale Fe0 particle

and Fe0/Pd0, Fe0 /Pt0, Fe0 /Ag0, Fe0/ Ni0 and Fe0/Co0 in environmental remediation.

These nanoparticles can reduce a variety of organic pollutants (e.g., chlorinated alkanes

and alkenes, chlorinated benzenes, pesticides, organic dyes, nitro aromatics, PCBs) and

inorganic anions (e.g., nitrates) in aqueous solutions to less toxic and recalcitrant by-

products. Fe0 and bimetallic Fe0 nanoparticles have also been successfully used to

reduce redox active metal ions such as Cr (VI) to less toxic and mobile Cr (III) species

(Zhang, 2003). The immobilization of metallo porphyrinogens in soil–gel matrices has

also been successfully used to prepare redox and catalytically active nanoparticles for

the reductive dehalogenation of chlorinated organic compounds (PCE, TCE and carbon

tetrachloride) in aqueous solutions (Dror et al., 2005).

NANOMATERIALS IN WATER PURIFICATION: CHALLENGES

Nanomaterials are the drivers of the nanotechnology revolution. However, the

environmental fate and toxicity of a material are critical issues in materials selection

and design for water purification. Not much is known about the environmental fate,

transport and toxicity of nanomaterials (Colvin, 2003; Lecoanet et al., 2004 a,b). No

systematic investigations of the hydrolytic, oxidative, photochemical and biological

stability of nanomaterials (e.g., dendrimers, carbonaceous nanoparticles, metal oxides,

etc) in natural and engineered environmental systems have been published in the peer-

reviewed literature to the best of our knowledge. Recently variety of inorganic nano

structured materials has been widely explored for remediation of metallic ions but

recently found to be associated with toxicity issues (Colvin, 2003; Paul and Richard,

53

2006; Karn et al., 2009). A number of in vitro and in vivo measurements of toxicity and

bio distribution have been carried out (Malik et al., 2000; Jevprasesphant et al., 2003;

Hong et al., 2004) in support of the use of dendrimers as DNA transfection reagents,

metal ion contrast agent carriers for magnetic resonance imaging, targeted drug and

therapeutic agent delivery vehicles and viral inhibitors (Fre´chet & Tomalia, 2001).

These overall studies suggest that non-toxic and biodegradable dendrimers can be

synthesized through a judicious selection of the dendrimer building blocks (e.g., core

and terminal group). The design and synthesis of biocompatible carbon nanotubes

(CNTs) and fullerenes are, on the other hand, much more challenging. Only a few peer

reviewed studies of the toxicity of CNTs and fullerenes have been published (Lam et

al., 2004; Oberdo¨ ster, 2004; Jia et al., 2005). These studies showed that underivatized

CNTs and fullerenes tend to be water insoluble and toxic. However, CNTs and

fullerenes can be functionalized with various functional groups (e.g. hydroxyl,

carboxyl, amines, etc.) to increase their water solubility and biocompatibility in some

cases (Sayes et al., 2004; Bianco et al., 2005). Finally, we would like to point out that

metal-containing particles also exhibit a size dependent toxicity (Chen, 2004). Thus, a

key challenge will be to gain regulatory and public acceptance for using

nanomaterials in water purification because of their unknown toxicity and

environmental impact.

One way to address such issues related to sustainability is to incorporate

renewable materials of miniaturized elements (Sain and Oksman, 2006) of plant

origin. The complexity of organic material represents the achievement of specific

structural order of many length scales and reflects interplay of perfection forming

the novel material under the domain of green nanotechnology. The ability to

control, manipulate and design organic materials on the nano scale by

implementation of green chemistry principles for the production of organic

nanoparticles in the development of technological processes with the use and

reuse of agricultural waste as biosorbent to remediate contaminants

simultaneously avoiding environmental hazardous is a major challenge of 21st

century.

54

Cellulose constitutes the most abundant and renewable polymer resource available

worldwide and comprises of repeating β-D-glucopyranose units covalently linked

through acetal functions between the OH groups of C4 and C1 carbon atoms providing it

hydrophilicity, chirality and reactivity properties. The increasing interest in

nanomaterials of plant origin and their unique properties have led to intensive research

in the area of nano cellulosic materials (Samir et al., 2005; Bondeson et al., 2006;

Visakh and Thomas, 2010). Nano cellulose, due to its nano-size nature, high surface

area and high aspect ratio, becomes an important material with the advantages of being

derived from renewable resources and making it suitable for environmental and health

applications, such as liquid filtration (Ma et al., 2011), biomedical applications (Czaja

et al., 2007), transparent materials for advanced nanocomposites for different purposes

(Lo´pez-Rubio et al. 2007; Shimazaki et al. 2007) etc. However, not much attention

has been paid on the application of nano cellulosic fibers for the remediation of toxic

metals from water bodies.

55

ARTIFICIAL NEURAL NETWORK MODELING

To achieve an optimum management for any control measure, the concept of modeling

for an efficient operation and design should be developed. A high quality representative

model can provide a favorable solution to the process control. It is likely to explain the

real process performance developing a continuous control strategy for such type of

technologies. The nature of the sorption process depends on physical or chemical

characteristics of the adsorbent systems and also on the system conditions. The

adsorption processes irrespective of the adsorbate/adsorbent are usually modeled using

the mechanistic or empirical based kinetic expressions. In addition to that numerous

empirical models have been employed to describe the biosorption equilibrium, namely

Langmuir, Freundlich, Brunauer–Emmett–Teller (BET), Sips, Dubinin–Radushkevich,

Temkin and Toth models. Langmuir and Freundlich equations are the most popular and

widely used models in a large number of studies. Nonetheless, in many cases, these

empirical models fall-short to represent the biosorption phenomena and its in-behind

physical meaning (Febrianto et al., 2009). In addition, predictive conclusions are hardly

drawn from systems operating at different conditions. However, any attempt to make a

relationship between the amount of solute uptake with all the operating variables,

namely, metal concentration, biomass dosage, initial solution pH, volume of solution

treated, and contact time, using the kinetic or mechanistic based models is impossible.

Neural networks are useful when a mathematical relationship is not available to

describe a phenomenon to be modeled. If the property in question can be modeled by

very complex and highly demanding computational techniques, neural networks

provide an alternative approach to obtain accurate numerical values in a

computationally less intensive fashion. Because of reliable, robust and salient

characteristics in capturing the non-linear relationships of variables in complex

systems, application of Artificial Neural Network (ANN) has been successfully

employed in environmental engineering [Shetty and Chellam, 2003; Park et al., 2004]

and bioremediation [Raj et al., 2010; Kardam et al., 2010].

56

NEURAL NETWORKS

A first wave of interest in neural networks (also known as ‘connectionist models’ or

‘parallel distributed processing’) emerged after the introduction of simplified neurons

by McCulloch and Pitts in 1943. These neurons were presented as models of biological

neurons and as conceptual components for circuits that could perform computational

tasks.

BIOLOGICAL NEURON MODEL

The human brain is estimated to have around 10 billion neurons each connected on

average to 10,000 other neurons. An artificial neuron is a computational model inspired

in the biological neurons. Biological neurons receive signals through synapses located

on the dendrites or membrane of the neuron (Figure 1.12). When the signals received

are strong enough (surpass a certain threshold), the neuron is activated and emits a

signal though the axon. This signal might be sent to another synapse, and might activate

other neurons.

Figure 1.12: Biological Neuron a), and Artificial Neuron b).

The complexity of real neurons is highly abstracted when modelling artificial neurons.

These basically consist of inputs (like synapses), which are multiplied by weights

(strength of the respective signals), and then computed by a mathematical function

which determines the activation of the neuron. Another function (which may be the

identity) computes the output of the artificial neuron (sometimes in dependence of a

certain threshold). ANNs combine artificial neurons in order to process information.

57

ARTIFICIAL NEURAL NETWORK

An Artificial Neural Network (ANN) is an information processing paradigm that is

inspired by the way biological nervous systems, such as the brain, process information.

The key element of this paradigm is the novel structure of the information processing

system. It is composed of a large number of highly interconnected processing elements

(neurones) working in unison to solve specific problems. ANNs, like people, learn by

example. An ANN is configured for a specific application, such as pattern recognition

or data classification, through a learning process.

ARTIFICIAL NEURON- BASIC ELEMENTS

Neuron consists of three basic components – weights, threshold and a single activation

function as shown in the figure 1.13.

Figure 1.13: Basic elements of artificial neuron model

WEIGHT FACTORS

The values W1, W2, W3...........Wn are weights to determine the strength of an input

vector X = [X1, X2, X3................... X1,]T. Each input is multiplied by the associated

weight of the neuron connection XT W. The positive weight excites and the negative

weight inhibits the node output.

THRESHOLD ɸ

58

The nodes internal threshold ɸ is the magnitude offset. It affects the activation of the

node output y as:

To generate the final output Y, the sum is passed to non linear filter f called activation

function or transfer function or squash function which releases the output Y.

THRESHOLD FOR A NEURON

In practice, neurons generally do not fire (produce an output) unless their total input

goes above threshold value. The total input of a neuron is the sum of the weighted input

to the neuron minus threshold value. This is then passed through the sigmoid function.

The equation for the transition in a neuron is:

ACTIVATION FUNCTION

An activation function f performs the mathematical operation on the signal output.

Figure 1.14 shows the most common activation functions:

Figure 1.14: Classical activation functions

The activation functions are chosen depending upon the type of problem to be solved

by the network.

59

NEURAL NETWORK ARCHITECTURES

Single Layer Feed Forward Network

The Single Layer Feed Forward Network consists of a single layer of weights,

where the inputs are directly connected to the outputs, via series of weights

(Figure 1.15). The synaptic links carrying weights connect every input to every

output, but not other way. This way it

is considered a network of feed-

forward type. The sum of the product

of the weights and the inputs is

calculated in each neuron node, and if

the value is above some threshold

(typically 0) the neuron fires and takes

the activated value (typically 1); other

it takes the deactivated value (typically

-1).

Figure 1.15: Single Layer Feed Forward Network

Multi Layer Feed Forward Network

As the name suggests, it consists of multiple layers. The architecture of this

class of network, besides having the input and output layers, also have one or

more intermediary layers called hidden layers. The computational units of the

hidden layer are known as hidden

neurons. The hidden layer does

intermediate computation before

directing the input to output layer

(Figure 1.16). A multi layer feed

forward network with l input neurons,

m1 neurons in the first hidden layers,

m2 neurons in the second hidden

layers, and in output neurons in the

output layers is written as (l-m1- m2-n). Figure 1.16: Multi feed- forward Network

Recurrent Network

The recurrent Networks differ from feed-forward architecture. A recurrent

network has at least one feed back as shown in figure. There could be neurons

60

with self feedback links; that is the output of a neuron is feedback into itself as

input (Figure 1.17).

Figure 1.17: Recurrent Neural Network

Learning methods in Neural Networks

The learning methods in neural networks are classified into three basic steps:

· Supervised Learning.

· Unsupervised Learning.

· Reinforced Learning.

These three types are classified based on; presence or absence of teacher and the

information provided for the system to learn. These are further categorized,

based on the rules used, as Hebbian, Gradient descent, Competitive and

stochastic learning (Figure 1.18).

Figure 1.18: Hierarchical representation of the learning algorithms

61

APPLICATION OF NEURAL NETWORK

Neural Network Application can be grouped in following categories:

Clustering:

A clustering algorithm explores the similarity between patterns and places

similar patterns in a cluster. Best known applications include data compression

and data mining.

Classification/pattern recognition:

The task of pattern recognition is to assign an input pattern (like handwritten

symbol) to one of many classes. This category includes algorithms

implementation such as associative memory.

Function approximation:

The task of function approximation is to find an estimate of the unknown

function subject to noise. Various engineering and scientific disciplines require

function approximation.

Predictive Systems:

The task is to forecast some future values of a time-sequenced data. Prediction

differs from function approximation by considering time factor, System may be

dynamic and may produce different results for the same input data based on

system state (time).

Figure 1.19: Application of Artificial Neural Network in different disciplines of scientific research

62

Table 1.4: Optimisation of Biosorption Studies using Artificial Neural Network Modeling

ARTIFICIAL NEURAL NETWORK MODELING:

EQUILIBRIUM & MODELING STUDIES : REFERENCES

Pb, Cu and Cd modeling by ANN using bacterial biomass,

Modeling of Adsorption Isotherm using Neural Networks,

La, Eu and Yb modelization using ANN,

Prediction of two-metal biosorption equlibria using ANN,

Copper biosorption by penicillium cyclopium using ANN,

Predictive Modeling of Competitive biosorption equilibrium,

Prediction of biosorption efficiency for the removal of Cu (II) using ANN,

Cd and Zn biosorption by Sargassum filipendula using ANN,

Adsorption in packed column by macro porous resins using NN,

ANN modeling of Pb (II) adsorption by Antep pistachio shells,

Neural network modeling of Pb2+ removal from waste water.

Artificial neural network modeling of fixed bed biosorption using radial basis

approach

Artificial Neural Network for predicting biosorption of methylene blue by

Spirulina sp.

Thomas and artificial neural network models for the fixed-bed adsorption of

methylene blue by a beach waste Posidonia oceanica (L.)

Modelling the solid–liquid adsorption processes using ANN trained by pseudo

second order kinetics

Comparison of the results of response surface methodology and artificial neural

network for the biosorption of lead using black cumin

Prediction of biosorption of total chromium by Bacillus sp. using artificial neural

network.

Chang and Huang 1998

Babu & Ramakrishnan 2002.

Texier et al. 2002

Chu 2004

Ianis et al. 2006

Chu and Kim 2006

Prakash et al. 2006

Fagundes-Klen et al. 2006

Du et al. 2007

Yetilmezsoy and Demirel 2007

Sadrzadeh et al. 2009

Saha et al., 2010

González et al. 2011

Cavas et al., 2011

Kumar and Porkodi et al., 2011

Bingöl et al., 2012

Masood et al., 2012

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