CHAPTER ONE INTRODUCTION 1

CHAPTER ONE
INTRODUCTION
1.1Background of the Study
Chlorophenols are of greater environmental concern because of their higher toxicity and carcinogenic with strong odour emission, not readily biodegradable and persistent in the environment and thus poses a serious ecological problem and public health risk to human and marine life. These compounds are widely distributed due to the anthropogenic contributions from the industrial wastes generated from bleaching, iron steel, paper and cellulose, pesticides and biocides, petrochemical, pharmaceutical, plastic, rubber proofing, textile, and wood preserving industries (Fattahi et al., 2007; Hamad et al., 2010). This makes it necessary to develop methods that allow one to detect, quantify and remove chlorophenols from aqueous solution as an important prior to discharging wastewater into the environment (Mahvi, 2008).

Consequently, numerous conventional methods are existing in the treatment of chlorophenols wastewater which includes anaerobic processes, biodegradation, biosorption, distillation, combined applications of flotation and coagulation processes, ion exchange, the electro Fenton method, membrane separation, pervaporation, precipitation, reverse osmosis, solvent extraction, stripping and oxidation, etc. (Busca et al., 2008). Adsorption process is considered better among a variety of methods used in chlorophenols wastewater treatment because it is easy to operate and convenient.

Adsorption is a phenomenon in which a substance (adsorbate) either in gas or liquid phase accumulates on a solid surface (adsorbent), which rely on the capability of porous materials with large surfaces to selectively keep compounds on the surface of the solid (adsorbent). The adsorption process of the adsorbate molecules from the bulk liquid phase into the adsorbent surface is supposed to involve the following stages:
Mass transfer of the adsorbate molecules across the external boundary layer towards the solid particle.

Adsorbate molecules transport from the particle surface into the active sites by diffusion within the pore–filled liquid and migrate along the solid surface of the pore.

Solute molecules adsorption on the active sites on the interior surfaces of the pores.

Once the molecule adsorbed, it may migrate on the pore surface trough surface diffusion (Mohamed, 2011).
Activated carbons are materials with large specific surface areas, high porosity; adequate pore size distributions and high mechanical strength which are extensively used as an adsorbent in the removal of heavy metals, hydrocarbons, and other hazardous chemicals that can be found in wastewaters (Bohli et al., 2013). Granule or powder form of activated carbons have good adsorptive capacity to attract soluble organic molecule materials from solution to its surface, but due to its high cost and difficulty in regeneration which limits its commercial application in large scale treatment of wastewater (Popuri et al., 2007). This has led to research for cheaper substitutes such as agricultural waste materials obtained as the by-products from the forestry and agricultural industries which is a ubiquitous green waste generally inexpensive, renewable source of activated carbons and often cause serious environmental pollution problem. Agricultural waste is a rich source for activated carbon production due to its low ash content and reasonable hardness (Bhatnagar & Sillanpaa, 2010). These are organic compounds comprised of cellulose, hemicelluloses, lignin, lipids, proteins, simple sugars, water, hydrocarbons, starch, and containing a variety of functional groups having potential sorption capacity for various organic pollutants. Therefore, conversion of agricultural wastes into low-cost adsorbents is a promising alternative to solve environmental problems such as disposal of waste and also to reduce the preparation costs (Ahmedna et al., 2000).

Different kinds of activated carbon have been achieved from different agriculture wastes and used as low-cost adsorbents with varying success for the removal of organic compounds from aqueous solution. Almond (Terminalia catappa) nut shells is one of the important agricultural materials belong to the family Combrataceae, it is a large spreading tree distributed throughout the tropics and coastal environment (Species profiles, 2006). The fruit is a sessile, laterally compressed, ovoid to ovate and smooth skinned drupe. The oil containing seeds are encased in a tough fibrous husk with a fleshy pericarp. The shells of almond are abundant, inexpensive and readily available lignocellulosic substance generally discarded as a waste material, and can be collected on community basis for reuse as adsorbent. The cell walls of almond shell consist of cellulose, silica, lignin and carbohydrates which have hydroxyl groups in their structures. Others agricultural waste products include: banana peels (Achak et al., 2009), banana stalk (Ogunleye et al., 2014), orange peels (Owabor & Audu, 2010; Agarry & Aremu, 2012), peanut husk (Hu et al., 2011), pineapple peels (Agarry & Aremu, 2012; Solidum, 2013), spent tea leaves (Hameed, 2009; Agarry et al., 2013a), etc. More and more interests are focused on developing these agricultural wastes as adsorbent for wastewater treatment due to their relative high sorption affinity, ubiquitous presence in the environment, and the ease of being modified to materials with higher efficiency (Chen et al., 2011; Agarry & Aremu, 2012).
1.2Statement of the Problem
One of the main problems causing environmental pollution of watercourses is industrial effluents that have high concentrations of dissolved organic compounds, with disagreements existing on the maximum values allowed by current legislation. The adsorption by absorbent is one of the methods used for the removal of pollutant (i.e. 2,6-Dichlorophenol) from aqueous waste stream using activated carbon obtained from agricultural waste material (Almond nut shells). The search for practical, efficient and low cost alternatives has been a constant to circumvent these problems.

1.3Aim and Objectives of the Study
The aim of this research work is to investigate the potentiality of using cellulose based wastes, almond nut shells (Terminalia catappa) as a non-conventional low cost adsorbent for 2,6-Dichlorophenol removal from aqueous solution.
Objectives
In order to achieve the broad goal of this study, the specific objectives are to;
Prepare adsorbate solution (2,6-Dichlorophenol) and the adsorbent (Almond nut shells).
Characterize the modified almond nut shells by Fourier Transform Infrared (FTIR) spectroscopy studies and other physicochemical parameters such as pH and Conductivity, Moisture Content, Ash Content, Bulk Density, Specific Density, Porosity, and Pore Volume.

Examine the effect of various factors such as time of contact, adsorbent dosage, pH and initial adsorbate concentrations on this adsorption process under batch equilibrium technique.
Analyse the experimental data by Langmuir and Freundlich models in order to describe the equilibrium isotherms.

Modelling of adsorption kinetic using Lagergren pseudo-first order, pseudo-second order and intra-particle diffusion (Weber-moris Model).
1.4Significance of the Study
The purpose of the study is of high importance to test the possibility, and provide summary information concerning the use of locally available materials as adsorbents for the removal of phenolic compound. Until this present work, little information is available on the suitability of using this selected cellulose-based agricultural waste (Almond nut shells) in the removal of toxic 2,6-Dichlorophenol and seldom work has been reported in literature so far. Finally, the study will be an important source of reference to the researchers and students of natural and applied sciences who might want to embark on scholarly investigation in future.

1.5Scope of the Study
For the aim and objectives of the study to be achieved, the scope of the study is specifically limited to the removal of 2,6-Dichlorophenol (adsorbate) from an aqueous solution employing cellulose-based almond nut shells (adsorbent) because of its availability.

CHAPTER TWO
LITERATURE REVIEW
The surge of industrial activities has intensified more environmental problems as seen for example in the deterioration of several ecosystems due to the accumulation of dangerous pollutants. Apart from the environmental damage, human health is likely to be affected as the presence of toxic wastes beyond a certain limit brings serious hazards to living organisms (Febrianto et al., 2009). Phenol and substituted phenols are one of the important categories of aquatic pollutants, which are considered as toxic, hazardous and priority pollutants (Bhatnagar & Minocha, 2009). The main sources of phenol which are released into the aquatic environment are the wastewater from industries such as coke ovens in steel plants, petroleum refineries, resin, petrochemical and fertilizer, pharmaceutical, chemical and dye industries (Ahmaruzzaman & Sharma, 2005).
Several treatment methods have been applied to remove phenolic compounds from aqueous solutions, such as biological treatment using live and dead organisms, catalytic wet oxidation and adsorption technology using activated carbons prepared from various precursors. Other methods include air stripping, incineration, ion exchange and solvent extraction. For instance, petrochemical and chemical industries are concentrated in the South Durban area in South Africa where there is extreme contamination of ground and surface water and members of the community have consistently complained of high levels of cancer (Butler & Hallowes, 2002).

Adsorption is gaining interest as one of the most effective processes for treatment of industrial effluent containing toxic materials. The occurrence of non-biodegradable wastes in streams and lakes threatens the use of water resources and various treatment methods have been used for the removal of these wastes. Among these methods, adsorption using commercial activated carbon has proven to be efficient, however it is highly expensive. Hence in recent years there has been a continuous search for locally available and cheaper adsorbent.

2.1Adsorption Process
In adsorption process, two substances are involved. One is the solid or the liquid on which adsorption occurs and it is called adsorbent. The second is the adsorbate, which is the gas or liquid or the solute from a solution which gets adsorbed on the surface.

Adsorbent: This is the substance on whose surface the adsorption occurs.

Adsorbate: This is the substance whose molecules get adsorbed on the surface of the adsorbent (i.e. solid or liquid). 
Adsorption is different from absorption. In absorption, the molecules of a substance are uniformly distributed in the bulk of the other, whereas in adsorption, molecules of one substance are present in higher concentration on the surface of the other substance.

  …… (2.1)
Adsorption is influenced by the nature of solution in which the contaminant is dispersed, molecular size and polarity of the contaminant and the type of adsorbent. Hence, it is important to be able to relate the amount of contaminant adsorbed from the wastewater stream to the amount of adsorbent needed to reduce the contaminant to acceptable levels (Rowe & Abdel-Magid, 1995). The presentation of the amount of solute adsorbed per unit weight of the adsorbent as a function of the equilibrium concentration in bulk solution at constant temperature is termed the adsorption isotherm. Adsorption isotherm models can be regarded as benchmark for evaluating the characteristic performance of an adsorbent.

2.1.1Types of Adsorption
Adsorption can be classified into two types based on the nature of forces that exist between adsorbate molecules and adsorbent:
1. Physical Adsorption (Physisorption): If the force of attraction existing between adsorbate and adsorbent are Vander Waal’s forces, the adsorption is called physical adsorption. It is also known as Vander Waal’s adsorption. In physical adsorption the force of attraction between the adsorbate and adsorbent are very weak, therefore this type of adsorption can be easily reversed by heating or by decreasing the pressure.

2. Chemical Adsorption (Chemisorption): If the force of attraction existing between adsorbate and adsorbent are almost same strength as chemical bonds, the adsorption is called chemical adsorption. It is also known as Langmuir adsorption. In Chemisorption the force of attraction is very strong, therefore adsorption cannot be easily reversed. 
Physisorption Chemisorption
Low heat of adsorption (20-40 kJ mol-1) High heat of adsorption (40-400 kJ mol-1)
Force of attraction are Van der Waal’s forces Forces of attraction are chemical bond forces
It usually takes place at low temperature and decreases with increasing temperature It takes place at high temperature
It is reversible It is irreversible
It is related to the ease of liquefaction of the gas The extent of adsorption is generally not related to liquefaction of the gas
It is not very specific It is highly specific
It forms multi-molecular layers It forms monomolecular layers
It does not involve any activation energy It involves activation energy
Fig. 2.1:Comparison between Physisorption and Chemisorption
(Source: Literature Survey)
Factors Affecting Adsorption:
 The extent of adsorption depends upon the following factors:
Nature of adsorbate and adsorbent.

The surface area of adsorbent.

Activation of adsorbent.

Experimental conditions. E.g., temperature, pressure, etc.

2.1.2Adsorption Isotherm Models
Analysis of the isotherm data is important to develop an equation which accurately represents the results and which could be used for design purposes and to optimize an operating procedure. Langmuir and Freundlich models are the most common theoretical equilibrium isotherms applied in solid/liquid systems (Ho, 2004; Basha et al., 2008), and the models are extensively used due to their Simplicity and ease of interpretation. Likewise, linear regression has been frequently used to evaluate the model parameters (Basha et al., 2008). However, equilibrium isotherms such as the Temkin, two site Langmuir, Langmuir-Freundlich (Sips isotherm), Redlich-Peterson, Toth, and Dubinin-Radushkevitch can also be used to model experimental data (Onyango et al., 2004).

2.1.2.1Langmuir Adsorption Isotherm
The Langmuir isotherm also called the ideal localized monolayer model was developed to represent chemisorption (Wang et al., 2009). Langmuir (1918) theoretically examined the adsorption of gases on solid surfaces, and considered sorption as a chemical phenomenon. The Langmuir equation relates the coverage of molecules on a solid surface to concentration of a medium above the solid surface at a fixed temperature. This isotherm is based on the assumption that; adsorption is limited to mono-layer coverage, all surface sites are alike and can only accommodate one adsorbed molecule, the ability of a molecule to be adsorbed on a given site is independent of its neighbouring sites occupancy, adsorption is reversible and the adsorbed molecule cannot migrate across the surface or interact with neighbouring molecules (Febrianto et al., 2009; Sarkar ; Acharya, 2006). By applying these assumptions and the kinetic principle (rate of adsorption and desorption from the surface is equal), the Langmuir equation can be written in the following hyperbolic form:
qe=qmaxKLCe1 + KLCe…… (2.2)this equation is often written in different linear forms (Febrianto et al., 2009):
1qe= 1KLqmax1Ce+ 1qmax….… (2.3)
Ceqe= 1qmaxCe+ 1KLqmax…… (2.4)
where qe is the adsorption capacity at equilibrium (mg/g), qmax is the theoretical maximum adsorption capacity of the adsorbent (mg/g) and, as such, can be thought of as the best criterion for comparing adsorptions (Ho et al., 1995), KL is the Langmuir affinity constant (l/mg) and Ce is the supernatant equilibrium concentration of the system (mg/l). However, it should be realized that the Langmuir isotherm offers no insights into aspects of adsorption mechanism (Liu ; Liu, 2008).

2.1.2.2Freundlich Adsorption Isotherm
Initially, the Freundlich isotherm was of an empirical nature which was later interpreted as sorption to heterogeneous surfaces or surfaces supporting sites of varied affinities. It is assumed that the stronger binding sites are occupied first and that as the degree of site occupation increases, the binding strength decreases. (Davis et al., 2003). Adsorption of organic and inorganic compounds on a wide variety of adsorbents can be described by Freundlich isotherm (Febrianto et al., 2009). According to this model the adsorbed mass per mass of adsorbent can be expressed by a power law function of the solute concentration as (Freundlich, 1906):
qe= KFCe1n…… (2.5)
where KF is the Freundlich constant related with adsorption capacity (mg/g), n is the heterogeneity coefficient (dimensionless). The linear expression of Freundlich equation is written in logarithmic form as follows:
logqe= logKF+ 1nlogCe…… (2.6)
The plot of log qe versus log Ce has a slope with the value of 1/n and an intercept magnitude of log KF. On average, a favourable adsorption tends to have Freundlich constant n between 1 and 10. Larger value of n (smaller value of 1/n) implies stronger interaction between the adsorbent and the adsorbate while 1/n equal to 1 indicates linear adsorption leading to identical adsorption energies for all sites. Generally, linear adsorption occurs at very low solute concentrations and low loading of the adsorbent (Site, 2001).

2.1.3Adsorption Kinetic Models
Adsorption equilibria studies are important in determining the efficiency of adsorption. Added spite of this, it is also necessary to identify the adsorption mechanism type in a given system. With the purpose of investigating the mechanism of adsorption and its potential rate-controlling steps that include mass transport and chemical reaction processes, kinetic models have been exploited to test the experimental data. In addition, information on the kinetics of metal/organic compound uptake is required to select the optimum condition for full-scale batch adsorbate removal processes. Adsorption kinetics is expressed as the solute removal rate that controls the residence time of the adsorbate in the solid–solution interface.

Generally, several steps are involved during the sorption process by porous sorbent particles: (i) Bulk diffusion; (ii) External mass transfer (boundary layer or film diffusion) between the external surface of the sorbent particle and the surrounding fluid phase; (iii) Intra-particle transport within the particle; and (iv) Reaction kinetics at phase boundaries.

In practice, kinetic studies were carried out in batch reactions using various initial adsorbate concentrations. Adsorption kinetic models have been proposed to clarify the mechanism of sorption from aqueous solution on to an adsorbent. Several adsorption kinetic models have been established to understand the adsorption kinetics and rate-limiting step. These include Lagergren’s pseudo-first and second-order rate model, Weber and Morris sorption kinetic model, Natarajan and Khalaf first-order reversible reaction model, etc.

2.1.3.1Lagergren’s Model
Lagergren’s kinetics equation has been most widely used for the adsorption of an adsorbate from an aqueous solution. Vast majority of the adsolutes in the adsorption systems from the articles studied were aqueous phase pollutants such as metal ions, dyestuffs, and contaminating organic compounds. At large, the adsorbents were activated carbon (Onganer & Temur, 1998; Kadirvelu & Namasivayam, 2000; Dai, 1994), materials of biological organic compounds (Yamuna & Namasivayam, 1993; Kandah, 2001), agricultural by-products such as banana pith (Namasivayam & Kanchana, 1992), palm-fruit bunch (Nassar, 1997), corn pith (Namasivayam et al., 2001), cow dung (Das et al., 2000), sago (Quek et al., 1998), coconut husk (Manju et al., 1998), and orange peel (Namasivayam et al., 1996) and inorganic adsorbents such as fly ash (Viraraghavan & Ramakrishna, 1999; Panday et al., 1985), polyacrylamide grafted hydrous tin(iv)oxide gel (Shubha et al., 2001), Fe(III)/Cr(III) hydroxide (Namasivayam et al., 1994), chrome sludge (Lee et al., 1996), magnetite (Ortiz et al., 2001), kaolinite (Atun & Sismanoglu, 1996), and bituminous shale (Tütem et al., 1998).

Lagergren’s original paper expressed the pseudo-first order rate equation for the liquid-solid adsorption system in 1898 and was summarised as follows:
axdt=kX -x ………………………(a)
X and x (mg g-1) are the adsorption capacities at equilibrium and at time t, respectively.

k (min-1) is the rate constant of pseudo-first order adsorption.

Equation (a) was integrated with boundary conditions t = 0 to t = t and
x = 0 to x = x:
lnXX – x=kt …………………………(b)
andx=X1-e-kt ……..…………………(c)
equation (b) may be rearranged to the linear form:
logX-x= logX-k2.303t ………….(d)
The most popular form used is:
logqe-qt= logqe-k12.303t…… (2.7)
qe and qt (mg g-1) are the adsorption capacities at equilibrium and at time t respectively. k1 (min-1) is the rate constant of pseudo-first order adsorption.

Consequently, the sorption data was also studied by second order kinetics
dqdt=k2(qe-qt)2 …………………………. (i)
where k2 is the rate constant of pseudo- second order adsorption.

After integration,
1qe-qt=1qe+k2t …………………………….. (ii)
This can be written in the linear form on further simplification
tqt=1k2qe2+tqe …… (2.8)
The applicability of this equation can be studied by a plot of t/qt vs. t.

2.1.3.2Intra Particle Diffusion
The most commonly used technique for identifying the mechanism involved in the adsorption process is by fitting the experimental data in an intra-particle diffusion plot. Previous studies by various researchers showed that the plot of Qt versus t0.5 represents multi linearity, which characterizes two or more steps involved in the adsorption process. According to Weber and Morris, an intra particle diffusion co-efficient Kp is defined by the equation:
Kp= Qtt0.5 or qt= Kpt12+C…… (2.9)
Thus the Kp (mg/g min 0.5) value can be obtained from the slope of the plot of Qt (mg/g) versus t0.5 and C is the intercept.

2.2Types of Adsorbent
2.2.1Commercial Adsorbents
2.2.1.1Zeolites
Zeolites are aluminosilicate minerals containing exchangeable alkaline and alkaline earth metal cations (normally Na, K, Ca and Mg) as well as water in their structural framework. The physical structure is porous, enclosing interconnected cavities in which the metal ions and water molecules are contained. Zeolites have high ion exchange and size selective adsorption capacities as well as thermal and mechanical stabilities (Wang et al., 2009). Also, zeolites can be either synthetic (Hui et al., 2006) or natural (Rubio, 2006). They have been used as water softeners (Ali ; El-Bishtawi, 1997), chemical sieves and adsorbents (Hui et al., 2005) for a long time. However, zeolites become unstable at high pH (Basu, et al., 2006) and for this reason; chemicals are added to adjust the pH, which makes this process expensive. The process of regenerating zeolite packed beds dumps salt water into the environment. Furthermore, the use of zeolites does not reduce the level of most organic compounds (Johnson, 2005).

2.2.1.2Silica gel
Silica gel is a non-toxic, inert and efficient support and is generated by decreasing the pH value of the alkali silicate solution to less than ten. The solubility of silica is then reduced to form the gel and as the silica begins to gel, cells in silica are trapped in a porous gel, which is a three-dimensional SiO2 network (Chaiko et al., 1998). Porous silica gel is an inorganic synthetic polymeric matrix often used to entrap cells and its use for entrapment is called the sol-gel technique (Weller, 2000). Reactive sites of silica gel exist in large numbers, and therefore, the number of immobilized organic molecules is high, which results in good sorption capacity for metal ions (Rangsayatorn et al., 2004; Chaiko et al., 1998).

2.2.1.3Activated alumina
Activated alumina is a filter media made by treating aluminium ore so that it becomes porous and highly adsorptive. It can also be described as a granulated form of aluminium oxide. Activated alumina removes a variety of contaminants that often co-exist with fluoride such as excessive arsenic and selenium (Farooqi et al., 2007).

The medium requires periodic cleaning with an appropriate regenerant such as alum or acid in order to remain effective. Activated alumina has been used as an effective adsorbent especially for point of use applications (Ghorai ; Pant, 2005; Bouguerra et al., 2007). The main disadvantage of activated alumina is that the adsorption efficiency is highest only at low pH and contaminants like arsenites must be pre-oxidized to arsenates before adsorption. In addition, the use of other treatment methods would be necessary to reduce levels of other contaminants of health concern (Johnson, 2005).

2.2.1.4Activated carbon
The most widely used adsorbent for industrial applications is activated carbon (Ho, 2004). In the 1940’s, activated carbon was introduced for the first time as the water industry’s main standard adsorbent for the reclamation of municipal and industrial wastewater to a potable water quality (Huang, et al., 2009). It has been found as a versatile adsorbent due to its high capacity of adsorption because of small particle sizes and active free valences. The structure consists of a distorted three dimensional array of aromatic sheets and strips of primary hexagonal graphic crystallites (Stoeckli, 1990). This structure creates angular pores between the sheets of molecular dimensions which give rise to many of the useful adsorption properties of activated carbon (Stoeckli, 1990; Innes et al., 1989). In spite of this, due to its high cost of production, activated carbon could not be used as the adsorbent for large scale water treatment. Moreover, the regeneration of activated carbon is difficult due to the use of costly chemicals, high temperatures, and hence, its regeneration is not easily possible on a commercial scale. Commercial activated carbon, which has high surface area and adsorption capacity, is a potential adsorbent for removing heavy metals and dissolved organic compounds from wastewater. However, preparing activated carbon is relatively complicated and involves carbonization and activation stages.

According to the IUPAC definitions the pore sizes of activated carbon can roughly be classified as micropores (; 2 nm), mesopores (2 – 50 nm) and macropores (; 50 nm) (Stoeckli et al., 2002). The macropores act as transport pathways, through which the adsorptive molecules travel to the mesopores, from where they finally enter the micropores. Thus, macro- and mesopores can generally be regarded as the highways into the carbon particle, and are crucial for kinetics. The micropores usually constitute the largest proportion of the internal surface of the activated carbon and contribute most to the total pore volume (Rodriguez-Reinoso ; Linares-Solano, 1989).

Activated carbon has both chemical and physical effects on the substance where it is used as a treatment agent. Activity can be separated into adsorption, mechanical filtration, ion exchange and surface oxidation. Adsorption is the most studied of these properties in activated carbon (Cheremisinoff ; Morresi, 1978). Heavy metal removal by adsorption using commercial activated carbon has been widely used. However, high costs of activated carbon and 10-15% loss during regeneration makes its use prohibitive in the developing countries like South Africa (Vimal et al., 2006). Commercial activated carbon also requires complexing agents to improve its removal performance for heavy metals. Therefore this situation no longer makes it attractive to be widely used in small-scale industries because of cost inefficiency (Sandhya ; Kurniawan, 2003). This has led to a search for cheaper carbonaceous substitutes. In order to overcome the problems associated with the activated carbon, low cost adsorbents derived from agricultural waste is proposed in the present work.

2.2.2Low Cost Adsorbents
In a developing country like South Africa, materials which are locally available in large quantities such as agricultural wastes and industrial by-products can be utilized as low cost adsorbents. Conversion of these materials into adsorbents for wastewater treatment would help to reduce the cost of waste disposal and provide an alternative to commercial activated carbon (Kurniawan et al., 2006). The adsorption of toxic waste from industrial wastewater using agricultural waste and industrial by-products has been massively investigated (Basu et al., 2006; Wan ; Hanafiah, 2007; Srivastava et al., 2006). Several reviews can be referred to that discuss low-cost adsorbents application for industrial wastewater treatment (Kurniawan et al., 2006; Babel and Kurniawan, 2003; Crini, 2005; Pollard et al., 1992).

Fig. 2.2:Possible classification of low-cost adsorbents
(Source: Literature Survey Compiled by Grassi et al., 2012)
2.2.2.1Agricultural Wastes
Production of activated carbon from agricultural wastes serves a double purpose by converting unwanted, surplus wastes to useful, valuable material and provides an efficient adsorbent material for the removal of pollutants from wastewater. In recent years, more attentions have been gained by the biomaterials which are by-products or the wastes of large-scale industrial processes and agricultural waste materials. A range of adsorbents such as orange peel, grass, leaf, wheat shells, heartwood, rice husk, saw dust of various plants, bark of the trees, groundnut shells, coconut shells, black gram husk, hazelnut shells, walnut shells, cotton seed hulls, waste tea leaves, Cassia fistula leaves, maize corn cob, jatropa deoiled cakes, apple, banana, soybean hulls, grapes stalks, water hyacinth, sugar beet pulp, sunflower stalks, coffee beans, arjun nuts, and sugarcane bagasse have been reported to be used to remove or recover heavy metals and dissolved organic compounds from aqueous solutions.

Karnitz et al. (2007) reported the use of chemically modified sugarcane bagasse to adsorb heavy metal ions and Mukherjee et al. (2007) studied the adsorption of phenol using an adsorbent derived from sugarcane bagasse as well. This shows that agricultural wastes are versatile; they can be used for sorption of both inorganic and organic wastes.
Effective use of biomass wastes has become one of the promising fields of the treatment of heavy metals due to the low cost as well as their environmentally friendly nature (Shao et al., 2011). Wong et al. (2003) investigated this agricultural wastes were extensively used for the removal of heavy metals due to their abundance in nature. Besides that, it has been used for adsorbing metal ions due to the characteristic functional groups (Tarley et al., 2004).
Agricultural waste materials being economic and eco-friendly due to their unique chemical composition, availability in abundance, renewable, low in cost and more efficient are seem to be viable option for heavy metal remediation. These promising agricultural waste materials are used in the removal of metal ions either in their natural form or after some physical or chemical modification (Sud et al., 2008). But, many studies have shown that the adsorption capacity of these adsorbents may be increased by their treatment with chemical reagents (Tarley et al., 2004). In general, raw lignocellulosic adsorbents were modified by various methods to increase their sorption capacities because metal ion binding by lignocellulosic adsorbents is believed to take place through chemical functional groups such as carboxyl, amino, or phenolics. More recently, great effort has been contributed to develop new adsorbents and improve existing adsorbents. Many investigators have studied the feasibility of using low-cost agro-based waste materials (Demirbas, 2008).

2.2.2.2Industrial By-Products
Many industrial wastes are high in carbon content and offer significant potential for conversion into carbonaceous chars which may then be further activated to yield porous adsorbents. Like agricultural waste, industrial by-products such as fly ash, used tyres, waste iron, metallic iron, hydrous titanium oxide, and blast furnace slag are inexpensive and abundantly available (Kurniawan et al., 2006). These materials can be chemically modified to enhance their removal performance. However, unlike those from agricultural waste, adsorbents from this source can be obtained from industrial processing only. In South Africa, several such wastes currently pose a variety of disposal problems due to bulk volume, auto reactivity or physical nature like oily wastes and scrap tyres. Thus, the controlled pyrolysis of these wastes combined with the reuse of porous products contributes to a minimisation of handling difficulties (Pollard et al., 1992). Some of these industrial by-products combine good adsorption capacities and buffering effect, which assure almost complete removal of heavy metal ions without preliminary correction of the initial pH being necessary.

Fly ash, an industrial solid waste of thermal power plants is one of the cheapest adsorbents having excellent removal capabilities for different wastes. South Africa produces approximately 28 million tons of coal fly ash per annum (Reynolds et al., 2002). Only 5% of the fly ash is used as a construction material while the rest is stored in ash damps, which in turn have to be rehabilitated increasing the cost of ash handling (Woolard et al., 2000). Sen and De (1987) carried out a study on the adsorption of mercury using coal fly ash and it was reported that the maximum adsorption capacity of 2.82 mg Hg2+/g took place at a pH range of 3.5 – 4.5 and that adsorption followed the Freundlich model. In another work, a comparative adsorption study was carried out by Jain et al. (2001) using carbon slurry waste obtained from a fertilizer plant and blast furnace sludge, dust, and slag from steel plant wastes as adsorbents for the removal of dyes. It was found that carbonaceous adsorbent prepared from the fertilizer plant waste exhibited a good potential for the removal of dyes as compared to the other three adsorbents prepared.

2.3Mechanism of Adsorption
Sud et al. (2008) reported that the removal of metal ions from aqueous streams using agricultural materials is based upon metal adsorption. The process of adsorption involves a solid phase (sorbent) and a liquid phase (solvent) containing a dissolved species to be sorbed. Due to high affinity of the sorbent for the metal ion species, the latter is attracted and bound by rather complex process affected by several mechanism involving chemisorptions, complexation, adsorption on surface and pores, ion exchange, micro precipitation, heavy metal hydroxide condensation onto the biosurface, and surface adsorption, chelation, adsorption by physical forces, entrapment in inter and intrafibrillar capillaries and spaces of the structural polysaccharides network as a result of the concentration gradient and diffusion through cell wall and membrane (Sud et al., 2008).

In order to understand how metals bind to the biomass, it is essential to identify the functional groups responsible for metal binding. Most of the functional groups involved in the binding process are found in cell walls. Plant cell walls are generally considered as structures built by cellulose molecules, organized in microfibrils and surrounded by hemicellulosic materials (xylans, mannans, glucomannans, galactans, arabogalactans), lignin and pectin along with small amounts of protein (Dewayanto, 2010).

2.3.1Various Adsorbents Used for Adsorption of Phenol and Its Derivatives
In recent years literature surveys show that a large number of alternative adsorbents have been studied to replace activated carbon. The review presents the summary of the removal of phenol and its derivatives by using following adsorbents by investigators in research works. Also the comparison of adsorption capacities for various phenolic compounds on adsorbents was shown in the Figure 2.3.

Fig. 2.3: Comparison of adsorption capacities for phenolic compounds on various adsorbents
(Source: Literature Survey Compiled by Bazrafshan et al., 2016)
Zarei et al., (2013), studied the efficiency of Moringa peregrina tree shell ash for the removal of phenol from aqueous solutions; the examination was carried out in a batch system. According to the results of this study, it was found that the Moringa peregrina tree shell ash is not only a low-cost adsorbent but also has a high performance in the removal of phenol from aqueous solutions (Zarei et al., 2013). In another research, the adsorption potential of pistachio-nut shell ash in a batch system was studied by Bazrafshan et al. (2012b) for the removal of phenol from aqueous solutions. The possibility of using rice husk and rice husk ash for removal of phenol from aqueous solution was investigated by Mahvi et al. (2004). Activated carbon prepared from rubber seed coat (RSCC), an agricultural waste by-product has been used for the adsorption of phenol from aqueous solution by Rengaraj et al. (2002b). Rao and Viraraghavan, (2002), have investigated the use of nonviable pretreated cells of Aspergillus niger to remove phenol from an aqueous solution. Five types of non-viable pretreated A. niger biomass powders were used as a biosorbent to remove phenol present in an aqueous solution at a concentration of 1000 g l-1. Sulfuric acid pretreated A. niger biomass was found effective in the removal of phenol present in an aqueous solution at a concentration of 1000 g l-1 (Rao ; Viraraghavan, 2002). Findings of Tor et al. (2006) on the application of neutralized red mud for removal of phenol from aqueous solution showed that the neutralized red mud was an effective adsorbent for the removal of phenol from aqueous solutions. Higher phenol removal by neutralized red mud was possible provided that the initial phenol concentration was low in the solution (Tor et al., 2006). The potential of tendu (Diospyros melanoxylon) leaf refuse from local industry, which itself is a solid waste disposal menace and its chemically carbonized product to adsorb phenol was investigated by Nagda et al. (2007). Activated carbon derived from avocado kernels (AAC) was evaluated for its ability to remove phenol by Rodrigues et al. (2011).
Adsorption of phenol on natural clay for phenol removal from aqueous solutions have investigated by Djebbar et al. (2012). The phenol removal potential of clay, a low cost and abundantly available material has been investigated by Nayak and Singh (2007). Activated carbon derived from rattan sawdust (ACR) was evaluated by Hamid and Rahman (2008) for its ability to remove phenol from an aqueous solution in a batch process. Abdelwahab and Amin (2013) have analyzed the removal of phenol from aqueous solution by adsorption onto Luffa cylindrical fibers (LC). Adsorption study for phenol removal from aqueous solution on activated palm seed coat carbon (PSCC) was carried out by Rengaraj et al. (2002a). A comparative study with a commercial activated carbon showed that PSCC is two times more effective than commercial activated carbon (CAC) (Rengaraj et al., 2002a). The vegetable sponge of cylindrical loofa, a natural product which rows in the north of Algeria, was used by Cherifi et al. (2009). Abdelkreem (2013) explored the possibility of using olive mill waste to remove phenol from aqueous effluents. The experimental studies on removal of phenol from waste water in a fluidized bed column using coconut shell activated carbon as an adsorbent have been reported by Kulkarni et al. (2013). Arris et al. (2012) showed that cereal by-product, an abundant natural material, can be used effectively and efficiently for the removal of phenol from wastewater. Rushdi et al. (2011) showed that Jordanian zeolite tuff can be used as a low cost adsorbent for the removal of phenol from water.
Another investigation of the use of three carbonaceous materials, activated carbon (AC), bagasse ash (BA) and wood charcoal (WC), as adsorbents was studied by Mukherjee et al. (2007). Srivastava et al. (2006) research deals with the adsorption of phenol on carbon rich bagasse fly ash (BFA) and activated carbon-commercial grade (ACC) and laboratory grade (ACL). The study showed that the bagasse fly ash (BFA) is an effective adsorbent for the removal of phenol from aqueous solution (Srivastava et al., 2006). Karatay and Donmez (2014) have carried out the research on an economical phenol bio-removal method using Aspergillus versicolor an agricultural wastes as a carbon source. Viraraghavan and Alfaro (1998) examined the effectiveness of less expensive adsorbents such as peat, fly ash and bentonite in removing phenol from wastewater by adsorption. Batch adsorption research by Kilic et al. (2011) for the removal of phenol from aqueous solution have been carried out by using activated carbon obtained from tobacco residue by chemical activation using K2CO3 and KOH as activation agents. A natural bentonite modified with a cationic surfactant, cetyl trimethyl ammonium bromide (CTAB), was used as an adsorbent for removal of phenol from aqueous solutions by Senturk et al. (2009). Application of a chemically modified green macro alga as a biosorbent for phenol removal has carried out by Aravindhan et al. (2009). The potential of bentonite for phenol adsorption from aqueous solutions was investigated by Banat et al. (2000). The removal of phenol (Ph) and 2-chlorophenol (2-CPh) from aqueous solution by native and heat inactivated fungus Funalia trogii pellets investigated by Bayramoglu et al. (2009). Batch adsorption experiments were conducted by Bahdod et al. (2009) to investigate the removal of phenol from wastewater by addition of three apatites {porous hydroxyapatite (PHAp), crystalline hydroxyl- (HAp) and fluoroapatite (FAp)}. The adsorption of phenol from aqueous solutions was investigated using a carbonized beet pulp in the inert nitrogen atmosphere by Dursun et al. (2005). Results in comparative studies on adsorptive removal of phenol by three agro-based carbons, which have investigated by Srihari and Das (2008) showed that the black gram husk (BGH) is an effective adsorbent for the removal of phenol from aqueous solution when compared with green gram husk (GGH) and rice husk (RH).
Activated carbons prepared from tamarind nutshell, an agricultural waste by-product, have been examined by Goud et al. (2005). Another Experiment have been conducted by Kermani et al. (2006) to examine the adsorption of phenol from aqueous solutions by rice husk ash and granular activated carbon (GAC). Phenol removal from aqueous system by jute stick has studied by Mustafa et al. (2008). In Siboni et al. (2013) research activated red mud containing iron and calcium as major components was applied to treat synthetic wastewater in a batch reactor. In another research, the adsorption of phenol from wastewater was investigated using sawdust as adsorbent by Dakhil (2013). Moyo et al. (2012) investigated the possibility of Saccharomyces cerevisiae as an alternative adsorbent for phenol removal from aqueous solution. Adsorption of phenol from aqueous solution was investigated using sodium zeolite as an adsorbent by Saravanakumar and kumar (2013). The application of Colocasia esculenta as an alternative adsorbent for the removal of phenol from aqueous solution was investigated by Obi and Woke (2014). The potential of employing wheat husk for phenol adsorption from aqueous solution was studied by Jagwani and Joshi (2014). The potential of activated carbon prepared from Typha orientalis Presl to remove phenol from aqueous solutions was studied by Feng et al. (2015). Coconut shell has been converted to activated carbon through chemical activation with KOH by Hu and Srinivasan (1999). The properties of the carbon produced were dependent on the impregnation ratio and the activation temperature. The removal capabilities were found to be 206, 267 and 257 mg/g for phenol, 4-chlorophenol and 4- nitrophenol respectively. It was found that the adsorption increased with increase in agitation time and initial concentration while acidic pH was favourable for the adsorption of TCP. The maximum adsorption capacity was 716.10 mg/g.

Coconut husk was used to remove 2,4,6-trichlorophenol under optimized conditions by Hameed et al (2008). The effect of activation temperature, activation time and KOH to char impregnation ratio were studied. The adsorption capacity was found to be 191.73 mg/g. Namasivayam and Kavitha (2006) utilized coir pith carbon has an adsorbent for understanding the mechanism of the phenol removal. It was found that the adsorption capacities of 48.31, 19.12 and 3.66 mg/g were obtained for phenol, 2,4-dichlorophenol and p-chlorophenol. The FTIR studies shows that the participation of the specific functional groups in adsorption interaction, while SEM studies visualized the formation of the adsorbed white layer on the phenol surface. The applicability of shells, seed coat, stone and kernels of various agricultural products as adsorbents for the removal of toxic pollutants from water has been investigated. The feasibility of activated carbon from almond shell, hazelnut shell, walnut shell and apricot stone for the removal of phenol has been investigated by Aygun et al. (2003), and found that the adsorption capacity of 70.4, 100, 145 and 126 mg/g was obtained for almond shell, walnut shell, hazel nut shell and apricot stone respectively. It was found that the impregnating agents and activating agents had influence on phenol removal. The effectiveness of the almond shell carbon for the treatment of pentachlorophenol from water was performed by Santos et al. (2008), and a saturation adsorption capacity of 9.6 mg/g was obtained under continuous flow experiments. The nature of sorption on almond shells carbon was understood by focusing on the structural and chemical characterization of the carbons.
2.3.2Properties of Agricultural Adsorbent
Agricultural materials particularly those containing lignin and cellulose as the main constituents shows potential adsorption capacity for metals and organic compounds. Other components are hemicellulose, extractives, lipids, proteins, simple sugars, starches, water, hydrocarbons, ash and many more compounds that contain a variety of functional groups present in the binding process. (Dewayanto, 2010).
The functional groups present in biomass molecules are acetamido groups, carbonyl, phenolic, structural polysaccharides, amido, amino, sulphydryl carboxyl groups alcohols and esters. These groups have the affinity for metal complexation. The presence of various functional groups and their complexation with heavy metals during adsorption process has been reported by different research workers using spectroscopic techniques that facilitate metal complexation which helps for the sequestering of heavy metals (Sud et al., 2008).

Agricultural waste usually has high moisture content that required removal through physical treatments which include natural drying under the direct sunlight, room drying, and oven drying at certain temperature. Dried materials are normally ground to obtain the specific granular size and can directly be applied as an adsorbent or transformed into carbonaceous adsorbent by pyrolysis (Dewayanto, 2010).
Chemical treatment of agricultural wastes can extract soluble organic compounds and enhance chelating efficiency using different kinds of modifying agents such as base solutions (sodium hydroxide, calcium hydroxide, sodium carbonate), mineral and organic acid solutions (hydrochloric acid, nitric acid, sulfuric acid, tartaric acid, citric acid), organic compounds (ethylenediamine, formaldehyde, methanol), oxidizing agent (hydrogen peroxide), and dye (Reactive Orange 13). Chemically modified adsorbents can provide better performance for removing soluble organic compounds, eliminating coloration of the aqueous solutions and increasing efficiency of metal adsorption (Dewayanto, 2010).

2.4Chlorophenols
2.4.1Sources and Usage of Chlorophenols
Most of the commercially important chlorophenols are obtained by direct chlorination of phenol using chlorine gas or for the higher chlorinated phenols, the chlorination of lower chlorinated phenols at high temperatures (WHO, 1989). In the technical product, there are impurities of other chlorophenol isomers or chlorophenols with more or less chlorine. The heavy chlorophenols are mainly contaminated by polychlorophenoxyphenols, chlorodibenzoparadioxins and chlorodibenzofurans. Emissions are mainly due to the manufacture, storage, transportation and application of chlorophenols. Because the higher chlorinated phenols are produced at higher temperature, the contamination of the higher chlorinated phenols is greater than that of the lower chlorinated phenols (WHO, 1989). However, due to their broad-spectrum antimicrobial properties, chlorophenols have been used as preservative agents for wood, paints, vegetable fibres and leather and as disinfectants. In addition, they are used as herbicides, fungicides and insecticides and as intermediates in the production of pharmaceuticals and dyes, (WHO, 1989).

2.4.2Effects of Chlorophenols
Chlorophenols can be absorbed through the lungs, the gastro-intestinal tract and the skin, 80% of it can be excreted via the kidneys without undergoing any transformation. The toxicity of chlorophenols depends upon the degree of chlorination, the position of the chlorine atoms and the purity of the sample. Chlorophenols have an irritating effect on the eyes and on the respiratory tract. Toxic doses of chlorophenols cause convulsions, shortness of breath, coma and finally death. After repeated administration, toxic doses may result in damage to the inner organs (primarily liver) and the bone marrow. Pentachlorophenol has a toxic effect on embryos in animal experiments (lethal at higher concentrations). Technical PCP may possibly be carcinogenic not least due to contamination. Mutagenic potential cannot be excluded, (WHO, 1989).

In the aquatic environment, chlorophenols may be dissolved in free or complexed form or adsorbed on suspended matter. Removal is mainly by way of biodegradation which is rapid when adapted microorganisms are already present. However, PCP is biodegraded much more difficultly than other chlorophenols. Chlorophenols are also removed from water by photodecomposition and volatilisation. Finally, adsorption of chlorophenols on suspended matter plays a role in the amount of chlorophenols in water: light chlorophenols are hardly fixed whereas PCP is fixed very strongly, (WHO, 1989).

2.5Almond Nut Shells
The tropical Almond Terminalia catappa (Indian almond) belongs to the family Combrataceae is a fruit of a large spreading tree distributed throughout the tropics and coastal environment (Species profiles, 2006). The fruit is a sessile, laterally compressed, ovoid to ovate and smooth skinned drupe. The oil containing seeds are encased in a tough fibrous husk with a fleshy pericarp. This corky fibrous endocarp (nut) of the fruit and shells are waste materials and can be collected on community basis for reuse. Almond nut shells are abundant, inexpensive and readily available lignocellulosic material.

Plate 1:Almond (Terminalia catappa) nut shells
2.6Instrumentation of FT-IR Spectroscopy
Fourier Transform infrared spectroscopy (FTIR) originates from Fourier transform (a mathematical process) which is required to convert the raw data into the actual spectrum. It is a technique which is used to obtain an infrared spectrum of absorption or emission of a solid, liquid or gas. It takes advantage of asymmetric molecular stretching, vibration and rotation of chemical bonds as they are exposed to designated wavelengths of light, and transform the signal from the time domain to its representation in the frequency domain (Wikipedia.org).

Fig. 2.4:Fourier Transform Spectrometer
(Image Source: Wikipedia Commons)
The normal instrumental process is as follows:
1. The Source: Infrared energy is emitted from a glowing black-body source. This beam passes through an aperture which controls the amount of energy presented to the sample (and, ultimately, to the detector).

2. The Interferometer: The beam enters the interferometer where the “spectral encoding” takes place. The resulting interferogram signal then exits the interferometer.

3. The Sample: The beam enters the sample compartment where it is transmitted through or reflected off of the surface of the sample, depending on the type of analysis being accomplished. This is where specific frequencies of energy, which are uniquely characteristic of the sample, are absorbed.

4. The Detector: The beam finally passes to the detector for final measurement. The detectors used are specially designed to measure the special interferogram signal.

5. The Computer: The measured signal is digitized and sent to the computer where the Fourier transformation takes place. The final infrared spectrum is then presented to the user for interpretation and any further manipulation.

Because there needs to be a relative scale for the absorption intensity, a background spectrum must also be measured. This is normally a measurement with no sample in the beam. This can be compared to the measurement with the sample in the beam to determine the “percent transmittance.”
This technique results in a spectrum which has all of the instrumental characteristics removed. Thus, all spectral features which are present are strictly due to the sample. A single background measurement can be used for many sample measurements because this spectrum is characteristic of the instrument itself.

The light passes through a beamsplitter, which sends the light in two directions at right angles.  One beam goes to a stationary mirror then back to the beamsplitter.  The other goes to a moving mirror.  The motion of the mirror makes the total path length variable versus that taken by the stationary-mirror beam.  When the two meet up again at the beamsplitter, they recombine, but the difference in path lengths creates constructive and destructive interference ‘an interferogram’.
The recombined beam passes through the sample.  The sample absorbs all the different wavelengths characteristic of its spectrum, and this subtracts specific wavelengths from the interferogram.  The detector now reports variation in energy versus time for all wavelengths simultaneously.  A laser beam is superimposed to provide a reference for the instrument operation (Wikipedia.org).
A mathematical function called a Fourier transform allows to convert an intensity-vs.-time spectrum into an intensity-vs.-frequency spectrum.
The Fourier transform:   A(r) and X(k) are the frequency domain and time domain points, respectively, for a spectrum of N points.
2.7Instrumentation of UV-Visible Spectroscopy
Ultraviolet–visible spectroscopy or ultraviolet-visible spectrophotometry (UV-Vis or UV/Vis) refers to absorption spectroscopy or reflectance spectroscopy in the ultraviolet-visible spectral region. This means it uses light in the visible and adjacent (near-UV and near-infrared (NIR)) ranges. UV/Vis spectroscopy is routinely used in analytical chemistry for the quantitative determination of different analytes, such as transition metal ions, highly conjugated organic compounds, and biological macromolecules. Spectroscopic analysis is commonly carried out in solutions but solids and gases may also be studied.

The functioning of this instrument is relatively straightforward. A beam of light from a visible and/or UV light source (coloured red) is separated into its component wavelengths by a prism or diffraction grating. Each monochromatic (single wavelength) beam in turn is split into two equal intensity beams by a half-mirrored device. One beam, the sample beam (coloured magenta), passes through a small transparent container (cuvette) containing a solution of the compound being studied in a transparent solvent. The other beam, the reference (coloured blue), passes through an identical cuvette containing only the solvent (Michigan State Univ.edu).

Fig. 2.5: Schematic for a UV-Vis spectrophotometer
(Image Source: Wikipedia Commons)
The intensities of these light beams are then measured by electronic detectors and compared. The intensity of the reference beam, which should have suffered little or no light absorption, is defined as I0. The intensity of the sample beam is defined as I. Over a short period of time, the spectrometer automatically scans all the component wavelengths in the manner described. The ultraviolet (UV) region scanned is normally from 200 to 400 nm, and the visible portion is from 400 to 800 nm (Michigan State Univ.edu). If the sample compound does not absorb light of a given wavelength, I = I0. However, if the sample compound absorbs light then I is less than I0, and this difference may be plotted on a graph versus wavelength, as shown on the right. Absorption may be presented as transmittance (T = I/I0) or absorbance (A = log I0/I). If no absorption has occurred, T = 1.0 and A = 0. Most spectrometers display absorbance on the vertical axis, and the commonly observed range is from 0 (100 % transmittance) to 2 (1 % transmittance). The wavelength of maximum absorbance is a characteristic value, designated as ?max . Different compounds may have very different absorption maxima and absorbances. Intensely absorbing compounds must be examined in dilute solution, so that significant light energy is received by the detector, and this requires the use of completely transparent (non-absorbing) solvents. The most commonly used solvents are water, ethanol, hexane and cyclohexane. Solvents having double or triple bonds, or heavy atoms (e.g. S, Br ; I) are generally avoided. Because the absorbance of a sample will be proportional to its molar concentration in the sample cuvette, a corrected absorption value known as the molar absorptivity is used when comparing the spectra of different compounds. This is defined as:
Molar Absorptivity, ? = A/cl
(where,  A = absorbance, c = sample concentration in moles/liter ; l = length of light path through the cuvette in cm).

CHAPTER THREE
MATERIALS AND METHODS
This chapter includes the materials and general methods implemented to carry out this work and which are not completely described through the research articles content.

3.1Materials and Equipments
A. Reagents:
All the reagents used for this current investigation were of analytical grade (AR) obtained from different manufacturer: NaOH and HCl (Merck India Ltd.), 2,6-Dichlorophenol (Kem-Light Laboratories PVT. Ltd. Mumbai, India).B. Apparatus and Equipments:
Standard test sieve (1.68 mm), glass bottles, pestle and mortar, weighing machine, pH/conductivity meter (Jenway 430 pH/cond.), petri dish, beakers, conical flasks, volumetric flasks (1000 ml, 100 ml capacity), 50 ml pycnometer, funnel, 1000 ml round and flat bottom flasks, measuring cylinders (10 ml, 100 ml, 1000 ml capacity), porcelain dish, electric oven, dessicator, stirrer, electric burner, water bath, electric muffle furnace, glass stoppered 250 ml erlenmenyer flasks, rubber stopper, mechanical shaker, centrifuge machine, filter papers,  FT-IR spectrophotometer (Perkin-Elmer infrared spectrometer ASCII PEDS 1.60),  UV spectrophotometer, IR-grade KBr in an agate mortar.

3.2Collection and Preparation of Adsorbent Sample (Almond Nut Shells)
The common reagents used for the preparation and treatment of adsorbents are hydrochloric acid, phosphoric acid, sodium hydroxide and zinc chloride. But in this present study, the adsorbent was subjected to acid treatment using hydrochloric acid since it is an inexpensive and non-volatile agent compared to phosphoric acid, while sodium hydroxide was utilized for the alkali treatment preferred to zinc chloride which constitute problems of additional environmental contamination by zinc.
3.2.1Collection of Almond Nut Shells Sample
In this work, the corky fibrous endocarp (nut) shells of the fruit (Almond) were collected from the premise of News Agency of Nigeria (NAN) National Headquarters, Central Business District Abuja-FCT (Plate 2).

Plate 2: Raw and Processed Almond (Terminalia catappa) nut shells
3.2.2Preparation and Modification of Almond Nut Shells
Fruit seed shells of Almond (Terminalia catappa) were crushed using wooden mallet and thoroughly washed with double distilled water for several times to remove all the foreign matters, and sun dried for some days. Then the dried seed shells was homogenized to a fine powder using pestle and mortar, and the powdered particles were sieved to obtain a desired average particle size of 1.68 mm using standard test sieve (Plate 2). The modification process was carried out using 150 g of the powdered, sieved adsorbent which was pre-treated with chemical solvent to increase the 2, 6-dichlorophenol uptake efficiency. For this purpose, adsorbent was first treated by boiling in 0.1 N HCl for three hours. After decanting the solution, the residue was boiled again with 0.1 N NaOH for three hours. The treated sorbent was washed well several times with double distilled water. Later, it was soaked in water for sufficient time interval, to ensure swelling, as it would make more sorption sites available; and finally, the sorbent material was dried in the oven, after which it was stored in an air tight plastic container prior to use as an adsorbent. The chemically treated Terminalia catappa nut shell powder was used for further experiments and henceforth shall be denoted as MTCNS in the forthcoming discussions.
3.3Preparation of Adsorbate (2,6-Dichlorophenol)
A stock solution was prepared by dissolving 1.0 g of 2,6- Dichlorophenol (DCP) in 1litre of sterilized de-ionized water. From this original stock solution, five test working solutions with various concentrations (100, 200, 300, 400, and 500 mg/l) were obtained by successive dilution with de-ionized distilled water (DDW). Before mixing the adsorbent, the pH of each 2,6-Dichlorophenol (DCP) solution was adjusted to the required value by 0.1 M NaOH or 0.1 M HCl solution (Agarry ; Ogunleye, 2014).

3.4Characterization of Modified Adsorbent
The procedures for physicochemical and surface characteristics of the modified adsorbent are compiled as follows:
3.4.1pH and Conductivity
Approximately 1.0 gram of MTCNS (adsorbent) was weighed and transferred to 250 ml beaker. 30 ml of freshly boiled and cooled double distilled water (adjusted to pH 7.0) was added and heated to boiling. After 10 minutes, the solution was filtered and the first 15 ml of the hot filtrate was discarded. The remaining filtrate solution was cooled to room temperature. The pH and conductivity was determined using Jenway 430 pH/cond. Meter (Ademiluyi et al., 2008).

3.4.2Moisture Content
Approximately 0.25 g of MTCNS (adsorbent) was weighed in petri dish and placed in an electric oven maintained at 383±5 K for about 2 hours. The dish was covered and cooled in desiccators and then weighed. Heating, cooling and weighing were repeated at 30 minutes intervals until the difference between two consecutives weighing was less than 5 mg (Abdul Halim et al., 2001).

Moisture Content %=(W-X)W ×100…3.1
where, W= Weight of the material (g)
X = Weight of the material after drying (g)
3.4.3Bulk Density
The MTCNS (adsorbent) was placed in a 10 ml graduated measuring cylinder, tapped several times until constant volume obtained and then weighed. The bulk density was calculated as the ratio of the weight of MTCNS (adsorbent) to its volume and expressed in g/ml (Mudoga et al., 2007).

3.4.4Specific Gravity
Approximately 2.5 g of MTCNS (adsorbent) was weighed and placed in a small porcelain dish, 25 ml of double distilled water was added and the content was heated to boil gently for 3 minutes to expel the air. After cooling in a water bath to 288 K, the sorbent suspension was transferred to 50 ml pycnometer and weighed (Wc). Later, the pycnometer was filled with double distilled water and weighed (Wb) (Agarry ; Ogunleye, 2014).

Specific gravity= Weight of MTCNS (adsorbent) (Wa)Volume of displaced water (V) … 3.2
where, V= Wa +Wb +WcDensity of water Wa = Weight of MTCNS (adsorbent)
Wb = Weight of pycnometer with water
Wc = Weight of pycnometer with MTCNS (adsorbent) residue
3.4.5Pore Volume
Approximately 2.0 g of MTCNS (adsorbent) was weighed and transferred completely into a 10 ml graduated measuring cylinder and its height in the cylinder was recorded. This was poured into a beaker containing 20 ml of deionized water and boiled for 5 minutes. The content in the beaker was filtered and measured. The pore volume of MTCNS was determined by dividing the increase in weight of the adsorbent by the density of water (Aneke ; Okafor, 2005).
3.4.6Porosity
Porosity was determined by dividing the pore volume (Vp) of the MTCNS by its total volume (Vt) (Aneke ; Okafor, 2005).s
Porosity (Pt)= Pore Volume (Vp)Total Volume (Vt) ×100 … 3.3
where,Vt = Vs + VpandVs = solid volume (ml)
3.4.7Ash Content
Approximately 2.0 g of MTCNS (adsorbent) was weighed (Ws) and placed in a pre-weighed porcelain crucible (We). The crucible and it content was placed in an electric oven at 383±5 K for about 5 hours. The crucible was removed from the oven and the content was ignited in an electric muffle furnace at a temperature of 800 K for about 2 hours. The crucible was removed and cooled in a desiccators and then weighed (Wc). Heating, cooling and weighing was repeated at 30 minutes intervals until the difference between two consecutives weighing was less than 5.0 mg (Shetty ; Rajkumar, 2009). The ash content was calculated as percentage by weight using the relation:
Ash Content %=Wc-WeWs×100 …3.4
3.4.8FT-IR Spectra Analysis
Fourier transform infrared spectra analysis of MTCNS (adsorbent) sample was performed by using a Perkin-Elmer infrared spectrometer ASCII PEDS 1.60. This was carried out as a preliminary and qualitative analysis to determine the type of functional groups present in the sorbent that might have involved in the 2,6-dichlorophenol uptake. The MTCNS (adsorbent) was blended with IR-grade KBr in an agate mortar and pressed into pellets. The spectrum of MTCNS (adsorbent) was recorded within the range of 400 – 4000 cm-1.

3.5Batch Mode Adsorption Studies
Adsorption experiments were carried out in batch mode at ambient temperature. The influence of various experimental parameters such as initial adsorbate concentration, pH, MTCNS (adsorbent) dosage and contact or exposure time on the adsorption efficiency of 2,6-DCP were conducted under optimized conditions. Only one of the parameters was changed at a time while others were maintained constant.

3.5.1Adsorption Experiments
Adsorption equilibrium experiments were conducted in a set of glass-stoppered 250 ml Erlenmenyer flasks, where 100 ml of working volume with different initial concentrations (100, 200. 300, 400 and 500 mg l-1) of 2,6-DCP having a solution pH of 7 were added in these flasks. A weighed amount (2.0 g) of adsorbent (MTCNS) was added to the solution. The flasks were agitated at a constant speed of 150 rpm for 150 minutes in a temperature controlled water-bath shaker at 30 oC. Samples were collected from the flasks at predetermined time intervals of 30 minutes for analyzing the residual 2,6-DCP concentration in the solution. Prior to analysis, samples were centrifuged to separate adsorbent from the adsorbate and minimize interferences. At time t = 0 and equilibrium, the 2,6-DCP concentrations were determined using UV-spectrophotometer at an absorbance wavelength of 340 nm. Three replicate per sample were done and the average results are presented. The amount of adsorption at equilibrium, qe (mg/g) was calculated according to the expression (Crisafully et al., 2008):
qe=Co-CeVW ….3.5
where Co and Ce (mg/l) are the initial and final (equilibrium) concentrations of 2,6-DCP in aqueous solution. V (ml) is the volume of the aqueous solution and W (g) is the mass of dry adsorbent used.

3.5.2Batch Adsorption Kinetic Studies
The procedures of kinetic studies were basically identical to those of batch equilibrium studies. The amount of 2,6-DCP sorbed at time t , qt was calculated according to Eq. (3.6) (Xun et al., 2007):
qt=Co-CtVW ….3.6
where Ct is the concentration of 2,6-DCP in aqueous solution at time t.

The percentage of 2,6-DCP removal was calculated using Eq. (3.7) (Hamad et al., 2011):
Removal Efficiency (%)=Co-Ct 100Co ….3.7
3.5.3Effect of pH
The effect of pH on the amount of 2,6-DCP removal was analysed over the pH range from 2 to 10. In this study, 100 ml of 2,6-DCP solution 100 mg l-1 was taken in stoppered conical flask and agitated with 2.0 g of MTCNS (adsorbent) using a temperature controlled water-bath shaker at a constant speed of 150 rpm for 30 minutes at 30 oC. The samples were centrifuged, and the left out concentration in the supernatant solution were analysed using a UV spectrophotometer at an absorbance wavelength of 340 nm (Moyo et al., 2012).
3.5.4Effect of Adsorbent Dosage
The effect of MTCNS (adsorbent) mass on the amount of removal of 2,6-DCP solution was obtained by contacting 100 ml of 2,6-DCP solution of initial concentration of 100 mg l-1 at the optimal pH of 7, with different weighed amount ranging from 2.0 g to 10 g. Each sample was then agitated in a temperature controlled water-bath shaker at a constant speed of 150 rpm for 30 minutes at 30 oC. The samples were then centrifuged and the concentrations were then analysed as before (Moyo et al., 2012).
3.5.5Effect of Contact Time
The effect of contact time on the removal of 2,6-DCP was carried out at different intervals ranging from 30 – 150 minutes. In each case 100 ml of 2,6-DCP solution of initial concentration 100 mg l-1 was added to each of the conical flasks. Corresponding masses of approximately 2.0 g of MTCNS (adsorbent) were added to each of the flasks and the mixture agitated in a temperature controlled water-bath shaker at a constant speed of 150 rpm at 30 oC. After the stated time the samples were removed from the rotary shaker and centrifuged. The supernatant solution was then analysed using the UV spectrophotometer at an absorbance wavelength of 340 nm (Moyo et al., 2012).
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
4.1Characterization of MTCNS (Adsorbent)
4.1.1Physicochemical Properties
Table 4.1 depicts the various physicochemical parameters of the modified almond nut shells (MTCNS).

Table 4.1: Physicochemical Characteristics of MTCNS (adsorbent)
Parameters Mean Values ± Standard Deviation
pH 4.42 ± 0.13
Conductivity (µs/cm) 197.00 ± 3.61
Moisture Content (%) 7.73 ± 0.61
Bulk Density (g/ml) 0.30 ± 0.02
Specific Gravity 1.58 ± 0.20 
Porosity (%) 39.95 ± 2.00
Ash Content (%) 2.32 ± 0.23
Pore Volume (ml) 4.93 ± 0.42
pH: The pH value determines whether the activated carbon is acidic or basic. The acid or basic nature of an activated carbon depends on the means it was prepared, inorganic matter and chemically active groups on its surface as well as the kind of treatment applied. The pH value obtained in this present investigation revealed that MTCNS with pH of 4.42 as presented in Table 4.1 is acidic in nature which was consistent with the result of Almond shells activated carbon (ASAC) subjected to phosphoric acid treatment having pH of 4.5 as reported by Bhatti et al. (2007). Also, this value was in agreement with the finding carried out by Cheremisinoff and Ellerbusch (1978) that the pH of either raw or modified agricultural by-products in water suspension can vary between 4 and 12, hence, it can be deduced that MTCNS is a good activated carbon material.
Conductivity: This is a measure of the ability of water to allow the passage of an electrical current, and the unit is in micromhos per centimetre (µmhos/cm) or microsiemens per centimetre (µs/cm). Conductivity can be affected by many factors which includes the presence of inorganic dissolved solids (ions that carries negative and positive charges such as Cl-, NO3-, SO42- , PO43-, Ca2+, Na+, Mg2+, Al3+, etc.); organic compounds (like oil, phenol, alcohol ; sugar); and temperature (the warmer the water, the higher the conductivity). From the result obtained, it was observed that MTCNS studied has conductivity of 197 µs/cm as revealed in Table 4.1. In a similar research work, the conductivity of the phosphoric acid activated (ASAC) sample obtained by Bhatti, et al. (2007) was discovered to be 40 µs/cm.

Moisture Content: The moisture content of a sample refers to the amount of water physically bound on the sample under normal condition. The laboratory result of the moisture content for MTCNS was determined to be 7.73 % as shown in Table 4.1; however, this was slightly higher than 7.21 % moisture content of Almond shells as reported by Erhan, et al., (2004) in their studies. The permissible limit of moisture content is 3 – 8 %; low moisture content is desired by activated carbon because its presence increases the rate of adsorption of contaminants into the microspore of the activated carbon (Inyang, et al., 2010). High moisture content allows penetration of more contaminants into the matrix of the adsorbent thus reducing working capacity of the adsorbent (Appendix A2).

Bulk Density: Bulk density is the ratio of mass of the aggregate to the volume of aggregate particles with voids between them; hence, it is used to convert quantities by mass to quantities by volume. The bulk density of activated carbon depends on several factors such as size; shape and degree of compaction of individual particles, and its data are useful to Engineers for the estimation of tank, cartridge or packing volume. The American Water Work Association has set a lower limit on bulk density at 0.25 g/ml for Granular Activated Carbon (GAC) to be of practical use (AWWA, 1991). The bulk density of prepared MTCNS (adsorbent) sample used for this work is within that limit, which is calculated to be 0.30 g/ml (Appendix A3).
Specific Gravity: This is ratio of the weight of a given volume of material (activated carbon) to the weight of an equal volume of water, indicating how much heavier (or lighter) the material is than water. The knowledge is necessary in the computation of fine particle properties like void ratio, degree of saturation, size distribution etc. The result obtained from this present study of specific gravity of MTCNS (adsorbent) was found to be 1.58 (Appendix A4), meanwhile 4.45 was obtained from activated carbon prepared from chemically treated Terminalia catappa nut shells (TTCNS) (Andal ; Gohulavani, 2013).

Porosity: This is used to explain how much empty or void, space is present in a given sample. It shows the capacity of activated carbon in terms of its efficiency. Porosity of the studied MTCNS (adsorbent) was evaluated to be 39.95 % (Appendix A5). Activated carbon used in determining pore volume by Aneke and Okafor (2005) gave porosity of 21.4 %.

Pore Volume: Pore volume is of importance in the facilitation of the adsorption process by providing sites and the appropriate channels to transport the adsorbate. The result obtained for MTCNS (adsorbent) was estimated to be 4.93 ml (Appendix A5). But in a similar research work carried out by Andal and Gohulavani (2013) using chemically treated Terminalia catappa nut shells (TTCNS), the pore volume was discovered to be 6.80 ml which shows that MTCNS is a good activated carbon with highly developed porous structure.
Ash Content: The ash content of a sample is the inorganic (non-carbon) residue left after the organic matter has been burnt off which is not chemically combined with the carbon surface; also the ash content primarily depends on the types of raw material used for the production of the activated carbon. The percentage of ash content for MTCNS (adsorbent) sample studied was found to be 2.32 % (Appendix A6) which was consistent with Romero Gonzalez, et al., (2001) reported result of 2.14 % for almond shells. The obtained value for MTCNS was favourable because the ash content serves as interference during the adsorption (Kha, et al., 2009). High ash content is not desirable and is considered as an impurity for activated carbon since it reduces the mechanical strength of carbon and affects its adsorptive capacity. The lower the ash content, the better the quality of the activated carbon.

4.1.2Fourier Transform Infrared Analysis
Figure 4.1 surmise the FTIR spectrum obtained in order to give an idea about the organic functional groups present in modified almond nut shells (MTCNS) sample that can participate in bonding with 2,6-DCP during adsorption process. The peaks emerging in the FTIR spectrum were assigned to a variety of functional groups in accordance to their respective wave numbers as stated in literatures.
MTCNS (Adsorbent)

Fig. 4.1: FT-IR Spectrum of Modified Terminalia catappa Nut Shells (MTCNS)
Table 4.2: FT-IR Spectrum Elucidation of MTCNS (Adsorbent)
? (cm-1) Assignment (Suspected Functional Group)
Adsorption Peak Intensity 3777.89 Sharp OH (non-bonding) Free
3394.00 Strong,
Broad OH (stretch), N-H (stretch)
2923.13 Sharp, Medium C-H (stretch)
1607.35 – 1734.38 Sharp, Medium C-H (alkane), C=C (stretch), C=O (stretch)
1247.17 – 1442.00 Weak C-H (bend), C-O (alcohol), C-N, OH (carboxylic acid)
1046.88 Strong C-O (alcohol), C-H (in plane), C=N (bend)
604.70 Weak C-H (bend), C=C (out of plane)
? is the wave number
Table 4.2 shows the FT-IR spectrum elucidation of modified almond nut shells (MTCNS). A sharp peak is recognized around 3777.89 cm-1 which is attributed to non-bonding (free) hydroxyl (–OH) group of water. The strong and broad absorption peak at 3394.00 cm-1 depicts that of OH bond of alcohol and carboxylic acid groups; and N-H bond of amide groups with stretched vibrations. The peak observed at 2923.13 cm-1 was associated with the stretching vibrations of C-H bond of methyl, methylene and methoxy groups (Feng et al., 2008), and those peaks appearing around 1607.35 – 1734.38 cm-1 corresponded to C-H (alkane), C=C (aromatic) and C=O stretch. On the other hand, the absorption bands 1247.17 – 1442.00 cm-1 were ascribed to C-H bend, C-O (alcohol), C-N, and OH (carboxylic acid) and the one at 1046.88 cm-1 to C-O (alcohol), C-H and C=N bend (nitriles) respectively. The weak band with wave number of 604.70 cm-1 was assigned to C-H bend and C=C which are out of plane. Consequently, the FT-IR spectra indicates that hydroxyl, carboxyl, and carbonyl groups were very important (hetero-atoms) functional groups which participate in the binding of 2,6-DCP to the surface of MTCNS (adsorbent).
4.2Adsorption Process Studies
4.2.1Effect of pH on Adsorption
pH of an aqueous solution is an essential operational parameter prevailing the adsorption process of organic chemicals or metals in solution as it not only affects the solubility of the chemical ions concentration of the counter ions on the functional groups of the adsorbent, but also influences the degree of ionization of adsorbate during reaction (Agarry et al., 2013b). The effect of variation of pH in the range of 2 -10 on the adsorption of 2,6-DCP by MTCNS (adsorbent) was studied from the data by keeping other parameters constant as presented in Table 4.3. The relations between removal percentage and pH were revealed in Fig. 4.2. It was observed that the percentage of 2,6-DCP removal increased from 92.24 % at pH 2 to 96.92 % at pH 6 which is the maximum uptake and decreased to 94.52 % at pH 10. The apparently high adsorption of 2,6-DCP at lower pH was due to high electrostatic attraction between the negatively charged 2,6-DCP molecules and positively charged adsorption sites. Increase in the pH present fewer H+ ions in the solution, consequently more negatively charged sites were made available which facilitate a decreased in 2,6-DCP removal due to electrostatic repulsion (Morlu ; Bareki, 2017).
Table 4.3: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various pH
pH Ce
(mg/L) Qe = Co-Ce (mg/L) qe
(mg/g) Removal Efficiency (%)
2 7.76 92.24 4.612 92.24
4 5.46 94.54 4.727 94.54
6 3.08 96.92 4.846 96.92
8 4.26 95.74 4.787 95.74
10 5.48 94.52 4.726 94.52
Co = 100 mg/L, mass = 2 g, contact time = 30 minutes.

Fig. 4.2: Effect of pH for the Adsorption of 2,6-DCP onto MTCNS
4.2.2Effect of Adsorbent Dosage on Adsorption
In this study, five different dosages of MTCNS were selected ranging from 2.0 to 10.0 g, while other parameters were kept constant. The results are presented in Table 4.4 while the relationship between adsorbent dosage and removal efficiency of 2,6-DCP is shown in Fig. 4.3. It can be explain from this figure that as adsorbent dosage increases there is an increase in the removal efficiency. This kind of a trend is mostly ascribed to an increase in the adsorptive surface area and the availability of more active binding sites on the adsorbent surface (Das ; Mondal, 2011).
Table 4.4: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various Adsorbent Doses
Mass (g) Ce 
(mg/L) Qe = Co-Ce (mg/L) qe
(mg/g) Removal Efficiency (%)
2 4.52 95.48 4.774 95.48
4 3.34 96.66 2.417 96.66
6 1.19 98.81 1.647 98.81
8 0.47 99.53 1.244 99.53
10 0.67 99.33 0.993 99.33
Co = 100 mg/L, pH = 7, Contact Time = 30 minutes.

Fig. 4.3: Effect of Adsorbent Dosage for the Adsorption of 2,6-DCP onto MTCNS
However, significant changes in value of adsorbent dosage (from 8.0 to 10.0 g) yield little or no change in percentage adsorption of the 2,6-DCP. This revealed that the adsorption sites remain unsaturated during the adsorption reaction whereas the number of sites available for adsorption increases by increasing the adsorbent dose. Furthermore, maximum 2,6-DCP removal efficiency of 99.53 % was recorded at 8.0 g adsorbent dose of MTCNS.
4.2.3Effect of Contact Time on Adsorption
The variation in contact time (30 – 150 minutes; 30 mins. Interval) on the adsorption of 2,6-DCP by MTCNS (adsorbent) was investigated at fixed adsorbent dose of 2 g, pH of 7.0 and initial concentration of 100 mg/l, the results are shown in Table 4.5. The effect of contact time on removal of 2,6-DCP by MTCNS as a function of time is depicted in Fig. 4.4. It can be seen that the removal efficiency of 2,6-DCP increased considerably until the optimal removal efficiency reached within about 100 minutes contact time, where a saturation adsorption has been shown. Further increase in contact time beyond this point did not show significant changes. In general, the rate of removal of adsorbate increases with an increase in contact time to a certain extent, further increase in contact time does not increase the uptake due to deposition of adsorbate on the available adsorption site on adsorbent material (Ansari ; Mosayebzadeh, 2010).
Table 4.5: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various Period of Contact
Time (mins.) Ce 
(mg/L) Qe = Co- Ce (mg/L) qe
(mg/g) Removal Efficiency (%)
30 4.52 95.48 4.774 95.48
60 3.61 96.39 4.820 96.39
90 1.11 98.89 4.945 98.89
120 0.83 99.17 4.959 99.17
150 0.75 99.25 4.963 99.25
Co = 100 mg/L, pH = 7, mass = 2 g

Fig. 4.4: Effect of Contact Time for the Adsorption of 2,6-DCP onto MTCNS
4.2.4Effect of Initial Concentration on Adsorption
The adsorption of 2,6-DCP onto the MTCNS (adsorbent) was studied for different concentrations ranging from 100 – 500 mg/l keeping pH 7, adsorbent dose 2.0 g and exposure time 30 minutes fixed in all the samples. The data obtained are provided in Table 4.6. The removal efficiency of 2,6-DCP was found to decrease with the increase in the initial concentration as shown graphically in Fig. 4.5. Maximum removal efficiency of 95.68 % occurred for low initial concentration which showed gradual reduction when initial concentration was raised. It could be attributed to the fixed amount of adsorbent.
Table 4.6: Amount of 2,6-DCP Adsorbed (qe), Removal Efficiency (%) and Amount Adsorbed at Equilibrium (Ce) by MTCNS at Various Concentrations
Co
(mg/L) Ce
(mg/L) qe
(mg/g) Ce/qe
(g/L) Removal Efficiency (%)
100 4.32 4.784 0.903 95.68
200 12.22 9.389 1.303 93.89
300 21.30 13.935 1.529 92.90
400 32.28 18.386 1.756 91.93
500 43.95 22.803 1.927 91.21
pH = 7, mass = 2 g, contact time = 30 minutes.

Fig. 4.5: Effect of Initial Concentration for the Adsorption of 2,6-DCP onto MTCNS
The adsorption sites were occupied and attained saturation at low concentration, with increase in 2,6-DCP concentration no further adsorption will be achieved at high concentration due to non-availability of active sites which resulted to reduced removal efficiency. Similar results have been reported in literature on the extent of removal of dyes, the initial adsorbate concentration provides an important driving force to overcome mass transfer resistance of ions between the aqueous and solid phases (Donmez ; Aksu, 2002).

4.2.5Adsorption Isotherm Modelling
The adsorption isotherm play a vital role in describing the interaction between adsorbate and adsorbent, it gives an insight about the adsorption capacity of the adsorbent. This indicates how the adsorption molecules between the liquid and solid phases distribute in order to attain equilibrium state during adsorption process. The surface phase may be considered as a monolayer or multilayer (Salleh et al., 2011). In this present study, Langmuir and Freundlich isotherm models relating to adsorption equilibrium are tested.

The Langmuir isotherm is described mathematically by equation (2.3) or (2.4), where qmax and KL are Langmuir constants related to adsorption capacity (maximum amount adsorbed per gram of adsorbent) (mg g-1) and energy of sorption (L mg-1), respectively. Values of qmax and KL can be calculated from the slope and intercept of the linear plot of Ce/qe against Ce (Appendix C) as illustrated in Fig. 4.6 with a correlation coefficient (R²) of 0.9433, thus indicating that the adsorption equilibrium data conform well to the Langmuir isotherm model, confirming monolayer adsorption and the monolayer adsorption capacity (qmax) was found to be 40.49 mg/g. Similar research conducted by Sathishkumar et al. (2009) obtained 17.94 mg/g as the maximum monolayer adsorption capacity of maize cob carbon for the adsorption of 2,4-DCP while Agarry et al. (2013) obtained 14.25 mg/g as the maximum monolayer adsorption capacity of modified plantain peels for the adsorption of 2,6-DCP. The essential characteristics of the Langmuir isotherm can be expressed in terms of dimensionless constant Separator Factor (RL) which is defined as
RL= 11+ KLCo ………… (4.1)where Co is the initial 2,6-DCP concentration. The value of RL indicates the type of isotherm to be either unfavourable (RL ; 1), linear (RL = 1), favourable (0 ; RL ; 1), or irreversible (RL = 0) (Hameed, et al., 2008). The values of RL (Table 4.8) in the present investigation was calculated with initial concentration range 100 – 500 mg/L were between (0 ; RL ; 1) which is consistent with the requirement for a favourable adsorption of the 2,6-DCP onto MTCNS, indicating that the adsorbent is good for the removal of 2,6-DCP from aqueous solution.

Fig. 4.6: Langmuir Isotherm for the Adsorption of 2,6-DCP onto MTCNS

Fig. 4.7: Freundlich Isotherm for the Adsorption of 2,6-DCP onto MTCNS
Table 4.7: Isotherm Parameters and Correlation Coefficients (R2) for 2,6-DCP Adsorption onto MTCNS (Adsorbent)
Isotherm Models Langmuir Ceqe=1qmaxCe+ 1KLqmaxFreundlich logqe=logKf+1nlogCeParameters qmax (mg/g) KL (L/mg) R2 Kf n R2
Values 40.49 0.027 0.9433 1.773 1.48 0.9996
Table 4.8: Values of Separator Factor (RL) for Adsorption of 2,6-DCP on MTCNS (Adsorbent)
Initial Concentration Co(mg/L) RL Values
100 0.27
200 0.16
300 0.11
400 0.09
500  0.07
The empirical Freundlich model which is known to be satisfactory for low concentrations and based on sorption on a heterogeneous surface is denoted by equation (2.6), where KF and n are Freundlich constants related to the adsorption capacity and adsorption intensity respectively. On average, a favourable adsorption tends to have Freundlich constant, n between 1 and 10. Larger value of n (smaller value of 1/n) implies stronger interaction between the adsorbent and the adsorbate while n equal to 1 indicates linear adsorption leading to identical adsorption energies for all sites (Site, 2001). These parameters can be calculated from the intercept and the slope of the linear plot of log qe versus log Ce (Appendix C) as shown in Fig. 4.7.
In this study, the Freundlich isotherm model was also found suitable and fitted well for the experimental data with high correlation coefficient (R2) of 0.9996 which is close to unity, verifying multilayer adsorption. The value of KF and n obtained from the plot are 1.773 and 1.48 respectively. The value of ‘n’ greater than 1 implies favourable nature of adsorption. In a similar observations, Agarry et al. (2013b) obtained KF and n of 2.79 and 3.03 for the adsorption of 2,6-DCP onto modified plantain peels; while Achak et al. (2009) obtained KF and n of 0.13 and 1.13 for the adsorption of phenolic compounds from olive mill wastewater onto banana peel.

Table 4.7 summarized the model parameters together with the R2 values (goodness of fit criterion) corresponding to the Langmuir and Freundlich isotherms established at 30 o C. Generally, a comparison of R2 values for the two tested isotherm models fitted well to the experimental data with high correlation coefficient. The order of fitness of data to isotherm models were: Freundlich ; Langmuir. However, the Freundlich isotherm model provided the best fit with a higher correlation coefficient hence considered desirable model to describe the adsorption process.

4.2.6Adsorption Kinetic Models
In this study, three (3) different models were applied to evaluate the experimental data of the adsorption kinetic of 2,6-DCP onto MTCNS namely: Lagergren’s Pseudo-first-order and Pseudo-second-order, and Webber-Moris intra-particle diffusion models. The pseudo-first order kinetic model equation describes the rate of adsorption is directly proportional to the number of unoccupied sites by the solutes (Lagergren & Svenska, 1898). Pseudo-second-order equation describes the rate of occupation of adsorption sites is proportional to the square of the number of unoccupied sites (Dada et al., 2012). Intra-particle diffusion plays a significant role in controlling the kinetics of the adsorption process. The linear forms of these three models are expressed by equations (2.4), (2.8) and (2.9) respectively, where the terms qe and qt have the same meaning as previously described in chapter 2 with unit mg g -1 while k1, k2 and kp are pseudo-first-order, pseudo-second-order and intra-particle diffusion model rate constants, expressed in min-1, g / mg min and mg / g min0.5 respectively.
Table 4.9: Kinetic Study Data for the Removal of 2,6-DCP at Different Initial Concentration
Time (t) Min. Initial 2,6-DCP Concentration (Co) in mg/L
100 mg/L 200 mg/L 300 mg/L 400 mg/L 500 mg/L
Ct qt Ct qt Ct qt Ct qt Ct qt
30 4.32 4.784 12.22 9.389 21.30 13.935 32.28 18.386 43.95 22.803
60 3.61 4.820 11.72 9.414 20.22 13.989 31.72 18.414 43.05 22.848
90 1.11 4.945 10.84 9.458 19.14 14.043 30.92 18.454 41.85 22.908
120 0.83 4.959 10.20 9.490 18.60 14.070 29.96 18.502 41.05 22.948
150 0.75 4.963 9.92 9.504 18.18 14.091 29.32 18.534 40.80 22.960
Note: Final 2,6-DCP Concentration (Ct) in mg/L and Adsorption Capacity (qt) in mg/g @ Time (t)
The slopes and intercepts of plots were used to calculate qe, k1, k2 and kp as illustrated in Figures 4.8 – 4.10. These model parameters and constants along with the corresponding linear regression coefficient R2 values are depicted in Table 4.10. The applicability of the kinetic model is compare by judging the correlation coefficient R2 and the agreement between the calculated and experimental qe values.
Table 4.10: Kinetic Parameters and Correlation Coefficients (R2) obtained for the Adsorption of 2,6-DCP onto MTCNS (Adsorbent)
Kinetic Models Parameters Initial Concentration Co (mg/L)
100 200 300 400 500
qe, Exp. (mg g-1)  4.963 9.504   14.091  18.534  22.960Pseudo First Order logqe-qt=logqe-k12.303tk1 (min-1) 0.045 0.023 0.023 0.017 0.028
qe, Cal. (mg g-1) 1.070 0.292 0.344 0.286 0.480
% ?qe 78.44 96.93 97.56 98.46 97.91
R2  0.9284 0.9163   0.9812  0.9058  0.9179
Pseudo Second Order
tqt=1k2qe2+tqek2 (g mg-1 min-1) 0.107   0.113  0.132 0.120  0.122 
qe, Cal. (mg g-1) 5.028 9.560 14.144 18.587 22.989
% ?qe 1.29 0.59 0.37 0.29 0.13
R2 0.9999  1.0000   1.0000   1.0000   1.0000 
Intra-particle Diffusion
qt=Kp.t1/2+Ckp (mg g-1 min-0.5) 0.0301  0.0312  0.0237  0.0225  0.0248 
C (mg g-1) 4.6176 9.1444 13.8080 18.251 22.665
R2 0.8843   0.9233  0.9891 0. 9697  0.9804
It can be observed that the correlation coefficients (R2) obtained from the plots of log (qe  – qt) versus time (t) (Appendix D) for pseudo-first-order equation (Fig. 4.8) were moderately high (0.9058 – 0.9812), but the calculated qe values from pseudo-first-order kinetic plots were deviating (% ?qe) much as compared to the experimental qe values, and were not in agreement with the experimental qe values suggesting that the removal of 2,6-DCP by adsorption on MTCNS did not fit the pseudo-first-order model.

Fig. 4.8: Pseudo-first-order Kinetic plots for Removal of 2,6-DCP by MTCNS

Fig. 4.9: Pseudo-second-order Kinetic plots for Removal of 2,6-DCP by MTCNS

Fig. 4.10: Intra-particle Diffusion Kinetic plots for Removal of 2,6-DCP by MTCNS
On the other hand, the R2 values from the plots of t/qt versus time (t) (Appendix D) for pseudo-second-order model (Fig. 4.9) were extremely high (0.9999 – 1) for all the initial concentrations of 2,6-DCP. The calculated qe values were closer to the experimental qe values and the calculated qe values agreed well with the experimental ones. This indicated that the kinetics data fitted perfectly well with the pseudo-second-order model. This model assumes that, the rate-controlling step in the removal of 2,6-DCP by adsorption with MTCNS is chemisorptions involving valence forces through sharing or exchanging of electrons between adsorbent and adsorbate (Parate & Talib, 2015).

According to Intra-particle diffusion model, the intercept (C) of the plots qt versus t1/2 (Appendix D) give an idea about boundary layer thickness. The larger the intercept, greater the boundary layer effect, and if the plots qt versus t1/2 pass through the origin then intra-particle diffusion is the rate-controlling step. When the plots do not pass through the origin, this is indicative of some degree of boundary layer control and this further show that the intra-particle diffusion is not the only rate-limiting step, but also other kinetic models may control the rate of adsorption, all of which may be operating simultaneously (Arami et al., 2008). It can be seen from Figure 4.10; the interception of the line does not pass through the origin showing that the mechanism of adsorption is not solely governed by intra-particle diffusion process.

In a view of these both considerations, we may conclude that the pseudo-second-order mechanism is predominant. Similar observations have been reported for the adsorption of chlorophenols onto other single adsorbents (Wang et al., 2011; Agarry et al., 2013).

CHAPTER FIVE
CONCLUSION AND RECOMMENDATIONS
5.1Conclusion
Based on the experimental results obtained within the framework of this study, it appears that activated carbon prepared from almond nut shells constitutes a good adsorbent for the removal of 2,6-DCP from its aqueous solution, because they are readily available, low cost hence reducing pollution. The following points can be concluded from the current investigation:
The adsorbent (MTCNS) had considerable physicochemical characteristics such as ash content, porosity, bulk density, pH, conductivity, specific gravity, moisture content and pore volume which signifies the effectiveness of the adsorbent.
The FTIR study revealed the types of functional groups responsible for the adsorption.

It was found that the adsorption clearly depends on the parameters like contact time, adsorbent dosage, pH, and initial 2,6-DCP concentration.

The fitting of equilibrium data was found to be satisfied by Langmuir and Freundlich isotherm in order Freundlich > Langmuir, therefore Freundlich is the most suitable isotherm model for the studied adsorption system.
The adsorption kinetic modelling confirmed that the pseudo-second-order was good fitted the experimental data as compare to the pseudo-first-order with a very good correlation coefficient, and the intra-particle diffusion was not the sole rate controlling factor.

5.1Recommendations
In the future the following works can be done:
The adsorption data could also be fitted to other adsorption isotherm and kinetic models such as the Sips isotherm, Redlich-Peterson, Toth, Dubinin-Radushkevitch, Bangham, Elovick, etc.

Studies on the effects of other process parameters such as temperature, particle size and ionic strength can also be an interesting area of future study.
It could also be of particular interest to study the adsorption performance of almond nut shells from different sources.

Study on the adsorption properties of almond shell nuts in the form of pellets rather than granular form.

Studies with actual industrial wastewater to evaluate parameters for field applications.

Investigative studies on the morphological characteristics of almond nut shells by using Scanning Electron Microscope (SEM), X-ray diffractometer (XRD), and energy-dispersive X-ray spectroscopy (EDX).
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APPENDIX A
Characterization Data
Appendix A1: pH and Conductivity
Observation Number 1 2 3
pH 4.50 4.28 4.48
Conductivity (µs/cm) 204.00 189.00 198.00
Mean pH ± Standard Deviation 4.42 ± 0.13
Mean Conductivity (µs/cm) ± Standard Deviation 197.00 ± 3.61
Appendix A2: Moisture Content (%)
Observation Number 1 2 3
W= Weight of the (MTCNS)material (g) 0.25 0.25 0.25
X = Weight of the (MTCNS)material after drying (g) 0.232 0.229 0.231
Moisture Content %=(W-X)W ×1007.20 8.40 7.60
Mean Moisture Content (%) ± Standard Deviation 7.73 ± 0.61
Appendix A3: Bulk Density (D)
Observation Number 1 2 3
Weight of Empty Measuring Cylinder (g) 16.00 16.00 16.00
Weight of Measuring Cylinder + MTCNS Sample (g) 18.50 18.30 18.30
Weight of MTCNS Sample (g) 2.50 2.30 2.30
Volume of MTCNS Sample in Cylinder (ml.) 8.20 7.90 7.20
Bulk Density (D) = Weight of MTCNS SampleVolume of MTCNS Sample (g/ml.) 0.30 0.29 0.32
Mean Bulk Density (D) (g/ml.) ± Standard Deviation 0.30 ± 0.02
Appendix A4: Specific Gravity (S)
Observation Number 1 2 3
Wa = Weight of MTCNS Sample (g) 2.50 2.50 2.50
Wc = Weight of Pycnometer + MTCNS Sample + Water (g) 80.00 79.80 80.20
Wb = Weight of Pycnometer + Water (g) 79.10 79.10 79.10
Density of Water (g/ml.) 1.00 1.00 1.00
Volume of Displaced Water V= Wa+ Wb- WcDensity of water (ml.) 1.60 1.80 1.40
Specific Gravity S= WaV1.56   1.39 1.79
Mean Specific Gravity (S) ± Standard Deviation 1.58 ± 0.20
Appendix A5: Pore Volume (Vp) and Porosity (Pt)
Observation Number 1 2 3
Wi = Initial Weight of MTCNS Sample (g) 2.00 2.00 2.00
Wf = Final Weight MTCNS Sample (g) 7.40 6.60 6.80
Wf – Wi = Increase in Weight of MTCNS Sample (g) 5.40 4.60 4.80
Density of Water (g/ml.) 1.00 1.00 1.00
Vs = Volume of MTCNS Sample in the Cylinder (ml.) 7.40 7.40 7.40
Vp=Pore Volume= Wf – WiDensity of Water (ml.) 5.40 4.60 4.80
Vt = Total Volume = Vs + Vp (ml.) 12.80 12.00 12.20
Pt=Porosity=VpVt ×100 (%) 42.19 38.33 39.34
Mean Pore Volume (ml.) ± Standard Deviation 4.93 ± 0.42
Mean Porosity (%) ± Standard Deviation 39.95 ± 2.00
Appendix A6: Ash Content (%)
Observation Number 1 2 3
Ws = Weight of the (MTCNS) adsorbent (g) 2.00 2.00 2.00
We = Weight of pre weighed porcelain crucible (g) 18.80 18.80 18.80
Wc= Weight of porcelain crucible + (MTCNS)
adsorbent (after heating & cooling) (g) 18.846 18.851 18.842
Ash Content %=Wc-WeWs×1002.30 2.55 2.10
Mean Ash Content (%) ± Standard Deviation 2.32 ± 0.23
APPENDIX B
Calibration Plot and Absorbance Data
Calibration Data
Concentration (mg/L) Absorbance (%)
100 0.1965
200 0.4020
300 0.6120
400 0.8160
500 1.0200

Calibration Plot Absorbance in % versus Concentration mg/L
Ce= AbsorbanceSlope= Absorbance0.002Absorbance Data for Determining Effect of Initial Concentration
Co (mg/L) Absorbance (1) Absorbance (2) Absorbance (3) Average Absorbance Ce  (mg/L)
100 0.0085 0.0088 0.0085 0.0086 4.32
200 0.0248 0.0245 0.0240 0.0244 12.22
300 0.0434 0.0428 0.0416 0.0426 21.30
400 0.0645 0.0652 0.0640 0.0646 32.28
500  0.0878  0.0888 0.0869 0.0879 43.95
Absorbance Data for Determining Effect of pH
pH Absorbance (1) Absorbance (2) Absorbance (3) Average Absorbance Ce  (mg/L)
2 0.0148 0.0161 0.0156 0.0155 7.76
4 0.0111 0.0110 0.0106 0.0109 5.46
6 0.0058 0.0065 0.0063 0.0062 3.08
8 0.0086 0.0083 0.0086 0.0085 4.26
10 0.0110  0.0113  0.0106 0.0110 5.48
Absorbance Data for Determining Effect of Contact Time
Time
(Min.) Absorbance (1) Absorbance (2) Absorbance (3) Average Absorbance Ce  (mg/L)
30 0.0089 0.0090 0.0092 0.0090 4.52
60 0.0072 0.0076 0.0069 0.0072 3.61
90 0.0027 0.0022 0.0018 0.0022 1.11
120 0.0016 0.0015 0.0019 0.0017 0.83
150 0.0015  0.0017  0.0012 0.0015 0.75
Absorbance Data for Determining Effect of Adsorbent Dosage
Mass (g) Absorbance (1) Absorbance (2) Absorbance (3) Average Absorbance Ce  (mg/L)
2 0.0089 0.0090 0.0092 0.0090 4.52
4 0.0066 0.0069 0.0066 0.0067 3.34
6 0.0025 0.0027 0.0020 0.0024 1.19
8 0.0010 0.0008 0.0010 0.0009 0.47
10 0.0013 0.0015 0.0012 0.0013 0.67
APPENDIX C
Langmuir and Freundlich Adsorption Isotherms Data for 2,6-DCP Removal using MTCNS
Co
(mg/L) Ce
(mg/L) qe
(mg/g) Ce/qe
(g/L) log Ce log qe
100 4.32 4.784 0.903 0.635 0.680
200 12.22 9.389 1.303 1.087 0.973
300 21.30 13.935 1.529 1.322 1.144
400 32.28 18.386 1.756 1.509 1.264
500 43.95   22.803 1.927  1.643  1.358 
APPENDIX D
Adsorption Kinetics Data (Pseudo-first-order, Pseudo-second-order and Intra-particle Diffusion) for 2,6-DCP Removal by MTCNS
Time (t) Min. 100 mg/L
qt (mg/g) qe
(mg/g) qe – qt (mg/g) log (qe – qt) t/qt (min g/mg) t0.5 (min0.5)
30 4.784 4.963 0.179 -0.747 6.271 5.477
60 4.820 4.963 0.143 -0.845 12.448 7.746
90 4.945 4.963 0.018 -1.745 18.200 9.487
120 4.959 4.963 0.004 -2.398 24.198 10.954
150  4.963  4.963  –  –  30.224 12.247 
Time (t) Min. 200 mg/L
qt (mg/g) qe
(mg/g) qe – qt (mg/g) log (qe – qt) t/qt (min g/mg) t0.5 (min0.5)
30 9.289 9.504  0.115 -0.939 3.230 5.477
60 9.414 9.504  0.090 -1.046 6.374 7.746
90 9.458 9.504  0.046 -1.337 9.516 9.487
120 9.490 9.504  0.014 -1.854 12.645 10.954
150 9.504  9.504    –  –  15.783 12.247 
Time (t) Min. 300 mg/L
qt (mg/g) qe
(mg/g) qe – qt (mg/g) log (qe – qt) t/qt (min g/mg) t0.5 (min0.5)
30 13.935 14.091  0.156 -0.807 2.153 5.477
60 13.989 14.091  0.102 -0.991 4.289 7.746
90 14.043 14.091  0.048 -1.319 6.409 9.487
120 14.070 14.091  0.021 -1.678 8.529 10.954
150 14.091  14.091    –  – 10.645  12.247 
Time (t) Min. 400 mg/L
qt (mg/g) qe
(mg/g) qe – qt (mg/g) log (qe – qt) t/qt (min g/mg) t0.5 (min0.5)
30 18.386 18.534  0.148 -0.830 1.632 5.477
60 18.414 18.534  0.120 -0.921 3.258 7.746
90 18.454 18.534  0.080 -1.097 4.877 9.487
120 18.502 18.534  0.032 -1.495 6.486 10.954
150 18.534   18.534  –   –  8.093 12.247 
Time (t) Min. 500 mg/L
qt (mg/g) qe
(mg/g) qe – qt (mg/g) log (qe – qt) t/qt (min g/mg) t0.5 (min0.5)
30 22.803 22.960  0.157 -0.804 1.316 5.477
60 22.848 22.960  0.112 -0.951 2.626 7.746
90 22.908 22.960  0.052 -1.284 3.929 9.487
120 22.948 22.960  0.012 -1.921 5.229 10.954
150 22.960   22.960  –  –   6.533 12.247