CHAPTER ONE INTRODUCTION 1

CHAPTER ONE
INTRODUCTION
1.1 Background
Information and Communication Technology is defined as an assortment of technologies that are used for collecting, accumulating, getting back, transforming, examining and transferring of information (Ritchie & Brindley, 2009). These technologies include such as desktop computers, laptops, handheld devices, wired or wireless intranet, software that are meant for manufacturing in the firm for instance text editor and spreadsheet, software that are meant for activities in firms, storage devices for data and information and security, making sure that the network in the firm is secure and so on (Ashrafi And Murtaza, 2008). According to Kollberg and Dreyer (2006), ICT provides ways of storing, processing, distributing and exchanging information for the companies and their customers.
ICT improves productivity, growth and economic progress and is an essential component in the pursuit of a high value, knowledge-based economy. It improves efficiency and increases productivity through; improving resource allocation, reducing transaction costs and enabling technical improvement, leading to the outward shift of the production function (GOK, 2006).
The adoption of ICT is viewed as an important factor for competitive growth of Small-scale manufacturing firms in global and regional markets through new markets, new products, and new distribution channels (Minton, 2013). ICT has provided improved efficiency, and closer customer and supplier relationship (Steinfield, Larose & Chew, 2012). With ICT, small-scale manufacturing firms are able to gain information that would improve their competitiveness (Swash, 2008). It is therefore surprising that there are slow adoption and integration of ICT by Small-scale manufacturing firms (Small Bone et al, 2011; Dawn et al, 2012). The anticipation that Small-scale manufacturing firms might advance like large firms in ICT acceptance has not been recognised (Mpofu, C., 2007).
Through the use of ICT, Small-scale manufacturing firms can gain from developing capabilities for managing; information intense resources enjoy reduced transaction costs, bringing information together and distributing of international level and gain access to very quick access of information (Minton, 2013). OECD (2002) argues that ICT is able to provide a new approach for the organization, from a production that is lean production and teamwork to a good relationship with the customers. Through ICT, firms are able to introduce changes in the organization in areas like re-engineering, decentralization, flexible work arrangements and outsourcing. Firms are able to be more flexible, shorten the product cycle so as to satisfy the customer preference. By having organizational innovation and ICT, the firms are said to have complementary factors.
Businesses around the world have been influenced and have spread through the use and adoption of ICT. By adopting ICT, businesses are rapidly changing their product, work business methods, trade and consumption patterns in and between enterprises and consumers. This is as a result of the demand improves their products. The strategies that are to be used by the small-scale manufacturing firms assist in the development of competitive advantage both within the locality, the country and internationally (Hitt, Hoskinson &Ireland, 2007). Continuous improvement in product and service are necessary because of changing customers demand.
Adoption of ICT by the small-scale manufacturing firms are able to reduce costs by improving their internal processes, their products through faster communication with the available and future customers and they are also able to better promote and distribute their products by having a presence on the online platform. As the global economies rely on ICT to receive and send information, the Small-scale manufacturing firms in Kenya have yet to reap these benefits. This can be attributed to the ability of the Small-scale manufacturing firms to engage the regional and the global economic business networks where they can in turn demand for provision of the required level of access to and use of ICT (Dixon et al, 2012).
The way globalization is expanding has encouraged the way information is transmitted and this would be facilitated by the use of ICTs in small-scale firms. Sharma and Bhagwat (2006) information flow is considered as a source of survival in an organization and this assists in its operation regardless of the size. ICT adoption in small-scale manufacturing firms is able to provide a means of entry, process and assign a very large amount of data and information very fast in order for the firms to make mindful decisions that would help in fields which including manufacturing, environmental analysis, data processing and result making processes (Jimmy and Li, 2003). Hence, there is the need for small-scale manufacturing firms to embrace the state-of-the-art technologies in order for them to penetrate the international markets and remain competitive despite challenges posed by globalization, liberalization and technological changes. With state-of-the-art technologies, small-scale manufacturing firms can be able to leverage as a resource and to benefit from the value of the information (Sharma and Bhagwat, 2006, Dangayach and Deshmukh, 2001).
There is not enough response in the organizational changes that would assist in the adaption when it comes to the business environment so that these firms would make better use of knowledge, technology, and human resources to respond to new demand and customers and to use of ICT in a better manner. The examination will concentrate on whether ICT has any effect on the better performance in Kenya. These have resulted in productions being high, competition is very stiff due to the imported goods, credits being high, those that induce drought, and doubts (KNBS, 2013).
1.1.1 Small-scale manufacturing firms and Information communication and Technologies adoption
Manufacturing firms are defined as any business that changes raw materials into finished or semi-finished goods using machines, tools, and labor. Those that go through the manufacturing process include food, chemicals, textiles, machines, and equipment (Briens & Williams, 2004). Manufacturing is the process of converting raw materials into a finished product especially by means of a large-scale industrial operation. According to Awino (2011), manufacturing is important in Kenyan economy and it makes a considerable contribution to the country’s growth.
Manufacturing firms in Kenya are classified under large-sized (with assets that go above Kenya shillings 100 million), medium-sized (with assets are between Kenya shillings 40 million and 100 million) and small-sized (with the asset are below Kenya shillings 40 million), (KAM, 2011). The firms whose performances are in terms of machinery requirement, labor force and insensitivity are high as well as the deployment of resources necessary to make the end product.
With this, ICT is considered as the incentive to productivity, growth and economic progress and is an imperative component in the quest for quality, knowledge-based economy. By gaining knowledge, the firms can be able to attain structured information that is difficult to organize and expand due to its inherent inability to be divided into parts or forms or inseparable. It is embodied in individuals and firms. With the availability of attaining knowledge and considered to be part of absorbing and selecting the available information as a human being, knowledge is considered as capital in any firm. When it comes to attaining knowledge, the access is considered to be limited and very partial. The knowledge space is itself disentangled, therefore implying that the decisions are made under a good sense of surrounding. According to Audrestch et al. (2006), innovation opportunities are the result of organized and strong-willed efforts so as to create knowledge and new ideas by devoting all the resources on products that are being produced or looking for ways of remodeling old commodities.
The rate of advancement of new technologies has led to the growth of innovation in products. The available technologies are in themselves innovations and have led to many new innovations and ability to bring to light ideas that were never there before. There is a need to focus on understanding how, why and at what rate, innovative ideas and technologies spread into the social systems. In the diffusion of innovation, diversity is only supposed to take place at innovation and not the people who are consuming the product (Les Robinson, 2009). Efficiency and increase productivity through can be improved by the use of ICT through; a resource that is allocated in the production is improved, making sure that the transaction costs are reduced and technical improvement and this would lead to the noticeable transfer in the manufacturing exercise (GOK, 2006).
The significance of ICT to small-scale manufacturing firms being a critical pillar, the adoption of ICT is expected to be an important factor for the competitive growth of small-scale manufacturing firms in global and regional markets. When it comes to the manufacturing firms’ location, there is a positive effect on the concentrated environment on the location of firms. Environments characterized by the small firm’s causes better ways of doing business by lowering the effective costs through the development of self-reliant suppliers, together with a larger and a more assorted supply. The growth of aggressive press forces small-scale manufacturing firms to fight for modern business areas of state-of-the-art products and prevailing dissemination routes (Minton, 2013).
By using ICT, small-scale manufacturing firms are able to advance in the development capabilities in management, in-depth information resources, enjoy reduced transaction costs, develop a scope for information gathering and distribution to a level that is in the international scale and gain access to a swift flow of information (Minton, 2013). New business models and market compositions enabled by information technology, including business process outsourcing, provide small-scale manufacturing firms with access to new a market and new sources of competitive edge.
For instance, in India, small-scale manufacturing firms have been doing way much better in comparison to the large companies on most important areas like advancement in production and growth in employment. The category accounts for 40 percent of the factory-made production, 35 percent of the total products being distributed beyond the borders of the country and brings in about 80 percent of jobs and business transactions in the streamlined sector (Sharma and Bhagwat, 2006). In Singapore, 51 percent of the total people who are recruited in the small- and medium-scale region, and to be more specific, SMEs in the industrialized zone adds up to 15 percent of a massive domestic commodity (Lukacs, 2005). In Hong Kong, SMEs hires the highest number people and they are estimated to be over 1.4 million people and in Japan 81 percent of the people employed to put in the category of the SMEs (Lukacs, 2005).
The employment in Africa by the SMEs has been put to higher than 40 percent of all new entrants because the demand for labor is higher than large firms and are thus better placed to eliminate the lack of jobs (Muuka, 2002). Further research has shown that SMEs have pitched in greatly to job creation and in promoting social economic advancement (Mutula and Brakel, 2006).
Their lower level technology and poor managerial capabilities have often shown to be a challenge when it comes to their effective use of new technologies (Caldeira and Ward, 2002). Whereas ICT is not a remedy for all development problems, it offers a fighting chance for the small-scale manufacturing firms. It continuously grants the small-scale manufacturing firms a chance to fully participate in the knowledge economy by aiding in connectivity; helping to create and deliver products and services on a wider market, and providing access to new markets and new sources of competitive advantage to boost income growth.
1.2 Statement of the problem
Companies today are adopting ICT in all aspects of their businesses, not only for improving business processes and task efficiency but also for improving engagement and communication with their customers (Mutula and Van Brakel, 2007). According to the organization for economic co-operation and development (2003), ICT has the potential to enhance communication within a company, leading to efficient resource management. Additionally, ICT applications such as enterprise resource planning (ERP) provide businesses with a viable source to store, share, and utilize acquired business knowledge and know-how (OECD, 2003).
Small-scale manufacturing firms are an important component of many economies in the world (Mutula and Van Brakel, 2007, Jones, Packham, Beynon and Pickernell, 2011). By not adopting ICT, it is convincing that the Small-scale manufacturing firms can be able to compete effectively and efficiently in both local and international markets (Moyi ; Njiraini, 2005).
Advancements in ICT, especially the internet, have brought about a lot of changes in the way the world economies and markets work, both in developed and developing countries (Montazemi, 2006). Companies in all economies and regardless of size want to expand and increase their market reach, this is easily achieved by utilization of ICT which enables them to communicate with virtually any individual throughout the globe, acquire vital business information and run their organizations more effectively (Montazemi, 2006).
It seems SMEs operators often lament of poor profits and high operational cost. Also, Small-scale manufacturing firms in Nyeri County in Kenya primarily employ traditional methodologies and tools to operate. These traditional means of operations very often has resulted in the collapse and lack of competition by the Small-scale manufacturing firms within the first 2-6 years of existence (Apulu and Lathen, 2009).
The livelihood of most people in the rural areas comes from the informal economic activities. This is because access to the formal employment has been decreasing as the public sector is retrenching its employees. Kenya is faced with several developmental challenges including poor manufacturing products. To the majority of Kenyan consumers, manufactured products are important features within the lifestyle choices. Therefore the small-scale manufacturing firms need to avail and access timely and up-to-date products and services. This can be achieved through the adoption of ICT. By identifying the benefits that come with the use of ICT, it is necessary to identify the reasons that caused the small-scale manufacturing firms not to fully and ably using ICT in their business the challenge found by most small-scale manufacturing firms, is the decision of whether or not to adopt ICT (Payne, 2005).
The study, therefore, will investigate factors affecting the adoption of ICT among rural-based small-scale manufacturing firms in Kenya (Ihua,2009).
1.3 Objectives
The general objective of this research is to evaluate factors affecting the adoption of ICT among rural-based small-scale manufacturing firms in Kenya.
1.3.1 Specific objective
1. To determine the effects of cost on the adoption of ICT in small-scale manufacturing firms in Nyeri.
2. To determine skills development affects the adoption of ICT in small-scale manufacturing firms in Nyeri.
3. To determine the effect of administrative support in the adoption of ICT in small-scale manufacturing firms in Nyeri.
1.4 Research questions
2. Are the costs of ICT and the infrastructure preventing ICT adoption by small-scale manufacturing firms?
3. Do skills affect the adoption of ICT by the small-scale manufacturing firms?
4. Is there any effect of administrative support in the small-scale manufacturing firms?
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter carefully looks at the quality of appropriate forthis research which includes both empirical as well as theoretical literature. The study has demonstrated that empirical study focuses on earlier studies on the same topic, their findings and their impact on the factors affecting the adoption of ICT among the small-scale manufacturing firms in Nyeri County. Theoretical literature deals with concepts and principles that guide the small-scale manufacturing firms. The literature review section shall also consider the knowledge gaps.
2.2 Theoretical overview
The theoretical overview is a composition that backs the path to a research study. The theoretical framework brings into use and gives into detailed account the bone of contention as to why the study is being done (Abend ; Gabriel, 2008). This potion scrutinizes the theories that the researcher considers applicable to the study. It reviews the Kirzner’s ‘alert’ theory of entrepreneurship, the Schumpeterian theory of innovation, and innovation diffusion theory on perceived usefulness.
2.2.1 Kirzner’s ‘alert’ Theory of Entrepreneurship
The entrepreneur is at the center stage in the market process of economic development. According to Kirzner (1997) the success of any capitalist market economies is brought out by what is recognized to be true in form of habit where less efficient, less imaginative ideologies, are to be replaced by what is considered to be newly identified, remarkable ways of serving customers by manufacturing better goods or by taking advantage up till now unknown, but available, source of resource supply. According to Kirzner, the entrepreneur who is in the market will identify and take advantage of opportunities so as to gain profits by bringing in or restoring of a less efficient, less imaginative systems with better ways of serving customers.
By having an entrepreneur and expounding on her role in driving the development of serenity is critical to understand the market as a process that is changing. Kizner relaxes on the assumption that what goes hand-in-hand with this framework is the perfect knowledge of assumption. It is not the success that is looked at but instead calculates the most favorable blueprints that are based on any given data. When it comes to the decision-making process, the entrepreneur has to make the decision to remove a product that is less or not preferred or even valued by the consumer for something that is more preferred and valued and sought after.
Kizner (1973) emphasizes that the plans he has can be explained, in terms of cutting down of the most favorable allocations and magnifying where his plans can be shown to be essentially uninterrupted in terms of data which complements and makes up his knowledge of all present and future circumstances that is relevant to his situation. Goods are always sold at the same price and where the goods are sold differently, the explanation of the different prices is available. The explanation has to bring out the differences in the transaction costs, like differences in transportation costs between the space where the good was produced and the two point-of-sale.
Kirzner’s view of the world, when it comes to knowledge is imperfect and imperfectible. This imperfect knowledge allows the entrepreneur to have a function and for the market process to take place. The imperfect knowledge, according to Kirzner (1973) brings out the thought that there is a king of investments that are meant to capture the slight differences in price and when there is a difference in price of a product on two different markets, the person buying, buys the product at a lower price and then sells it at a higher price. This scenario exists, where there is an imperfect knowledge, buyers and sellers can make errors of having too many expectations which may lead to frustrated plans and errors that are above despair and leads to unexploited opportunities. According to Kirzner, individuals and entrepreneur are alert to trading that is programmed and may lead to opportunities and it is their alertness to these opportunities that explains the tendency of the entrepreneur to bring a balance in the market. Kirzner (2000) explains that each market is characterized by opportunities for pure entrepreneurial errors which have resulted in shortages, surplus, and misallocation of resources.
Deliberate searching for profit opportunities is altogether different as compared to an entrepreneur who is being alert to and so discovering profit opportunities. Engaging or deciding to engage can only come to pass if the costs of acquiring the knowledge required are less than the returns that come with the search. The costs can only be calculated if the individual has some degree of knowledge about the prospect and the likelihood of finding what he is looking for. For this to happen, the individual has to be given an advance search which has to be profitable in form of opportunities and the ability to discover the opportunity that will be of benefit for that particular kind of search.
According to Kirzner, entrepreneurship is a balancing force that consists of (and is reducible to) an alert to programs of trading that brings opportunities which are readily appreciable and detectable and that only exist because of the widespread ignorance in the market.
For markets that have multiple periods and commodities, market participants are less likely to commit errors because of their insensitivity but they are not aware of best course of action available to them and that is where the scope of creativity and judgment comes in handy. They are not able to unquestionably make sense of their circumstances and their options which leads to situations that can be analyzed.
Kirzner demonstrates that if there has to be a balance in the market where there is an elimination of errors of over-optimism and over-pessimism, there is need to have agents who can economize and be alert to profits and opportunities that will bring out pure entrepreneur.
2.2.2 Schumpeterian Theory of Innovation
The factors of production, improvements in the efficiency of allocation across economic activities, knowledge and the rate of innovation are considered as a sign of growth in a firm. For one to be considered employed and efficiently allocated, growth has to be put in place by build-up or amassing of both knowledge and innovation. For innovation to be viewed as an incentive structure there has to be access to existing knowledge and a more systematic part. Innovation penetrates and improves or boots an already existing knowledge, thereby serving as an avenue for realizing knowledge in excess.
Schumpeter (1911/1934) explains in details about the economic functions and development of the entrepreneur in three separate sections which include: the first stage, Technical discovery expands invention and can be defined as identifying new things or identifying new activities. Technological innovations are realized by the entrepreneur who has credit which can be equated to an expansion of the economy while if there is a decrease in credit by not paying the lenders then there is a fluctuation that is experienced in the market. Where profits are experienced in the market, the same proportion of losses can be attained. The entrepreneur is identified as an innovator and a catalyst to change and this is seen through the introduction of new technological products and processes. The second stage is the innovation where there is successful trading of the new goods or services. By introduction of entrepreneurs into the market, there is a need to identify or spot new technological opportunities and to understand possible technological and economic applications of new a scientific breakthrough. This is considered an important factor in understanding the steps taken in the introduction of new technologies and there are specific economic and technological characteristics. This approach compliments or commends the role of small-scale manufacturing firms as a point of the compass in terms of new technologies and suggesting that only high creation levels of firms can sustain rates of technological change (Antonelli, 2009). Finally, there is the combination of knowledge which includes both new and the old. The entrepreneur has to be innovative in the production order, using invention so as to come up with new goods or be able to manufacture products previously produced by the firm in a new form that would attract consumers and find new resources of raw materials or markets for their products.
The entrepreneur has to have the ability to be inventors which is considered to be crucial for further adoption and provide custom specific innovation thus reducing uncertainty in the firm’s capacity. Entrepreneurs and small-scale firms’ take advantage of the existing knowledge through their network and link to other knowledge producers in order to satisfy their specific needs in the manufacturing of goods. It’s through this that they are able to produce knowledge even if it does not crop up.
Small-scale firms have a great chance for development, use and introduce profound market-making products that give the firm a competitive edge over an already existing company who are set out to an established growth target and less ability to adapt to change (Casson 2002a, 2002b, Baumol 2007a). The small-scale firms are able to compete through innovations and this can be seen through the process of holding a more extreme view through new ideas that will more likely stem from new ventures (Scherer 1980, Baumol 2004) and in particular if small and new firms have access to knowledge surplus from the available stock of knowledge.
Schumpeter’s idea of holding a more extreme view is to initiate innovations that will essentially change the way things are manufactured, the type of products being produced, how the firm is methodically organized and the way people transport things and communicate suggest that long currents or influx are caused by the bringing together of innovations.
There is an advantage when it comes to the habit of any firm and this can be seen through the access of finance and services, higher flow of ideas, larger markets and less fluctuation of demand, together with lower entry costs which are considered to be advantages. The firms can be able to supply a go-between to the final goods manufactured and which link entrepreneurs to qualitative and structural change and with the increased numbers of small manufacturing firms into the market imply more of variety or assortment and higher regional growth.
A market that has small-scale firms causes more business or trading capabilities by lowering the effective costs of entry through the improvement of self-sufficient suppliers, together with larger and more diversified supply of investment capital where risk capital investors more easily can spread the risks.
Success and entrepreneurship can be said to be important when the regional economic surrounding is demonstrated in terms of culture, knowledge base and the business attitude (Camagni,1991). The firms will need networks which offer more favorable conditions for innovative entrepreneurship and they can be accessed through knowledge, skills, destiny, and opportunities (Nijkamp 2003).
2.2.3 Innovation Diffusion Theory on Perceived Usefulness
It is the customer who benefits when there is a new idea, product or service and the manufacturer only gets a relief when the innovation that has come from the firm is considered valid and the end user accepts it wholly. Diffusion can only be considered important when the end user can fully adopt and use it. This can be done when the users can be able to comfortably do their research on the benefits and the disadvantages of any innovation that comes up before using it and it can be done by the use of the internet.
The understanding of Innovation diffusion theory, is brought about by factors like how, why and how fast these ideas and technologies penetrate into the social system (Rogers, 1962). There is no need to wait for the people to change, but there has to be changed on the way products are reinvented and the behaviors so as to make them better for the individuals and groups who will need them. Innovation diffusion theory further explains that it, not the people who are supposed to change in favor of the product but the innovation that is coming up from any firm (Les Robinson, 2009). Diffusion is defined as the process through which innovation is passed on through certain avenues over time among the community (Rogers, 2003). According to Fichman (2000), diffusion is described as the process through which a technology is able to spread across any population of an organization. The understanding of diffusion of innovation can be interpreted as the widening of ideas from one society to another or from a focus or institution within a society to other parts of that society (Rogers, 1962). Theory of innovation diffusion is divided into four main elements (Ismail Sahim, 2006). Innovativeness brings out the adoption as being new to how individual in the form of an idea, practice, objects or other entities (Rogers, 1983). Innovation is when the product and service is presented to the user differently or in a way that the user cannot explain. The product and service have to be for use by the consumer.Communication system is the medium through which users can be able to pass information with each other. It is the means through information is passed from one channel to another between users. Rogers believes that it is the relational path that is more important for the diffusion of new innovations or technology. Time in terms of innovation diffusion process is duration it takes for adoption in terms of categories and rate to happen. It weighs how a product and innovation is able to penetrate from the moment it is created to the moment it stops to exist in the market. It documents the proportion at which the innovation is distributed into the society and adopted by the different users. Social Systems is a set of units that are not related but combine effort so as to solve a problem in order to accomplish a common goal (Rogers, 2003). Innovation becomes useless if it is not adopted, recognized, accepted and get to the point of sharing the information through social systems. Barnett (1991) suggested that for a particular innovation and product to be adopted by individual, the people have to think about using it. Rogers (2003) described the innovation-diffusion as the process of reduction of doubt about innovation brought into the market. Once the process of accepting the innovation, the implements the innovation and proceeds to confirm their decision which can go through five stages:
Knowledge stage (Demir, 2006) where the individual is exposed to the technology and explores how it functions. The individual attempts to determine “what innovation is, how and why it works” (Rogers, 2003).Persuasion where the individual has to do his or her own research regarding the innovation, analyses that information that has been obtained, determine if the sources are reliable and if these sources are credible, there will be a determination if the peers have any attitude towards the technology. The acceptance or rejection does not that the innovation has been rejected, an attitude is towards the idea is perceived when it exists (Rogers, 1983). Uncertainty is said to be present when a wrong word of mouth or wrong publicity becomes too much while a reduction of uncertainty comes when there is a positive feedback from people around you like friends and peers is spread. According to Sherry (1997) people tend to trust information from close circle peers and family members about an innovation and there is always an ability to refine innovation that comes from outside this circle. The decision comes when there is a participation in activities that would lead to the acceptance or the rejection of technology or innovation. The individual is able to verify whether the technology or innovation provides a benefit, which if it does greatly enhance acceptance (Rogers, 2003). There is a very good chance that the individuals may end up changing their mind over time. Implementation is where the innovation is used on a daily basis or one can say the innovation is put into practice. The implementation involves a change in behavior, as the new idea is indeed put into use (Rogers, 1983). The adoption can be hampered by the newness of an innovation and uncertainties by the individual and this is because of the information flow is not directed to the user of the innovation or product but to other people. Confirmation is identified when the behavior of human beings change from a state of imbalance, state of mind that is uncomfortable and leads to elimination or the individual seeks to reduce (Rogers, 1983). According to Rogers, research about innovation is always sought, even after an adoption decision is made about an innovation, so that they can feel motivated or to do away with the innovation. This stage plays a huge role in convincing a person to continually adapt to or discontinue the adaption. The concept of reinvention is defined as the innovation or modification by a user in the process of adoption and implementation (Rogers, 1983). The definition of invention is the process where a new idea is identified or created, while adoption is described a process of making full use of an innovation as the best line of action available. Computers are the tools that consist of many possible opportunities and applications, so computer technologies are more open to reinvention (Ismail Sahin, 2006).
2.3 Empirical review
This section offers information on the research objectives idea in relation to other researcher’s opinion. Relying on other research practical conclusions, the researcher attempts to react to the research objectives. The idea is to improve the efficiency, quality, manufacture value and sustainability need to spend the necessary resources so as to move to the digital platform so that they can be able to improve the profitability (Schulte, 2002). The businesses can be able to use the technology to grow by identifying the required customers. This can also be done by improving productivity, training and the necessary procedures.
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2.3.1 Factors affecting the adoption of Information Communication and Technologies among small-scale manufacturing firms
2.3.1.1 Skill development on adoption of Information Communication and Technologies
With training, small-scale manufacturing firms can be able to attract new employees, as well as turnover reduction methods for the current staff. The major reasons for making training priorities are the overall IT staffing shortages and the expense of the turnover. Training provides a relatively simple way of creating new IT professionals plus, moral will climb as IT employees learn new skills and are continuously challenged. Small-scale manufacturing firms that offer current, exciting opportunities have an easier time extracting highly experienced IT staffers. It is less expensive to keep current IT staff that know the business and train them on the latest technologies. Other considerations include the cost of hiring a replacement for the people lured away and hidden costs of missed deadlines.
When the SME is able to access skills, they can be able to adjust to changes in the competitive environment, in particular to changes that take the form of continuous shifts. Upgrading in skill has notably been identified as an approach used by firms to adjust to increasing market shares by foreign competitors. In order to increase in competition by low-wage countries, firms have to increase the skill-content of their products and move up the quality of their products (Montfort Et Al., 2008). Kumar Chugan and Rawani (2012) emphasize the need to upgrade skills in small-scale firms where they are losing the market.
One of the most difficult things among the small-scale manufacturing firms is the ICT staffing shortage who are skilled and training is considered as a reason that hampers ICT adoption by the small-scale manufacturing firms in Kenya (Lange et al., 2000; Arendt.,2008).
Apulu and Lattam (2009) are certain that owner/managers are usually reluctant to invest in the training of employees because they are afraid that following the completion of such training the employees will leave and find employment in large companies that offer better salaries. Kumar and Ravindran (2012) found that ease of use was not considered a factor but rather it represents intellectual beliefs formed by indirect information. Also, perceived ease of use was found to have an insignificant effect on consumer intention to use e-commerce (Wei et al., 2009). Without the necessary skills, Small-scale manufacturing firms will have a challenge of implementing all the necessary ICT skills. Options available for the small-scale manufacturing firms in solving these problems include training, retention management, and outsourcing the work.
Individuals may also not invest in training because they do not have all the necessary information about the future returns from such training or because the trainers do not give all the information on the quality of training they give and therefore hesitate to make the necessary investments. Both problems fall under of decision-making failures. Co-ordination failures typically arise at the high end of the category of skills because of the existence of external innovation (Robalino et al., 2012).
Folorunso et al (2006) and Owoseye (2010) also identifies cost implementation and lack of funds as challenges of ICT adoption in Kenyan small-scale manufacturing firms. Abor and Quaterly (2010) confirm that Small-scale Manufacturing Firms development is inevitably constrained by the limited availability of financial resources and cost implementation to meet a variety of operational needs. Many Small-scale Manufacturing Firms in Kenya struggle with the high cost involved in the implementation of ICT hence they sometimes ignored the adoption or effective utilization of ICT.
H01 There is no significant relationship between ICT adoption and Skill development on adoption of Information Communication and Technologies.
2.3.2 Cost of Information Communication and Technology materials on adoption of Information Communication and technologies.
A firm’s coordination costs can be divided into two distinct categories: internal coordination costs and external coordination costs (Kim & Mahoney, 2006). Internal coordination costs represent the economic costs incurred for communications, data transfer, and other economic expenditures on managing dependencies between activities within a firm. External coordination costs represent the economic costs of locating suppliers, writing contracts, and other economic costs of market procurement. Bloom, Garicano, Sadun, and Van Reenen (2014) find that the adoption of ICT within the same organization can reduce communication costs as well as knowledge acquisition costs. ICT can be able to support increased responsiveness to customers easily. This helps to tie customers to a company. Being responsive gives customers the feeling that they are treated well by the company. Moreover, Dosi et al. (2008) find that the size distribution of firms and industrial concentration in the advanced economies over the past few decades have not changed despite the increasing role of ICT. ICT adoption can reduce internal coordination costs and provide management with the capability to manage an organization more effectively and facilitate increased integration. On the other hand, adoption of ICT can directly reduce market transaction costs by providing a cost-effective means to access and process market information, thus, reducing external coordination costs to the firm. ICT can also reduce market transaction costs by facilitating closer inter-firm links through information sharing and mutual monitoring. Rangan and Sengul (2009), for instance, argue and provide empirical evidence for increased internationalization at arm’s length associated with increased ICT spending.
There is research that has been made on the cost factor indicating that there is a direct and significant relationship between cost and adoption technology (Seyal and Rahim, 2006). According to Oliver (2002), cost refers to an amount paid or to be paid for a purchase to acquire, produce or maintain goods or services. Adoption according to this study refers to the application of ICT in small-scale manufacturing firms. The cost of ICT training materials is considered to be among the problems that could negatively affect the implementation of ICT in most small-scale manufacturing firms. Personnel costs include recruiting, hiring, training/retraining and resolve ongoing human resource management issues. The firms need to address a changing corporate culture which results from a move that is web-based in transaction that often requires firms to hire professionals who are experienced in managing such an exchange.
The higher the cost of computers and their accessories, the fewer computers one can buy with the limited resources. The adoption of ICT by small-scale manufacturing firms is avoided because of the complexity of use that comes with them. The costs that come with the use of ICT are different because of the unknown associated with both the technology and marketing strategy. As ICT evolves, new costs of various types of hardware, software and business processes will disappear. Hardware costs can be incurred based on redundancy and reliability needs. Many firms purchase or lease hardware so that they can get through peak usage times and because such hardware is not fully utilized, the lease expense can become a loss. Software costs may be incurred depending on the firm’s industry, location and the type of ICT initiative (Macgregor et al., 2006).
H02 There is no significant relationship between the Cost of Information Communication and Technology materials and the adoption of Information communication and technologies.
2.3.3 Effect of administrative support on the adoption of ICT
Malik and Malik (2008) state that the lack of supportive organizational culture and structure may hamper technology initiatives in any organization. Eruban and Dejong (2006) also state that culture can influence actual behavior through its influence on attitudes and subjective norms.
For the Small-scale manufacturing firms to successfully adopt ICT, the management has to change their perception (Martin and Matlay, 2001). Top management support can encourage the adoption of ICT through positive attitude towards its adoption and usage (Premkumar and Roberts, 1999). The positive attitude will then lead to a high likelihood of allocating the necessary resources for ICT adoption and usage. There can also be ease of adoption of ICT in the firm if they can be able to spearhead through effective communication with the employees. Additionally, Molla and Licker (2005) found that competition of senior management of e-commerce activities significantly affects the maturity level of e-commerce adoption.
In order to maintain shareholder and customer confidence in a rapidly changing environment, the leadership has to attain skills in organizations where ICT plays a crucial role in creating competitive advantage. The understanding of leadership in an e-business context, it is necessary to make the difference between the new economy and traditional bricks-and-mortar firms.
Investing in technology is important, but the critical success factor is getting people to use it effectively and the leaders can do this by communicating their vision on the role of the internet when it comes to achieving the company aims. The effectiveness of a leader is determined by the extent to which the workforce understands and embraces the vision. That is, it is the followers that attribute leadership. If there are no followers, then there is no leadership (Grint, 2005).
The owners of small-scale manufacturing firms should get all the staff/departments in the business involved so that they can assist in the re-engineering of business processes. In order for this to work, effectively, everybody should have some knowledge and the necessary skills of ICT and internet, by providing the introductory courses for employees who have no contact with the internet in order to make their contributions more valuable and reduce their fear of new medium. They will have that there are possibilities, along with some basic applications such as email. There has to be connectivity between computers being used by the employees so that there is ease in exchanging of files, information, and emails.
H03 There is no significant relationship between ICT adoption and administrative support.
Figure 1.1: The conceptual framework

Operationalisation of Variables
Refers to how you will define and measure a specific variable as it is used in the study. Operationalization of variables shows the measure of the dependent variable (ICT adoption) and independent variables (cost of materials, skills development in ICT, infrastructure, administrative support).
Table 2. 1: Operationalization of Research Variables

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OBJECTIVES VARIABLES INDICATORS
To establish the effect of costs on materials ICT adoption in small-scale firms. Cost of materials ? Price of computer.
? Installation costs.
? Maintenance fee.
To investigate whether skills on ICT use affects its adoption. Skills development ? Ordinary level.
? Advanced level.
? Tertiary level.
To establish how infrastructure is affecting the ICT adoption in small-scale firms. Infrastructure ? Computer equipment.
? Software.
? Internet.
? Networks.
To establish whether the administration is affecting the adoption of ICT in small-scale firms. Administrative support ? Technical assistance.
? Financial assistance.
? Managerial assistance.

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CHAPTER THREE
RESEARCH DESIGN AND METHODOLOGY

3.1 Introduction
This study used descriptive research design where the people of concern were small-scale manufacturing firms. The design was considered to be appropriate because the main interest was to investigate the factors affecting the ICT adoption in small-scale medium manufacturing firms in Nyeri County.
Descriptive design method provided quantitative data from a cross-section of the chosen population. The questionnaires were administered and later collected from the respondents. According to Kothari, (2008) the descriptive research accumulates data so as to answer the questions concerning the present status of the subject being studied. The target population for this study was small-scale manufacturing firms who were in Nyeri County.
3.2 Research Design
The research design used a descriptive survey design so as to get both the prevailing and clear-cut objectives of the study. According to (Orodho, 2004), a descriptive survey design is a scientific method which involved observing and describing the behavior of the one who is liable or accountable without influencing it in any way. This is because the researcher’s view is to get detailed information and description from the respondents regarding the manufacturing sector in the area.
3.3 Target population
Population refers to the entire group of people, events or things of interest that the researcher wishes to investigate (Creswell, 2003). A study population is defined as the entire group or an assembly of cases or units the researcher is ready and willing to draw an outcome.
The target population in statistics is defined as a specific population about which information is yearned for. According to Ngechu (2004), a population is well-defined as the setting or surrounding that includes a set of people, services, elements, and events, group of things or households that are being investigated. The target population of this study was made up of all small-scale manufacturing firms in Nyeri County. According to the national survey on Kenya labor force (RPED, KMES, RPED, 2000), food, furniture, clothing, leather, soap, textile, wood, and metal comprises about 73 percent of the manufacturing sector in Kenya.
Table3.0: population size
Business Total number of registered business
Furniture 130
Textile 110
Clothing 277
Soap 100
Leather 105
Total 722
The Municipal Council of Nyeri- classification and distribution of SMEs across the Nyeri County.
3.4 Data collection techniques
Primary data was collected through a structured questionnaire (Appendix II). As Kumar (2010) argues, primary data is first-hand information collected, compiled and published for some purpose. The study involved the use of a questionnaire because they were not only convenient but also effective in the collection of data. For the purposes of this study, questionnaires were self-administered and collected after a few days while others were answered promptly. The types of questions included both open and closed-ended. Closed-ended questions were used to ensure that the given answers were relevant. The questions were phrased clearly in order to make clear dimensions along which respondents were properly analyzed. The questionnaires were administered on the basis of drop and pick later as per the agreement that the researcher made with the respondents.
3.5 Sampling technique
Sampling refers to the process by which a relatively small number of individual, object or event is selected and analyzed in order to find out something about the entire population from which it will be selected. A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen
A sample of 217 small-scale manufacturing companies was selected from a sample of 722 small-scale manufacturing firms. In this case, with a population of 722 small-scale manufacturing firms, sampling was made random because each employee will have an equal probability of being chosen to participate in the research.
At least 30% of the total population is representative (Borg ; Gall, 2003). Thus, 30% of the accessible population is enough for the sample size. A simple random sampling technique was used to select a sample size of 217 small-scale manufacturing firms from a population of 722 firms.
The sampling fraction is f=n/N*100= (217/722)*100%=30%
Easton and McColl (2002) proposed the use of simple random sampling technique because they considered that it was quite possible to use a section of the population to represent the entire population. According to the researchers, individuals who were chosen by chance had an equal probability of representing the entire population. The essence of randomization in this regard was to achieve a sample that was unbiased. It is demonstrated as follows:
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Table3.1: population size
Business Total number of registered business Percentage % Sample
Furniture 130 30 39
Textile 110 30 33
Clothing 277 30 83
Soap 100 30 30
Leather 105 30 32
Total 722 30 217
3.6 Data analysis
Both quantitative and qualitative approaches were used for data analysis. Quantitative data from the questionnaire were coded and then entered into the computer for computing of descriptive statistics. The data collected from the field was assessed and comparison made so as to select the most accurate and quality information from the feedback given by various respondents. This involved assessing and evaluating the questionnaires and other sources of both primary and secondary data. STATA was used to run descriptive statistics which included frequency and percentages so as to present the quantitative data in form of tables and graphs based on the major research questions.
Regression analysis was done to evaluate the linearity and the correlation of data using the following equation;
Y =? + ?1X1 + ?2X2 + ?3X3 + ?4X4 + ?
Where;
Y = Dependent variable (Firm performance)
? = Constant
?1 ?2, ?3 and ?4 =Coefficients of the independent variables
X1=Cost of materials
X2= Skills development
X3 = Administrative support
X4 = Independent variables (infrastructure)
? -Error term
All the necessary regression diagnostics were collected conducted including Multicollinearity Test, Homoscedasticity, and Normality test, Sampling Adequacy, Chi-square Tests and Linearity Test. The research findings were presented in charts, tables, frequencies and percentages

CHAPTER 4
DATA ANALYSIS AND INTERPRETATION
4.1 Introduction
This chapter discusses data analysis, presentation, and interpretation of the research findings in line with the objectives of the study. The data obtained were presented in tables to reflect different response rate amongst the respondents. Analysis of the response rate, general information, and independent variables was conducted and the obtained data was subjective to quantitative analysis. Analysis, therefore, may be categories as descriptive analysis and inferential analysis which is often known as statistical analysis.
4.1.1 Response rate
The study targeted 217 respondents and to determine the actual number of the respondents who actively participated in the research study by filling and submitting back the questionnaires, the analysis of the response rate was conducted as follows;
Table 4.1: Response rate
Frequency Percentage
Responded 135 62.22%
Not respond 82 37.78
Total 217 100%

Table 4.1 shows that 135 respondents were able to fill and return the questionnaires which amounted to a response rate of 62.22%. According to Kumar ( 2010) a response rate of 50% and above is acceptable for statistical analysis.
4.1.2 Reliability analysis
The reliability is expressed as a coefficient between 0 and 1.00; where the higher the coefficient, the more reliable the test is.
Table 4.2: Reliability analysis
Cronbach’s Alpha
Cost of materials 0.9748
Skills development in ICT 0.9832
ICT infrastructure 0.9861
Adoption of ICT 0.9798

4.2 Demographic information
The study sought to establish the respondents’ demographic information which included gender, occupation, and age. The respondents answered questions concerning the same in the questionnaire for the date to be obtained.
Table 4.3: Gender of the respondent
Frequency Percentage
Male 67 49.63%
Female 68 50.37%
Total 135 100

From the findings the study found and demonstrated on table 4.3, that male respondents were 49.63% while female respondents were 50.37%. This shows that the researcher was not biased and collected the data considering all the respondents irrespective of their gender. This can be demonstrated in table 4.3.

Fig 4.1: Gender

Table 4.4:The occupation of the respondents
Frequency Percentage
Furniture 20 14.81%
Textile 25 18.52%
Clothing 45 33.3%
Soap 25 18.52%
Leather 20 14.81%
Total 135 100

According to table 4.4, the findings showed that most of the respondents were in the clothing as shown by 33.3% while the rest were in furniture at 14.81%, textile at 18.52%, soap18.52%, and leather was 14.81%. This indicated that majority of the respondents could understand the subject under study.
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Table 4.5: Age of the Respondent
Frequency Percentage
Between 18-25 years 35 25.93%
Between 25-35 years 45 33.33%
35 years and above 55 40.77%
Total 135 100%

According to table 4.5, the study results revealed that there were respondents aged between 18 to 25 years as shown by 25.93%, aged between 25 to 35 years as shown by 33.3% and aged 35 years and above as shown by 40.77%. This implies that the respondents who participated in the study were mature enough to cooperate in giving reliable information on the subject under study.

Fig 4.2: Age
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4.3 Factors affecting the cost of ICT
4.3.1 Cost
Table 4.6: The extent to which the availability of funds have affected the adoption of ICT

In table 4.7, at cost of materials, 2 disagree, 3 moderately agree, 4 Agree and 5 strongly agree. According to the table, 48.15% of the respondents strongly agree that cost affects the adoption of ICT, 46.67% agree, 3.7% disagree while 1.48%moderately agree.

Fig 4.3: Cost
Table 4.7: The extent to which the cost of materials affect the adoption of ICT

Table 4.7 explains the study by stating that the mean is 4.392593 when it comes to the adoption of ICT.
Table 4.8: Demonstration of how the routine and maintenance has affected the adoption of ICT

In table 4.7, at cost of materials, 2 disagree, 3 moderately agree,4 Agree and 5 strongly agree. According to table 4.8, 50.37% of the respondents strongly agree that the routine and maintenance affect the adoption of ICT while 45.1% agree, 2.22% agree and 2.22% disagree.

Fig 4.4: Routine and maintenance

Table 4.9: Shows the extent to which maintenance and servicing affect the adoption and use of ICT
According to table 4.9, the mean is 4.437037
Table 4.10: This table demonstrates how migration has affected the adoption of ICT

At migration of data to the electronic state, 2 disagree, 3 moderately agree,4 Agree and 5 strongly agree. Table 4.10 states that 55.6% of the respondents agree that the costs that come with migrating of records to electronic forms are a cause for not adopting ICT while 40.7% strongly agree, 1.5% moderately agree, and 2.2% agree.

Fig 4.5: Migration

Table 4.11: Shows the extent to which the migration to electronic records affects the adoption of ICT.

Table 4.11 shows a mean of 4.50
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4.3.2 Skills development and adoption

Table 4.12: Demonstrates the lack of specialized ICT Knowledge and skills.

In knowledge and skills, 2 disagree, 3 moderately agree,4 Agree and 5 strongly agree. Table 4.12 illustrates that 37.04% of the respondents agree that lack of the necessary skills is affecting the adoption of ICT, while 32.59% strongly agree, 13.33% disagree11.11% moderately agree while 5.93% strongly disagree.

Fig 4.6: ICT Knowledge and Skills

Table 4.13: shows the extent to which the members of the firm are lacking the necessary ICT knowledge and skills

According to table 4.13, the mean is 3.77037
Table 4.14: Shows how the higher capability have a higher potential of the employees

In the table 4.14, at cost of materials, 2 disagree, 3 moderately agree,4 Agree and 5 strongly agree. According to table 4.14, 42.22% of the respondents strongly agree there is a higher potential in small-scale manufacturing firms when ICT is used,17.78% agree, 17.78% moderately agree, 11.85% disagree while 10.37Strongly disagree.

Fig 4.7: Capabilities
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Table 4.15: Demonstrates how the firm can have a lot of potentials when ICT is adopted

According to table 4.15, the mean was 3.77037.
Table 4.16: The table demonstrates the extent the firm is willing to train its staff

On table 4.16, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to table 4.16, 44.29.63% of the respondents that the management and staff should be educated and trained on computer use while 22.22% disagree, 18.52%Agree, 14.81Strongly disagree and 14.81%moderately agree.
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Fig 4.8: Training

Table 4.17: Shows the extent the firm is willing to train their staff on ICT use

The mean in accordance to table 4.17 is 3.259259
Table 4.18: Shows if the management has the necessary skills and their willingness to apply.

In table 4.18, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree and it shows that 37.04% strongly agree that management is lacking the necessary skills when it comes to the adoption of ICT, 22.22%Agree, 16.30% moderately agree, while 14.07% disagrees and 10.37% strongly disagree.

Fig 4.9: Management Skills and Development

Table 4.19: Demonstrates the level of management has the necessary skills and their willingness to employ ICT

The mean according to table 4.19 is 3.614815
4.3.3 ICT infrastructure
Table 4.20: Show the potential that ICT has in a firm

According to table 4.20, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 44.44% of the respondents agree that ICT can cause a firm to have a lot of potentials, 29.63% strongly agree, 11.11% strongly disagree, 7.41% disagree, 7.41% moderately agree.

Fig4.10: Potential

Table 4.21: Demonstrates the potential a firm would have if ICT is applied.

Table 4.21 shows the mean to be 3.740741
Table 4.22: shows how Communication shifted from a largely manual to digital communication

According to table 4.22, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 44.44% of the respondents strongly agree that communication has shifted from manual to digital communication, 29.63%agree, 11.11% strongly disagree, 7.41%disagree, 7.41%moderately agree.

Fig 4.11: Communication

Table 4.23: Demonstrates that communication has changed from manual to electronic communication

The mean as demonstrated in table 4.23 states the mean 3.888889
Table 4.24: shows how the Internet has become an important part of the firm

According to table 4.24, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 40.74% of the respondents agree that internet has not become an important part of their business, 37.04%strongly agree, 10.37%moderately agree, while 8.15% disagree and 3.70% strongly disagree.

Fig 4.12: Internet
Table 4.25: Demonstrates that the internet has not become part of their firm.
According to table 4.25, the mean has been stated as 3.992593
Table 4.26: Shows that there are challenges that the firms are going through in the firm when it comes to ICT adoption.

According to table 4.26, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 44.44%agree that there are constraints that are preventing them from adopting ICT, 21.48% strongly agree, 17.04%moderately agree, 9.63%agree while7.41% strongly disagree.

Fig 4.13: Constraints

Table 4.27: Demonstrate the challenges in firms

According to table 4.27, the mean is stated as 3.62963.
4.3.4 Adoption of ICT
Table 4.28: Shows the extent to which ICT improves quality

According to table 4.28, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 58.52% of the respondents strongly agree that the quality of the product is improved when ICT is adopted, 37.78% Agree, 2.22% moderately agree, while 1.48% disagree.

Fig 4.14: Improvement of quality
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Table 4.29: Demonstrates how ICT is adopted

According to table 4.29, the mean is stated as 4.533333
Table 4.30: Elaborates whether ICT increase knowledge when it comes to the running of the firm

According to table 4.30, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 66.67% of the respondent strongly agrees that using ICT in manufacturing does indeed increase knowledge, 21.48% agree, 5.19%disagree,4.44% moderately agree, while 2.22% strongly disagree.

Fig 4.15: Increase of knowledge

Table 4.32: Demonstrates on the knowledge level when it comes to ICT adoption

According to table 4.32, the mean based on the knowledge level is 4.451852
Table 4.33: Illustrates how ICT adoption would affect the efficiency of a firm

According to table 4.33, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 61.48% of the respondents strongly agreed that ICT adoption does indeed affect the efficiency of the firm, 29.63% Agree, 3.70% moderately agree,2.96% disagree while 2.22% strongly disagree.

Fig 4.16: Improve Efficiency

Table 4.34: Demonstrates how ICT has affected the efficiency of the firm

According to table 4.34, states the mean 4.451852
?
Table 4.35: Shows how competition is affected by the adoption of ICT

In the table 4.35, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 62.96% of the respondents strongly agree that ICT does indeed improve the completion of firms, 25.93% agree, 7.41% moderately agree, 3.70% disagree.

Fig 4.17: Improved Competition

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Table 4.36: Demonstrates the impact of ICT adoption when it comes to competition.

According to table 4.36, the mean is 4.481481
Table 4.37: Illustrates how ICT affects the finances in form of speed

In table 4.37, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 55.56% of the respondents strongly agree that ICT affects the financial services in terms of speed, 29.63% agree, 5.93%strongly disagree,5.19%disagree, while 3.7%moderately agree.

Fig 4.18: Speed in financial services

Table 4.38: Demonstrates how Finances are affected by the use of ICT

The mean in accordance to table 4.38 is 4.237037
Table 4.39: Illustrates how ICT affects the finances in form of accuracy

In table 4.39, 1 is strongly agree,2 disagree,3 moderately agree,4 agree and 5 strongly agree. According to the table, 59.26% of the respondents strongly agree that ICT affects the financial services in terms of speed, 29.63% agree, 7.49% moderately agree, 2.22% strongly disagree while 1.48% disagrees.

Fig 4.19: Accuracy in financial services

Table 4.40: Elaborates how ICT affects the finances in form of accuracy

The mean in accordance to table 4.38 is 4.422222
4.4 Pearson’s product moment correlation
A Pearson’s Product Moment Correlation was conducted to establish the strength of the relationship between the variables. The findings are presented in Table 4.41.
Table 4.41: Correlations

scalars:
r(N) = 135
r(rho) = 1
matrices:
r(C) : 2 x 2
In table 4.41, the study reveals reveal that there is a positive and significant correlation between cost and ICT adoption(r=0.9530, p. value 0.0000). Further, the study reveals that there is a positive and significant correlation between skills and adoption(r=0.979, p. value=0.9679). Finally, the study reveals that there is a positive and significant correlation between infrastructure and adoption of ICT (r=0.9855, p. value=0.0000).
4.5 Regression analysis
Multiple regression analysis was used to test the relationship between variables which shows how the dependent variables are influenced by the independent variables. The study sought to determine the effect of costs of availability of funds, skills development and development, ICT infrastructure, and the adoption of ICT.
Table 4.42: Model summary

a. skills development in ICT, ICT infrastructure, cost of ICT
Table 4.42, is a model fit which establishes how fit the model equation fits the data. The adjusted R2 was found to be 0.9944 implying that 99.4% of the variations in performance of firms in the small-scale manufacturing firms are explained by changes in the cost of materials, skills development in ICT, ICT infrastructure and the adoption of ICT. This established that the model equation fits the data.
Table 4.43: ANOVA

a. Dependent Variable: Adoption of ICT
b. Predictors: (Constant), cost of materials, skills development, and ICT infrastructure
The probability value at the model is 0.000 indicates that the regression relationship was significant in determining how the cost of material, skill development in ICT, ICT infrastructure affects the adoption of ICT.
Table 4.43: Coefficients

a. Dependent Variable: Firm Performance
The established model for the study was:
Y=10.99175+7.67822X2+4.14002X3+1.30287X4
Where:-
Y= Firm Performance
?0=constant
X2= Cost
X3= Skills
X4= Infrastructure
The regression equation above has established that taking (Cost, Skills, infrastructure) constant at zero, firm performance will be 10.99175. This shows that if all the independent variable were held constant, the performance will be increasing.
The findings presented also show that a unit increase in the scores of Cost would lead to a 7.67822 increase in the scores of firm performance. This variable was not significant since 7.67822

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.

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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 

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