Ijsrtm vol 2 (3) may 2014

Vol 2 (3) May 2014

International Journal of Students’

Research in Technology & Management

(IJSRTM)

International Journal of Students Research in Technology & Management

Vol 2 (03), May 2014, ISSN 2321-2543, pg. 93-102

http://www.giapjournals.org/ijsrtm.html 93

A Review of Optimum Sizing Techniques for Off-Grid Hybrid PV-Wind Renewable Energy Systems

Ahmed Said Al Busaidi, Hussein A Kazem, Mohammad Farooq Khan

Lecturer , Nizwa College of Technology, Oman Professor, Sohar University, Oman

Lecturer, Nizwa College of Technology, Oman

Presenting author email: [email protected]

Abstract – Hybrid renewable energy power systems have proven their ability to address limitations of single renewable energy system in terms of power stability, efficiency and reliability while running at minimum cost. In the present decade, lots of research and practical experiences have been done. This paper will present an overview of the different hybrid solar (PV)- wind renewable energy systems for power generations. Different criteria of selecting the right sizing of different component of hybrid renewable energy power plant at the most preferable economical, logistical environmental considerations will be discussed. In some cases when the weather data are not available, this paper will discuss some optimization approaches which are used to compare the performance and energy production cost of different system configurations using simulation techniques. Based on the fact that, potential of the wind and solar energy is not equal in Oman, this paper will discuss the optimum sizing process of two proposed hybrid solar-wind plants in Oman. Key Words: Hybrid Energy, wind, solar, sizing and optimization.

I. INTRODUCTION

Hybrid Renewable Energy Systems are defined as an electric energy system which is made up of one renewable and one conventional energy source or more than one renewable with or without conventional energy sources, that works in off-grid (stand alone) or grid connected mode [1]. The main feature of hybrid renewable energy systems is to combine two or more renewable power generation and so they can address efficiency, reliability, emissions and economical limitations of single renewable energy source [2].

Hybrid Renewable Energy Systems are becoming popular for stand-alone power generation in isolated sites due to the advances in renewable energy technologies and power electronic converters [3]. Based on the availability of the natural local resources, there are some advantages of the hybrid system. Higher environmental protection, especially CO2 and other emissions reduction is expected due to the lower consumption of fuel. The cost of wind energy, and also

solar energy can be competitive with nuclear and the diversity and security of natural resources who are abundant, free and inexhaustible [4]. Most of these appliances can be easily installed and they are rapidly deployed. Financially, the costs are predictable and not influenced by fuel price fluctuations [5-8]. However, because of the solar-wind unpredictable nature and dependence on weather and climatic changes, a common drawback to solar and wind power generations is that both would have to be oversized to make their stand- alone systems completely reliable for the times when neither system is producing enough electric power [9].

Many areas are concerned with the applications of the hybrid renewable energy generation. Researches [1, 10] have focused on the performance analysis of demonstration systems and the development of efficient power converters, such as bi-directional inverters and the Maximum power point trackers [11-12]. Other researches focused on the storages devices and the battery management units [6].

In the last decade, various hybrid energy systems have been installed in many countries, resulting in the development of systems that can compete with conventional, fuel based remote area power supplies [13]. However, there are several combinations of hybrid energy system which mainly depend on the natural available resources, the wind or the solar energy practically represents one source of the hybrid renewable energy systems.

With the advance development of the hybrid solar-wind systems for electrical power generation, the target to achieve efficient and reliable performance became complicated task. So the need to select and configure the right sizing of all components is important in order to obtain the initial minimum capital investment while maintaining system reliability [14-15]. This paper will overview three common used sizing methods for hybrid solar-wind systems. Beside that, the paper will discuss some optimization approaches of the solar-wind hybrid renewable energy systems. These

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approaches are used to compare the performance and energy production cost of different system configurations using simulation techniques.

This work will focus on the off-grid solar (PV)-wind hybrid energy systems as both solar and wind has the highest potential in Oman compared to the others [16-17]. Two research cases will be discussed as a practical implementation of the right sizing and optimization of the Masirah Island and Al-Halaniyat Island proposed hybrid renewable energy plants.

II. HYBRID SOLAR- WIND ENERGY

In fact, the use of small isolated hybrid energy systems is expected to grow tremendously in the near future [18], both in industrialized and developing countries. Solar and wind are naturally complementary in terms of both resources being well suited to hybrid systems [19]. Hybrid electric systems combine solar-wind systems to make the most of the area's seasonal wind and solar resources; with wind relatively more available in winter months and at night time, and solar relatively more available in summer months and during winter's sunlit days [13].

These hybrid systems provide a more consistent year-round output than either wind-only or solar-only systems and can be designed to achieve desired attributes at the lowest possible cost [20]. Most hybrid systems have backup power through batteries and/or and diesel engine generator.

Moreover, Fig 1 compares PV system capital costs of

three common PV types. The cost of electricity of the three

PV types has dropped 15- to 20 times; and grid-connected PV

systems currently sell for about $5-$10 per peak Watt (20 to

50¢/kWh), including support structures, power conditioning,

and land. In contrast, the system efficiency of the three types

has increased for about 10-13% [21].

Fig 1 PV system capital cost The wind capital cost of class 4 and 6 wind turbines is

shown in Fig 2. The cost of both systems has dropped by 180$ per KW in the last two decades.

Fig 2 Wind capital cost

However both Fig 1 and Fig 2 show promised figures for the real investments of the PV and wind renewable energies, the optimum design of hybrid system becomes complicated through uncertain renewable energy supplies uncertain load demand, non-linear characteristics of the resources with the increased complexity, the need for practical sizing and optimum configuration becomes an issue [22].

Researcher and industries faces some challenges in the

developments of the hybrid solar-wind energy systems. The

following may be considered, poor efficiency of the solar PV

sources as the efficiency cannot reach more than 17.5 % , high

manufacturing cost which leads to longer payback time [16-

17].

Beside all of the technical considerations there are other factors which must be included such as the financial investment, social aspects, local infrastructure and the whole system durability. Furthermore, references [1, 10] has presented some steps which must be taken into account before installing PV-wind hybrid systems. They include selecting the most suitable location for installation, acquiring data on the local natural potential of available wind energy and solar energy and the annual energy consumption must be determined. Then the right sizing of the whole system can be set as it will discuss in the following section.

III. SIZING METHODS OF SOLAR -WIND HYBRID SYSTEMS

Before setting up or installation of a new hybrid renewable energy system, it is essential to do the right sizing of the individual components to obtain the initial capital investment[18]. Unit sizing is basically a method of determining the right practical sizing of the hybrid system components by minimizing the system cost [14] while maintaining system reliability. The right sizing is to determine the wind generator capacity (number and size of wind turbines), the number of PV panels and number and capacity of battery needed for the stand-alone system. Note that it is


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important to maintain optimum resource management in a hybrid generation system in order to avoid wrong sizing. Over sizing the system components will increase the system cost whereas under sizing can lead to failure of power supply [14]. References have presented three methods of sizing.

A. The Yearly Monthly Average Sizing Method

The PV panels and wind generators size are measured from the average annual monthly values of energies statement. This calculates is basing on the average annual monthly data of sunning and the wind.

B. The Most Unfavorable Month Method

The PV and wind generators are being calculated in the most unfavorable month. Generally the month most unfavorable in wind is favorable in irradiation. Here the system must be dimensioned in two most unfavorable months (unfavorable irradiation month and unfavorable wind month). When the system functioned in this month it’s automatically functioned in the author month.

C. Loss of Power Supply Probability (LPSP) Method

The LPSP is the probability that an insufficient power supply results when the hybrid system is not able to satisfy the load demand [23-24]. This method consists in determining the optimal number of the batteries and the photovoltaic modules according to the optimization principle knowing: the reliability, which is based on the concept of the probability of loss of energy [25-26], and on the cost of the system. This method presents the advantage that the introducing of the wind generator permits to minimize the cost of the photovoltaic stand-alone system, by the minimizing the size of the photovoltaic generator and the storage number of battery [27]. Two methods can be used for the application of the LPSP in designing a grid-off hybrid solar–wind system. The first one is based on chronological simulations. The second approach uses probabilistic techniques to incorporate the fluctuating nature of the resource and the load, thus eliminating the need for time-series data.

IV. OPTIMIZATION METHODS OF SOLAR -WIND HYBRID

SYSTEMS

Optimization approaches of the solar-wind hybrid renewable energy systems are used to set up the optimum configuration of renewable energy configuration. Simulation techniques are used to compare the performance and energy production cost of different system configurations. Several software tools [28] are available for designing of hybrid systems, such as homer, hybrid2, hoga and hybrids [29]. Depends on the availability of the metrological data, two approaches are followed to achieve the right optimum sizing. The conventional techniques are based on the energy balance

and reliability of supply and they make use of the metrological weather data. If the weather data are not available, the system must be optimized using different methods as will be discussed in this section.

A. Graphic Construction Technique

This technique can configure the optimum combination of PV array and battery for a stand-alone hybrid solar–wind system based on using long-term data of solar radiation and wind speed recorded for every hour of the day for very long years [2]. For given load and a desired LPSP, the optimum sizing of the hybrid PV-wind can be achieved by assuming that the total cost of the system is linearly related to both the number of PV modules and the number of batteries. The minimum cost will be at the point of tangency of the curve that represents the relationship between the number of PV modules and the number of batteries.

B. Probabilistic approach

The effect of variation of the solar radiation and wind speed are the main factors in the system design of this method. Reference [29] has proposed a sizing method treating storage energy variation as a random walk. The probability density for daily increment or decrement of storage level was approximated by a two-event or three- event probability distribution. This method was extended to account for the effect of correlation between day to day radiation values.

Other applications presented the probabilistic approach based on the convolution technique. The fluctuating nature of the resources and the load is incorporated, thus eliminating the need for time-series data for the assessment [30].

C. Iterative Technique

Reference [31] proposed a Hybrid Solar–wind System Optimization (HSWSO) model, which utilizes the iterative optimization technique following the LPSP model and Levelised Cost of Energy model for power reliability and system cost respectively. Three sizing parameters are considered, i.e. the capacity of PV system, rated power of wind system, and capacity of the battery bank. For the desired LPSP value, the optimum configuration can be identified finally by iteratively searching all the possible sets of configurations to achieve the lowest Levelised Cost of Energy.

Similarly, in [32] an iterative optimization method was presented by to select the wind turbine size and PV module number using an iterative procedure to make the difference between the generated and demanded power (DP) as close to zero as possible over a period of time. From this iterative procedure, several possible combinations of solar–wind generation capacities were obtained. The total annual cost for


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each configuration is then calculated and the combination with the lowest cost is selected to represent the optimum mixture.

D. Artificial intelligence methods

There are different artificial intelligence methods which are widely used to optimize a hybrid system in order to maximize its economic benefits [32], such as Genetic Algorithms, Artificial Neural Networks and Fuzzy Logic. Genetic Algorithms are also widely used in the design of large power distribution systems because of their ability to handle complex problems with linear or non-linear cost functions. [33] Proposed one optimum sizing method based on Genetic Algorithms by using the Typical Meteorological year data, while desired LPSP with minimum Annualized Cost of System is maintained. Two optimization variables that are not commonly seen, PV array slope angle and turbine installation height have been considered. Such algorithms are applicable for the conventional optimization methods such as dynamic programming and gradient techniques [33]. Ref [18] has compared between all optimization techniques and listed all advantages and disadvantages.

V. DISCUSSION OF SOLAR- WIND ENERGIES IN OMAN

Study [17] has discussed and addressed all renewable energy resources in Oman, solar and wind energy present the highest potential for applicability in the country. The following sections overview these energies and their potential applications.

A. Solar Energy

In Oman, solar energy is the main renewable energy resource which is currently utilized in Oman for some local small applications. Oman is one of the highest solar energy densities in the world, the received solar radiation ranging from 5,500-6,000 Wh/m2 a day in July to 2,500-3,000 Wh/m2 a day in January[17].

A solar energy evaluation study [34] covered several years in order to estimate the long term average solar energy resources. The average global isolation data which is the sum of direct and diffuse radiation from 1987 to 1992 for six locations in Oman is depicted in Fig 3.

Fig 3 Global average radiation for 1987-1992 for the stations included in this study.

As shown in Fig 3, the solar isolation varies from 4.5 to 6.1 kWh/m2 per day which corresponding to 1640– 2200 kWh per year per square meter. Salalah and Sur have a significant lower insolation compared with other stations; this is due to the summer rain period in Salalah and the frequent period with fog in Sur. Relatively high solar energy density is available in all region of Oman. The total solar energy resources in Oman are enormous and can cover all energy demands as well as could provide export [17].

The consumption of energy is higher during the summer time due to the need for air conditioning. During the winter time the surplus production can be exported to Europe where the need for energy is highest [17]. For real solar PV energy investment in Oman, the following points must be considered [8]:

The solar PV technology is suitable for use in northern parts of Oman.

The solar PV technology is also suitable for electricity generation in off-grid power plants in rural desert areas where the solar energy can reduce diesel fuel use. The efficiency of PV cells is influenced by high air temperature and dust contamination.

It was found that highly suitable land for PV applications in Oman can provide more than 600 times the current electric energy demand if Thinfilm PV technology is used [8].

A research paper [35] has investigated the economical

prospect of the solar PV in Oman for a 25 location assuming a 5MW plant as shown in Fig 4.

Global solar radiation. Average 1987-1992. kWh/m2 per day

4.51 4.53

5.38 5.485.69

6.09

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Salalah Sur Buraimi Seeb Fahud Marmul

kWh

/m2

per

day


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Fig 4 The COE for 25 locations in Oman

The research results revealed the following:

The renewable energy produced each year from 5MW PV power plant vary between 9000 MWh at Marmul to 6200 MWh at Sur while the mean value is 7700 MWh of all the 25 locations.

The capacity factor of PV plant varies between 20% and 14% and the cost of electricity varies between 210 and 304 US$/MWh for the best location to the least attractive location.

The study has also found that the PV energy at the best location is competitive with diesel generation without including the externality costs of diesel.

An average of about 6000 tons and 5000 tons of GHG emissions can be avoided for each implementation of PV station that is currently using diesel and natural gas, respectively.

Theoretically, it is possible to power Oman by utilizing

about 280 km2 of desert from solar collectors, corresponding to 0.1% of the area of the country [35].

B. Wind Energy

Wind power has become a major source of energy today, it is free, clean, and inexhaustible source of energy. In 2007, wind power capacity increased by a record-breaking 20,000MW, bringing the world total to 94,100MW, which is sufficient to satisfy the residential electricity needs of 150 million people. Existing wind power capacity grew by 29% in 2008 to reach 121GW, more than double the 48GW that existed in 2004 [36].

The assessment of the wind energy resources in Oman is based on the hourly wind speed data measured at twenty one stations in 2005 under the responsibility of Directorate General of Civil Aviation & Meteorology (DGCAM) [34]. The wind data is measured at 10 m and estimated at 80 m above ground level to represent a hub height of a modern large wind turbine (capacity 2-3 MW). Five stations with the

highest wind speeds were identified and the annual mean wind speed is shown in Fig 6.

Fig 5 Annual mean wind speed at 10m and at 80 m above ground level at five

meteorological stations

Fig 6 Energy content in the wind at 80 m above ground level at five meteorological

stations

Further assessment was done to estimate the annual energy content at each of the five stations. The energy is specified as kWh per year through a vertical area of one m2, kWh/year/m2. The maximum expected energy is at Thumrat for an almost 4.5 kWh/m2/year. The assessment results are shown in Fig 6.

The main findings of the study are:

The high wind speeds are found along the coast from Masirah to Salalah. The highest wind speeds are in the Dhofar Mountain Chain north of Salalah. The low wind speed areas are in the north and western part of Oman.

The highest wind energy speeds are observed during the summer period. The summer period is also the period with the highest electricity demand in Oman.

The study reveals that at the present gas price of 1.5 US$/MMBtu wind energy is not economical. The wind energy at Quiroon Hariti, the highest wind potential in Oman, becomes marginally economical at a gas price of 6 US$/MMBtu [34].

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

Qairoon Hariti Thumrait Masirah Joba SurAn

nu

al m

ean

win

d s

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d, m

/s

Location


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This clearly shows that wind application for large wind farms is not presently economical. However, the wind energy remains a suitable option for hybrid applications.

VI. HYBRID SOLAR- WIND ENERGY SYSTEMS IN OMAN

Study [13] has investigated different combinations of hybrid systems of diesel generator, wind turbine, PV array, battery, and power converter for Masirah Island in Oman. The wind and the solar assessment for Masirah Island are presented in Fig 7 & Fig 8 respectively.

Fig 7 Masirah monthly average wind speed in m/s and monthly load in MW (2008)

Fig 5 & Fig 7 show that the average yearly wind speed is 4.99 m/s and the measured wind speed happens to be quite high when the electrical load requirement is also high. Moreover, wind speeds are generally higher during the months of April to September compared to other months. The average monthly solar radiation between 2004- 2008 is shown Fig 8.

Fig 8 Masirah monthly average solar radiation between 2004- 2008

TABLE 1

TECHNICAL DETAILS OF THE LOAD AND DIFFERENT SOURCES AT MASIRAH ISLAND

Item Details Remark Site information (Masirah Island )

Area of about 649 km2 population estimated to 12,000

Scattered in 12 villages

Average wind speed

4.99 m/s

1997-2008 At 10-m height

Solar average daily radiation

6.4 kWh/m2 from 2004 till 2007

Annual electrical energy demand

43,624,270 kWh Year 2011 minimum load 550kW maximum load 9530 kW

Using the above metrological weather background and the technical details given Table 1, the optimum sizing of the system components was selected based on the monthly

average sizing approach. The optimum sizing results are

illustrated on table 2.

TABLE 2 OPTIMUM SIZING CONFIGURATION FOR THE PROPOSED MASIRAH ISLAND

HYBRID PLANT

Proposed Diesel generator details

10 units Capacity between 200kW to 3300kW

The actual diesel price for Masirah Island is 0.468 US$/L

Number of the PV panels

1.6 MW PV, Cost = 3000 US$/kW O & M cost=10US$/year/kW

Proposed Wind turbine

Rated power=250kW @ Height= 31m Rated wind speed =13m/s

Batteries Type 6CS25P, Nominal voltage 6V, Nominal capacity 1156 Ah Nominal energy capacity of Each battery (VAh/1000) 6.94 kWh

Converter Cost=900US$/kW Efficiency=90%

Level of RE penetration

25%

Furthermore, a comparison of cost of energy of different hybrid solar –wind- battery- diesel systems was developed as shown in table 3.


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TABLE 3 A COMPARISON OF COST OF ENERGY FOR DIFFERENT SYSTEMS

COE

Battery unit

PV–diesel hybrid system

Wind–diesel

hybrid system

PV–Wind- diesel hybrid

system

Yes

0.186 US$/kWh with 28 minutes battery system

0.189 US$/kWh with 28 minutes battery system.

0.182 US$/kWh with the presence of 28 minutes battery unit

No

0.189 US$/kWh without batteries and the annual diesel consumption will increase by 1%

0.187 US$/kWh with no battery system used

0.185 US$/kWh if the battery unit is removed from the hybrid system

It is shown here that using PV–wind–diesel hybrid system

with a battery unit will produce the lowest COE (0.182 US$/kWh) compared to other hybrids. It can be noticed that the combination and the ratio of the types of energy depending greatly on the resources locally available in each geographical area ref.

The second study is presented in for Al Hallaniyat Island in Oman [37]. The technical and economic viability of utilizing different configurations of hybrid system (Wind, PV, diesel) was investigated using the weather data from 2004-2008. Al Hallaniyat’s annual electrical energy demand for the year 2008 was 1,303,290 kWh with a minimum load of 50kW and a maximum load of 320kW . The average wind speed at 10m height was 5.19 m/s and the yearly average daily value of solar radiation was 6.8 kWh/m2 [37].

Fig 9 Monthly average wind speed at Al Hallaniyat Island

The wind assessment at Al Hallaniyat Island shows that wind speeds are generally higher during the months of May to

August when compared with other months. The wind duration analysis indicated that the wind speeds are less than 4 m/s for about 40% of the time during the year, as shown in

Fig 9.

The monthly average solar radiation for the 2004–2007 is plotted in Fig 10. The insolation level is high during the high electrical load season (March–May) when compared with other months. The yearly average daily value of solar radiation is 6.8 kWh/m2.

Fig 10 Monthly average daily global radiation at a site near Al Hallaniyat Island

The technical details of the site and the load are summarized in table 4. Since both wind and PV are promising systems in this location, a hybrid system was considered in the analysis consisting of the following combinations: wind–PV–

diesel with batteries and wind–PV–diesel without batteries. Fig 11 shows the proposed hybrid system which can be implemented Al Hallaniyat Island. Using the above metrological weather background and the technical details given Table 1, the optimum sizing of the system components was selected based on the monthly average sizing approach. The optimum sizing results are illustrated on table 5.

TABLE 4

TECHNICAL DETAILS OF THE LOAD AND DIFFERENT SOURCES AT AL

HALANIYAT ISLAND

Item Details Remark Site information (Al Hallaniyat Island )

Area of about 56 km2 population estimated to 150

Among five the Khuriya Muriya Islands

Average wind speed 5.19 m/s at 10m height

Average daily value of solar radiation

6.8 kWh/m2 from 2004 till 2007

Annual electrical energy demand

1,303,290 kWh Year 2008 minimum load of 50kW and a maximum load of 320kW


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Fig 11 Proposed hybrid solar-wind- diesel system for AL Halaniyat

TABLE 5 OPTIMUM SIZING CONFIGURATION FOR THE PROPOSED AL HALANIYAT

ISLAND HYBRID PLANT

Proposed Diesel generator details

10 units Capacity 1080.8 kW

Diesel Price : US$0.508 l−1

Number of the PV panels

70 kW with 30 min storage capacity

A standard cost of $3000kW−1 Lifetime 25 years O&M cost US$10 per kW per year

Proposed Wind turbine

Rated power=60kW@ 11.3 m/s Height= 10m Rated speed =11.3m/s

capital cost US$60,000 Replacement cost US$40,000 Lifetime 30 years

Batteries Type US305HC, Nominal voltage 6V, Nominal capacity 305 Ah Nominal energy capacity of Each battery (VAh/1000) 1.83 kWh

Converter Cost=900US$/kW Efficiency=90%

Level of RE penetration

From 10 -25%

This study has investigated the technical and economic

viability of utilizing different configurations of a hybrid system. The main finding is that potential site for deployment of a PV and wind power station, especially with the diesel fuel price $0.5081 − 1. The simulation results showed that for a hybrid system composed of 70kW PV, 60 kW wind, 324.8kW diesel generators together with a battery storage, with renewable energy penetration of 25%, the total COE was found to be $0.222 kWh−1. Moreover, removing the 30 min

battery unit from the hybrid system will increase the COE to $0.225 kWh−1.

VII. CONCLUSION

This paper addresses the concepts of off-grid hybrid renewable energy sources for electrical power generation. Hybrid renewable energy system allows high improvement in the system efficiency, power reliability and reduce the system requirements for storages devices. Most of the advantages of the hybrid PV-Wind hybrid systems were given and the difficulties which faces these industries were also discussed. The paper has also presented different methods of sizing off-grid hybrid solar PV-wind renewable energy sources. Right sizing of a new hybrid renewable energy system can significantly help to determine the initial capital investment while maintaining system reliability at minimum cost. The optimization techniques of the hybrid solar-wind renewable energy systems were also discussed. The optimization approaches compare the performance and energy production cost of different system configurations and that will help to set up the optimum configuration of renewable energy configuration using simulation techniques.

Two proposals for optimum sizing of off-grid hybrid solar-wind power system are discussed. The first was for Masirah Island 12 MW hybrid PV-wind solar plant and the other one was for Al Halaniyat Island.

Optimum sizing analysis showed using PV–wind–diesel hybrid system with a battery unit will produce the lowest COE (0.182 US$/kWh) compared to other hybrids for Masirah Island. For AL Halaniyat Island, the analysis showed that for a hybrid system composed of 70kW PV, 60 kW wind, 324.8kW diesel generators together with a battery storage, with renewable energy penetration of 25%, the total COE was found to be 0.222 US$/kWh.

ACKNOWLEDGMENT

The research leading to these results has received Research Project Grant Funding from the Research Council of the Sultanate of Oman, Research Grant Agreement No. ORG/EI/13/011. The authors would like to acknowledge support from the Research Council of Oman.

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International Journal of Students Research in Technology & Management Vol 2 (03), May 2014, ISSN 2321-2543, pg.103-108


Activity Concentrations of Natural Radionuclides in Soils of Rainforest Sites in Western Ghats

*P.K.Manigandan, and K.K.Natrajan

*Departemnt of Engineering, Al Musanna College of technology-Oman

*[email protected]

Abstract– Assessments of naturally occurring radionuclides in soil collected from a tropical rainforest forest of western Ghats, India were conducted. These radionuclides were distributed unevenly in the forest soil. For all soil samples, the terrestrial gamma dose rate and the corresponding outdoor annual effective dose equivalents were evaluated. The activity concentration of 232Th and average outdoor gamma dose rates were found to be higher than the global average which appears to affects Western Ghats environment in general, the radiological hazard indices were found to be within the International Commission on Radiological Protection recommended limits. Hence, obtained results for natural radionuclides in the forest soils were within the range specified by UNSCEAR (2000) report for virgin soils except 232Th.

Key words– Naturally occurring radionuclides, Western Ghats, Monazite, radiological hazard

I. INTRODUCTION

We have previously reported that activity concentration of thorium was high in the region of Western Ghats especially around the Nilgiri hill station due to the presence of monazite sand (Manigandan. 2009; Selvasekarapandian. 2000; Iyengar et al. 1990) [1-3]. The external radiation levels from monazite sands in India are higher than that of radiation level reported from Brazil. High content of thorium and traces of uranium are also reported from these areas. These thorium and uranium may be redistributed during igneous, sedimentary and metamorphic cycles of geological evolution, which might have resulted in small concentrations of deposits under favorable geological processes. Literature indicates that the deposit of monazite on the coastal areas of Kerala and Tamil Nadu were formed due to the weathering of rocks in Western Ghats. Monazite sands consist of phosphate minerals of elements such as cerium which occur as small brown crystals in the Kerala sands (these monazite sands are mined for both cerium and radioactive thorium oxide). The sands originate in the granites and gneisses

of the Western Ghats and are transported to the coast by more than 47 streams that indent the Kerala coastline (Valithan et al.1994) [4] and it is shown in the Figure 1.

The study of the radioactive components in soil is a fundamental link in understanding the behavior of radionuclides in the ecosystem and contributes to the total absorbed dose via ingestion, inhalation andexternal irradiation. Forest soils in comparison with agriculture soils are more suitable for radionuclide investigations, because they not are usually disturbed by cultivation over long period of time. Characteristics of forest soils may modify radionuclide transfer in the and their bioaccumulation in comparison with other ecosystems (Segovia et al. 2003)[5]. These are important factors that might result in additional population exposure due to external irradiation or intake of radioactivity by the people. This might have economic consequences due to possible recreational or industrial use of the forest or its products (Gaso et al. 1998: Vaca et al. 2001) [6-7]. Therefore, thorough knowledge about the level of exposure to natural radiation from natural gamma-emitting radionuclides is important to the authorities and policy makers for making the right decisions.

II. MATERIALS AND METHODS

A. Study Area

The soils analyzed were collected from elevations of between 2000 and 2400 m the Nilgiri Highlands, Tamil Nadu, South India, which are situated between 11° 00' and 11° 30' N and between 76° 00' and 77° 30' E. The Nilgiri massif is located at the junction between the Eastern and Western Ghats, and is bounded by abrupt slopes. The study area is shown in Fig. 1. The vegetation above 2000 m in the highlands is a mosaic of high-elevation evergreen forests, called „shola‟ locally, and

grasslands with different compositions of flora, including C4 grasses (Sukumar et al. 1995; Rajagopalan et al. 1997) [8-9].




B. Sample Collection

The study area was divided into a 4-km grid and soil samples were collected from 15 sampling points in the natural, uncultivated, and grass-covered level areas within the grid, conforming to International Atomic Energy Agency recommendations (IAEA 1989)[10]. The 15 sampling points followed a zig-zag pattern. Five 20-cm-deep samples were collected at equal distances along a 1-m circle around the center of each sampling point. This sampling method was used to improve the representativeness of the samples. The position and elevation of each sampling point was determined using a global positioning system.

C. Sample Processing

The soil samples were transported to the laboratory and plant roots and other unwanted materials were removed. The samples were then dried in an oven at 105 °C for 12–24 h, ground, and passed through a 2-mm sieve. About 400 g of dry sample was weighed into a plastic container, which was capped and sealed. The container was sealed to ensure that none of the daughter products of uranium and thorium that were produced, particularly radon and thoron, could escape. The prepared samples were stored for 1 month before counting to ensure that equilibrium had been established between radium and its short-lived daughters. Detailed gamma-ray spectrometry analysis was performed on the soil samples.

D. Activity Determination

The samples were analyzed using a NaI(Tl) spectrometer coupled with TNIPCAII Ortec model 8K multi-channel analyzer . The 232Th-series, 238U-series, and 40K activities were estimated, as were the amounts of these radionuclides that would enter the air from the soil. A 3 inch × 3 inch NaI(Tl) detector was used, with adequate lead shielding, which reduced the background by a factor of 95. The energies of interest were found using an International Atomic Energy Agency standard source and the appropriate geometry. The system was calibrated in terms of both the energy response and the counting efficiency. Sample with a density of 1.3 g/cm3 was used for the calibration, which was the same as the mean density of the soil samples analyzed (1.24 g/cm3), the detector was very well shielded, and the counting time was 20,000s for each sample. The minimum detectable concentrations, defined as 3 × σ (the standard

deviation), were 7 Bq/kg for the 232Th-series, 8.4 Bq/kg for the 238U-series, and 13.2 Bq/kg for 40K.

The concentrations of the radionuclides of interest were determined using the counting spectrum for each sample. The peaks corresponding to 1.46 MeV (40K), 1.76 MeV (214Bi), and 2.614 MeV (208Tl) were considered when evaluating the 40K, 238U-series, and 232Th-series activities, respectively. The crystal detector resolution was 6% for 40K, 4.4% for the 232Th-series, and 5.5% for the 238U-series. The gamma-ray spectrum activities for each soil sample were analyzed using dedicated software, and references were chosen to achieve sufficient discrimination.

In addition to the gamma-ray spectrometric analysis, a low-level survey environmental radiation dosimeter (type ER 705; Nucleonic System PVT Ltd., Hyderabad, India) meter was used to measure the ambient radiation levels in the forest in the study area. The dosimeter had a halogen quenched Geiger–Müller detector (Ind. lnc., U.S.A ) powered by a rechargeable battery, and was designed to read the exposure rate at two levels, 0.1 μR/h and 1 μR/h. The dosimeter was calibrated using a standard

source before use.



III. RESULTS AND DISCUSSION

The activity concentration of naturally occurring radionuclides in forest soil of Western Ghats is shown in Table 1. The mean activity concentration ranges for 238U in soil was 15.12 to 41.21Bq/ kg with an averages of 26.26 + 9.1Bq /kg. This shows that, similar activity concentration was found throughout the forestland with less variation. At the same time, samples that were collected from interior parts of the forest showed high concentration of thorium, since the samples collected from these areas were covered with bushes and trees of various species where soils were generally undisturbed much by weathering.

TABLE 1

THE ACTIVITY CONCENTRATION OF NATURALLY OCCURRING

RADIONUCLIDES AND RAEQ VALUES IN SOIL SAMPLES

* σ is Standard Deviation

On the other hand, the activity concentration of 232Th was much higher than 238U at all the locations. The activity of 232Th in soil ranged from 39.17 to 76.13Bq/kg with a mean of 53.61 + 10.4Bq/kg. The spectral measurement clearly exposed the spectral photo peaks at 238.3, 373.3, 510.7, 727.3, 911.2, 916, 1587 and 2614KeV which were due to the daughter products of 232Th series viz, 212Pb, 228Ac, 208TI, 208Tl,

212Bi, 228Ac, 212Bi and 208Tl, respectively. Hence, this observation endorses presence of 232Th series in soil and also the deposits of monazite on the coastal areas of Kerala and Tamil Nadu were formed due to the weathering of rocks in Western Ghats.

The activity of 40K in soil ranged from 127.54 to 248.12Bq/ kg with a mean of 204.08 + 30.4Bq/ kg. The previous background radiation survey by Selvasekarapandian et al (2000)[2] showed that mean activity of 232Th-series, 238U-series and 40K are 4.4, 1.9 and 0.742 time was higher than the world average values reported by the UNSCEAR 2000 Report (Such as 238U, 232Th and 40K were 35Bq/kg, 30Bq/kg and 400Bq/kg respectively)[11] . The mean activity of 232Th observed in the present work is 1.5 times higher than the world average value whereas the mean activity of 238U and 40K was observed to be lower than the world average. These variations in the activity concentration may be explained by the difference in natural ecosystems and the terrestrial ecosystems. There are several important features, the main one being that, in terrestrial ecosystems, soils are periodically ploughed and fertilized, while in natural systems they exhibit a more or less clear subdivision in the upper, mainly organic horizon and the lower, mineral horizon. They differ in several important characteristics such as pH, moisture, nutrient status, biological activity etc. (Frissel et al. 1990) [12].

While comparing radionuclides from different decay chains (232Th and 238U), it was observed that both the series are linearly related i.e. concentration of 232Th-series increases with increase of 238U-series, but Y- intercept is clearly different from zero. This fact reflects that the 232Th/238U activity ratio is not constant across the forest soil.

A graph is plotted between 232Th/238U activity ratios with the 238U concentration. The curves reflect the variation of activity ratio and expressed mathematically a hyperbolic function:

x = aCsb

Where X is the activity ratio, Cs is concentration of 238Uradionuclide in the soil and a and b parameters to determined. Using the above equation, the following function is obtained.

232Th/238U= 9.2 (238U)-0.456,

(With regression coefficients of –0.9)

This correlation reflects that the activity ratio remains constant only for high concentration of 238U in the soil. For low activity concentration, contamination of radionuclides from 232Th decay chain seems to be undistinguished.

Location Activity Concentration

[Bq/kg] Radium

Equivalent (Raeq)

Observed Dose(ERD)

[nGy/h] 238U 232Th 40K S-1 33.42 61.32 224.56 138.40 115.72 S-2 41.21 70.28 233.71 159.71 118.23 S-3 44.11 76.13 248.12 172.08 123.81 S-4 37.91 64.61 221.5 147.36 100.82 S-5 19.99 46.5 127.54 96.31 90.45 S-6 27.9 51.86 218.06 118.85 82.95 S-7 18.57 46.96 201.14 101.21 89.77 S-8 24.38 48.67 148.89 105.44 93.18 S-9 18.56 44.14 211.19 97.94 90.91

S-10 30.12 58.46 214.56 130.24 98.9 S-11 15.12 39.17 198.79 86.44 93.98 S-12 21.03 45.89 205.37 102.47 96.59 S-13 19.99 47.76 202.77 103.90 86.36 S-14 21.42 48.91 195.39 106.41 94.32 S-15 20.19 53.55 209.67 112.91 78.41

Range 15.12

- 41.21

39.17- 76.13

127.54 -

248.12

86.44-172.08

78.41-123.81

Mean + σ *

26.26 + 9.1

53.61 + 10.4

204.08 + 30.4

118.66 + 25.3

96.96 + 12.9



y = 9.2 x-0.456

0

0.5

1

1.5

2

2.5

3

0 20 40 60

Th

-232/

U-2

38

U-238 in Bq/kg

Fig-2

Fig 2. 232Th/238U activity ratio vs concentration of 238U in soil

A. Dose Calculation 1) Absorbed and observed dose rate: The mean activity

concentrations of 232Th and 40K are converted in to dose rate based on the conversion factor given by UNSCEAR (2000) [11] (Table 2).

D = nGy/h

………(1)

Where D is calculated the absorbed dose rate (nGy/h) CU ,

CTh and CK are the activity concentrations (Bq/kg) of 238U,232Th and 40K in soil samples respectively. The range of calculated absorbed dose rates is from 38.93 nGy/h to76.71 nGy/h with an average of 53.03 + 11.2nGy/h that similar the world average value of 51nGy/h reported in UNSCEAR (2000) [11].

The outdoor gamma dose rates were measured 1 m above the ground by a portable digital ERD at all the sampling sites. A total of five readings were recorded at each spot and the average was taken (Table 1). Other studies indicate an average outdoor gamma dose rate of 60 nGy/h in the world ranging from 10 to 200nGy/h (Taskin et al. 2009)[13] but also similar to our determination within the experimental range.

The present study in Western Ghats shows that in the field, measured average gamma dose rate is 96.96 +

12.9nGy/h, which is slightly higher than the world average. The level of gamma radiation is directly associated with the activity concentrations of radionuclides in the soil and cosmic rays (Taskin et al. 2009) [13]. The excess dose measured in the field with the portable dosimeter (96.96±12.9 nGy/h) in comparison with the absorbed dose expected on the basis of radionuclide concentrations determined in soil samples (53.03±11.2 nGy/ h) is due to the significant contribution from the cosmic radiation in the present study area, located at 2400m above the sea level, where the contribution of cosmic ray is much higher than the normal one.

B. The Annual Effective Dose Equivalent (AEDE):

The absorbed dose to effective dose conversion coefficient (0.7 Sv/Gy) and an outdoor occupancy factor (0.2), which have been proposed by UNSCEAR (2000)[11], were used to estimate the annual effective dose rates, as shown in Eq. 2.

…….. (2)

The outdoor annual effective dose equivalents obtained for the samples are presented in Table 2 and it was found to be 65.03 + 13.8μSv which is within the world average value

of 70μsv (Orgun et al. 2007) [14].

C. Radiological Hazard Indices:

The Gamma ray radiation hazards caused by the specified radionuclides in samples were assessed by calculating the different indices. Even though total activity concentration of radionuclides is calculated, it does not provide the exact indication of total radiation hazards. Also, these hazard indices are used to select the right materials, because soil potentially contaminated is used for making earthen huts, bricks and pottery items.

The gamma–ray radiation hazards due to the specified radionuclides were assessed by two different indices (Radium-equivalent activity and external radiation hazard). A widely used hazard index (reflecting the external exposure) called the externalhazard index Hex is defined as follows:

…………….(5)

where CU, CTh and CK are mean activity concentrations of 238U, 232Th and 40K in Bq/kg respectively, Hazard indices of all sites samples were found to be less than unity (permissible level)(Orgun et al. 2007) [14].



TABLE 2

RADIOLOGICAL PARAMETERS FOR THE SOIL SAMPLES

* σ is SD (Standard Deviation)

D. Radium Equivalent (Raeq):

Exposure to radiation can be defined in terms of many parameters. It is well known that, Radium equivalent activity (Raeq) is also a widely used Radiation hazard index. The

indices were defined as below (Beretka and Mathew 1985) [15]

…………….(4)

Where AU, ATh and AK are the activity concentration of 238U, 232Th and 40K (Bq/kg) in the soil samples respectively. Radium equivalent activity index (Raeq) represents a weighted sum of activities of the above-mentioned natural radionuclides and is based on the assumption that 259 Bq/kg of 232Th, 370 Bq/kg of 226Ra and 4810 Bq/kg of 40K produce the same gamma radiation dose rates. The use of materials whose radium equivalent activity concentration exceeds 370 Bq/kg is discouraged to avoid radiation hazards. The annual effective dose for Raeq of 370 Bq/kg corresponds to the dose limit of 1.0 mSv for the general population (Tahir et al. 2005) [16]. The calculated average radium equivalent activity value in the present study is 118.66 + 25.3Bq/kg which are lower than above said value of 370Bq/kg.

IV. CONCLUSION

The average values for 238U and 40K in all areas under investigation are within the world wide values reported by UNSCEAR (2000). The thorium concentration in the Western Ghats region is on the higher side of the world wide range which could be due to the existence of monazite sand in the area of study. The average outdoor gamma dose rate is higher than the world average, and thus Western Ghats region comes under above average background radiation in the world. In spite of all these, the other calculated radiological hazard indices are within the acceptable limits, (Safety Limit) and thus we can conclude that forest environment of Western Ghats has slightly high background radiation, but despite of this, it will not pose much radiological risks regarding harmful effects of ionizing radiation from the naturally occurring radionuclides in soil to the population. Also, the results of measurements will serve as base line data and, as a reference level for soil samples of Western Ghats.

ACKNOWLEDGEMENT

The authors are thankful to Dr. A. Natarajan, Head, HASL, IGCAR, Dr. A.R. Lakshmanan. HASL, IGCAR, Dr. A.R.Iyengar, Head, ESL, Kalpakkam for their constant encouragement throughout the period of work.

REFERENCE

[1] Manigandan P K (2009). “Activity concentration of radionuclides in plants in the environment of Western Ghats, India”. African Journal of Plant Science 3 (9): 205-209,

Location

D, Absorbed Dose (nGy/h)

External Hazard Index (Hext)

Outdoor Annual

effective dose

Equivalent

(μSv/y)

238U 232Th 40K Total

S-1 15.44 37.04 9.36 61.84 0.37 75.84

S-2 19.04 42.45 9.75 71.23 0.43 87.36

S-3 20.38 45.98 10.35 76.71 0.46 94.07

S-4 17.51 39.02 9.24 65.78 0.40 80.67

S-5 9.24 28.09 5.32 42.64 0.26 52.29

S-6 12.89 31.32 9.09 53.31 0.32 65.37

S-7 8.58 28.36 8.39 45.33 0.27 55.59

S-8 11.26 29.40 6.21 46.87 0.28 57.48

S-9 8.57 26.66 8.81 44.04 0.26 54.01

S-10 13.92 35.31 8.95 58.17 0.35 71.34

S-11 6.99 23.66 8.29 38.93 0.23 47.75

S-12 9.72 27.72 8.56 46.00 0.28 56.41

S-13 9.24 28.85 8.46 46.54 0.28 57.07

S-14 9.90 29.54 8.15 47.59 0.29 58.36

S-15 9.33 32.34 8.74 50.42 0.30 61.83

Range 6.99-20.38

23.66-45.98

5.32 -10.35

38.93-76.71 0.23 - 0.46

47.75 - 94.07

Mean+ σ 12.13 + 4.2

32.38 +6.3

8.51+1.27 53.02+11.2 0.31+0.07 65.03+13.

8



[2] Selvasekarapandian S, Manikandan N, Sivakumar R (2000). “Natural radiation distribution of soil at Kotagiritaluk of Nilgiris biosphere in India”. Eighth International Conference, October 16-20, Ibaraki, Japan.

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International Journal of Students Research in Technology & Management Vol 2 (03), May 2014, ISSN 2321-2543, pg. 109-112


An Assessment of Temperature and Precipitation Change Projections in Muscat, Oman from Recent

Global Climate Model Simulations Abdulaziz Al-Ghafri #1, Luminda Gunawardhana #2, Ghazi Al-Rawas #3

# Department of Civil and Architectural Engineering, Sultan Qaboos University P.O. Box 33, Postal code 123, Al-Khoud, Sultanate of Oman

1 [email protected] 2 [email protected] 3 [email protected]

Abstract— Oman is vulnerable to the impacts of climate

change, the most significant of which are increased temperature, less and more erratic precipitation, see level rise (SLR) and desertification. The objective of this research is to investigate the potential variation of precipitation and temperature in Muscat, the capital city of Sultanate of Oman in future. We used the MIROC general circulation model (GCM) output (maximum and minimum temperatures and precipitation) from the Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0 and 8.5 scenarios of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) for assessing changes in climate in the period of 2080-2099 compared to the baseline period of 1986-2005. The spatial mismatch between GCM grid scale and local scale was resolved by applying the LARS stochastic Weather Generator (WG) model. The results obtained for 4 scenarios indicate a significant warming in future, which ranges from 0.93ᴼC (minimum temperature by 1.1ᴼC and maximum temperature by 0.86ᴼC) for the lowest scenario, RCP 2.6, to 3.1ᴼC (minimum temperature by 3.2ᴼC and maximum temperature by 3.0ᴼC) for the highest one, RCP 8.5, relative to baseline level. The differences in the precipitation projections between the scenarios are much greater compared to consistent warming depicted in temperatures. The results reveal -36.4% and -36.0% decreases in precipitation for the RCP 2.6 and RCP 4.5 scenarios, respectively, while, RCP 6.0 and RCP 8.5 scenarios predict increase in precipitation in a range from 9.6% to 12.5%, respectively during 2080-2099 compared to 1986-2005 period. These results need to be further improved by adopting more GCMs, which will provide potential changes in a consistent range.

I. INTRODUCTION

Observed and projected increases in temperature and precipitation variability are perhaps the most influential climate driven changes to impact water systems (Parry et al., 2007). Located in an arid region, the climate of Oman is vulnerable to the potential impacts of climate change, the most significant of which are increased average temperatures, less and more erratic precipitation, sea level rise (SLR) and desertification. Oman is primarily concerned due to its chronic water stress and lack of resilience (institutional, infrastructure and social) against climate change.

Groundwater represents about 78% of the water supply in Oman. Owing to a lack of data and the very slow reaction of groundwater systems to changing recharge conditions, impacts of climate change on groundwater are poorly understood. Groundwater resources are related to climate change through hydrologic processes, such as precipitation and evapotranspiration, and through interaction with surface water. With increased evapotranspiration as a result of higher air temperature and decreased precipitation, the impact of climate change will result in declining groundwater recharge (Eckhardt and Ulbrich, 2003; Brouyere et al., 2004) and alter the associate temperature distribution in the subsurface. For example, in the Ogallala Aquifer region, projected natural groundwater recharge decreases more than 20% in all simulations with warming of 2.5°C or greater (Rosenberg et al., 1999).

Integrating climate change mitigation and adaptation in development strategies and policies is a must for Oman which is at the early stage of economic and industrial development. Thus far in Oman, the scientific knowledge about the climate change and its impacts on the hydro-meteorological extremes has not been fully studied thereby making it difficult to assess future risks. Therefore, the main objective of this research is to investigate the potential variation of precipitation and temperature in Muscat, the capital city of Sultanate of Oman in future.

II. STUDY AREA

Oman located in south-Eastern corner of the Arabian Peninsula, encompasses a diverse range of topography, including mountain ranges, low land, coastal areas and arid deserts. The coastal line of Oman extends over 3165 km and experiences very severe tropical cyclones. The supper cyclonic storm, hurricane Gonu in 2007 led to the worst natural disaster on record in Oman, with total rainfall reached 610 mm near the cost. The cyclone and flash flood caused about $4 billion in damage (2007 USD) and 49 deaths (Rafy and Hafez, 2008). The climate of the country is mainly arid or semiarid, which receives less than 100 mm in annual rainfall on average compared to annual global average of 1123 mm.




Muscat, the capital city of Oman is a coastal city (Fig.1), which accommodates 29.5% of the total population in Oman in 2010. The average precipitation of Muscat is 81 mm over the period 1977-2011 and it shows statistically weak increasing trend at an average rate of 6 mm/10 yr. The rainfall pattern proved to be irregular, averaging rain on only around 7 days per year. Consequently, number of consecutive days of no rainfall is relatively high, which was estimated to be about 225 days per year over the period 1977-2011. The daily maximum temperature in Muscat fluctuates between 17 and 49C, and the daily minimum temperature is between 10 and 40C over the period 1986-2011.

Fig. 1. Study area in Oman

III. METHODOLOGY

Observed precipitation and temperature data in the Muscat airport meteorological station for the period of 1986-2005 were obtained. For the future climates, we used the MIROC general circulation model (GCM) output (maximum and minimum temperatures and precipitation) from the Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0 and 8.5 scenarios in the periods of 1986-2005 and 2080-2099. The four RCPs are based on multi-gas emission scenarios. They are being used to drive climate model simulations planned as part of the World Climate Research Programme’s

Fifth Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2009).

The spatial mismatch between GCM grid scale and local scale was resolved by applying statistical downscaling method. A stochastic weather generator (LARS-WG) used in this study serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which incorporate changes in both mean climate and in climate variability (Semenov & Barrow, 1997). LARS-WG can be used for the simulation of weather data at a single site under both current and future climate conditions. These data

are in the form of daily time-series for a suite of climate variables, namely, precipitation (mm), maximum and minimum temperature (°C) and solar radiation (MJm-2day-1). The simulation of precipitation occurrence is based on distributions of the length of continuous sequences, or series, of wet and dry days. To developed future scenarios, the mean of the empirical distributions for wet and dry spell length from the baseline (1986-2005) and future time period (2080-2099) were calculated for each month. The relative change in length of wet (or dry) series was calculated as follows.

Relative change in length = length2080-2099 / length1986-2005 (1)

Similarly, the relative change in standard deviation

(St.dev.) of minimum and maximum temperatures and the mean change in precipitation amount were calculated as follows.

Relative change in St.dev = St.dev.2080-2099 /

St.dev.1986-2005 (2) Relative change in Precipitation = pre.2080-2099

/pre.1986-2005 (3) For monthly mean changes in maximum and minimum

temperatures, the monthly mean changes in these values between the future and baseline periods were considered.

IV. RESULTS AND DISCUSSIONS

We used the MIROC model output of maximum and minimum temperatures and precipitation from the RCPs 2.6, 4.5, 6.0 and 8.5 scenarios for assessing changes in climate in the period of 2080-2099 compared to the baseline period of 1986-2005. An example of the scenario file developed for the RCP4.6 is shown in Fig. 2.

The scenario files developed for each scenario were used with the observations during 1986-2005 to produce time series of weather variable for the period of 2080-2099. Table 1 shows a summary of weather variables and their relative change in future compared to baseline time period. Figure 3 depicts accumulative probability distributions of three variables in baseline period and four scenarios in the future. According to figures 3 a) and b), there is a consistent warming in both minimum and maximum temperatures with different magnitudes for all scenarios during 2080-2099 compared to observations in 1986-2005 period. RCP8.5, which is representative of the high range of non-climate policy scenarios (subsequent radiative forcing of 8.5Wm-2 in 2100), predicts the highest warming of 3.2 and 3.05C for minimum and maximum temperatures, respectively. Similarly, RCP2.6, which assumes drastic policy interventions to reduce greenhouse gas emissions (subsequent radiative forcing of 2.6 Wm-2 by 2100), predicts the lowest warming of 1.01 and 0.86C for minimum and maximum temperatures, respectively. Warming predicts by two other scenarios varies between RCP8.5 and RCP2.6 in similar magnitudes.



Fig. 2. A scenario file developed to use in the LARS-WG for the MIROC global climate model for the RCP4.6 scenario for the time period 2080–2099

at Muscat.

The differences in the precipitation projections between the scenarios are much greater compared to consistent warming depicted in temperatures. The results reveal -36.4% and -36.0% decreases in precipitation for the RCP 2.6 and RCP 4.5 scenarios, respectively, while, RCP 6.0 and RCP 8.5 scenarios predict increase in precipitation in a range from 9.6% to 12.5%, respectively during 2080-2099 compared to 1986-2005 period. Figure 3 c) depicts that the RCP8.5 scenario predicts the potential of very extreme precipitation events in future, which may increase the risk of floods in our study area.

V. CONCLUSIONS AND RECOMMENDATIONS

This study was conducted to investigate the potential variation of precipitation and temperature in Muscat, the capital city of Sultanate of Oman in future. We used the MIROC general circulation model output (maximum and minimum temperatures and precipitation) from the RCPs 2.6, 4.5, 6.0 and 8.5 scenarios of the IPCC Fifth Assessment Report for assessing changes in climate in the period of 2080-2099 compared to the baseline period of 1986-2005. LARS-WG was used for the simulation of weather data under both current and future climate conditions.

Fig.3 Probability distributions of weather variables in the baseline and future time periods

The results obtained for 4 scenarios indicate a significant

warming in future, which ranges from 0.93ᴼC (minimum temperature by 1.1ᴼC and maximum temperature by 0.86ᴼC) for the lowest scenario, RCP 2.6, to 3.1ᴼC (minimum temperature by 3.2ᴼC and maximum temperature by 3.0ᴼC) for the highest one, RCP 8.5, relative to baseline level.

// Columns are: // [1] month // [2] relative change in monthly mean rainfall // [3] relative change in duration of wet spell // [4] relative change in duration of dry spell // [5] absolute changes in monthly mean min temperature // [6] absolute changes in monthly mean max temperature // [7] relative changes in daily temperature variability // [8] relative changes in mean monthly radiation [VERSION] LARS-WG5.5 [NAME] Muscat_RCP46 [BASELINE] 1986 [FUTURE] 2080 [GCM PREDICTIONS] Jan 0.68 0.94 1.06 1.09 1.09 0.86 1 Feb 1.40 0.80 0.66 1.09 1.09 0.71 1 Mar 0.34 0.86 3.99 1.09 1.09 1.04 1 Apr 1.62 0.84 1.51 1.09 1.10 0.94 1 May 0.41 1.11 1.39 1.09 1.08 0.86 1 Jun 0.58 0.74 0.84 1.06 1.05 0.78 1 Jul 3.69 0.81 1.28 1.04 1.02 0.97 1 Aug 4.47 1.70 0.52 1.03 1.02 0.77 1 Sep 1.34 0.76 1.17 1.03 1.02 0.93 1 Oct 9.27 1.54 1.12 1.05 1.05 1.00 1 Nov 0.08 0.68 2.06 1.06 1.06 0.80 1 Dec 0.50 0.78 1.55 1.09 1.09 0.70 1 [END]

a)

b)

c)



TABLE I A SUMMARY OF WEATHER VARIABLES CHANGE DURING 2080-2099 COMPARED TO 1986-2005 PERIOD

Data

Minimum Temperature Maximum Temperature Rainfall

Annual average value

(°C)

Relative change (°C)

Annual average value

(°C)

Relative change (°C)

Annual total (mm)

Relative change (%)

Observations 23.89 32.99 79.79

RCP 2.6 24.9 1.01 33.85 0.86 50.75 -36.40

RCP 4.5 25.57 1.68 34.74 1.75 51.09 -35.97

RCP 6.0 25.81 1.92 34.83 1.84 89.76 12.50

RCP 8.5 27.09 3.2 36.04 3.05 87.41 9.55

The results reveal -36.4% and -36.0% decreases in precipitation for the RCP 2.6 and RCP 4.5 scenarios, respectively, while, RCP 6.0 and RCP 8.5 scenarios predict increase in precipitation in a range from 9.6% to 12.5%, respectively during 2080-2099 compared to 1986-2005 period. RCP8.5 scenario alone predicts the probability of very extreme precipitation events in future which may have implications for planning and decision making for flood mitigation infrastructures, land-use regulations and building codes. The differences in the precipitation projections between the scenarios are much greater compared to consistent warming depicted in temperatures. These results need to be further improved by adopting more GCMs, which will provide potential changes in a consistent range. The results of this study can be integrated with hydrological and flood models so that risk scenarios can be constructed for future time periods.

ACKNOWLEDGMENT

Authors wishes to acknowledge Prof. M. Semenov in Rothamsted Research Center for providing LARS-WG model. We are also grateful to the National Center for Atmospheric Research, Colorado for providing us the NCAR command Language (http://dx.doi.org/10.5065/D6WD3XH5).

REFERENCES [1] M. Parry, O. Canziani, J. Palutikof, P. V. Linden and C. Hanson,

“Climate change 2007: Impacts, Adaptation and Vulnerability. Summary for policymakers”, Cambridge University Press, New York, 2007.

[2] K. Eckhardt and U. Ulbrich, “Potential impacts of climate change on

groundwater recharge and streamflow in a central European low mountain range,” Journal of Hydrology, vol. 284, pp. 244-252, 2003.

[3] S. Brouyere, G. Carabin and A. Dassargues, “Climate change impacts

on groundwater resources: modelled deficits in a chalky aquifer, Geer basin, Belgium,” Hydrogeology Journal, vol. 12, pp.123-134, 2004.

[4] N. J. Rosenberg, D. J. Epstein, D. Wang, L. Vail, R. Srinivasan and J. G. Arnold, “Possible impacts of global warming on the hydrology of the Ogallala Aquifer region,” Climatic Change, vol. 42, pp. 677–692, 1999.

[5] M. E. Rafy, and Y. Hafez, “Anomalies in meteorological fields over

northern Asia and its impact on Hurricane Gonu,” 28th Conference on Hurricanes and Tropical Meteorology, pp. 1–12, 2008.

[6] K. Taylor, R. J. Stouffer and G. A. Meehl, “A summary of the CMIP5 Experiment Design” [Online]. Available: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf

[7] M. A. Semenov and E. M. Barrow, “Use of a stochastic weather generator in the development of climate change scenarios,” Climatic Change, vol. 35, pp.397-414, 1997.

http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf

http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf



Assessment of Embodied Energy in the Production of Ultra High Performance Concrete (UHPC)

Aysha. H^, T. Hemalatha*, N. Arunachalam**,

A. Ramachandra Murthy* and Nagesh R. Iyer*

^project Student, *CSIR-SERC, Chennai, Tamil Nadu, India – 600113.

** Dean - Bannari Amman Institute of Technology, Sathy, Tamil Nadu, India – 638 503.

Presenting author email: [email protected]

ABSTRACT - There is a growing interest towards quantifying the direct and indirect emission of carbon (embodied energy) in the production and utilization of new types of concrete. Advanced technological development of concrete and demand for high strength and high performance construction materials have lead to the evolution of Ultra High Performance Concrete (UHPC). This material is primarily characterized with high strength and durability and when reinforced with steel fibers or steel tubes exhibits high ductility. Existing UHPC preparation methods involve costly materials and classy technology. This may increase the embodied energy of UHPC, which is not in favor of green environment for a sustainable technology and development.

Embodied energy is the energy required to produce any goods or services, which is incorporated or embodied in the product itself. Embodied energy assessment aims in finding the sum of total energy necessary for an entire product life-cycle. To make UHPC an eco-friendly material, the embodied energy involved in its production should be reduced by the application of simple technology. Many research works are being done in replacing certain amount of cement with silica fume (SF), fly ash (FA), ground granulated blast furnace slag (GGBS) etc. in order to achieve an environmental friendly UHPC of high strength of more than 150 MPa and an elevated level of durability. This study is focused on the assessment of embodied energy involved in the production of UHPC with alternate cementitious material. With the knowledge of embodied energy for UHPC, implications can be deliberated by varying the constituents and replacing cement with certain amount of eco-friendly materials, so as to reduce the environmental impact of construction with UHPC.

Key Words - embodied energy, fly ash, GGBS, sustainable concrete, UHPC.

I. INTRODUCTION

The net cement production in the world has increased from about 1.4 billion tonnes in the year 1995 to almost 2 billion tonnes in the year 2010. This has lead to the emission of about 2 billion tonnes of CO2 in the atmosphere every year [1]. The global cement industry has reduced its specific net CO2 emissions per tonne of product by 17 % since 1990, from 756 kg/tonne to 629 kg/tonne. Meanwhile, cement production increased by 74 % between 1990 and 2011, according to the World Business CSI, which released

its 2011 data update to the project Council for Sustainable Development’s Cement Sustainability Initiative (CSI).

“Getting the Numbers Right” or GNR, which tracks global CO2 emissions for participating companies in the cement industry, reports the evidence of significant reduction of CO2 emissions and improved efficiency. According to CSI, the four main drivers for the reduction in emissions are (i) investment in more efficient kiln technology, (ii) increasing the use of alternative fuels such as biomass, (iii) reduction in clinker content and (iv) 8 % decrease in electricity use per tonne of cement since 1990. Between 2010 and 2011, cement production volume covered by the GNR increased from 840 million tons to 888 million tons, and specific net CO2 emissions decreased from 638 kg/ton to 629 kg/ton of product.

As a building material, concrete is the most used man-made material in the world, utilized at double the rate of all other building materials, according to CSI. There are several essentials which can reduce the environmental impact factor and CO2 intensity of concrete used for construction, which include maximizing the concrete durability, conservation of materials, use of waste and supplementing cementing materials and recycling of concrete [3]. Partial replacement of cement with waste and supplementary cementitious materials such as fly ash, GGBS, silica fume, rice husk ash and metakaolin not only improves the concrete durability and reduce the risk of thermal cracking in mass concrete but also emits less CO2 than cement. By doing so, it ensures the proper utilization of such waste materials in an effective manner which otherwise are being dumped creating hazard to the environment.

II. RESEARCH SIGNIFICANCE

Ultra high performance concrete belongs to the family of engineered cementitious composites (ECC) and is defined as cement based concrete with compressive strength equal to or greater than 150 MPa. The ductility of UHPC is attained by adding steel fibres to it and these generally transform the developed cracks into larger number of small width cracks, which increases the strength and durability of UHPC members. It is a high strength ductile material formulated




from a special combination of constituent materials which include Portland cement, silica fume, quartz powder, fine sand, high range water reducer, water and steel fibres. With the present focus on sustainability, green concrete is achieved by optimizing the mixture proportions and material substitutions, so that energy and CO2 impact can be reduced

Replacement of certain amount of cement with silica fume and other cementitious materials in the production of UHPC itself leads to lesser consumption of cement. UHPC, being a highly efficient material with good mechanical and durability characteristics is used in the production of thinner elements which in turn consumes less volume of cement. Hence, UHPC employs lesser volume of cement both in the production and utilization phases. The present study focuses on the assessment of embodied energy of UHPC, with partial replacement of cement with eco friendly materials like silica fume, fly ash, GGBS etc. Also, an optimum UHPC mix proportion with less embodied energy, without compromising the strength and durability criteria are obtained.

III. SUSTAINABLE CONSTRUCTION

The principles of sustainable development and green buildings have penetrated the construction industry at an accelerating rate in recent years. The concrete industry in particular, because of its enormous environmental footprint, has a long way to go to shed its negative image [4]. Sustainability is given prime importance in the field of construction for the social progress which recognises the needs of everyone, effective protection of the environment, prudent use of natural resources and maintenance of high and stable levels of economic growth and employment. The use of GGBS or fly ash in concrete, either as a mixer addition or through a factory made cement can significantly reduce the overall greenhouse gas emissions associated with the production of concrete, and thereby reducing the embodied energy.

A. Embodied Energy

Embodied energy is an accounting method which aims to find the sum of the energy necessary for an entire product life-cycle, which constitutes assessing the relevance and extent of energy into raw material extraction, transport, manufacture, assembly, installation, disassembly, deconstruction and/or decomposition as well as human and secondary resources as shown in Fig. 1. Materials that have a lower embodied energy are more sustainable than those with a higher embodied energy. Energy inputs usually entail greenhouse gas emissions in deciding whether a product contributes to or mitigates global warming. Different methodologies produce different understandings of the scale, scope of application and the type of energy embodied. Typical embodied energy units used are MJ/kg (mega joules of energy needed to make a kilogram of product).

Fig. 1 Breakdown of embodied energy calculations

1) Embodied Energy Methodologies: Different methodologies use different scales of data to calculate the energy embodied in products and services of nature and human civilization. International consensus on the appropriateness of data scales and methodologies is still pending. This difficulty can give a wide range in embodied energy values for any given material. In the absence of a comprehensive global embodied energy public dynamic database, embodied energy calculations may omit important data. Such omissions can be a source of significant methodological error in embodied energy estimations. The following are the widely used methodologies, 1. Input-Output embodied energy analysis and 2. Process life cycle assessment.

2) Standards on Embodied Energy: The UK Code for Sustainable Homes and USA LEED are methods in which the embodied energy of a product or material is rated along with other factors, to assess a building's environmental impact. Embodied energy is a concept for which scientists have not yet agreed absolute universal values because there are many variables to take into account, but most agree that products can be compared to each other to see which has more and which has less embodied energy.

B. Supplementary Cementitious Materials

There are some materials obtained as industrial by-products, which is actually a waste, but can be used as a supplementary cementitious material, by partially replacing the cement. In this study, the analysis of embodied energy of UHPC is undertaken, by partial replacement of cement with silica fume, fly ash and ground granulated blast furnace slag (GGBS).

1) Silica Fume: This siliceous material is a by-product of the semiconductor industry. When added to concrete, this greatly improves both strength and durability, and hence modern high performance concrete mix designs as a rule call for the addition of silica fume. There have been several research works, which have identified the benefits of silica fume both as a pozzolanic and a filler material [5], [6]. Nowadays silica fume is produced specifically for the concrete industry, apart from that available as an industrial

Site to grave

Cradle to Site



by-product due to its massive usage. The beneficial aspect of silica fume is the presence of around 98 % of SiO2.

2) Fly Ash: The utilization rates of fly ash vary greatly from country to country, from as low as 3.5% in India to as high as 93.7% in Hong Kong [7]. Fly ash, an important pozzolanic material has numerous advantages when compared with regular Portland cement. Firstly, lesser heat of hydration makes it a popular cement substitute for mass structures, resulting in the development of high volume fly ash concrete mixes. Perhaps, the most significant advantage of fly ash is that it is a byproduct obtained from coal combustion, which otherwise involves the greater cost for disposal of the waste product. Moreover, concrete produced with fly ash can have better strength and durability. After all, the cost of fly ash is lesser than Portland cement. The main disadvantage of fly ash is its slow rate of strength development and hence accelerators are used to speed up the hydration rates of fly ash concrete mixes. The quality of fly ash is an important issue, because of considerable variation in the physical and chemical properties, since the primary source of coal varies widely. In recent years, after the increased usage of fly ash, technologies have developed to separate the unburned residues for the quality improvement.

3) Ground Granulated Blast Furnace Slag: This is a glassy granular material, which is a by-product of the steel industry, formed when molten blast furnace slag is rapidly chilled, when immersed in water [8]. Like fly ash, GGBFS improves mechanical and durability properties of concrete and generates less heat of hydration. GGBFS is not only used as a partial replacement for portland cement, but also as an aggregate. The optimum cement replacement level is often quoted to be about 50% and even sometimes as high as 70% to 80%. The cost of slag is generally same as that of portland cement, but is being extensively used due to its beneficial properties [5]. Many suggest that the concrete industry offers ideal conditions for the beneficial use of such slag and ashes because the harmful metals can be immobilized and safely incorporated into the hydration products of cement.

IV. METHODOLOGY

For the assessment of embodied energy of UHPC, initially a base mix with quartz powder (40% of cement) designated as UHPC-I is taken into consideration, whose mix proportions are given in Table I. The optimum mix proportion of the base mix is obtained from various trials at the laboratory, satisfying the criteria of UHPC. The main constituents of the base mix are cement, silica fume, quartz powder, sand, water, superplasticizers and steel fibres. The mix developed is a kind of reactive powder concrete, whose material proportions are determined in part by optimizing the granular mixture. The basic idea is to completely eliminate the coarse aggregate to attain greater homogeneity. The cost effective optimal

dosage of steel fibres is 2% by volume of concrete. The fine sand used in this case acts as a filler material and superplasticizer is added to improve the workability of the mix. The compressive strength of this base mix with silica fume (25% of cement) is found to be 196 MPa with hot air curing at 200°C. The embodied energy of the base mix is ascertained by replacing 25% of cement with silica fume (SF), fly ash (FA) and ground granulated blast furnace slag, GGBS (BS).

Also, in order to arrive at the optimum value of embodied energy of UHPC with varying percentage of silica fume, fly ash and GGBS, several literature [9]-[16] are identified to obtain the mix proportions of UHPC with higher strength and durability criteria. Out of those literature, three are finally chosen [10], [15] & [16], and the mix proportions of UHPC taken from those literatures are presented in TABLE II

(UHPC-II), TABLE III (UHPC-III) and TABLE IV (UHPC-IV) respectively. The mixes are so identified, that one set of mix contained steel fibres but no coarse aggregate; the other set contained coarse aggregate but no steel fibres and the third set contained neither steel fibres nor coarse aggregate. All the three sets of mixes had varying percentage of silica fume, fly ash, GGBS and quartz powder, to achieve several mix proportions having higher strength and durability, satisfying the UHPC norms. The embodied energy of all the three set of mixes with varying combinations of silica fume, fly ash and GGBS are ascertained. A comparative analysis is made with the embodied energy and compressive strength of all the mixes, and the influence of the compressive strength on the embodied energy of a particular mix is also studied.

V. MATERIAL DESCRIPTION

The supplementary cementitious materials silica fume, fly ash and GGBS are abbreviated as SF, FA and BS respectively. Three mix proportions of UHPC-I with silica fume, fly ash and GGBS are designated as UHPC-I-SF, UHPC-II-FA and UHPC-III-BS respectively.

UHPC-II mixes have 6 different mix proportions containing varying percentage of fly ash and GGBS, which are given in TABLE II. In addition to the basic materials, it contained steel fibres, silica fume and quartz powder, but no coarse aggregate. The mix denoted as BS0FA0 contained neither GGBS nor fly ash; BS10FA10 contained 10% GGBS as well as 10% fly ash; BS10FA20 contained 10% GGBS, 20% fly ash; BS10FA30 contained 10% GGBS and 30% fly ash; FA20 contained no GGBS but 20% fly ash and BS40 contained 40% GGBS but no fly ash.



TABLE I

MIX PROPORTION of BASE MIX UHPC-I WITH DIFFERENT % of SILICA FUME, FLY ASH and GGBS

S. No Material

Embodied energy

(MJ/kg)

Quantity (kg/m3) Total Embodied energy (MJ/m3)

UHPC-I-SF

UHPC-I-FA

UHPC-I-BS

UHPC-I-SF

UHPC-I-FA

UHPC-I-BS

1 Cement 5.50 788.00 788.00 788.00 4334.00 4334.00 4334.00

2 Fly ash 0.10 0.00 197.00 0.00 0.00 19.70 0.00

3 GGBS 1.60 0.00 0.00 197.00 0.00 0.00 315.20

4 Silica fume 0.036** 197.00 0.00 0.00 7.09 0.00 0.00

5 Quartz powder 0.850* 315.00 315.00 315.00 267.75 267.75 267.75

6 Coarse aggregate 0.083 0.00 0.00 0.00 0.00 0.00 0.00

7 Fine aggregate 0.08 866.80 866.80 866.80 70.21 70.21 70.21

8 Water 0.01 173.00 173.00 173.00 1.73 1.73 1.73

9 Superplasticizer 9.00** 14.77 14.77 14.77 132.93 132.93 132.93

10 Steel fibres 36.00*** 157.00 157.00 157.00 5652.00 5652.00 5652.00

Total value of each mix (MJ/m3) 10465.71 10478.32 10773.82

* Green Building Challenge Handbook, 1995.

** Minerals Products Association, The Concrete Industry Sustainability Performance Report, 1st Report

*** Steel Wires (Virgin) from ICE Database.

Others – The Inventory of Carbon & Energy Database (ICE)

TABLE II

MIX PROPORTIONS of UHPC-II WITH DIFFERENT % of FLY ASH and GGBS (WITH STEEL FIBRES and WITHOUT COARSE AGGREGATES)

S. No Material (kg/m3) BS0FA0 BS10FA10 BS10FA20 BS10FA30 FA20 BS40

1 Cement 830.00 664.00 581.00 498.00 664.00 498.00

2 Fly ash 0.00 83.00 166.00 249.00 166.00 0.00

3 GGBS 0.00 83.00 83.00 83.00 0.00 332.00

4 Silica fume 291.00 205.00 157.00 141.00 195.00 173.00

5 Quartz powder 244.00 260.00 266.00 264.00 257.00 269.00

6 Coarse aggregate 0.00 0.00 0.00 0.00 0.00 0.00

7 Fine aggregate 733.00 781.00 800.00 794.00 773.00 810.00

8 Water 151.00 151.00 151.00 151.00 151.00 151.00

9 Superplasticizer 55.00 35.00 34.00 33.00 38.00 35.00

10 Steel fibres 234 234.00 234.00 234.00 234.00 234.00



TABLE III

MIX PROPORTION of UHPC-III WITH DIFFERENT % of SILICA FUME and GGBS (WITHOUT STEEL FIBRES and WITH COARSE AGGREGATES)

S. No Material (kg/m3) 1-SF10 2-SF10 3-SF10 SF10BS20 SF10BS40

1 Cement 450.00 630.00 810.00 630.00 450.00

2 Fly ash 0.00 0.00 0.00 0.00 0.00

3 GGBS 0.00 0.00 0.00 180.00 360.00

4 Silica fume 50.00 70.00 90.00 90.00 90.00

5 Quartz powder 0.00 0.00 0.00 0.00 0.00

6 Coarse aggregate 1195.00 1073.00 923.00 923.00 923.00

7 Fine aggregate 797.00 715.00 616.00 616.00 616.00

8 Water 90.00 126.00 162.00 162.00 162.00

9 Superplasticizer 18.00 18.00 18.00 18.00 18.00

10 Steel fibres 0.00 0.00 0.00 0.00 0.00

TABLE IV

MIX PROPORTION of UHPC-IV WITH DIFFERENT % of FLY ASH and GGBS (WITHOUT STEEL FIBRES and COARSE AGGREGATES)

UHPC-III mixes have 5 different mix proportions containing

S. No Material (kg/m3) FA0BS0 FA20 FA40 FA60 FA80 BS20 BS40 BS60 BS80

1 Cement 850.00 680.00 510.00 340.00 170.00 680.00 510.00 340.00 170.00

2 Fly ash 0.00 170.00 340.00 510.00 680.00 0.00 0.00 0.00 0.00

3 GGBS 0.00 0.00 0.00 0.00 0.00 170.00 340.00 510.00 680.00

4 Silica fume 260.00 260.00 260.00 260.00 260.00 260.00 260.00 260.00 260.00

5 Quartz powder 212.00 212.00 212.00 212.00 212.00 212.00 212.00 212.00 212.00

6 Coarse aggregate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

7 Fine aggregate 850.00 787.00 724.00 661.00 598.00 838.00 826.00 814.00 802.00

8 Water 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00

9 Superplasticizer 45.00 45.00 45.00 45.00 45.00 45.00 45.00 45.00 45.00

10 Steel fibres 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00



varying percentage of silica fume and GGBS, which are given in TABLE III. In addition to the basic materials, it contained coarse aggregate but no steel fibres, fly ash and quartz powder. The mix represented as 1-SF10, 2-SF10 and 3-SF10 comprised only 10% silica fume with varying quantity of cement as presented in Table III. The mix symbolized as SF10BS20 consisted of 10% silica fume, 20% GGBS and SF10BS40 comprised 10% silica fume, 40% GGBS.

UHPC-IV mixes have 9 different mix proportions containing varying percentage of fly ash and GGBS, which are given in TABLE IV. In addition to the basic materials, it contained silica fume and quartz powder, but no coarse aggregate and steel fibres. The mix symbolized as FA0BS0 has neither fly ash nor GGBS; FA20 included 20% fly ash; FA40 included 40% fly ash; FA60 included 60% fly ash; FA80 included 80% fly ash; BS20 included 20% GGBS; BS40 included 40% GGBS; BS60 included 60% GGBS and BS80 contained 80% GGBS.

IV. RESULTS AND DISCUSSIONS

The embodied energy of the UHPC mixes are calculated based on the embodied energy values of each constituent material in terms of Mega Joules per kilogram (MJ/kg). These embodied energy values for different constituents are taken from three different sources for this study [17]-[19]. The quantity of the constituent materials in terms of kilogram per cubic metre (kg/m3) is multiplied with the basic embodied energy values to get the total embodied energy of the constituent material in MJ/m3. The sum of all the embodied energy values of the constituent materials in the mix would represent the final embodied energy of the mix in terms of MJ/m3. The embodied energy value for steel fibres is not found in any source, and hence the value of steel wires (virgin) from ICE database is taken as the embodied energy value for steel fibres, as far as this study is concerned.

The embodied energy values of UHPC-I mixes presented in TABLE I, represents that the embodied energy is lesser for the mix with silica fume with superior strength of 196 MPa than the mix with GGBS with comparatively lesser strength. This is because the embodied energy value of GGBS is higher than that of silica fume.

From Figs. 2, 3 and 4, it is evident that the embodied energy as well as the compressive strength of UHPC-II mixes is very high when compared with the other two mixes. This is obvious due to the presence of steel fibres in the mix, for which the embodied energy is very high about 36 MJ/kg (Steel wires – ICE data base). The steel fibres are included in the mix to impart ductility, because it is certain that the high strength mixes are very brittle in nature. This type of ultra high

performance mix is used for specific purpose, where strength and durability are the governing factors.

Fig. 2 Embodied energy Vs Compressive strength for UHPC mix with steel fibres and without coarse aggregates

The embodied energy of the other two mixes UHPC-III and UHPC-IV without steel fibres is in the range of 1500 to 5000 MJ/m3depending upon the mix proportions. Their compressive strength is in the range of 70 MPa to 140 MPa, which is less compared to UHPC-II mixes, whose compressive strength is more than 200 MPa. These mixes satisfy the criteria of UHPC and also have a less embodied energy, which can be termed as “high strength green concrete”.

From Fig. 2 and TABLE II (with steel fibres and without coarse aggregate), it is recognized that the embodied energy is highest of about 13763 MJ/m3 for the mix without fly ash and GGBS and the compressive strength is highest of about 212 MPa for the mix with 20% fly ash and 10% GGBS. The optimum mix among the UHPC-II mixes would be the mix with 10% GGBS and 30% FA, having an embodied energy of 11913 MJ/m3 and a compressive strength of 206 MPa. Similar strength of 202 MPa is achieved with the mix without fly ash and GGBS but with the highest embodied energy of 13763 MJ/m3, which is actually not a good proportioning in embodied energy perception. Hence, this mix would require partial replacement of cement with optimum levels of fly ash and GGBS.

From Fig. 3 and TABLE III (without steel fibres and with coarse aggregates), it is apparent that the embodied energy as well as the compressive strength is highest for the mix with 10% of silica fume (with 810 kg/m3 of cement) of about 4748 MJ/m3 and 137 MPa respectively, which is due to the presence of high cement content. The optimum mix among the UHPC-III



mixes would be the mix with 10% of silica fume (with 450 kg/m3 of cement), having an embodied energy of 2803 MJ/m3 and compressive strength of 131 MPa. The weakest mix would be the mix with 10% SF and 40% GGBS, having the least compressive strength of about 110 MPa and high embodied energy of 3345 MJ/m3.

Fig. 3 Embodied energy Vs. Compressive strength for UHPC mix without steel

fibres.

From Fig. 4 and TABLE IV (without steel fibres and coarse aggregate), it is evident that the embodied energy is highest for the mix without fly ash and GGBS of about 5340 MJ/m3 and the compressive strength is highest for the mix with 40% fly ash and no GGBS of about 126 MPa. The optimum mix among the UHPC-IV mixes would be the mix with the highest compressive strength of 126 MPa and an embodied energy of 3494 MJ/m3

containing 40% fly ash and no GGBS. The mixes which would require a re-proportioning are, 1) The mix containing 80% of GGBS, with an embodied energy of 2684 MJ/m3 and a low compressive strength of 82 MPa and 2) The mix with the highest embodied energy of 5340 MJ/m3 and a compressive strength of 113 MPa having no fly ash and GGBS, because the same strength of 113 MPa is achieved with a lesser embodied energy of 2570 MJ/m3 with the mix containing 60% of fly ash. This reduction in embodied energy with considerable strength can be due to the replacement of high volume of cement with fly ash.

Fig. 4 Embodied energy Vs. Compressive strength for UHPC mix without steel

fibres and coarse aggregates.

V. CONCLUSIONS

Basically, all the UHPC mixes contain silica fume as a base material, which has very low embodied energy value of 0.036 MJ/kg, when partially replaced for cement produces a high strength low embodied energy ultra high performance concrete. An efficient mix is identified as the mix with partial replacement of cement by 10-25% of silica fume, 20-40% of GGBS and 30-60% of fly ash, which results in the reduction of cement usage and in turn results in lesser embodied energy without compromising the strength. To obtain the most favorable UHPC mix, the proportioning of the cementitious materials needs to be taken utmost care, because higher percentage of replacement of supplementary cementitious materials can lead to a poor mix having higher embodied energy and lower strength. Hence, the optimum levels of cementitious materials as a replacement for cement can be arrived by trial and error only.

ACKNOWLEDGEMENT

We acknowledge with thanks the technical support provided by the Computational structural mechanics group (CSMG), CSIR-SERC. This paper is being published with the kind permission of the Director, CSIR-SERC, Chennai, India.



REFERENCES

[1] Alain, Bilodeau and V.Mohan. Malhotra, “High volume fly ash system: Concrete solution for sustainable development”, ACI mterials journal, Vol. 97 (1), pp. 41-47, 2000.

[2] Mark, Reiner and Kevn, Rens; “High volume fly ash concrete; Analysis and application”, Practice periodical on structural design and construction, Vol. 11 (1), pp. 58-64, 2000.

[3] M. L. Berndt, “Properties of sustainable concrete containing fly ash, slag and recycled concrete aggregate”, Construction and building materials, Vol. 23, pp. 2606-2613, 2009.

[4] C Meyer, “The greening of the concrete Industry”, Cement and concrete composites, Vol. 31, pp. 601-605, 2009.

[5] ACI Committee 234. Guide for the use of silica fume in concrete. Farmington Hills, MI: American Concrete Institute Report 234R-06; 2006.

[6] CANMET/ACI. In: 8th CANMET/ACI International conference on fly ash, silica fume, slag, and natural Pozzolans in concrete. Farmington Hills (MI): American concrete institute, pp. 963, 2004. (Special publication SP-221)

[7] Malhotra VM. “Role of supplementary cementing

materials in reducing greenhouse gas emissions”, Concrete technology for a sustainable development in the 21st century. London: E & FNSpon, pp. 226-235, 2000.

[8] [ACI Committee 233, “Ground granulated blast-furnace slag as a cementitious constituent in concrete”, Farmington Hills, MI: American Concrete Institute Report ACI, Vol. 233, pp. R-95, 1995.

[9] A. M. T. Hassan, S. W. Jones, G. H. Mahmud, “Experimental test methods to determine the uniaxial

tensile and compressive behavior of ultra high performance fibre reinforced concrete”, Construction and building materials, Vol. 37, pp. 874-882, 2012.

[10] Halit Yazici, Mert Yucel Yardimci, Serdar Aydin, Anil S. Karabulut, “Mechanical properties of reactive powder

concrete containing containing mineral admixtures under

different curing regimes”, Construction and building materials, Vol. 23, pp. 1223-1231, 2009.

[11] Ming-Gin Lee, Yung-Chih Wang, Chui-Te Chui, “A

preliminary study of reactive powder concrete as a new repair material”, Construction and building materials, Vol. 21, pp. 182-189, 2007.

[12] Bassam A. Tayeh, B. H. Abu Bakar, M. A. Megat Johari, Yen Lei Voo, “Mechanical and permeability properties of the interface between normal concrete substrate and ultra high performance fibre concrete overlay”, Construction and building materials, Vol. 36, pp. 538-548, 2012

[13] Eduardo N. B. S. Julio, Fernando A. B. Branco, Vitor D. Silva, Jorge F. Lourenco, “Influence of added concrete

compressive strength adhesion to an existing concrete substrate”, Building and environment, Vol. 41, pp. 1934-1939, 2006.

[14] F. A. Farhat, D. Nicolaides, A. Kanellopoulos, B. L. Karihaloo, “High performance fibre reinforced cementitious composite – Performance and application to retrofitting”, Engineering fracture mechanics, Vol. 74, pp. 151-167, 2007.

[15] Chong Wang, Changhui Yang, Fang Liu, Chaojun Wan, Xincheng Pu; “Preparation of ultra high performance

concrete with common technology and materials”, Cement and concrete composites, Vol. 34, pp. 538-544, 2012.

[16] Halit Yazici, “The effect of curing conditions on

compressive strength of ultra high strength concrete with high volume mineral admixtures”, Building and environment, Vol. 42. pp. 2083-2089, 2007.

[17] Hammond G. P. and Jones C. I., “Inventory of (embodied) Carbon & Energy Database (ICE)”, Version 2.0, UK - University of Bath, 2011.

[18] “Minerals Products Association; the Concrete Industry Sustainability Performance Report”, 1st Report, 2009.

[19] Green Building Challenge Handbook, 1995.



Design and Development of an Infrared Heater For Waste Plastic Gasification

Zuhair E. M. Haruon

Department of Electrical Engineering, Cape Peninsula University of Technology [email protected]

Abstract— This paper outlines the design, manufacture and analysis of a far infrared ceramic heater for plastic gasification purposes. The study includes the theoretical overview of the mathematical modelling of the far infrared ceramic heater. This study gives a novel energy conversion system of waste plastic materials. In this system, waste plastics are converted into gaseous fuel by gasification using infrared gasifier system. The derived gaseous fuels can then be used in fuel cell for purposes of electricity production. In this study two types of waste plastics (high density polyethylene, low density polyethylene) have been used as feedstock for the infrared gasifier. Analysis of the spectral properties of the waste plastics has been performed. Gasification of plastic waste as carbonaceous material, basic reactions during the gasification of plastics and gasification results has been analysed. The ceramic infrared heaters developed in this research are fully functional and all test results obtained are accurate to a very fair degree. The results obtained from the gasification experiment shows that using infrared heaters on gasification is practically sound because of significant advantages of infrared heating compared to the landfill and incineration. The work is intended to develop a low-cost ceramic infrared heater solution to be used in plastic waste gasification.

Keywords — Gasification, Infrared heater, Plastic waste, LabVIEW.

I. INTRODUCTION

The energy demand has been increasing, and the combustion of fossil fuel to cover energy demand is associated with serious environmental problems through the emission of CO2. The researchers have been researching for clean and reliable energy source that could substitute fossil fuel. Hydrogen gas is clean fuel with no CO2 emission used in fuel cell for electricity generation. It is valuable gas as clean fuel, it has high energy content, 122kj/g [1], therefore, demand on hydrogen has increased considerably in recent years. Hydrogen production methods include electrolysis of water, steam reforming of hydrocarbons and auto-thermal process.

With the modern lifestyle, the consumption of plastics continues to increase every year and therefore the amount of plastic waste has also increased. Traditional ways of plastic waste disposal have been either to bury or burn them in landfill incinerators, respectively. Landfills and incinerations however are associated with serious environmental concerns.

There are different technologies such as gasification and pyrolysis which transform waste plastic to useful fuels or petrochemicals. One of these technologies is infrared radiation heating which is often said to be more efficient and cost effective. The use of infrared radiant heating in the gasification of waste plastic is performed because infrared radiation does not emit harmful fumes and do not require air movement [2]. This project is concerned with the use of infrared energy in the disposal of plastic waste.

II. BACKGROUND REVIEW

Waste plastics are one of the most promising resources for fuel production because of useful gases that it contains. Plastic recycling can be divided into three methods; mechanical recycling, chemical recycling and energy recovery. Chemical recycling which, converts plastic materials into useful chemicals have been recognized as an advanced technology process [3]. In recent years the gasification of plastics has been intensively conducted and some useful results have been seen in different studies [4], [5]. Two types of fuel-coal and polyethylene, were gasified in a 250mm across 3.4m high drop tube furnace, the study conclude that the gasification of coal has produce H2, CO with concentrations of 15% and 25% respectively. Gasification is a process that converts carbonaceous materials such as plastic, coal and petroleum into carbon monoxide and hydrogen. The gas yield from the gasification process is called syngas. Equation 1 and 2 represent the raw material decomposition and reaction with oxygen during gasification.

A two-stage thermal gasification process for plastics has been studied and developed by Tashiro [6], [7]. Polyethylene (PE), polypropylene (PP) and polystyrene (PS) have been gasified using two stage thermal degradation. Plastics have been transformed to liquid and then to gas during gasification. A gasification process which converted waste plastics to synthetic gases (CO, H2), at a high temperatures (over 1600K) has also been studied by Takatoshi [8].



III. METHODOLOGY

A. Overview of Methodology

The plastic samples were washed with hot water prior the gasification tests to remove dirt and any possible contaminants on the surface of the samples. Samples were also weighed using measuring scale to measure the mass of each sample before and after gasification. Samples of High density polyethylene (HDPE) and Low density polyethylene (LDPE) from municipal solid waste were collected and cut into squares.

The methodology used within this study;

Mathematical modeling of an infrared ceramic heater Wavelength measurements of waste plastics using

Fourier Transform Infrared Spectroscopy (FTIR). The design of a data acquisition system to verify results.

Using Quadrupole Mass Spectrometer 200 gas analyser for gas analysis.

IV. MODELLING AND DESIGN

The design of a ceramic infrared heater which has a surface temperature lower than 800℃ shall be considered. The heater is made of a ceramic body with resistance wire (filament) embedded in it. A fibre blanket placed behind the filament to avoid heat loss from the back of the heater. Ceramic infrared heaters are designed to emit wavelengths in the far infrared range at certain operational temperatures. As voltage is applied, a current and resistive loss in the filament that translates to heat build-up. The higher the temperature the higher the filament resistivity, with a reduction in the amount of current and power consumed. The rise in filament temperature results in heat transfer by means of conduction to the ceramic body and then radiation to the environment. The passage of electric current through the filament when voltage is applied is given by

( )

[ √

]

Where is constant.

⁄

⁄

⁄

Where U is energy storage.

The mechanism involved in the heat transfer in ceramic heaters is conduction from the filament to the ceramic body. A

Fourier equation can be used to calculate the rate of heating and cooling of the heater:

Where ( ).

Where are the running temperature of the surface of the heater after the time , the initial temperature of the heater, and the temperature of the medium respectively, a is the diffusivity, equal to the product of the heat capacity of the heater, its density and the thermal conductivity of the insulating sheath, N is the heat transfer coefficient characterizing heat exchange with the medium, r is the depth of penetration of the heat pulse. The heating element reliability and stability are determined by the extent to which the heater remains constant over it is service life. The relation in equation (5) describes the rate of degradation.

[

]

Where is a constant dependent on the composition and method of production of the material of the conducting phase, the electrical insulator, or the casing, Q is the energy of activation of the aging process, which depends on the ambient conditions and the thermo-mechanical stability of the material of the heater, T is the working temperature of the heater.

A. Energy Balance Energy balance is when the rate at which energy is

transferred from the heater surface to the surface of the target is equal, given mathematically as follows:

Considering ceramic heaters as having a resistance wires of diameter D and length L initially at thermal equilibrium with the ambient air and its surroundings, this equilibrium condition is only distributed when an electric current I is passed through the wire. An equation that could be used to compute the variation of the wire temperature with time during the passage of the current is developed using the first law of thermodynamics, often used for determining unknown temperatures. Relevant terms involve heat transfer by radiation from the surface of the heater, internal energy generation due to electrical current passage through the wire, and a change in internal energy storage. For determining the rate of change of temperature and applying the first law of thermodynamics to a system of length L of the wire, it follows that:

Where the energy generation due to the electric resistant heating is given by:



Energy outflow due to net radiation leaving the surface is given by:

The change of energy storage due to the temperature change is:

Where and are the density and specific heat, respectively of the wire material and is the volume of the

wire (

)

Substituting the rate equations into the energy balance, it follows that:

(

)

Hence the time rate of change of the wire temperature is:

(

)

The heat transfer is defined as one dimensional conduction in the reflector which itself is considered as an opaque body. The equation in the reflector is defined as:

Since there is an insulation blanket, the boundary condition at the back of the heater is:

The boundary conditions at the front surface of the heater involve radiation and it is represented as follows:

V. TESTING AND MEASUREMENTS

Testing of the manufactured heaters has been conducted by connecting the heater leads to the wall socket, which normally gives 220 to 230 volts, and the temperature sensor has been mounted in front of the heater to sense the temperature of the heater surface. The maximum values of temperature recorded were as follows: 187.8 ℃ and 234.9 . “Table 1” shows the manufactured heaters specifications including temperatures and calculated wavelength.

The wavelength of the manufactured heaters is calculated using Wien's displacement law. The wavelength of the emitter is inversely proportional to the temperature, and is given by:

Where b is a constant.

Different samples of plastic waste have been tested in terms of absorbtivity and transmitivity in order to determine the exact wavelengths at which high density polyethylene, low density polyethylene, teraphthalate perfectly absorb infrared radiation. The results showed that the absorption of the infrared radiation by any sample of plastics strongly depends on the thickness of the sample: the thinner the sample the better and stronger the absorption, while the opposite also held true - the thicker the sample the poorer the absorption.

Transmittance measurements of LDPE and HDPE using FTIR spectroscopy were conducted and the results are shown in “Fig. 1 and 2”. A sample wavelength measurement was implemented to determine the infrared absorption wavelength of LDPE and HDPE. Peak absorption values shown in “Table 2” were calculated after conversion from cm-1 to m.

TABLE 1

MANUFACTURED HEATER SPECIFICATIONS

Parameter Heater 1 Heater 2

Size (mm) 265×198 216×122

Typical operating temperature

187.8 234.9

Wavelength (μm) 6.2 5.70

TABLE 2

MEASURED WAVELENGTH VALUES

Sample Measured Peak Value Absorption Wavelengths (m)

LDPE 3.3 6.6 12

HDPE 3.3 4.5 6.6

VI. GASIFICATION OF PLASTICS

The manufactured ceramic infrared heaters were carefully placed inside the gasifier before establishing the electrical connections. The input voltage to the gasifier was 230 volts and the rated current was 4.2A. The gasification tests then conducted on each sample separately.



Fig 1: Transmittance spectrograph of LDPE

A. Sample Preparation The plastic samples were washed with hot water prior to

the gasification tests to remove dirt and any possible contaminants that stick on the surface of the samples. Samples were also weighted using measuring scale to determine the mass of each sample before and after gasification, “Table 3” shows the measured mass of samples.

TABLE 3

MASS OF SAMPLES

Sample Mass (grams)

LDPE 153.9

HDPE 190.05

Fig 2: Transmittance spectrograph of HDPE

VII. RESULTS

In order to test and validate the manufactured ceramic infrared heaters for the gasification process and the production of

syngas, gasification experiments were conducted. The infrared gasifier was left for 20 minutes to reach an operating temperature of 457 . The gasifier was heated to reach

temperature of 457 before feeding the samples to the gasifier. After feeding the LDPE sample and during the gasification, the emission of gases started after 10 seconds. Gases continue to yield for 12 minutes before it stops completely, results of gasification tests are shown in “Table 4”

below. The temperature measurements inside and outside the gasifier and the temperature of the samples during gasification were performed using a Fluke Ti20 (Thermal imager). The total gas yield of LDPE and HDPE were 96.7wt% and 95 wt% each at a temperature of 457 . The formation of carbonaceous residue or coke was 3.3wt%, 5.2wt% for LDPE and HDPE respectively. After taking all the plastic samples, the test run is considered finished and the gasification then concluded.

TABLE 4

GASIFICATION RESULTS

Sample

Mass (a)

(Grams)

Duration

(Minute)

Mass (b)

(Grams)

temperature

(˚C)

yield

Wt%

LDPE 153.9 10 5.06 457 96.7

HDPE 190.5 12 9.95 457 95

Fig 3: LDPE gas spectrum analysis

CO2 CO

H2



Fig 4: HDPE gas spectrum analysis

VIII. ANALYSIS OF RESULTS

The comparison of the wt% of coke residue and the wt% of the feed reveals the fact that the carbonaceous residue is very low, less that 5%, which makes the use of ceramic infrared heaters efficient. The plastics started react at 457˚C, the

wavelength emitted by the manufactured infrared heaters was successfully absorbed by the plastic samples. The short period gasification time of the plastic sample during gasification confirms the high thermal efficiency of the infrared gasifier and therefore the validity of infrared technology used in gasification of plastics. Whereas in this experiments the difference of gasification residence time between samples referred to the difference in the samples thickness. Gasification process shows that the amount of the produced gases increased when gasification temperature increased. Gasification results derived from this project were compared to other models (Toshiro, 2009) (Takatoshi, 2001). The comparison has shown that the production of syngas is comparable to models and the designed gasifier has low coke formation less than 5wt%. Gasification time and formation of residue needs further modification in the infrared gasifier compared to the two stage gasification procedures.

IX. GAS ANALYSIS

Quadrupole Mass Spectrometer 200 gas analyser was used to further analyse the resultant gas derived from the gasification of plastic samples. Analog scan mode has been chosen for analysis, it is the spectrum analysis mode common to all gas analisers. X-axis represents the atomic mass range chosen in the mass spectrometer. The Y-axis represents the amplitude of every mass increment measured.

Atmospheric scan inside the gasifier was performed and set as reference for any increase in gas yield. The experiments

concentrated in the production of H2, CO and CO2. Gases derived from the gasification of the two plastic samples then carefully injected to the gas analyser. Gas sample analyses have shown increase in hydrogen production for LDPE. Increase in CO2 was also observed for HDPE. In all samples,

TABLE 5

GAS ANALYSIS RESULTS

Sample Gasification temperature (˚C)

H2 production

CO production

CO2 production

LDPE

457

Increased

No change

No change

HDPE

457

No change

No change

Increased

the production of CO stayed unchanged during the analysis. “Figure 1and 4”shows the gas analysis of chosen samples, where “Table 4” summarises the gas analysis results.

X. CONCLUSION

Gasification results derived from this project were compared to other models, the comparison of the coke residue and the feedstock reveals that carbonaceous residue is very low, which makes the use of ceramic infrared heaters very efficient. The plastics reacted at 457 because of the good match of the heaters wavelength and the absorption characteristics of the samples. The short gasification times of the plastic samples during gasification confirms the high thermal efficiency of the infrared gasifier and therefore the validity of infrared use in the gasification of plastics. In this experiment the difference in gasification time between samples referred to the difference in samples thickness. Gasification process shows that the amount of the produced gases increased when gasification temperature increased. The comparison has shown that the production of syngas is comparable to models and the designed gasifier has low coke formation less than 5wt%.

REFERENCES

[1] M. Hamai, M. Kondo, M. Yamaguchi, G. Piao, Y. Itaya and S. Mori. “Gasification of organic waste materials for power

generation using fuel cell”, In environmentally conscious design and inverse manufacturing, pp. 103-106, IEEE., 2001.

[2] K. Kathiravan and K. H. Kaur and S. Jun and I. Joseph and D. Ali, “Infrared heating in food processing: An overview”,

Comprehensive reviews in food science and food safety, Vol. 7, N. 1, pp. 2-13, 2008.

[3] P. Martin, T. Norbert, J. Eberhard, W. Ernst, “Recycling of

plastics in Germany” ,Resources, Conservation and Recycling

Vol. 29, N. 1, pp. 65-90, 2000. [4] V. Cornelia, A. B. Mihai, K. Tamer, Y.J ale, D. Hristea,

“Feedstock recycling from plastics and thermosets fractions of used computers II Pyrolysis oil upgrading”, Fuel, Vol. 86, N. 2,

pp. 477-485, March 2007.

H2 CO2

CO



[5] K. Peter, M.A.Barlaz, R. P. Alix, A. Baun, A. Ledin, T. H. Christensen,“Present and Long-Term Composition of MSW Landfill Leachate: A Review”, Critical review in environmental science and technology, Vol. 32, N. 4, pp. 297-336, 2002.

[6] T. Tashiro, Yoshikitanaka, S. Toshiharu, U. Osamu, I. Hironori, “Two stage thermal gasification of plastics”, Proceedings of the

1st ISFR, Tohoku University Press, Sendai, pp. 211-214, 1999. [7] T. Toshiro, H. Akito, “Gasification of waste plastics by steam

reforming in fluidized bed”, Journal of Material Cycle and

Waste Management, Vol. 11, No. 2 pp. 144-147, 2009. [8] T. Shoji, K. Shindoh, Y. Kajibata , A. Sodeyama, “Waste

plastics recycling by an entrained flow gasifier”, Journal of

Material Cycles and Waste Management, Vol.3, No.2, pp. 75-81, 2001.

Principal Author:

Zuhair Hauron holds a BSc (Hons) degree in Electronics and Computational physics from Al-Neelain University of Technology and is a Master’s student at the Cape Peninsula

University of Technology.

http://www.springerlink.com/content/?Author=Takatoshi+Shoji

http://www.springerlink.com/content/?Author=Kenjiro+Shindoh

http://www.springerlink.com/content/?Author=Atsushi+Sodeyama



Piezoelectric Based Energy Suagto Hazra

Electrical and Electronics S’O’A University (ITER)

Bhubaneswar, Orissa, India

[email protected]

Abstract— In the present paper a review on piezoelectric sensing of mechanical deformations and its uses is highlighted.

I. INTRODUCTION

Recent advances in energy harvesting have been intensified due to urgent need of portable, wireless electronics with extensive life span. The idea of energy harvesting is applicable to sensors that are placed and operated on some entities for a long time, or embedded into structures or human bodies, in which it is troublesome or detrimental to replace the sensor module batteries. Such sensors are commonly called” self-powered sensors”.The

energy harvester devices are capable of capturing environmental energy and supplanting the battery in a stand-alone module, or working along with the battery to extend substantially its life. Vibration is considered one of the most high power and efficient among other ambient energy sources, such as a solar energy and temperature difference. Piezoelectric and electromagnetic devices are mostly used to convert vibration to ac electric power. For vibratory harvesting, a delicately-designed power conditioning circuit is required to store as much as possible of the device-output power into a battery. The design for this power conditioning needs to be consistent with the Electric characteristics of the device and battery to achieve Maximum power transfer and efficiency. This study offers an overview on various power conditioning electronic Circuits designed for vibratory harvester devices and their Applications to self powered sensors. Comparative Comments are provided in terms of circuit topology Differences, conversion efficiencies and applicability to a Sensor.

II. WORKING PLAN

A. Harvest Energy from Vibration of Railway Tracks Using Peizosensors

Piezoelectricity is an effect that occurs when mechanical stress is applied to certain materials .An electrical polarization is set up in the crystal with the result that the faces become electrically charged. The charge reverses if the compression changes to tension. Because the effect is reversible electric fields applied across the material causes it to contract or expand according to the sign of the field. The piezoelectric stress and an electrical voltage in solids .It is reversible: an applied Effect describes the relation between a mechanical stress will generate a voltage

and an applied voltage will change the shape of the solid by a small amount (up to a 4% change in volume).In physics the piezoelectric effect can be described as the link between electrostatics and mechanics. The piezoelectric effect occurs only in non conductive materials .Piezoelectric materials can be divided in 2 main groups: crystal and ceramics.

III. FEASIBILITY

Rail transport is one of the most common modes of carrying cargo and passengers from one place to another. The prevalence of road transportation created the need of train crossings and the danger associated with them. In many developed countries, trains are tracked in real-time using a variety of sensors and communication technologies. The data provided by the sensors allows transport authorities to stop vehicles from crossing the track when a train is near, or direct train traffic on rail turnouts or switches. Even with these high-tech solutions, transport authorities still lose contact with the rail cars due to lack of cellular service. Some trains use a third rail to provide communications, among other uses. These third rails are more reliable than wireless communications, but still have challenges. Organic build up due to foliage and dead leaves can cause the train to lose connection and “disappear” on

the rail.

A. Feasible Idea

I make use of piezo-electric devices to generate electricity. The specific application I was going for was in the railroad realm whereby such transducers could be placed in the roadbed under the track structure itself and when deformation in the track occurred as a result of a train rolling on top of it, by virtue of this action, pressure would be placed upon the piezo-electric transducer itself, thereby changing applied pressure into electricity. I figured, based on the number of transducers placed one next to another and so on down a railroad line, for example, with miles and miles of such placed just beneath the track, a considerable amount of electricity could be generated.

IV. TECHNICAL INFORMATION

Much of the abundant mechanical energy around us is irregular and oscillatory and can be somewhat difficult to efficiently tap into. Typical energy harvesting systems tend to be built for low power applications in the mill watts range. A new patent-pending electromagnetic energy



harvester capable of harnessing the vibrations of a locomotive thundering down a stretch of track to power signal lights, structural monitoring systems or even track switches. As a train rolls down the track, the load it exerts on the track causes vertical deflection. This displacement could engage a regenerative device like an electromagnetic harvester and generate enough power to operate local railway applications, which is especially useful in remote areas where electrification is not cost effective. Harvesting such energy is much more efficient with regular, unidirectional motion, but track vibrations caused by a moving train are pulse-like, bidirectional and somewhat erratic. A new harvester is required which capable of converting irregular, oscillatory rail track vibrations into regulated unidirectional rotational motion, similar to the way that an electric voltage rectifier converts AC voltage into DC.

Fig. 1 Data of the experiment performed connecting such transducers in parallel

V. UNIQUE SELLING POINT

Low cost High sensitivity High mechanical stiffness Broad frequency range Exceptional linearity Excellent repeatability Unidirectional sensitivity Small size

VI. IMPLEMENTATION IN REAL LIFE

Ultrasonic transmitters and receivers. Frequency references. (USE AS ENERGY

HARVESTOR FROM RAILWAY TRACK) Temperature sensors (resonant frequency changes

with temperature) Accelerometers (used with a seismic mass) (See

discussion in section 5-3.3 in Cars tens text). See notes on accelerometer

calibration in 9.7 and 9.8 DRM Microphones and loudspeakers (small loudspeakers

with poor audio characteristics = Beepers) Pressure sensor Force sensor

ACKNOWLEDGMENT

I like to acknowledge Dr.RN PANDA (Asst Professor, PHD-sambalpur University) for helping me trough out the project and help me to investigate the matter properly which probably aims at developing something new for mankind.Inspite of having energy all around us we are unaware how to harness it. So here we develop a new way out.

REFERENCES

[1] www.spinger.com/books [2] books.google.com/books/../piezoelectric [3] E resource center@ ITER CAMPUS [4] Safaribooksonline.com [5] M. Young, The Technical Writer’s Handbook. Mill Valley, CA:

University Science, 1989.



Social Emotional Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

K. Lenin1, B. Ravindranath Reddy2, M. Surya Kalavathi3

Jawaharlal Nehru Technological University Kukatpally

Hyderabad 500 085, India [email protected]

Abstract— The main feature of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the specified limits. Human society is a complex group which is more effective than other animal groups. Therefore, if one algorithm mimics the human society, the effectiveness maybe more robust than other swarm intelligent algorithms which are inspired by other animal groups. So in this paper Social Emotional Optimization Algorithm (SEOA) has been utilized to solve ORPD problem. The proposed algorithm (SEOA) has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the proposed algorithm converges to best solution.

Keywords— Social emotional, Optimal reactive power, Transmission loss

I. INTRODUCTION

Reactive power optimization plays an important task in optimal operation of power systems. Many papers by various authors has been utilized various methods to solve the ORPD problems such as, gradient based optimization algorithm [1,2], quadratic programming, non linear programming [3] and interior point method [4-7]. In recent years standard genetic algorithm (SGA) [8] and the adaptive genetic algorithm (AGA) [9], Partial swarm optimization PSO [10-11] have been applied for solving ORPD problem. Due to the problem of unparalleled generation and transmission capability growth and also due to continuous increase in demand of electrical power the ORPD problem has become very complex. The inability of the power system to meet the demand for reactive power to preserve regular voltage profile in stressed situations is playing very important role for causing voltage collapse. In the past many pioneering algorithms such as Evolutionary Algorithm [12-13], Genetic algorithm [14-15], Evolutionary strategies [16-18], Differential Evolution [19-20], Genetic programming [21] and Evolutionary programming [22] are used to solve many firm problems in optimization. In SEOA methodology, each individual represents one person, while all points in the problem space constructs the status society. In this practical world, all individuals aim to seek the higher social status. Therefore, they will communicate through cooperation and competition to increase personal status,

while the one with highest score will win and output as the final solution. In the experiments, social emotional optimization algorithm (SEOA) has a remarkable superior performance in terms of accuracy and convergence speed [22-26]. In this research paper social emotional optimization algorithm has been utilized to solve the ORPD Problems. This algorithm (SEOA) is applied to obtain the optimal control variables so as to improve the voltage stability level of the system. The performance of the proposed method has been tested on IEEE 30 bus system and the results are compared with the standard GA and PSO method.

II. PROBLEM FORMULATION

The Optimal Power Flow problem has been considered as general minimization problem with constraints, and can be mathematically written as :

Minimize f(x, u) (1)

Subject to g(x,u)=0 (2)

and

(3)

Where f(x,u) is the objective function. g(x.u) and h(x,u) are respectively the set of equality and inequality constraints. x is the vector of state variables, and u is the vector of control variables.

The state variables are the load buses (PQ buses) voltages, angles, the generator reactive powers and the slack active generator power:

( ) (4)

The control variables are the generator bus voltages, the shunt capacitors and the transformers tap-settings:

( ) (5)

or

( )

(6)

Where Ng, Nt and Nc are the number of generators, number of tap transformers and the number of shunt compensators respectively.



III. OBJECTIVE FUNCTION

A. Active power loss

The goal of the reactive power dispatch is to minimize the active power loss in the transmission network, which can be mathematically described as follows:

∑ (

) (7)

or

∑ ∑ (8)

Where gk : is the conductance of branch between nodes i and j, Nbr: is the total number of transmission lines in power systems. Pd: is the total active power demand, Pgi: is the generator active power of unit i, and Pgsalck: is the generator active power of slack bus.

B. Voltage profile improvement

For minimization of the voltage deviation in PQ buses, the objective function formulated as:

(9)

Where ωv: is a weighting factor of voltage deviation.

VD is the voltage deviation given by:

∑ | | (10)

C. Equality Constraint

The equality constraint g(x,u) of the ORPD problem is represented by the power balance equation, where the total power generation must envelop the total power demand and the power losses:

(11)

D. Inequality Constraints

The inequality constraints h(x,u) imitate the limits on components in the power system as well as the limits created to guarantee system security. Upper and lower bounds on the active power of slack bus, and reactive power of generators:

(12)

(13)

Upper and lower bounds on the bus voltage magnitudes:

(14)

Upper and lower bounds on the transformers tap ratios:

(15) Upper and lower bounds on the compensators reactive powers:

(16)

Where N is the total number of buses, NT is the total number of Transformers; Nc is the total number of shunt reactive compensators.

IV. STANDARD SOCIAL EMOTIONAL OPTIMIZATION ALGORITHM

In human society, all people do their work hardly to boost their social status. To obtain this purpose, people will try their bests to find the path so that more social wealth’s

can be compensated. Inspired by this phenomenon, a new population-based swarm, social emotional optimization algorithm has been proposed and in which each individual simulates a virtual person whose choice is guided by his emotion. In social emotional optimization algorithm methodology, each individual represents a practical person in each generation selection is based on his behaviour according to the corresponding emotion directory. After the behaviour is done, a status value is feedback from the society to confirm whether this behaviour is right or not. If this choice is right, the emotion index of the person will increase or otherwise it will decrease.

All individual’s emotion indexes are set to 1 in first step, with this value; they will choice the following behaviour:

(17)

Where represents the social position of j's individual in the initialization period, the corresponding fitness value is denoted as the society status. Symbol means the operation, in this paper, we only take it as addition operation +. Since the emotion index of j is 1, the movement phase Manner1 is defined by:

∑ ( ) (18)

Where k1 is a parameter used to control the emotion changing size, rand1 is one random number sampled with uniform distribution from interval (0,1). The worst L individuals are selected to provide a reminder for individual j to avoid the incorrect behaviour. In the initialization period, there is small emotion affection, therefore, in this period, there is a little good experience can be referred, so, Manner1 simulates the affection by the wrong experiences.

In t generation, if individual j does not obtain one better society status value than previous value, the j's emotion index is decreased as follows:

(19) Where Δ is a predefined value, and set to 0.05, this value is

coming from experimental tests. If individual j is rewarded a new status value which is the best one among all previous iterations, the emotion index is reset to 1.0:

(20) If then .



In order to simulate the behaviour of human, three kinds of manners are designed, and the next behaviour is changed according to the following cases:

If , then (21) If , then (22) Otherwise (23) Parameters TH1 and TH2 are two thresholds aiming to

restrict the different behaviour manner. For Case1, because the emotion index is too small, individual j prefers to simulate others successful experiences. Therefore, the symbol Manner2 is updated with:

( )

( ) (24)

Where represent the best society status position obtained from all people previously.

{ ( | )} (25) is defined as

( )

( )

∑ ( ) (26)

Where denotes the best status value obtained by individual j previously, and is defined by

{ ( | )} (27) For is defined as

( )

∑ ( ) (28)

Manner2 ,Manner3 andManner4 refer to three different emotional cases. In the first case, one individual's movement is protective, aiming to preserve his achievements in Manner2 due to the still mind. With the increased emotion, more rewards are expected, so inManner3 ,a temporized manner in which the dangerous avoidance is considered by individual to increase the society status. Furthermore, when the emotional is larger than one threshold, it simulates the

individual is in surged mind, in this manner, he lost the some good capabilities, and will not listen to the views of others, Manner4 is designed to simulate this phenomenon.

SEOA Algorithm for reactive power problem

Step 1. Initializing all individuals respectively, the initial position of individuals randomly in problem space.

Step 2. Computing the fitness value of each individual according to the objective function.

Step 3. For individual j, determining the value . Step 4. For all population, determining the value . Step 5. Determining the emotional index according to

Eq. (21)-(23) in which three emotion cases are determined for each individual.

Step 6. Determining the decision with Eq. (24)-(28), respectively.

Step 7. Creation of mutation operation. Step 8. If the criterion is satisfied, output the best

solution; otherwise, go to step 3.

V. SIMULATION RESULTS

SEOA algorithm has been tested on the IEEE 30-bus, 41 branch system. It has a total of 13 control variables as follows: 6 generator-bus voltage magnitudes, 4 transformer-tap settings, and 2 bus shunt reactive compensators. Bus 1 is the slack bus, 2, 5, 8, 11 and 13 are taken as PV generator buses and the rest are PQ load buses. The variables limits are listed in table 1.

TABLE 1 INITIAL VARIABLES LIMITS (PU)

Control variables

Min.value

Max.value Type

Generator: Vg 0.90 1.10 Continuous

Load Bus: VL 0.95 1.05 Continuous

T 0.95 1.05 Discrete Qc -0.12 0.36 Discrete

The transformer taps and the reactive power source

installation are discrete with the changes step of 0.01. The power limits generators buses are represented in Table2. Generators buses are: PV buses 2,5,8,11,13 and slack bus is 1.the others are PQ-buses.

TABLE 2 GENERATORS POWER LIMITS IN MW AND MVAR

Bus n° Pg Pgmin Pgmax Qgmin

1 98.00 51 202 -21 2 81.00 22 81 -21 5 53.00 16 53 -16 8 21.00 11 34 -16 11 21.00 11 29 -11 13 21.00 13 41 -16



TABLE 3

VALUES OF CONTROL VARIABLES AFTER OPTIMIZATION AND ACTIVE POWER LOSS

Control

Variables (p.u)

SEOA V1 1.0420 V2 1.0388 V5 1.0202 V8 1.0350 V11 1.0719 V13 1.0415 T4,12 0.00 T6,9 0.02 T6,10 0.90 T28,27 0.90 Q10 0.10 Q24 0.10 PLOSS 4.2908 VD 0.8990

The proposed approach succeeds in maintenance the

dependent variables within their limits. Table 4 summarize the results of the optimal solution obtained by PSO, SGA and SEOA methods. It reveals the decrease of real power loss after optimization.

TABLE 4 COMPARISON RESULTS

SGA[9] PSO[10] SEOA

4.98 Mw 4.9262Mw 4.2936Mw

VI. CONCLUSION

In this paper, the proposed SEOA has been successfully implemented to solve ORPD problem. The main advantage of the algorithm is solving the objective function with real coded of both continuous, discrete control variables, and easily handling nonlinear constraints. The proposed algorithm has been tested on the IEEE 30-bus system .And the results were compared with the other heuristic methods such as SGA and PSO algorithm reported in the literature.

REFERENCES

[1] H.W.Dommel, W.F.Tinney. Optimal power flow solutions. IEEE, Trans. On power Apparatus and Systems, VOL. PAS-87, octobre 1968,pp.1866-1876.

[2] Lee K, Park Y, Ortiz J. A. United approach to optimal real and reactive power dispatch. IEEE Trans Power Appar. Syst. 1985; 104(5):1147-53.

[3] Y. Y.Hong, D.I. Sun, S. Y. Lin and C. J.Lin. Multi-year multi-case optimal AVR planning. IEEE Trans. Power Syst., vol.5 , no.4, pp.1294-1301,Nov.1990.

[4] J. A. Momoh, S. X. GUO, E .C. Ogbuobiri, and R. Adapa. The quadratic interior point method solving power system optimization problems. IEEE Trans. Power Syst. vol. 9, no. 3, pp. 1327-1336,Aug.1994.

[5] S. Granville. Optimal Reactive Dispatch Through Interior Point Methods. IEEE Trans. Power Syst. vol. 9, no. 1, pp. 136-146, Feb. 1994.

[6] J.A.Momoh, J.Z.Zhu. Improved interior point method for OPF problems. IEEE Trans. On power systems; Vol. 14, No. 3, pp. 1114-1120, August 1999.

[7] Y.C.Wu, A. S. Debs, and R.E. Marsten. A Direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows. IEEE Transactions on power systems Vol. 9, no. 2, pp 876-883, may 1994.

[8] L.L.Lai, J.T.Ma, R. Yokoma, M. Zhao. Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Electrical Power & Energy System, Vol. 19, No. 5, p. 287-292, 1997.

[9] Q.H. Wu, Y.J.Cao, and J.Y. Wen. Optimal reactive power dispatch using an adaptive genetic algorithm. Int. J. Elect. Power Energy Syst. Vol 20. Pp. 563-569; Aug 1998.

[10] B. Zhao, C. X. Guo, and Y.J. CAO. Multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Syst. Vol. 20, no. 2, pp. 1070-1078, May 2005.

[11] J. G. Vlachogiannis, K.Y. Lee. A Comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE trans. on Power Syst., vol. 21, no. 4, pp. 1718-1728, Nov. 2006.

[12] A.E. Eiben and J.E. Smith. Introduction to Evolutionary Computing. Springer-Verlag, Berlin, 2003.

[13] Charles Darwin. The origin of species. John Murray, London, UK, 1859.

[14] D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, MA, 1996.

[15] John H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA, 1992.

[16] Thomas B¨ack. Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, 1996.

[17] Fister, M. Mernik, and B. Filipiˇc. Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm. Computational optimization and applications, pages 1–32, 2012.

[18] F. Neri and V. Tirronen. Recent advances in differential evolution: a survey and experimental analysis. Artificial Intelligence Review, 33(1–2):61–106, 2010.

[19] J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer. Selfadapting control parameters in differential evolution: A comparative study on numerical benchmark problems. Evolutionary Computation, IEEE Transactions on, 10(6):646–657, 2006.

[20] S. Das and P.N. Suganthan. Differential evolution: A survey of the state-of-the-art. Evolutionary Computation, IEEE Transactions on, 15(1):4–31, 2011.

[21] John R. Koza. Genetic programming 2 - automatic discovery of reusable programs. Complex adaptive systems. MIT Press, 1994.

[22] Cui ZH and Cai XJ. (2010) Using social cognitive optimization algorithm to solve nonlinear equations. Proceedings of 9th IEEE International Conference on Cognitive Informatics (ICCI 2010), pp.199-203.

[23] Chen YJ, Cui ZH and Zeng JH. (2010) Structural optimization of lennard-jones clusters by hybrid social cognitive optimization algorithm. Proceedings of 9th IEEE International Conference on Cognitive Informatics (ICCI 2010), pp.204-208.

[24] Cui ZH, Shi ZZ and Zeng JC. (2010) Using social emotional optimization algorithm to direct orbits of chaotic systems,



Proceedings of 2010 International Conference on Computational Aspects of Social Networks (CASoN2010), pp.389-395.

[25] Wei ZH, Cui ZH and Zeng JC (2010) Social cognitive optimization algorithm with reactive power optimization of power system, Proceedings of 2010 International Conference on Computational Aspects of Social Networks (CASoN2010), pp.11-14.

[26] Xu YC, Cui ZH and Zeng JC (2010) Social emotional optimization algorithm for nonlinear constrained optimization problems, Proceedings of 1st International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO2010), pp.583-590.

K. Lenin has received his B.E., Degree, electrical and electronics engineering in 1999 from university of madras, Chennai, India and M.E., Degree in power systems in 2000 from Annamalai University, TamilNadu, India. Presently pursuing Ph.D., degree at JNTU, Hyderabad,India.

Bhumanapally. RavindhranathReddy, Born on 3rd September,1969. Got his B.Tech in Electrical & Electronics Engineering from the J.N.T.U. College of Engg., Anantapur in the year 1991. Completed his M.Tech in Energy

Systems in IPGSR of J.N.T.University Hyderabad in the year 1997. Obtained his doctoral degree from JNTUA, Anantapur University in the field of Electrical Power Systems. Published 12 Research Papers and presently guiding 6 Ph.D. Scholars. He was specialized in Power Systems, High Voltage Engineering and Control Systems. His research interests include Simulation studies on Transients of different power system equipment.

M. Surya Kalavathi has received her B.Tech. Electrical and Electronics Engineering from SVU, Andhra Pradesh, India and M.Tech, power system operation and control from SVU, Andhra Pradesh, India. she received her Phd. Degree from JNTU, hyderabad and Post doc. From

CMU – USA. Currently she is Professor and Head of the electrical and electronics engineering department in JNTU, Hyderabad, India and she has Published 16 Research Papers and presently guiding 5 Ph.D. Scholars. She has specialised in Power Systems, High Voltage Engineering and Control Systems. Her research interests include Simulation studies on Transients of different power system equipment. She has 18 years of experience. She has invited for various lectures in institutes.



Firdouse Rahman Khan1 [email protected]

Abstract— Entrepreneurship Development makes a powerful impact on the economic development of the country. The success of the entrepreneur depends on the environmental factors such as social, economic, legal, political and technological factors which influence their activities thus leading to successful entrepreneurship. The socio-economic factors are the major key factors influencing the entrepreneurial behavior and operation of the business and thus the need for the study and the due influence. This paper analyzes the impact of socio-economic factors in relevance to entrepreneurship development of Small and Medium Enterprises (SMEs) across Chennai, Tamil Nadu State, India.

This paper attempts to explain the infrastructure that has to be developed in order to cultivate the quality of leadership among potential enterprising young men. Attempts are being made to inculcate the spirit of entrepreneurship.

Our empirical results reveal that most of the selected entrepreneurs of SMEs perceive the relevance of these factors to the highest degree. They are tempted to enter the entrepreneurship sector because of the perceived opportunities available to make appreciable profit. The study also reveals that the factors which are not considered to be of high importance in the bygone days, such as Education, Religion, Previous Experience, Family Type and Legal Status have significant influence on the entrepreneurial behavior and the operational performance of the selected SMEs’ business, in the recent period.

Thus, there exists necessity for the Government and the related sponsoring institutions to look into these factors and encourage the young entrepreneurs who in turn will render their full support towards national economy.

Keywords— Entrepreneurship, Factors’ influence on entrepreneurship development, Small and Medium Enterprises, Socio-economic factors

I. INTRODUCTION

Schumpeter (1967, p.621) has pointed out that economic development depends to a large extent on the active and enthusiastic participation of intelligent entrepreneurs in the economic process. Haggen E (1961, pp.191-224) viewed economic development is seen almost exclusively as a process of technological change which is brought in by the creativity of the entrepreneurs. Studies have shown that

small-scale industries in many countries provide the mechanism for promoting indigenous entrepreneurship, enhancing greater opportunities per unit of capital invested and aiding the development of local technology (Nils-Henrik and Morch, 1995). Research work on small-scale industries has shown that small-scale forest-based processing enterprises form a very large part of the overall forest products processing total in employment terms (FAO, 1995). Thus, in any country, economic developmental activities are centered on the entrepreneurship of the people of that country. The small scale industries are the hub of many economic activities in a developing country like India. The social economic transformation of India cannot be achieved without paying adequate attention to the development of this labor intensive and capital sparing factor (Prasain & Singh, 2007, p.13). Poverty eradication has been the major goal of small enterprise development in most developing countries. The small and medium scale industries represent 80 percent of industrial base of most of the developed countries (Mathew, 1999, p.23).

The role played by these industries in the economic activity of advanced industrialized countries is also very significant. In modern India the small scale industries have been a success story, they have emerged vibrantly in the face of rising threats from large scale sectors inside the country and of multinationals from abroad. The small scale units constitute about 95 percent of the total industrial units and produce more than 7500 products with associated technology varying from traditional to state of the art (Suryanarayana & Krishnamohan, 2005, p.11). In addition, small enterprises provide employment to nearly 20 million persons, account for about 40 percent of the value added in the manufacturing sector, 34 percent of total national export and 7 percent gross domestic product. Hence, the role of SMEs sector in the economic development has been a matter of great concern for policy makers, researchers, national and international agencies. The growth of Small Scale Industries Sector has been a dominant feature of Indian economic development strategy since independence (Neetubala, 2007, p.9). The governments in most developing countries such as Nigeria were criticized for paying inadequate attention to the

EDITORIAL ARTICLE



need for accelerated economic growth and for not harnessing the abilities of their own citizens for technological innovations and entrepreneurship (Anamekwe, 2001) and it is no bar for India as well. Thus, keeping in mind what a modern entrepreneur looks into the different factors, mainly the infrastructural conveniences, the Industrial Estates were established in different parts of the country.

The Tamil Nadu Small Industries Development Corporation Limited, popularly known as SIDCO, an enterprise of the Government of Tamil Nadu was set up in the year 1970 with the main objective of developing and assisting the SMEs in Tamil Nadu. The Tamil Nadu State has been a pioneer in the establishment of functional industrial estates and has 51 Industrial Estates where infrastructural facilities have been provided so as to create an environment conducive for the growth of industries. Industrial Estates Programmes provide the suitable factory space required for setting up of industries with facilities of water, transport, electricity, steam, bank, post office, canteen, watch and ward and first aid, provided with special arrangements for technical guidance and common service facilities. Thus, the entrepreneurs are saved from diverting their limited resources on unproductive factory sheds for carrying on their industrial activity (TNIDC, 1985, p.15). Public policies are designed in developing countries to increase the pool of entrepreneurs and to promote the formation of certain types of business at the micro and small-scale levels which foster technological activities (Litvak, 2002). Chennai, the state capital of Tamil Nadu, has the largest number of small scale units.

Government and non-governmental organizations through Banks have come forward to assist the entrepreneurs in motivating to start Small and Medium Enterprises (SMEs). However, the small-scale units established in these estates are getting sick despite all the facilities provided by the Government. While large-scale industries are established with expatriate capital, SMEs need to have a domestic entrepreneurial and industrial base. Low capital investment on capital goods and lack of division of labor in production makes these enterprises remained weak. It is a fact that many Micro, Small and Medium Enterprises (MSMEs) are dying out owing to lack of financial support from the government and other citizens. Further, the factors such as lack of technology, inadequate entrepreneurial skills and the absence of effective management techniques hinder the advancement of SMEs to such an extent. This has made the focus on SMEs is relatively little and therefore SMEs tend to concentrate on traditional industries where low entry barriers, low minimum production scales, and relatively large labor force are the potential advantages. However, the traditional industries have not been immune to the recent technological revolution taking place in the field (Adubifa, 1990). Hence, the goods produced by the SME units are constrained by lack of access to critical resources viz. capital, labour, land, infrastructures, and latest technology.

Thus, the focus of the study is to find out the socio-economic factors that impede the advancement of SMEs, thereby to reduce/eliminate the impediments and to derive technological strategies to improve the economic growth.

II. OBJECTIVES OF THE STUDY

The main objective of this study is to show the influence of the socio-economic factors on the entrepreneurship development of the SMEs in the industrial estates of Chennai, the state of Tamil Nadu, India. Thus, the key objective of the study is to identify the salient impacts of socio-economic factors on the entrepreneurship development of the SMEs in the study area and to establish the productive prospects of progressive SMEs in the study area.

III. LITERATURE REVIEW

Hoselitz (1952, pp.193-220) pointed out that some writers identified entrepreneurship with the function of uncertainty bearing, other with the co-ordination of productive resources, some others with the introduction of innovations and skills. There are various factors such as need for independence, improving financial position, self-fulfilment, desire to be own boss etc motivates an entrepreneur (Savita Balhara & et.al, p.9). Some factors such as age, gender, and individual background such as education and former work experience have an impact on entrepreneurial intention and endeavor. Kristiansen, et al (2003, pp.251-263) found that human capital or human resource such as age, gender, education and experience is a further influence on the decision to become self-employed. Christopher’s (1974, p.109) study revealed that economic gain as the most important reason for starting the small industrial units. High demand for the product perceived, was the most encouraging factor. The basic rationale of developing SMEs is that they provide additional employment opportunities and ensure more equitable distribution of income and better standard of living. Appropriate technological guidance through establishment of entrepreneurship business development could only help entrepreneurship to gain guidance and counselling to improve their entrepreneur skills and talent in rural areas (Dipanjan Chakarborty and Ratan Broman, 2012, p.7). A study by Shenbaga Vadivu & Devipriya (2013, p.23) revealed that the most influencing motivating factors of the entrepreneurs are educational qualification, type of business, marital status, form of organization, source of fund, family type, age and choosing this business, lack of adequate educational background and/or education training institutions. The factors that affect this occupational choice depend broadly on an individual’s entrepreneurial ability,

the relative rates of return to entrepreneurship (Wim Naude, 2008, p.6). Various scholars have pointed out that the detrimental effects of technology and socio-economic changes as the driving forces of economic growth and development (Dey, 1975; Zeidenstein, 1975; Palmer, 1978; Whitehead 1985; Stevens 1985). Giacomin et. al. (2011,



p.12) found out that the socio-economic characteristics of the potential entrepreneur influence the opportunity or necessity dynamics to which the entrepreneurial process obeys. Aswathappa K (2009, pp. 5-11) found out that the influence exercised by factors such as people’s attitude to

work and wealth, role of family, marriage, religion and education; ethical issues and social responsiveness of business and the social and cultural environment is highly relevant for a business unit as the variety of goods the firm produces, the type of employees the firm gets and its obligation to society depends on the cultural milieu in which the firm operates. Louis L. Stern (1971, p.7) suggested that more educated the society becomes, more inter-dependent it becomes, and more discretionary the use of its resources, more marketing will become enmeshed in social issues. Zvirbule & Vilka (2012, p.44-46) stated that the social indicators may underlie economic development success and they have also identified the importance of socio-economic factors i.e. demographic patterns, size of the population, population growth rate, age composition, life expectancy, family size, spatial dispersal, occupational status, employment pattern, ethical issues and social responsiveness of business, people’s attitude to work and wealth, role of

family, marriage, consumption habits of the people, their language, beliefs and values, customs and traditions, tastes and preferences and education. Although SMEs face initial developmental problems, they are expected to take a leading role in economic reconstruction as they encompass alternative approaches to problem solving, thinking, operating and risk taking thus should possess entrepreneurial ability and skills to manage the firms (Khanka, 2007, p. 7). A study has confirmed that the inadequate entrepreneurial talents affect the development of small-scale manufacturing and processing industries (ILO, 1994, pp.8-12). Nagarajan K (2012, p.22) confirmed in his report that it is necessary to nurture the quality of entrepreneurship among the people & to avoid entrepreneurial failures. Tarakeswara Rao S, et. al (2012, p.35) stated that the women should be provided with adequate training in development of entrepreneurial skills covering management of enterprises, maintaining account, enhancing productivity, marketing, selling etc. so that they can undertake income generating activities.

IV. RESEARCH METHODOLOGY

The survey was conducted among various small scale industrial estates of Chennai, Tamil Nadu (India) and the 383 units surveyed were selected on the basis of random sampling and contacted personally interviewed through the structured questionnaire. The analysis involved various statistical analyses. ANOVA and DF were used to analyze to determine the problem which is most discriminate with the entrepreneur and the problem which is least discriminate to the Entrepreneur.

V. STUDY VARIABLES AND MEASUREMENTS

The research problem has been defined to obtain the objectives of the study with a set of variables which include: Gender, Age, Educational background, Business Type, Legal Status, Religion, Previous Experience, Family Type and Family Size.

Y = a0 + b1 X1 + b2 X2 + b3 X3 …… + bn Xn + s

Where

Y = Performance measured in terms of profitability

X1 = Gender (dummy variables where Male = 1 and Female = 2)

X2 = Age (in years)

X3 = Educational background (dummy variables)

X4 = Business Type (dummy variable) – Occupational Categories

X5 = Legal Status (Ownership)

X6 = Religion (dummy variable)

X7 = Previous Experience (dummy variable)

X8 = Family Type (dummy variable)

X9 = Family Size (dummy variable)

s = Stochastic error term

a0 = base constant

b1, b2,b3,…bn = Regression coefficients of X1 ... Xn .

The statistical significance of regression coefficient is based on the appropriateness of signs of multiple determinations (R2) and the explanatory variables were judged by t-value.

VI. FINDINGS, RESULTS AND DISCUSSION

The socio-economic factors of SMEs were analyzed in terms of their gender, age, educational qualification, previous work experience, religion, ownership pattern-legal status, business type-occupational categories, family type and family size.

The observations of the characters of the socio-economic factors made from the above referred table 1 are summarized and given below:

It is observed that out of 383 entrepreneurs, 276 (72.1%) are male entrepreneurs and 107 (27.9%) are female entrepreneurs. This clearly shows that the majority of the successful entrepreneurs are male members. Thus, it can be concluded that the industrial estates are still dominated by male entrepreneurs.



TABLE 1 SHOWING DEMOGRAPHIC INFORMATION ABOUT THE ENTREPRENEURS

Characteristics Freque-ncy

Percen-tage

Gender Male 276 72.1 Female 107 27.9

Age 19-29 98 25.6 30-39 226 59.0 40-49 49 12.8 50-59 8 2.1 >60 2 .5 Religion Hindu 270 70.5 Muslim 39 10.2 Christian 41 10.7 Others 33 8.6 Educational Qualification

Illiterate 34 8.9

School 78 20.4 Graduate/Diploma 213 55.6 Post Graduate 7 1.8 Professional

qualification 51 13.3

Legal Status(Ownership)

Proprietorship 279 72.8

Partnership 46 12.0 Hindu Undivided

Family 4 1.0

Private Limited 6 1.6 Public

Undertaking 23 6.0

Others 25 6.6 Family Size Less than 4

members 99 25.8

4 – 7 members 227 59.3 Above 7 members 57 14.9 Family Type Nuclear 234 61.1 Joint 149 38.9 Business Type Beauty Products 6 1.6 Cookery 24 6.3 Chemical Products 27 7.1 Drugs /

Pharmacists 5 1.3

Herbal Products 21 5.5 Electrical Items 22 5.8 Electronics 24 6.3 Engineering 22 5.8 Garments 61 15.9 Handicrafts 32 8.4 Jute Products 22 5.8 Leather Products 27 7.1 Plastics 29 7.6 Sport items 2 0.1 Stationary 49 12.8 Others 10 2.6 Previous Employment

Not working 225 58.7

Working 158 41.3 Source: Questionnaire

It is observed that out of 383 entrepreneurs, 34(8.9 %) entrepreneurs did not have any qualification, 78 (20.4 %) of the entrepreneurs are SSLC/HSC holders, 213 (55.6 %) are either graduates or diploma holders, 7 (1.8%) are post graduates and 51 (13.3 %) of the entrepreneurs are professionals. From the above noted facts, it can be concluded that the majority of the entrepreneurs (55.6 %) are either graduates or diploma holders. The distribution reveals that majority of the respondents i.e. 91 % are educated. This indicates that the entrepreneurs were able to generate maximum profit through their literacy – one of the factors which influence their performance.

The sample data clearly shows that 70.5 percent of the entrepreneurs were belonging to the Hindu religion. This shows that similar findings have been reported by (Walokar, D., 2001, p.50). Muslims and Christians were above 10 percent of the respondents and others were below 10 percent of the respondents. Thus a majority of the entrepreneurs in Chennai were from the Hindu religion.

It is observed that the analysis of the age structure of the sample survey shows that 25.6 % were between 19 to 29 years; 59.0 % were belonging to the age group between 30 to 39years, 12.8 % were belonging to the age group between 40 and 49 years and 2.1 % were belonging to 50 and 59 years, whereas only 0.5% were over 60 years. This reveals that the majority of the entrepreneurs (59.0 %) were within the working age group of 30 to 39 years which clearly purports that earlier the innovation, earlier the success and the work efficiency.

From the data it is quite evident that 59.3 % of the entrepreneurs belonged to medium size (4-7 members) family and 25.8 % of the entrepreneurs constituted a small family (Less than 4 members). Only 14.9 % belonged to a large family (above 7 members).

From the data it is clear that 61.1% of the respondents are from the nuclear family. Perhaps this may be the reason for them to become successful entrepreneurs. This pattern of family system helps them to spend time or earn more money to lead their life in a socialistic pattern.

From the data it is evident that the ownership pattern of the entrepreneurs under study is as follows: Proprietorship concerns are 72.8 %, Partnership firms are 12%, Hindu Undivided family is 1%, Privated Limited companies are 1.6 % and Public Undertakings are 6 % and other types are 6.6%. It is one of the crucial indispensable factors which affects the growth and diversification of the enterprises.

It is clear from the data that 15.9 % of them were engaged in textile/ garments activities according to the growing global market followed by 12.8% of them had selected to make stationary items; 8.4 % percent of the entrepreneurs were involved in manufacturing of handicrafts items. 7.6 % of them were engaged in plastic products; 7.1 % of the entrepreneurs were engaged in leather & chemical



activities. 7.1 % of them were engaged in plastic & furniture manufacturing activity; 6.3 % percent of the entrepreneurs were involved in cookery / electronics; 5.8 % percent were involved in Jute manufacturing activity & Jute products and Electronics, 5.5 % were engaged in herbal manufacturing activity, followed by fewer percentages in other activities 2.6 % , beauty products 1.6 %, Drugs and Pharmaceuticals 1.3%, sports products 0.1%.

The data clearly shows that 58.7% of the entrepreneurs either had no experience or unemployed before starting an enterprise and 41.3 % only had earlier experience and thus it indicates that it is not a must for an entrepreneur to have previous experience to start a new venture. This clearly indicates that the MSEs were dominated by self-employed youth and pre-occupational experience is not necessitated for them.

Of late, the units established by the entrepreneurs were becoming sick despite all the facilities provided by the Government. The role played by the small and medium enterprises towards economic development has been the subject of great concern for policy makers, researchers, national and international agencies. The growth of small scale industries sector has been a dominant feature of our economic development strategy since their goods and services are of relatively increase in demand against imports.

Nine variables were used to predict and explain the effects of socio-economic factors on the performance of the study.

TABLE 2 MODEL SUMMARY

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 .237 .056 .033 3.42299

The multiple coefficients of correlation determine the strength of the relationship between the dependent and independent variables. In this study, the performance of small-scale enterprises (Y) and the variables (X1 to Xn) showing a multiple regression coefficient of 0.237 which is found to be significant (vide Table 2).

TABLE 3 ANOVA

Sum of Squares df

Mean Square F Sig.

Regression 259.504 9 28.834 2.461 .01

Residual 4370.381 373 11.717

Total 4629.886 382

The analyses of variance (vide Table 3), for the regression analysis yields an F-value of 2.461, which is significant at 5 %. This confirms the regression equation as

a model of determinants of the impact of socio-economic factors on the performance of the selected enterprises.

The influence of Socio-economic factors on the performance of small scale enterprises and business operations in the study area are shown in Table 4.

TABLE 4 CO-EFFICIENTS

B Std. Error Beta T Sig

(Constant) 5.835 1.095 5.329 .000

X1 : Gender .326 .393 -.042 .829 .408

X2 : Age .259 .269 -.053 .965 .035 X3 : EDUCATION

.320 .168 .097 1.910 .037

X4: BUSTYPE .082 .045 -.097 1.830 .068 X5 : LEGAL STATUS

.086 .116 .038 .740 .046

X6 : RELIGION

.298 .204 .085 1.461 .145

X7 : PREVIOUS EXPERIENCE

.033 .131 .016 2.252 .001

X8 : Family Type

1.208 .483 .169 .502 .013

X9 : Family Size

.918 .417 -.166 -2.203 .028

Predictors: (Constant), Gender, Age, EDUCATION, BUSTYPE, LEGAL STATUS, RELIGION, PREVIOUS EXPERIENCE, Family Type, Family Size

Dependent Variable: Profit

Source: Questionnaire

VII. CONCLUSION AND MANAGERIAL IMPLICATIONS

In addition to this study, the nine salient variables account for 5.6% of the total variation in explaining the impact of socio-economic factors on the performance of the selected enterprises.

However, five of these explanatory variables found to have significantly contributed to the dependent variable (performance) and the significant variables are return on educational qualification of the respondents (X3), previous experience of the respondents (X7), religion of the respondents (X6), family type of the respondents (X8) and the ownership pattern of the respondents(X5).

As quoted by (Aworemi et.al, 2011, pp.92-99), the study therefore disagrees with the findings of Rondinelli (1983, pp.181-208) that there is no significant difference between socio-economic factors and performance in terms of educational background, previous experience, religion, family type and the ownership pattern, but it supports the finding of Bygrave (1989, pp.7-26) that there is significance



difference between socio-economic factors and performance in terms of growth in profitability.

From the above findings, it could be concluded that the socio-economic factors such as educational qualification background, religion, previous job experience and family type and legal status (ownership pattern) had significant influence on the performance of the selected small-scale enterprises in the study area.

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