Master Thesis
submitted within the UNIGIS MSc programme at Z_GIS
University of Salzburg
Coal Fired Thermal Power Plant Siting in Swaziland using GIS Integrated Method
By
Dumisani Gift Shongwe
UN102717
A thesis submitted in partial fulfilment of the requirements of the degree of
Master of Science (Geographical Information Science & Systems) – MSc (GISc) Advisor:
Ann Olivier
Mbabane, Swaziland, October 2015
Science Pledge
By my signature below, I certify that my thesis is entirely the results of my own work. I have cited all sources I have used in my thesis and I have always indicated their origin.
Mbabane, 15/09/2015
(Place, Date ) (Signature)
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Abstract
The electricity demand pattern in Swaziland requires that a base-load generation technology be considered, which is a key to economic empowerment. Renewable energy technologies such as, solar thermal generation and wind energy are peaking generation technologies, as they rely on natural conditions, which do not exist on a 24-hour basis. This research has applied GIS methodology to identify a new potential coal-fired thermal power plant site, in Swaziland, using the evaluation parameters and criterion weights of a coal thermal plant.
The required conditions for the establishment of thermal power plants were comprehensively evaluated. A conceptual model of power plant siting was designed, and modelled to produce a final suitability location model, for a TPP construction site in Swaziland. Two models were created and aggregated into an overall satisfaction degree model, which identified the suitable and most suitable parcels. The aggregation of individual satisfaction model into an overall satisfaction model simplified the decision process.
All analyses were conducted using ArcGIS 10.3 model builder, using spatial analyst extension. Once the weighted parameters were properly configured, the model executed all the required processes at once from the beginning to the end. The TPP suitability model identified optimal TPP sites. A model validation was accompanied by further analysis and manual field assessment, based on the size and proximity of critical TPP requirements..
The results identified fourteen suitable empty parcels, that represent proper candidate sites. Two were most suitable, based on TPP land approximate suitability analysis; for every MW of power generated there must be at least 1.2 hectares of land available for that purpose. The sites identified by the suitability model correlated closely with key thermal power plant required parameters; the proximity of coal fuel, national electricity grid, water streams, labor, roads, undulating land and rails transport network. The results conformed perfectly with the pattern of TPP suitability input criterions. Two sites were optimum identified for a TPP.
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Preface
This thesis is submitted in partial fulfilment of the requirements for a UNIGIS MSc Degree in Geographic information system. The thesis has been made solely by the author; most of the text, however, is based on the research of others. I have done my best to provide references to these sources.
Writing this thesis has been hard, but in the process of writing, I feel I have learned a lot in GEO- information technology. I have dealt with a lot of subjects, in an attempt to give this thesis a broad perspective, combining many aspects of cognitive Geo-information Science. It was very exciting to work on this project. It is no exaggeration to state that, my personal learning curve, which grew like a logarithmic function, has reached a considerable level.
However it goes without saying that, it took some pains to eventually round off this thesis. It proved to me that, nothing in the world can take the place of persistence and determination, these alone are omnipotent.
The slogan, ‘press on’ has solved, and always will solve, the problems of the human race.” -Calvin Coolidge.
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Acknowledgments
First and foremost I offer my sincerest gratitude to the almighty God and pay my obedience to him, God, the almighty to have bestowed upon me good health, courage, inspiration, zeal and the light while working on this project. Secondly, my sincere and deepest gratitude to my supervisor, Ann Olivier, who ploughed through several preliminary versions of my text, making critical suggestions, and posing challenging questions. Her expertise, invaluable guidance, constant encouragement, affectionate attitude, understanding, patience and healthy criticism added considerably to my experience. Without her continual inspiration, it would have not been possible to complete this study.
I take the opportunity to thanks Dr Kenneth Ndlovu a senior geologist, department of geology survey and mines Department, who helped me understand, the geological coal seam of Swaziland and provided the necessary inform on all coal depots in Swaziland.
For this dissertation I would like to thank my reading committee members: my brother Mfandzile Shongwe Senior Economist in the ministry of economic planning and Mrs. Constance Van Zuydam, an environmental scientist, for their time, interest, and helpful comments. They politely pointed out glaring mistakes, and always expanded my vocabulary.
The burden of writing this thesis was lessened substantially by the support, love encouragement and humor of my family; Londiwe, Phiwa my daughters and my son Muhle. Most of all my appreciation to my loving, supportive, encouraging, and patient wife Futhi, who stuck with me during the long months of writing and re-writing, even when I retreated to long days with my computer. Thank you!
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TABLE OF CONTENTS
Science Pledge ...I Preface ... III Acknowledgments ... IV Table of Contents ... V List o Figures ... VIII List of Tables ... IX List of Abbreviations ... X
1 CHAPTER 1: INTRODUCTION... 1
1.1 Research Motivation ... 1
1.2 Background of Thermal Power Plant ... 4
1.2.1 Working Process of a Thermal Power Plant ... 5
1.3 Important Thermal Power Plants Requirements ... 7
1.4 Aims and Objectives ... 11
1.4.1 Aims ... 11
1.4.2 Objectives ... 11
1.5 Conclusion ... 12
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2 CHAPTER 2: LITERATURE REVIEW ... 13
2.1 Introduction ... 13
2.2 Review of Location Analysis ... 14
2.3 Spatial Location Evaluation Methods ... 16
2.3.1 Multi-Attribute Decision Making (MCDM) ... 16
2.3.2 The Analytical Hierarchy Process (AHP)... 18
2.3.3 GIS-Based Multicriteria Decision Analysis (GIS-MCDA) ... 19
2.4 Conclusion ... 21
3 CHAPTER 3: STUDY AREA... 23
3.1 Introduction ... 23
3.2 Study Area Selection Criteria ... 23
3.3 Location of Study Area ... 26
3.3.1 Administrative ... 27
3.3.2 Physical Environment ... 28
3.4 Study Area Data Collection ... 30
3.4.1 Topographic Data ... 30
3.5 Data Quality ... 32
3.5.1 Utility Infrastructure ... 32
3.5.2 Availability of Coal ... 34
3.5.3 Coal Quality ... 36
3.6 Conclusion ... 41
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4 CHAPTER 4: METHODOLOGY ... 42
4.1 Introduction ... 42
4.2 Methodological Approach ... 44
4.2.1 Criteria Weights Rating And Ranking ... 45
4.2.2 Criteria for Site Suitability Analysis: ... 46
4.3 Conclusion ... 58
5 CHAPTER 5: RESULTS ASSESSMENT AND CONCLUSION... 59
5.1 Introduction ... 59
5.2 Results Validation of Selected Sites ... 61
5.2.1 Coal Fuel ... 61
5.2.2 Electricity Transmission Grid ... 62
5.2.3 Roads and Railway Line ... 62
5.2.4 Social Issues ... 62
5.2.5 Water ... 64
5.2.6 Slope ... 64
5.2.7 Geological Faults and Mining Zone ... 64
5.3 Future Work ... 67
5.4 Conclusion ... 67
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List of Figures
Figure 1.1: SAPP Power Transmission network Grid(GENI, 2012) ... 2
Figure 1.2: Diagram of a typical coal fired thermal power station. (IEA Clean Coal Centre, 2012)... 5
Figure 1.3: Diagram of a typical steam-cycle coal power plant.(IEA Clean Coal Centre, 2012) ... 7
Figure 1.4: Diagram of Data required for thermal plant Siting. ... 10
Figure 3.1 Physical geography of Swaziland Panoramic view (Maphill, 2012) ... 24
Figure 3.2: Regional map of Swaziland ... 25
Figure 3.3 Study Area Royal kraals (Imiphakatsi) ... 27
Figure 3.4 Photo of the Physical Environment of the study area. ... 28
Figure 3.5 Study Area Location Map ... 29
Figure 3.6 Utility infrastructure network Map ... 33
Figure 3.7 Coal belt of Swaziland Map ... 35
Figure 3.8 Coal exploration geological boreholes Map ... 37
Figure 3.9 Surface Aspect of the study Area ... 38
Figure 3.10 Surface Elevation of the study Area ... 39
Figure 3.11 Surface Slope of the study Area... 40
Figure 4.1 Boolean raster Cells ... 47
Figure 4.2 Modelled Restriction criteria map ... 49
Figure 4.3 Modelled Suitability criteria map ... 52
Figure 4.4 Map showing final suitability parcels ... 54
Figure 4.5 Suitability (Critical) Model ... 55
Figure 4.6 Restriction (constrained) Model ... 56
Figure 4.7 Final Suitability Aggregation Model ... 57
Figure 5.1 Map showing optimal thermal power plant suitable site... 63
Figure 5.2 Map showing homesteads restricting buffer within the study area. ... 65
Figure 5.3 Map showing restriction buffer. ... 66
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List of Tables
Table 3-1: Geographical coordinates of the study Area ... 26
Table 3-2 study Area datasets source ... 30
Table 3-3 land-use / land-cover Data accuracy assessment results ... 32
Table 3-4 Projected mineable coal reserves and life span ... 36
Table 4-1 suitability ranging scale ... 46
Table 4-2 Boolean ranking scale for restriction Criteria ... 47
Table 4-3 Index overlay ranking scale for Suitability Criteria ... 51
Table 4-4 table indicates identified Suitable parcels for a TPP location ... 53
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LIST OF ABBREVIATIONS
MW Megawatt (1 MW = 1,000 kW = 1,000,000 or 106 watts)
MCDA Multi Criteria Decision Analysis
MCE Multi Criteria Evaluation
GPS Global Positioning System
Ha Hectare(s)
ESRI Environmental Systems Research Institute
AHP Analytical Hierarchy Process
MCDM Multi-Criteria Decision Making
MODM Multi-Objective Decision Making
GNSS Global Navigation Satellite System
GLONASS Global Navigation Satellite System
TPP Thermal Power Plant
SADC Southern Africa Development Community
SAPP Southern African Power Pool
ESKOM Electricity Supply Commission
SMCE Spatial Multi-Criteria Evaluation
MC-SDSS Multi-criteria Spatial Decision Support System
SHP Small Hydroelectric Plant
CTPP Coal Thermal Power Plant
DSS Decision support system
LULC Land-Use/Land-Cover
DEM Digital Elevation Model
RTK Real Time Kinematic
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RMSE Root-Mean-Square Errors
UN United Nation
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1 CHAPTER 1: INTRODUCTION
1.1 Research Motivation
Coal thermal power plant has been the most effective way of power generation across the world. It is actually the simplest and vital step of an effective power shortage mitigation plan. Despite the intensive efforts in the other methods of power generation, coal based thermal power plant (TPP), still remain as the essential part of the energy mitigation management plans in majority of the world. A secure coal thermal plant is the most viable mode of power generation, the one that is mostly used in developed countries, (IEA Clean Coal Centre, 2012) and siting of a TPP system remains problematic.
The Southern Africa Development Community (SADC) is faced with a serious shortage of electricity energy, and Swaziland is no exception. At present 80 percent of total electricity supply in Swaziland is imported from the Southern African Power Pool (SAPP), thus the need of local generation capability. The development of a thermal power station, would enable the country to be self-reliant and generate revenue by exporting excess power to the SAPP grid (see Figure1), as the country is continuously asked by ESKOM to save power or reduce imports.
In the last few decades, SAPP utilities have been developing production plants and transmission systems to catch up with the rapid growth of power demand. According to (B Chand, 2011), power is the life blood of any nation especially in the current times; the power consumption graph is going up, while the generation is not able to keep up with this demand in most countries. Thermal power plants (TPP) are an indispensable ingredient in the mix of power units of a country.
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Figure 1.1: SAPP Power Transmission network Grid(GENI, 2012)
The electricity demand pattern in Swaziland requires that a base-load generation technology be considered, which is a key economic empowerment. Renewable energy technologies such as, solar thermal generation and wind energy are peaking generation technologies, as they rely on natural conditions, which do not exist on a 24-hour basis.
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Coal fired thermal based power generation technology would be a suitable solution for Swaziland’s dwindling generation systems, hence the need to identify candidate location of a thermal power plant.
Locating a new coal fired thermal power plant is a complicated process, requiring evaluation of many different criteria and considerable expertise in diverse social and environmental fields, such as soil science, engineering, hydro-geology, topography, land use, sociology, and economics (Nishanth,2010).
Environmental factors are very important, because the thermal plant may affect the biophysical environment, and the ecology of the surrounding area (Kontos et al. 2003). Economic factors must be considered too, in the siting of thermal plant, which include the costs associated with the acquisition, development, and operation of the site (Delgado et al. 2008.)
Site selection of an appropriate thermal power plant is a critical decision that could significantly affect the profit and loss of a project under investigation. Often, site selection significantly influences the life style of the surrounding communities; therefore, site selection expertise is a big business when measured in terms of budgets committed, stature of decision-makers involved, size of communities affected, and sustainability of the project. (Delgado et al. 2008.)
It is evident that in locating a coal fired thermal power plant, there are many factors, with spatial dimensions, that must be combined and evaluated in the site selection. Geographic information system (GIS) is ideal for such studies, due to its ability to collect, store, manipulate, process and analyze large volumes of spatial data, from a variety of sources (Sener et al. 2006)..
GIS has the capability to handle and integrate the necessary economic, environmental, social, technical, and political factors and constraints in site selection. In the last few years spatial data has motivated the predominance of geographical approaches, which allow for the integration of multiple attributes using geographic information systems (Chang et al. 2008). It has emerged as a very important tool for site suitability analysis, it can recognize, correlate and analyze the spatial relationship between mapped
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phenomena, thereby enabling policy-makers to link disparate sources of information, perform sophisticated analysis, visualize trends, project outcomes and strategize long-term planning goals (Malczewski, 2004).
The location of a power plant has significant effects, on the efficiency of electricity generation, environmental impacts, price of electricity, transmission and distribution lines. Therefore the location for a new power plant should be done very carefully, and take into account many affecting factors (Yousefi et al, 2007).
The potential advantage of a GIS-based approach in site selection arises from the fact that, it does not only reduce time and cost of site selection projects, but also provides a digital data bank for long-term monitoring of the site. Therefore this research will apply GIS methodology to identify a new potential coal-fired thermal power plant site, in Swaziland.
1.2 Background of Thermal Power Plant
Making new coal-fired power plants carbon capture ready has been recognized as crucial by a number of stakeholders’ academics, energy companies and regional government, due to the environmental issues, global climate change, and is recommended. A number of publications have investigated the definition, engineering requirements, economic and finance of carbon capture ready. Meanwhile, suitable sites for new thermal power plants calls for a convincing investigation due to the development of rural areas and the availability of coal quality and quantity, together with the rising concern over environmental and legal issues.(World Coal Association, 2013)
According to ( IEA Clean Coal Centre, 2012) thermal power plants (TPP) are modular systems which are used for decentralized generation of electricity and heat through the use of power-heat coupling. A special industrial combustion engine, designed for long-duration operation, drives the generator (electrical power) of the TPP. For the motor, a number of different fuels, both solid and liquid, can be used. (Figure2)
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Figure 1.2: Diagram of a typical coal fired thermal power station. (IEA Clean Coal Centre, 2012)
1.2.1 Working Process of a Thermal Power Plant
The working processes of a coal fired thermal power plant are chronologically given below;
· Coal is conveyed with the help of coal conveyer from an external stack and ground to a very fine powder by large metal spheres in the pulverized fuel mill.
· There it is mixed with preheated air driven by the forced draught fan.
· The hot air-fuel mixture is forced at high pressure into the boiler where it rapidly ignites.
· Water of a high purity flows vertically up the tube-lined walls of the boiler, where it turns into steam, and is passed to the boiler drum, where steam is separated from any remaining water.
· The steam passes through a manifold in the roof of the drum into the pendant super heater where its temperature and pressure increase rapidly to around 200 bars and 570°C, sufficient to make the tube walls glow a dull red.
· The steam is piped to the high-pressure turbine, the first of a three-stage turbine process.
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· A steam governor valve allows for both manual control of the turbine and automatic set point following.
· The steam is exhausted from the high-pressure turbine, and reduced in both pressure and temperature, is returned to the boiler Re-heater.
· The reheated steam is then passed to the Intermediate pressure turbine, and from there passed directly to the low pressure turbine set.
· The exiting steam, now a little above its boiling point, is brought into thermal contact with cold water (pumped in from the cooling tower) in the condenser, where it condenses rapidly back into water, creating near vacuum-like conditions inside the condenser chest.
· The condensed water is then passed by a feed pump through a De-aerator, and pre-warmed, first in a feed heater powered by steam drawn from the high pressure set, and then in the economizer, before being returned to the boiler drum.
· The cooling water from the condenser is sprayed inside a cooling tower, creating a highly visible plume of water vapor, before being pumped back to the condenser in cooling water cycle.
· The three turbine sets are coupled on the same shaft as the three-phase electrical generator which generates an intermediate level voltage (typically 20-25 kV).
· This is stepped up by the unit transformer to a voltage more suitable for transmission (typically 250- 500 kV) and is sent out onto the three-phase transmission system.
· Exhaust gas from the boiler is drawn by the Induced draft fan through an electrostatic precipitator and is then vented through the chimney stack.
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Figure 1.3: Diagram of a typical steam-cycle coal power plant.(IEA Clean Coal Centre, 2012)
A thermal power plant is meant for generating power, which obviously means that it will consume huge quantities of fuel (coal). The exact quantity would depend on the size of the plant and its capacity but it is a general fact that ample quantities of fuel must be available either in the vicinity or it should be reasonably economical to transport the fuel to the power plant. Since most thermal power plants use coal, it must be ensured that sufficient coal is available round the clock. Ideally, a power plant with 1000 MW capacity approximately would require more than ten thousand tons of coal per day, hence the necessity for continuous supply, and storage capability of coal in the power station. (R Nath, 2007)
1.3 Important Thermal Power Plants Requirements
Selecting a proper site for a thermal power plant is vital for its long term efficiency, and a lot many factors come into play when deciding where to install the plant. Of course it may not be possible to get everything which is desirable at a single place but still the location should contain an optimum mix of the requirements for the settings to be feasible for long term economic justification of the plant. (R Nath, 2007).
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The main factors influencing the location of a coal-fired power plant is proximity to a coal resource of suitable quality and an appropriate water supply. Other important factors that influence location include availability of land, environmental suitability, proximity to the market and availability of infrastructure such as roads, transmission power-lines and telecommunications. There must be ample space for the storage of coal, disposal of ash, building of the power plant, and residential colony of workers, markets and so forth.
An approximate analysis suggests that for every MW of power generated there must be at least 3 acres of land available for the purpose. Hence the power plant site needs to have good amount of land and this land should have good bearing capacity in order to survive the static and dynamic loads during the operation of the plant.(World Coal Association, 2013)
In developing and undeveloped countries generally coal thermal plants are located on unsuitable sites. In these countries adverse environmental impacts, public health problems and socio-economic challenges associated with coal thermal plant emissions have led to the issuance of stricter regulations and increases in public opposition to the siting of coal thermal power plant. Therefore suitability analysis in siting of coal thermal power plant becomes one of the important tasks involved in locating a thermal power plant.
(Yousefi et al, 2007).
In general, both the construction and operation of a power plant requires the existence of some conditions such as water resources and stable soil type. Still there are other criteria that although not required for the power plant, yet should be considered because they will be affected by either the construction or operation of the plants such as population centers and protected areas. Hence below is a list of factors to be studied and considered simultaneously in locating a coal fired thermal power plant:
v Transportation Network: Easy and enough access to transportation network is required in power plant construction and operation periods, road and rail.
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v Power Transmission Network: To transfer the generated electricity to the consumers, the plant should be connected to electrical transmission system. Therefore the nearness to the electric network can play a roll.
v Geology and Soil Type: The power plant should be built in an area with soil and rock layers that could stand the weight and vibrations of the power plant, avoided in the coal-bearing area.
v Earthquake and Geological Faults: Even weak and small earthquakes can damage many parts of a power plant intensively. Therefore the site should be away enough from the faults and previous earthquake areas.
v Topography: It is proved that high elevation has a negative effect on production efficiency of gas turbines. In addition, changing of a sloping area into a flat site for the construction of the power plant needs extra budget. Therefore, the parameters of elevation and slope should be considered.
v Rivers and Floodways: obviously, the power plant should have a reasonable distance from permanent and seasonal rivers and floodways.
v Water Resources: For the construction and operating of power plant different volumes of water are required. This could be supplied from either rivers or underground water resources. Therefore having enough water supplies in defined vicinity can be a factor in the selection of the site.
v Environmental Resources: Operation of a power plant has important impacts on environment.
Therefore, priority will be given to the locations that are far enough from national parks, wildlife, protected areas population centers, etc.
v Need for Power: In general, the site should be near the areas that there is more need for generation capacity, to decrease the amount of power loss and transmission expenses.
v Climate: Parameters such as temperature, humidity, wind direction and speed affect the productivity of a power plant and always should be taken into account.
v Land Cover: Some land cover types such as forests, orchard, agricultural land, pasture are sensitive to the pollutions caused by a power plant. The effect of the power plant on such land cover types surrounding it should be counted for.
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v Area Size: Before any other consideration, the minimum area size required for the construction of power plant should be defined. An approximate analysis suggests that for every MW of power generated there must be at least 1.2 hectares of land available for the purpose.
v Distance from Airports: Usually, a power plant has high towers and chimneys and large volumes of gas. Consequently for security reasons, they should be away from airports.
v Archeological and Historical sites: Usually historical building are fragile and at same time very valuable. Therefore the vibration caused by power plant can damage them, and a defined distance should be considered.
Figure 1.4: Diagram of data required for thermal plant siting.
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1.4 Aims and Objectives
Having outlined the thermal power plant background, this research therefore strives to determine the optimum location that would satisfy the proponents’ selection criteria. The selection process attempts to optimize a number of objectives desired for the thermal power plant. This optimization will involve numerous decision factors, which are frequently contradicting. As a result, the process will come up with a possible site with advantages and limitations, hence the research question below, around which this work is based:
ü How can GIS be employed effectively, in optimum site location of a suitable and sustainable coal based thermal power plant in Swaziland?
1.4.1 Aims
This research explores the affective utilization of GIS in optimum site location of a suitable and sustainable coal based thermal power plant in Swaziland.
1.4.2 Objectives
The research objectives are as follows;
1. Classify coal coverage according to quantity and quality, using geological data to predict the merit of establishing a coal mine for a thermal power plant.
2. Map land use cover by selective ground true-thing, to verify the land use classes within the study area.
3. Source or Map physical environment, biological environment, socio- economic of the study area.
4. Standardize and correlate both required study area primary and secondary GIS data.
5. Prepare site selection criteria and criteria maps using thermal power plant site suitability index.
6. Develop an integrated GIS weighted overlay model using ArcGIS model-builder.
7. Evaluate identify site location of the thermal power plant 8. Produce a thermal power plant location Map.
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1.5 Conclusion
The primary motivation for this research is to develop a GIS-based tool that serves in multiple practical site suitability analysis purposes. This work will integrate and expand on the work done by others in industrial site suitability analysis field, with a particular focus on coal fire thermal power plant siting. The foundation of this project involves compiling, Screening, and organizing the necessary data into a spatial decision model. It will support site suitability analysis, model development, sensitivity analysis, and the production of a site suitability map.
Therefore the working hypothesis is that, combining GIS spatial analysis and visualization capabilities with Multi criteria analysis, is an effective approach for solving complex spatial problems. This includes the thermal power plant siting, which must balance numerous geographic, technical, environmental, economic, and social variables. The rationale is that, this research can help ensure the achievability of this form of power generation, by making information more accessible to interested parties, and by facilitating discussion on the aesthetic, environmental, and economic issues surrounding coal fire TPP development.
By making this information more readily available to decision makers and the public, the assumption is that this work will stimulate and enhance discussions on the subject of a coal fired thermal power energy development in Swaziland. The intension is to provide a practical context for those discussions by creating a tool that assesses many of the critical criteria and constraints involved in TPP energy project siting.
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2 CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
Facility location, also known as location analysis, is a well-known concept in the literature. Most past research articles in facilities site location analysis have focused on the economic aspects of location. Lately, given the growing interest in sustainable development, location decisions regularity, location analysis now put emphases is on environmental and social consequences. When compared to past practice, not only industries and government, but also the general public is demanding more complex facilities siting. These facilities must also meet social and environmental goals, as a result, an increasing number of requirements need to be satisfied in the location decision process.
The study of facility decisions has a long history in literature; terms such as location analysis, site suitability analysis, and land use suitability analysis, are also terms used in most location literature review studies. A typical facility location problem involves optimal placement of facilities, by minimizing the costs associated with, or maximizing the desirability gained by the placement. Moreover, certain mutual relationship points, mostly in the form of supply and demand points, are involved in these kinds of decision making problems.
Locating a power plant, landfill, industrial area, distribution point, and manufacturing sites are classic examples, and are mostly discussed by scholars and researchers.(Bagdanavičiūtė and Valiūnas, 2013)
Geographical information system (GIS) has emerged as an ideal system, that entirely address facility location analysis predicament. This chapter attempts to discover and assess critically previous concept or frameworks published, that have been used in sustainable facility site location analysis. The main focus of this work is motivation for a coal fire thermal power plant siting in Swaziland.
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2.2 Review of Location Analysis
The inception of siting theory dates back to 1929, when Alfred Weber published his book titled, “Theory of the Location of Industries” (Weber, 1929). These initiatives formed the basis of descriptive and normative location theories respectively. Up to now, several researchers and authors have developed the topic, and many handbooks and scientific papers have been published on this subject. Descriptive models seek spatial socio-economic patterns that follow each placement; whereas, normative location theories aim at setting up decision making mathematical models for this purpose. The distinctions between normative and descriptive approaches to siting theory have remained in place until now, since their goals are different.
Location theory is rooted in the disciplines of geography, engineering, mathematics, and economics. The science of “where should it be” is truly multidisciplinary and continues to be of interest to practitioners and researchers alike, representing a variety of fields, ranging from business, operations research to computer science. The contributions of modern pioneers helped expand the underlying theory, as well as formulate relevant models for application.(Li Jia et al., 2011)
Since the first location algorithm was proposed in 1937 by Weiszfeld (Vasonyi, 1937), this field has relied on the computer to solve and analyze location problems. This reliance has been strengthened even more with the emergence of capable commercial modeling software packages as well as geographical information systems. GIS and optimization techniques are equally important tools in this maturing field. In fact, scholars and practitioners today need an understanding of both, GIS and optimization. Existing books on location tend to discuss the topic from a perspective of GIS or optimization, but not both. However, the science of
‘where should it be’ has matured to the extent that this dichotomy is no longer ideal, so researchers need to be firmly grounded in both subjects.
For instance, (Dobson, 1979) has improved the automated regional screening technique by incorporating multiple criteria across a range of economic, environmental impact, and socio-economic criteria in a power plant siting case study. Candidates’ locations were rated based on aggregating the importance criteria weights obtained from the two nominal groups involved. The procedure applied geographical based file of
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relevant and easily accessible information, a screening algorithm for application to the stored data, and one or more sets of siting criteria for each type of land use or facility. The outcomes were satisfactory.
(Geneletti, 2010) presents combining stakeholder analysis and spatial multi-criteria evaluation (SMCE) to select and rank inert landfill sites. SMCE techniques were applied to combine the criteria, obtain a suitability map of the study region. Through GIS modeling, these sites were compared and ranked according to their visibility, accessibility and dust pollution. The two-stage approach allowed first to identify potential invert landfill sites within the study region, and then rank them according to their preferences. The two stages were conducted using different sets of criteria and inputs from different groups of people. (Geneletti, 2010)These techniques were successful applied and yielded workable results.
(Dudukovic et al., 2005) used the term “sustainable industrial siting” in the title of their work in which they developed a suitability map and used a multi-criteria spatial decision support system (MC-SDSS) for the siting of industrial facilities. Although only a few geophysical and environmental criteria were taken into account, the taken approach is of relevance to the current siting research article. Their work was accepted for use.
(Tsoutsos et al., 2007) used the term “sustainable siting procedure” in the title of their work in which they proposed a sustainable siting procedure for siting a small hydroelectric plant (SHP) using the principles of sustainable spatial planning. They proposed the procedure for analyzing the environmental, economic, and social impacts of SHP’s during their installation and operation. The result were appealing.
(Farahani et al., 2010) have included the term “sustainable facility location” in the suggestions for future research section of the article without any further development or description: The most important reflection that we took into consideration, is how to measure these attributes related to social and environmental objective functions, thus the term like sustainable facility location.
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2.3 Spatial Location Evaluation Methods
Spatial decision problems typically involve a large set of feasible alternatives and multiple, conflicting and incommensurate evaluation criteria. The alternatives are often evaluated by a number of researchers, using the different siting spatial decision analytical methods. The researchers are typically characterized by unique preferences with respect to the relative importance of criteria. However, more reliable and analytical methods are needed for organizations to support spatial decision making (Jankowski et al., 2001). The following are reviewed spatial location evaluation methods found in most published siting articles, books and journals.
1. Multi-criteria decision making (MCDM)
2. The Analytical Hierarchy Process (AHP)
3. GIS-based multi-criteria decision analysis (GIS-MCDA)
2.3.1 Multi-Attribute Decision Making (MCDM)
The rationale of MCDM models is based evaluation of multiple criteria, to find a solution of a problem with multiple alternatives. These alternatives can be evaluated by their performance characteristics, in other words decision criteria (Jankowski et al., 2001). Basically, MCDM enables the decision maker to evaluate a set of alternatives according to conflicting, and incommensurate criteria. A criterion is a generic term which may be constituted by both attributes and objectives. Therefore, MCDM can be classified into two groups:
Multi-attribute decision making (MADM) and multi-objective decision making (MODM) (Malczewski, 1999).
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In the MADM approach, each alternative is evaluated with respect to various attributes and final choices are made among potential alternatives. On the other hand, MODM is based on the decision maker‘s objectives which can be a statement about the desired state of the system. Several different attributes might represent objectives. In other words, MODM problems deal with the objectives which require establishing specific relationships between attributes of the alternatives (Malczewski, 1999).
Further classification depends on decisions under certainty and decisions under ambiguity. If decision makers have adequate knowledge, about all the variables and parameters of the problem, the decision can be classified as decision under certainty which is also called deterministic decision-making. However, many real world decisions are very complex to be deterministic. Thus decision associated with a problem involving random and uncertain variables, and vague or incomplete data are considered as decision under ambiguity.
Two types of uncertainty may exist in a decision situation; uncertainty due to vague, incomplete or limited information or variability due to randomness. As a result, both MADM and MODM problems can be classified further into probabilistic and fuzzy decision making problems. Probability theory or statistics are used to solve problems involving random variables. On the other hand, fuzzy set theory tools are used to solve problems that involve vague and incomplete data.
Presence of incomplete information leads to results that may not be represented by crisp numbers but rather with degrees. These types of problems are handled with fuzzy sets theory (Zadeh, 1965). As mentioned before, MCDM provides solutions to decision problems which have multiple alternatives. Decision rules are used to choose the most preferred alternative between several options. In other words, decision rule is a course of action that allows selecting best alternative from a set of alternatives.
This procedure provides overall assessment of alternatives by integrating the data and decision maker‘s preferences (Malczewski, 1999). Although significant numbers of decision rule approaches are presented in the literature, there are limited applications of combined utilization of GIS and MCDM. The weighted
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summation, ideal reference point, and outranking methods are the examples of such approaches which allow integration of MCDM and GIS (Malczewski, 2006).
Combined utilization of MCDM and GIS is the ideal point of approach. The ideal point approach is based on the set of alternatives which are ordered with respect to their separation from an ideal point. This point corresponds to a hypothetical alternative (decision outcome). The best alternative is the closest to the ideal point. The ideal point approach is an attractive methodology if relationships between attributes are complex to verify or test (Malczewski, 1999).
2.3.2 The Analytical Hierarchy Process (AHP)
The Analytical Hierarchy Process (AHP) is another popular method which is based on the additive weighting model (Basnet et al., 2001). The AHP method has been used in two distinctive ways within the GIS environment. First, it can be employed to derive the weights associated with attribute map layers. Then, the weights can be combined with the attribute map layers in a way similar to the weighted additive combination methods. This approach is of particular importance for problems involving a large number of alternatives, when it is impossible to perform a pairwise comparison of the alternatives (Eastman et al., 1993). Secondly, the AHP principle can be used to aggregate the priority for all level of the hierarchy structure including the level representing alternatives. In this case, a relatively small number of alternatives can be evaluated (Jankowski and Richard., 1994).
There are many examples in the literature about AHP. For example, (Hill et al., 2005) investigate the new methods for selecting suitable sites for various land uses in Australia. MCDA and AHP are combined and used to determine biophysical, economic and infrastructure suitability of land use. New interfaces are produced in the ArcInfo Grid GIS environment.
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In another study, (Ercanoglu et al., 2008) used AHP to assess landslide vulnerability in the West Black Sea Region of Turkey. (Ying et al., 2007) used AHP with GIS in order to evaluate eco-environment information system in Hunan Province, China. The aim of this study was to identify regional features of eco- environment and main environmental problems of the study area. Natural environment, disaster, environment pollution and social economy factors were proposed as evaluation index system. As a result, the regional eco-environmental information system database and evaluated the eco-environmental quality of Hunan Province were established.
(Amir Foroughian and Eslami, 2015) presents a case study for the city Susa titled “Application of AHP and GIS for landfill site selection”. 15 layers were incorporation, including conservation areas, urban areas, rural areas, major rivers, roads, dams, highways, railways, historic areas, land use, soil texture, slope, elevation, wetlands and flood prone areas were used in AHP and analysis of fuzzy method using ArcGIS software.
The weighting of the criteria for analysis and classification parameters were categorized into technical and operational, ecological, biological, socio-economic and physical. After criteria determination, factor of their importance to standardize the criteria for comparison was performed using GIS and AHP method, The AHP method was used to extract the relative importance weights of the evaluation criteria. GIS was used to create the spatial determination of the evaluation criteria. The combination of AHP method with GIS in this research was able to identified possible solid waste landfill sites.
2.3.3 GIS-Based Multicriteria Decision Analysis (GIS-MCDA)
Many articles on spatial decision problems give rise to the GIS-based multicriteria decision analysis (GIS- MCDA). These two distinctive areas of research, GIS and MCDA, can benefit from each other (Laaribi et al.
1996, Malczewski, 1999, Thill, 1999), Chakhar and Martel, 2003). Indeed, GIS is often recognized as a decision support system involving the integration of spatially referenced data in a problem solving environment’ (Cowen, 1988). On the other hand, MCDA provides a rich collection of techniques, and procedures for structuring decision problems, and designing, evaluating and prioritizing alternative
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decisions. At the most fundamental level, GIS-MCDA can be thought of as a process that transforms and combines geographical data and value judgments (the decision-maker’s preferences) to obtain information for decision making.
It is in the context of the synergetic capabilities of GIS and MCDA that one can see the benefit for advancing theoretical and applied research on GIS-MCDA. There is now a well-established body of literature on GIS-MCDA e.g. (Ouma et al., 2011, Yoxas et al., 2011, Djenaliev, 2007, Velasquez and Hester, 2013).
The GIS-MCDA research has made considerable contribution to the participatory GIScience (Jankowski and Nyerges 2001). By their nature, the MCDA approaches integrate multiple views of decision problems. They may improve communication and understanding among multiple decision-makers and facilitate ways of building consensus and reaching policy compromises. Consequently, the GIS-MCDA support systems have the potential to improve collaborative decision making process, by providing a flexible problem-solving environment, where those involved in collaborative tasks can explore, understand, and redefine a decision problem (Feick and Hall 1999, Jankowski and Nyerges 2001, Kyem 2004).
An integration of MCDA into GIS can support collaborative work by providing a tool for structuring group decision-making problems and organizing communication in a group setting. MCDA provides a framework for handling the debate on the identification of components of a decision problem, organizing the elements into a hierarchical structure, understanding the relationships between components of the problem, and stimulating communication among participants. The primary purpose of GIS-MCDA is to process and synthesize a large number of value judgments and spatial data sets, and to examine the implications of those value judgments for planning and policy-making, therefore a more careful attention must be paid to the assumptions underlying the multi-criteria procedures.
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(Issa and Al Shehhi, 2012), presented GIS-MCDA methodology in the assessment and selection of areas for solid waste landfill using multi-criteria analysis and geographical information systems (GIS) a case study of Abu Dhabi, United Arab Emirate, titled “A GIS-based multi-criteria evaluation system for selection of landfill sites”.
Eight input map layers including proximity to urban areas, proximity to wells and water table depth, geology and topography, proximity to touristic and archaeological sites, distance from roads network, distance from drainage networks, and land slope in constraint mapping. The principle method applied divided the decision problems into smaller understandable components, analyze each component separately, and then integrate the components in a logical way (Malczewski 1997).
These techniques which utilize Multiple Criteria Analysis (MCA) and GIS together (Lin and Kao, 1998) or MCDA method and GIS were used for optimal CTPP siting within an area of study (Hipel, 1982). In all these studies, GIS was generally used to manipulate and present spatial data, while the MCDA was used to rank potential areas based on more important involved criteria.
2.4 Conclusion
The role that GIS based suitability models play in decisions relating to TPP facility siting, is quite significant. The researched literature discussed above illustrates how widespread the application of this approach is, and the underlying commonalities in design behind these suitability models. The literature reviewed elude that site selection is one of the basic vital decisions in the start-up process, in expansion or relocation of businesses of all kinds, in this case CTPP.
Construction of a thermal power plant system is a major long-term investment, and in this sense determining the location is a critical point on the road to success or failure of the plant. The main objective in TPP site selection is finding the most appropriate site with desired conditions defined by the selection criteria.
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Maximum use of the available information, siting techniques and management tools were emphasized on literature studies, to ensure the acceptable outcome by governmental environmental protection agency and stakeholders and the public, (Dorhofer and Siebert, 1998). Several techniques for CTPP siting and site selection have been introduced earlier (Balis et al., 1998). Some of these techniques were binary, since the final result discriminated the study zone to limited numbers of suitable/unsuitable areas, as indicated on the on most literature. (Yesilnacar and Uyanvk 2005).
Most of the data used by researchers and decision makers in industrial site selection are geographical, which means that industrial site selection process is a spatial decision problem. Such studies are becoming more and more common, due to the availability of the geographic information systems with user-friendly interfaces. Geographic information systems have been used in conjunction with other systems and methods such as systems for decision making (DSS) and the MCDM. Synergistic effect is generated by combining these tools contribute to the efficiency and quality of spatial analysis for industrial site selection.
A variety of studies have employed GIS for the purposes of suitability modeling. They illustrates the strength of this approach and its immense utility in answering questions concerned with pinpointing good candidate locations for industrial facilities, such as the thermal power plant. (Malczewski, 2006) In the reviews of the literature on GIS-MCDA, one can conclude that it is well suited for a TPP siting research, due to the explicitly spatial nature of the criteria being considered.
These models will serve as a guide for the development of an original TPP site selection suitability model for Swaziland, and are a justification for the nature of this research. Ultimately, this work seeks to apply and assess the accuracy of this type of analytical approach by comparing the output results of a suitability model to the actual locations of TPP energy facilities and further identify ways in which future models might be improved.
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3 CHAPTER 3: STUDY AREA
3.1 Introduction
Site selection requires comprehensive consideration of factors, and balancing of multiple objectives to identify a suitable territory to drill-into, when selecting a tentative study area for a thermal power plant (TPP) site suitability study (Al-Shalabi et al., 2006). Hence, it is imperative to study the existing pre-project physical, biological, and socio-economic conditions country wide to identify the study area location. This motivated a TPP site selection assessment baseline study for this work, to identify a regional territory to drill into and select the study area. It was done using the physical geography, and the coal fuel resource location of the country, with the help of literature on coal, and TPP site suitability factors.
Hence this chapter describes the study area location, its selection criteria, collected primary and secondary geospatial data, within the identified study area. It further evaluates the quality of the required collected geodata for the TPP suitability analysis project. This includes horizontal and vertical accuracy evaluation, to ensure the result of this work is accepted and certain for a TPP location. These datasets includes environmental, physical, biological and socio-economic datasets within the identified project study area.
3.2 Study Area Selection Criteria
Geographically, Swaziland is a country in Southern Africa, lying between Mozambique and South Africa, located at the geographic coordinates 26°30′S and 31°30′E. With an area of 17,363 square kilometers, of which 160 is water. See (figure 3.5).
It is divided into four distinct geographical regions running from north to south, each with its own climate and characteristics varying from 98, to 1,800 meters above sea level. The mountainous highveld in the west has rivers, gorges and waterfalls while the middleveld has fertile valleys. In the east, is the lowveld and the Lubombo Plateau which boarders Mozambique. It has four administrative regions namely Hhohho, Lubombo, Manzini and Shiselweni. These regions are subdivided into 55 tinkhundla boundaries and each subdivided into imiphakatsi (Royal Kraals). See (figure 3.2).
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Based on the physical Geography as indicated on (figure 3.1) and the coal resource location within the country, Lubombo region was identified as the most suitable region to work on for this study. According to(Geological Survey and Mines Department, 2006) the coal belt in Swaziland lays along the Lubombo region, the east part of the country. This allowed us to drill down to a tentative location of the study area within this region.
The region has an area of 5,849.11 km² and a population of 207,731 as of 2007 sense data. It borders all three other regions: Hhohho to the north, Manzini to the west, and Shiselweni to the south. See (figure 3.2).
The region’s historic administrative centre is Siteki (formerly Stegi); a charming little town set on a hill 1000 meters above sea level. On a clear day, the Indian Ocean may be seen. About 20 km east of Siteki is the small town of Mpaka which, until recently, was best known as a centre for Swaziland Railway.
Figure 3.1 Physical geography of Swaziland Panoramic view (Maphill, 2012)
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Figure 3.2: Regional map of Swaziland
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3.3 Location of Study Area
The study area was delineated based mainly on the factors influencing the location of a coal-fired power plant, and the proximity of coal resource of suitable quality. When evaluating the region for the study location, the region was analyzed based on literatures in TPP site suitability analysis factors.
The north of region has many activities, with a huge game reserve (Hlane Game Park) and an international airport at Sikhuphe. Further north is the sugarcane farming belt Mhlume, Simunye, Ngomane, Vulane, up to Tshaneni, all taking advantage of the great Komati River. While on the south there is also, a game park (Mkhaya Game Park), further south is the Great Usuthu River where lots sugar cane farming takes place.
This then lead to the center of the region where a number TPP suitability factors are found; the road network, railway line for coal transportation if needed, the proximity of coal resource as it encompass the once operational Mpaka coal mine, and electricity transmission line for easy connection to the power grid.
The downside of the study area was the scarcity of water resources, which can be addressed by underground water explorations. The location map of the study area is presented in (figure 3.2 and figure 3.3) and the coordinates of the study area boundary are provided in (Table 3.1).
NAME LATITUDE LONGITUDE
A 26 22' 45.026" S 31 55' 44.886" E B 26 22' 45.026" S 31 39' 44.886" E C 26 40' 35.026" S 31 39' 54.886" E D 26 40' 34.418" S 31 55' 55.139" E
Table 3-1: Geographical coordinates of the study Area
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Figure 3.3 Study Area Royal kraals (Imiphakatsi)
3.3.1 Administrative
The study area is cover by 3 tinkhundla administrative centers namely Dvokodweni1/2 and Mpolonjeni, With nine Royal Kraals ( Imiphakatsi) as indicated on (figure 3.3 and figure 3.5), with an area of 1820.11 Hectares , a population of 40 324 and 7 329 homesteads as of 2007 sense data.
The project site is found at Lubhuku, located approximately 30km west of Siteki Town; 60km north of Bigbend and almost 15km from the Manzini Lonhlupheko highway. The area covers the following communities; Lukhula, Lubhuku, Khushweni, Lokhayiza, Malindza, Mampempeni, Sigcaweni and Mpaka.
The land is mainly Swazi Nation Land and a small portion of private farms. The project area is flat and undulating with the infrastructure consisting of small access roads and tracks.
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3.3.2 Physical Environment
The Study Area is dominated by secondary vegetation. The vegetation consists of trees such as Acacia burkiea, Combretum sp. Acacia Nigrescence, Sclerocarya biirrea, Ficus sp, Acacia borleae, Ziziphus mucronat, Dichrostachys cenerea. Common grasses observed were Cynadon dactylon; Panicum maximum and Digitaria sp.Invasive plant species such as, Lantana camara were prevalent in the project area. See (figure 3.4)
Figure 3.4 Photo of the physical Environment of the study area.
Study area has high summer temperatures, with mean temperature ranging between 17 – 27oC. On an average 600 – 750mm of rainfall were received annually. It altitude is between 200 – 500 meters above sea level, see analyzed physical surface characteristics in terms of slope, aspects and elevation on (Figure 3.9, Figure 3.10 and Figure 3.11) of the study area. Other characteristics include winter frosts and fertile soils.
The area is prone to drought and climate variability (Kureya, Chipfupa & Nxumalo, 2009).
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Households rely on raising crops such as maize, beans, ground nuts, cotton, sweet potato, jugo beans, potatoes, sorghum, cowpeas, and pumpkins for livelihood. However, due to high temperatures and less rainfall, yields, in particular maize are poor in most years. As a result, the area has consistently received food aid over the years. They also rear livestock such as cattle, goats, poultry, pigs and sheep (Kureya et al., 2009). There was no systematic settlement and planned development in the study area, the road networks were not built following proper standards, homesteads are scattered all over the study area.
Figure 3.5 Study Area Location Map
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3.4 Study Area Data Collection
Different primary and secondary datasets were sourced for this work, mainly those that are important in TPP Site selection. The datasets and their sources are indicated on (table 3.2.)
DATASET TYPE RESOLUTION SOURCE
Topography(DEM) Raster 20m x 20m Grid Surveyor General's Department
Geological Data Raster 1:50000 Swaziland geological and mines
Hydrology Data vector 1:10000 Surveyor General's Department
Environmentally Sensitive Area
vector 1:10000 Surveyor General's Department
Landuse/Cover Data Raster 1: 10000 Primary data
Electricity powerlines vector Submeter Swaziland Electricity Company
Administrative vector 1:10000 Surveyor General's Department
Roads vector 1:10000 Ministry of Work and Transport
Demographic data vector 1:10000 Swaziland Central Statistics
Households vector 1:10000 Primary data
Table 3-2 study Area datasets source
3.4.1 Topographic Data
Detail topographical data collection and quality checks were carried out in identifying, and mapping land use, land cover, elevation, homesteads and other topographic features. A combination of semi-automated classification techniques that include; supervised and unsupervised pixel-based classification contextual, object-based feature extraction and manual delineation for defining the land-use/land-cover (LULC) of the study area was performed to derive the various LULC types, including field observations.
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LULC map were subjected to accuracy assessment. The land-use/land-cover ground verification was determined from field data, and original virtual earth Image accessible through the google earth interface.
The LULC data set created from image classification was updated with the classes extracted by feature analyst and manual digitizing. The input data for the land use suitability were prepared in GIS format using ArcGIS in order to easily use within the site selection analysis model. The sourced digital elevation model (DEM) (20m x 20m grid) and LULC data sets were re-projected to South Africa Lo 31, Transverse Mercator, Clark 1880 Arc, Cape datum, and were clipped to the study area using a digitized boundary layer.
The topographic data accuracy was evaluated and verified by sampling the study area using a real time kinematic (RTK) a global network satellite system (GNSS) that has the ability to track both glonass and global positioning system(GPS ) yielding a sub-meter horizontal and vertical accuracy .Check points were collected from topographic maps (1:10 000) for the vertical accuracy assessment. Using the check points, root-mean-square errors (RMSE) in the Z-coordinates were calculated. Assuming parallax difference correlation errors in the range of 0.5 to 1.0 (5–10 m), elevation errors (RMSEz) are expected to be in the ±12 m to ±26 m range (Chrysoulakis et al., 2004). By comparing the GNSS captured z-coordinate values at check points with those collected from the topographic maps, DEMs of the study area yielded RMSEz of 1±
to 5±. This result is considered high quality and DEMs were not applied correction. See results elevation of the study area (Figure 3.10).
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3.5 Data Quality
The accuracy was evaluated using computed, producer accuracy, user accuracy, overall accuracy and kappa index (Table 3.2). Almost all errors occurred in the boundary area between different land use/land covers.
The overall accuracy was 92.4% and overall Kappa is 0.89.
Class User Accuracy Producer Accuracy Total Accuracy Kappa
Bare ground 86% 90% 97% 0.89
Built-on 95% 99% 99% 0.99
Bushland 86% 87% 98% 0.79
Cultivation 82% 74% 97% 0.82
Households 85% 85% 96% 0.95
Water 95% 94% 99% 0.94
Woodland 82% 83% 97% 0.89
Total
87% 88% 98% 0.90Table 3-3 land-use / land-cover Data accuracy assessment results
3.5.1 Utility Infrastructure
Electricity powerline and substation datasets were provided with metadata, which indicated that the data is updated frequently; usinng an RTK GNSS that yield sub-meter Horizontal accuracy, projected to South Africa Lo 31, Transverse Mercator, Clark 1880 Arc, cape datum (figure 3.6). This data was accepted without any verification or adjustment. Water resource data was collected from secondary sources; river systems of the study were identified and verified at field level through physical observation.
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Figure 3.6 Utility infrastructure network Map
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3.5.2 Availability of Coal
The key factor in sustainability of a power plant is the reliable and uninterrupted fuel supply. One of the objectives of this work is to classify the potential quantity and quality of coal deposit in Swaziland. The geological and seismic issues have been taken care through secondary information and field observations.
The general geological features and the seismicity of the project and its surrounding areas were collected from available secondary literature and site visit of existing mines along the coal belt (Figure 3.7).
According to (Geological Survey and Mines Department, 2006) report on coal, Swaziland’s well defined coal depots is found along the Lubombo region (Lowveld) see (Figure 3.7). The study report defines coal depots, summarized mineable coal reserves and projected life with potential quantity and quality of coal along the coal belt as indicated on (Table 3.4).
According to (Geological Survey and Mines Department, 2006) The coal field of Swaziland occurs within the Lowveld (Lobombo region) of Swaziland. It consists of low lying plains ranging between 300 and 400 meters above mean sea level and receives the least amount of rainfall in the country which averages only 700mm per annum. The mineable coal reserves and projected life on identified potential coal mining area are indicated on (Table 3.4).
The coal field of Swaziland comprises of Karoo sediments and occupies about one sixth of the 17 364 km2 area of Swaziland. The coal seams dip 5 to 8 degree to the east with up to 20 coal seams. The indications here means that two type of mining can be applied open cast mining and shaft mining, further to the east, on to the, deeping coal seams.
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Figure 3.7 Coal belt of Swaziland Map
