This study introduces a new methodology referred to as geoinformation synthesis for multivariate evaluation of mineral resources and integration of diverse geoscience data. The most critical component is the development of the notion of intrinsic samples and the methods for their delineation. Intrinsic samples replace grid cells which are conventionally employed as the basic information reference. Grid cell sampling has imposed several serious limitations on the geoscience and genetic information that can be objectively related to mineral endowment. Methods based upon intrinsic samples moderate to a certain extent these problems and bring the critical genetic information into the geoscience information system which forms the basis for the quantitative evaluation of mineral resources. The second major component in this new methodology is the integration of factors describing exploration effects with other geodata and mineral endowment estimation; this combination effectively reduces the possibilities of biases in the estimates of mineral endowment and recoverable resources due to the incomplete knowledge on the control area and imperfect analogy with the study areas. The third component is the use in the qualitative models of synthesized geoinformation, which is considerably enhanced, instead of using directly the original measurements (geodata). Several multivariate techniques are proposed and employed for synthesis of diverse information and estimation of mineral endowment, including a priori weighted multivariate criterion, optimum discretization, coherency analysis, multidimensional scaling method (p(ijk), filtering analysis, and geochemical transportation models. These methods were developed, tested, and demonstrated on an actual case study of the epithermal gold-silver deposits in the Walker Lake quadrangle of Nevada and California using various data sets available for this region: geochemical, structural, gravity and magnetic, lithology, and alteration. Finally, the estimation of endowment in terms of epithermal gold-silver mineral occurrences is given for some selected intrinsic samples or information zones identified in the Walker Lake region.

Run-of-mine (ROM) coal sampling is one of the most important factors in determining the disposition of ROM coal for an overall emission control strategy. Determination of the amount of sample, or still better, the frequency of ROM coal sampling is thus essential to the analysis of overall emission control strategies. A simulation model of a portion of the Upper Freeport coal seam in western Pennsylvania was developed employing conditional simulation. On the simulated deposit, different mining methods were simulated to generate ROM coal data. ROM coal data was statistically analyzed to determine the sampling frequency. Two schemes were suggested: (1) the use of geostatistical techniques if there is spatial correlation in ROM coal quality, and (2) the use of classical statistics if the spatial correlation in ROM coal quality is not present. Conditions under which spatial correlation in ROM coal quality can be expected are also examined. To link the ROM coal and coals from other sources to coal stockpiles and subsequently to solve coal blending problems, where varying qualities of stockpiled coals are normally used, an interactive computer program was developed. Simple file-handling, for stockpiling problems, and multi-objective goal programming technique, for blending problems, provided their solutions. The computer program was made suitable for use on both minicomputer and microcomputer. Menu-driven and interactive capabilities give this program a high level of flexibility that is needed to analyze and solve stockpiling and blending problems at the power plant.

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The study begins by investigating Thailand’s administration of its mineral resources and those of some other leading mineral-exporting countries for comparison. The notion of ‘resource curse’, which affects many resource-rich countries, and an analysis how Thailand fought and won the ‘curse’ is critically explored. The principle of sustainable development and its implication to Thailand are presented, together with various computed indicators of sustainable development for Thailand. The role of mineral resources and Hotelling’s model in the context of sustainable development are discussed. The essence of this study is the development of economic models to determine the optimal extraction paths of Thailand’s gypsum resources based on Hotelling's concept of maximizing Net Present Value (NPV) of benefits accrued to the country. This study finds that under all assumptions and all scenarios, at a certain point in time, Thailand should stop exporting its gypsum and devote the remainder of its gypsum resources to domestic consumption only. In addition, Thailand should push gypsum price up to a certain level. The model determining gypsum consumption in Thailand and some countries imported gypsum from Thailand, which are the basis to determine the optimal extraction paths of gypsum in Thailand, is also developed. It shows that the price of gypsum had no effect on its consumption. In other words, the demand for gypsum might be highly inelastic. Finally, the long-term policies for Thailand to manage its gypsum resource are recommended.

M.Sc. (Environmental Management) / South Africa is exceptionally rich in biodiversity. South Africa has been recognized as the third most biologically diverse country in the world, and has three globally recognized biodiversity hotspots; namely the Cape floristic, the Succulent Karoo and the Maputaland-Pondoland regions (NSBA, 2004). South Africa’s seas straddle three oceans, and provide a range of habitats from cool water kelp forests, to tropical reefs and deep ocean abyss (NSBA, 2004). Unfortunately, due to various pressures, many ecosystems are in trouble: 34% of terrestrial systems, 82% of river signatures, 65% of marine biozones and 8 estuarine types are threatened (NSBA, 2004). Mining has been identified as one of the sectors impacting negatively on biodiversity; the other significant pressures being agriculture, afforestration, urban and industrial development, extractive fishing, alien invasives and climate change (NSBA, 2004). The concept of ‘biodiversity offsets’ is relatively new and there are only generic methods whichare ill suited to determine appropriate biodiversity offsets in the South African context. The rationale for biodiversity offsets in South Africa is two-fold: firstly, South Africa contains biodiversity that is unique globally; secondly, its ecosystems underpin socioeconomic development and delivery of important services such as the reliable supply of clean water, ecotourism and coastal protection. Land-intensive development poses a significant threat to the countries remaining biodiversity. South African policies have, over the past few years, increasingly prioritised the conservation of biodiversity and important ecosystem services (Department of Environmental Affairs and Development Planning, 2007). The motivation for this is a decline in global biodiversity (WWF & ZSL, 2012). Land use changes are the main motivation for identifying the need for creating a system within the planning process that tackles unavoidable and residual impacts to biodiversity. The implementation of EIA in South Africa in terms of the National Environmental Management Act (No. 107 of 1998) allowed for the formal evaluation of impacts to habitat, wildlife and other natural considerations to be done as a prerequisite for developers to receive approval for a project to go ahead (BBOP, 2000). The aim of this study was to compare the current South African biodiversity offset approach to that of offset banking and no net loss or net gain principles as a feasible and beneficial alternative. A structured interview process was conducted to ascertain current understanding and perceptions relating to biodiversity offsets, biodiversity offset guidelines and regulations, offset banking, relevant experience and perceptions to determine the current level of understanding in the mining sector and with environmental consultants. This aided in determining whether biodiversity offset practices in its current form in South Africa were understandable, can be implemented effectively and achieves the rationale of biodiversity offset banking.

Ph.D. / Except for agriculture, the mining industry is considered as not only the oldest but also the most important industry. Mining involves the removal of minerals from the earth's crust for usage by mankind. The disturbance during mining activities such as mining effluent has an effect on the natural aquatic environment. In any freshwater environment, the macroinvertebrates form a vital link between the abiotic envinronment and the organisms in higher trophic levels. It is thus true that specific environmental contaminants, such as mining effluent, may directly affect the survival of macorinvertebrates. The density and diversity of macroinvertebrates is in a direct relation with the water quality. For the purpose of this study, attention was given to the effects of gold and coal mine effluent on the macroinvertebrate fauna, as well as to the determination of metal accumulation from the water through the macroinvertabrates to fish. At Case Study Mine One, with an open water system, acidic conditions of the water caused a reduction in the number and diversity of macroinvertebrates. A closed water system, characteristic of Case Study Mine Two, presented a slightly more abudandant macroinvertebrate population than with the previous mine. The results lead one to conclude that the surface water in this study area is of a better quality. Case Study Mine Three had a complex water circuit and presented a greater number and diversity of macroinvertebrates, with the best water quality of the three mines investigated.

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A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Chemical Engineering, 2016 / In South Africa, the need for water treatment is increasing, especially in the mining sector. As active water treatment technologies are expensive, the mining sector has an increasing need for passive water treatment technology, with low maintenance and operating costs, yet efficient water treatment ability. Literature on passive water treatment suggests that these systems only offer a narrow range of treatment capabilities. Therefore, hybrid water treatment systems could be a solution to low-cost water treatment in South Africa. The Degrading Packed Bed Reactor (DPBR) is one of the units comprising the hybrid treatment group. The DPBR’s main action is to convert sulfates into sulfides and alkalinity. In practice, the main drawback of the DPBR is clogging. Clogging lessens the amount of Acid Mine Drainage (AMD) that comes into contact with Sulfur Reducing Bacteria (SRB) in the DPBR, thereby reducing the efficiency of the bioreactor. In this study, six small-scale DPBRs were constructed. Each was classified according to its unique organic source (manure, straw, vegetable food processing waste, wood shavings, chicken litter and a combined sample with layers of all the carbon sources). Synthetic AMD was fed through the six bioreactors for a period of three months. From the small-scale DPBRs, the permeability, sulfate, iron and pH of the exit samples were measured. On average, the carbon sources removed 50 % of the sulfates and 98 % of the iron from the fed AMD. The different carbon sources showed no significant difference between each other in terms their sulfate and iron removal. The range between the best performing carbon source and the poorest performing carbon source, in terms of sulfate removal, was 17%. For iron removal, the range between the best and poorest performing carbon sources was only 2%. It was found that the permeability of the carbon sources played a larger role in the efficiency of the DPBR than the type of carbon source used. Manure is highly effective in terms of pH improvement, sulfate and iron removal. However, this is at the expense of permeability, as its packing clogs very rapidly. Compost and straw have excellent permeabilities which do not change significantly over long timeframes. This is, however, at the expense of the remedial ability of the packing materials. The combined reactor, in every instance, offers a good compromise between these different behaviours. / GR2016

Master of Science in Engineering by advanced coursework and research: A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering Johannesburg, 2016 / The mining sector plays a significant role in the economy of Zimbabwe. The mining sector is the second largest contributor to the country’s GDP at over 20%. Zimbabwe as a country is endowed with abundant mineral resources. The top three commodities in terms of estimated resources are iron ore, coal and platinum with resources of 30 billion tonnes, 26 billion tonnes and 2.8 billion tonnes respectively. Zimbabwe’s vast mineral resources and reserves are of strategic importance to the Zimbabwe economy. Coal mining is one of the major economic contributors to the mining industry in Zimbabwe. The purpose of the study is to determine the optimal operational model for Makomo Resources from a mining and processing point of view. The study is based on a coal-mining project in the Zimbabwean mining industry. Makomo Resources is the largest privately owned coal mining company in the country, which has a mining licence to perform coal-mining activities in the north-west part of the Bulawayo Mining District of Zimbabwe. Makomo Resources applies a conventional strip mining method by means of truck and shovel to extract the coal reserves. Makomo Resources is supplying over 200,000 tonnes of coal per month to the local and export market. The mine has invested in USD20 million capital to commission a wash plant. The study investigates how to optimise the plant throughput by comparing two mining options: Mining Option 1 - crush and screen 2m power coal, crush & screen and wash a full 7m low ash coal seam and wash 2m of coking coal. Mining Option 2 – crush and screen 2m power coal, crush & screen a 3m low sulphur coal seam and wash low ash coal and coking coal of 4m and 2m respectively. The study investigated all the marketing, geology, mining and financial parameters in the Zimbabwean coal mining context. The study determines the appropriate mining methodology and explore to optimise the coal processing. Two financial models were developed to evaluate and compare the two proposed mining options, determine their feasibility and conclude the optimal mining model. Financial techniques were used to analyse and evaluate the two mining options. The financial models were used to analyse and evaluate the following:  The cashflow over the 10-year period.  The Net Present Value (NPV) and Internal Rate of Return (IRR) of each mining option.  The payback period of the washing plant.  Profitability Index per mining option. The NPV of a project determines the economic value of the mining project. The decision on a mining investment is mostly related to the NPV and IRR of the project. Discounted Cash flow (DCF) models were developed for both mining options that shows project cash in and out flows and calculates economic indicators, such as IRR and NPV. The NPV and IRR were the main methods for the evaluation of the two mining options. The resulting DCF models were developed in an Excel spreadsheet format designed for a 10-year Life of Mine (LOM) period. Mining Option 1 has a higher NPV of USD38.2 million in comparison to USD9.7 million for Mining Option 2. The IRR for Mining Option 1 was calculated at 48%, which is bigger than the IRR for Mining Option 2 of 26%. Mining Option 1 has a simple payback period and discounted payback period of 2.7 years and 4.9 years respectively. Mining Option 2 has a simple payback period and discounted payback period of 3.9 years and 11.9 years respectively. Mining Option 1 has a shorter payback period than Mining Option 2. Both mining options have a Profitability Index (PI bigger than one with Mining Option 1 and Mining Option 2 recording values of 1.87 and 1.18 respectively. Mining Option 1 has the better PI value and is therefore more profitable. Based on the economic evaluation, Mining Options 1 is by far more attractive than Mining Option 2, which results in a better return on the investment and profitability, therefore the preferred option. / MT2017