Appraisal of Nuclear Energy as an Alternative Option in South Africa’s Energy Scenario: A Multicriteria Analysis

South Africa is being confronted with an irregular power supply, leading to persistent load shedding due to aged and unreliable coal-fired power plants. Connected with coal as a generating source for electricity from fossil fuels are environmental concerns such as emissions of greenhouse gases and climate change impacts. Nuclear energy can allay the country’s dependence on coal as a source of energy. This article, therefore, reviews the feasibility of nuclear energy using a multicriteria analysis technique. A combination of Strengths, weaknesses, Opportunities, and Threats (SWOT) analysis and Analytical Hierarchy Process (AHP) was used to evaluate the external and internal factors that could either positively or negatively affect the country’s nuclear energy expansion drive. From the analysis, the country’s enabling laws and regulatory framework recorded the highest score of 39.2% under the strengths for the sector. In the case of the weaknesses, the high cost of construction and long construction framework recorded the highest weight, of 50.47%. Energy export and demand under the opportunities recorded a weight of 52.09%, ranking it as the highest opportunity for the sector. Seismic events were identified as the biggest threat for nuclear power expansion in the country, and the experts assigned a weight of 42.5% to this factor. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Acknowledgments: The authors acknowledge with thanks the Centre for Postgraduate Studies and the Postdoctoral Fellowship/Research Committee of the Cape Peninsula University of Technology, and the Directorate of Research and Innovations of the University of Venda

Influenza/common cold within the Slebi Phikwe Nickel-Copper exploitation environment, Botswana

No Abstract.AJPHERD Vol. 13 (4) 2007: pp. 473-49

Multiple data clustering algorithms applied in search of patterns of clay minerals in soils close to an abandoned manganese oxide mine

This paper proposes a multi-level approach to data clustering and provides a novel approach to characterisation of clay soils by, effectively, looking at the same clay sample from different angles. It is shown that using this approach can help avoid detection of spurious clusters or skipping vital natural grouping in data. Muscovite, illite and kaolinite were identified by X-ray diffraction (XRD) in <4 mu m fraction of soil samples obtained from the periphery of an abandoned manganese oxide mine and semi quantified as major, minor and trace. Based on information inherent in the data attributes, useful rules for grouping the samples were generated and with the aid of multiple data clustering, applied to characterize the clay minerals occurrences in the soils. The paper found that the presence of large quantities of illite and kaolinite heavily influence the formation of clusters. When the most influential variables-LJ and KJ were taken out, the resulting model showed that muscovite traces play a vital role in initial cluster building and the importance matrix of inputs suggested inter-dependence between muscovite, kaolinite and illite traces as well as between them and minor quantities of illite. Dwelling on aspects of clay mineralogy and modelling sciences, the paper marks a significant departure from the conventional approaches to clay characterisation by showing how effectively data mining methods can be adopted in the area. For a successful approach to characterisation of clay minerals in African soils, the paper recommends to set-up data repositories that will provide scientific data sources and forums in a multi-disciplinary environment. This is particularly important as capturing interesting patterns requires expert knowledge describing the emerging natural groupings. (C) 2009 Elsevier B.V. All rights reserved

Principal component analysis to evaluate the spatial variation of major elements in kaolin deposit

This paper explores the variations in major elements concentrations in kaolins from four different deposits in Botswana. The data were obtained from four different kaolin deposits with an additional four-class label based on particle sizes of the rock – providing a natural comparative basis between detected structural features with those of the original data attributes. Using principal component analysis (PCA), the paper reduces the data dimensionality and establishes inherent distinctive attributes of major elements accounting for the highest variation in chemical compositions of the kaolins. The principal components extracted are validated using graphical data visualization tools applied on a 28x11- dimensional data matrix of the oxides of Na, Mg, Al, Si, P, K, Ti, Mn and Fe, and loss on ignition (LOI). The validated results show that structures based on three retained components exhibit clearly discernible variations within the samples. Discretisation of the particle sizes is highlighted as both a challenge and an opportunity and it is recommended that it be used as a tuning parameter in gauging kaolin variations across samples and in validating new predictive modeling applications. Successful applications will depend on how clay and data scientists keep track, synchronise and share information relating to potentially dynamic data such as the impact of discretisation of kaolin particle sizes. DOI: http://dx.doi.org/10.4314/bcse.v29i1.

Self-organising feature map (SOFM) algorithms applied to manganese mineralisation in soils close to an abandoned manganese oxide mine

This paper proposes a multi-level self-organising map (SOFM) approach in studying manganese minerals interdependence in soils close to an abandoned Mn oxides mine. Multiple SOFM algorithms for data clustering were applied on Mn minerals identified by X-Ray diffractometry contained in four hundred soil samples from the periphery of the abandoned mine. Emerging structures from the Mn minerals (bixbyite, cryptomelane, ramsdellite, pyrolusite and braunite) were analysed using SOFM and two of the minerals (cryptomelane and braunite) were found to be influential in cluster formation. The findings of the study demonstrate the suitability of data mining in characterising Mn minerals interdependence in soils close to the abandoned Mn oxides mine and highlight the underlying, issues of which applicants of the method need to be aware of

Physico-chemical and Mineralogical Characterisation of Subsurface Sediments around Gaborone Landfill, Botswana

Studies were carried out on subsurface sediments obtained around the Gaborone landfill area Botswana, in order to characterize their mineralogy and physico-chemistry, appraise any contaminant inputs from the landfill and assess their ability to attenuate contaminants from the landfill. Physico-chemical properties investigated included particle size distribution (PSD), moisture content, bulk density (Db), porosity, surface area, pH, electrical conductivity (EC), and cation exchange capacity (CEC). The mineral phases occurring in the subsurface sediments were identified by use of X-ray powder diffraction (XRPD) technique. Grain size analyses of the subsurface sediments revealed sandy loam, silty loam, and silt as the main textural classes. The values obtained for Db and porosity is reflective of the textural classes of the different samples. Moisture content values of the studied samples are characteristic of sediments in semi-arid environments. Apart from two samples, which were acidic, others were alkaline. The EC values were indicative of significant amounts of total dissolved salts (TDS) especially along the eastern corner of the landfill. Cation exchange capacity values were relatively low. Minerals identified by XRPD included quartz, microcline, muscovite and kaolinite in bulk subsurface sediments, whereas kaolinite, smectite and /or illite occurred in the clay fractions. Sediment pH, EC and TDS suggests inputs from the landfill. The sediments seem to have a low attenuation capacity as a result of their physico-chemical and mineralogical properties. Further geophysical and hydrogeochemical research is needed to verify if the Gaborone landfill area is environmentally safe. Journal of Applied Sciences and Environmental Management Vol. 8 (1) 2004: 49 - 5

Graphical visualisation and domain partitioning of minerals in clay fraction of soils from Capricorn District, South Africa

Modeling techniques were used to study minerals in clay fraction of soils from Capricorn District Limpopo Province, South Africa. Minerals in the clay fraction of soils were identified by X-ray diffraction (XRD) technique and semi-quantified. The minerals were then subjected to a combination of exploratory data analysis (EDA), graphical visualisation and domain-partitioning techniques in order to determine their cross-influence to one another in terms of abundances. Quartz and kaolinite were major dominant minerals in the soils; smectite, feldspar and mica were in minor to trace quantities. Consensual associations among other traces and high quantities of minerals were detected. Evidence of relationship using EDA portrayed general skewness in favour of quartz and kaolinite. Quartz remained dominant in the soils but with a consistent high probability of co-existence with kaolinite. Where there is low quartz content, kaolinite increased with the drop in quartz made up for by a combination of smectite, mica and feldspar. The nested nature of interaction also revealed indirect relationship between quartz and mica. The tree model, which yielded 100% accuracy, showed smectite as the first important mineral in identifying whether there is high, medium or low quartz content in the sols. Down the line the model relies heavily on both mica and kaolinite. Collating the minerals contents and data modeling procedures, inter alia, it could be inferred that the weathering of feldspar and mica may have an impact on the mineralisation of kaolinite and smectite; which are both important minerals in several agricultural applications