Highly-motivated employee with desire to take on new challenges. Strong worth ethic, adaptability and exceptional interpersonal skills. Adept at working effectively unsupervised and quickly mastering new skills. Dedicated and focused individual who has and still is developing research qualitative and analytical skills. With my experience in academia and industry, I have the ability to develop goals, objectives and implement strategies to yield result. Proven ability to conceptualise problems and develop well-reasoned and integrated solutions are evident throughout my research degree and work experience. I am as familiar with ore comminution, extraction and refining as I am with metals' microscopic and macroscopic properties. Furthermore, I am vested in mineral processing, physical metallurgy, and environmental management practices. My training has taught me how to draw, read, understand and interpret processing flowcharts and make recommendations for improvement and perform metal reconciliation across each process stream. I In addition, I possess working knowledge of Microsoft office suite, statistical software package for mineral engineers and fluent in English language.
Development of a geometallurgical coarse ganguerejection indicator for gold ores.
Coarse gangue rejection upgrades low and uneconomic ore grades before intensive downstream processes. The Gangue Rejection Amenability Test (GRAT) is a characterisation method used to assess the suitability of gold ores for coarse gangue rejection through density and size separation. Various studies conducted with the Gangue Rejection Amenability Test (GRAT) indicated a variable coarse gangue rejection response across the different types of gold ore deposits tested. The variability identified has made the current project's objective to establish an early indicator that could be used to determine ores that are likely to respond better to coarse gangue rejection to avoid wasting resources and time. The early indicators could be used as the first stage in determining whether an ore is suitable for Coarse Particle Gangue Rejection (CPGR) before conducting an entire GRAT test work to ascertain the mass and metal recovery at both reject and accept streams, respectively. The result of this work could contribute to the growing knowledge of geometallurgy, which can have applications in predicting mineral process response to reduce energy, reagent and water consumption. In addition, better revenue forecasting and estimations can be made based on mine productivity, and measures can be taken early to mitigate any downside to productivity. The initial studies have identified using a machine learning approach such as Shapley Regression analysis with Random Forest to identify and quantify variable that influences the GRAT performance from the previous that is currently in the database. Variables such as Au, Fe, S and As feed grades significantly contributed to the coarse gangue rejection response. Based on these findings, The Shapley Regression with Random Forest can be used to evaluate which geometallurgical characteristics significantly contribute to the coarse gangue rejection response. The identified characteristic can then be used as an early indicator for coarse gangue rejection amenability. This forms the foundation for the next face of this project.
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