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Mill-to-Melt Energy Efficiency Opportunities
Published in Sheila Devasahayam, Kim Dowling, Manoj K. Mahapatra, Sustainability in the Mineral and Energy Sectors, 2016
Geometallurgy facilitates the “smart blasting” approach to target the sections of the ore body with the highest ore-grade concentration (Bye, 2011) and reduces energy use per tonne of metal by 10%–50% (CRC ORE, 2011). The selective/smart blast design technology raises the grade of ore being fed to the crusher and grinding mill by improving resource characterization and reducing the net total energy consumed at the crushing and grinding stages compared to conventional blasting (Powell and Bye, 2009). The benefits include: Less energy is required to crush the ore to the same product size if the feed-size to the primary crusher is decreasedAdditional macro- and micro-fracturing within individual fragments from the blasting makes fragments easier to fracture further, using less energy in the crushing and grinding phaseAn increased percentage of relatively small mineral particles can bypass stages of crushingSoftware packages assist in designing effective blasting techniques, including analyzing and evaluating energy, scatter, vibration, damage, and cost
Comparison between regression models and neural networks applied to forecast geometallurgical variables
Published in Christoph Mueller, Winfred Assibey-Bonsu, Ernest Baafi, Christoph Dauber, Chris Doran, Marek Jerzy Jaszczuk, Oleg Nagovitsyn, Mining Goes Digital, 2019
F.G.F. Niquini, J.F.C.L. Costa
Geometallurgy can be defined as the discipline which analyzes geological, mineralogical, chemical, physical and environmental data collected from a mineral deposit, aiming at explaining the factors affecting ore recovery at a processing plant. It can consider economical parameters, such as ore recoveries and concentrate grades or environmental variables, as water consumption, energy and tailings generation. More often feasibility studies embed geometallurgical variables, having in mind the benefits achieved with this practice: a better financial adherence in a project or in a mine under operation; a better use of the ore; and a better management of environmental impacts caused by the mine or the plant.
Utilisation of geometallurgical predictions of processing plant reagents and consumables for production scheduling under uncertainty
Published in International Journal of Mining, Reclamation and Environment, 2023
Christian Both, Roussos Dimitrakopoulos
Geometallurgy aims to describe, model and exploit the relationships between spatially distributed rock characteristics to be extracted from mineral deposits and their impact on processes further downstream in a mineral value chain. Geometallurgical relationships have long aided the design and risk assessment of processing plants [1–4]. However, the impacted processes are not restricted to metallurgical processing plants; rather, they can comprise activities in mines, waste dumps, tailings, material transport, smelting, and others. Today, geometallurgy is seen as interdisciplinary approach to maximise the economic value and reduce the technical risk of the entire value chain [5,6]. One important aspect of the modern geometallurgical approach is the incorporation of geometallurgical components, as well as their geological uncertainty, into mine production scheduling [7–9].
Geometallurgy-oriented mine scheduling considering volume support and non-additivity
Published in Mining Technology, 2022
Pedro Henrique Alves Campos, João Felipe Coimbra Leite Costa, Vanessa Cerqueira Koppe, Marcel Antônio Arcari Bassani
Geometallurgy is the integration of geological, mining, metallurgical, environmental, and economic information into spatial models through geometallurgical variables (Dowd et al. 2016). Metallurgical recovery is a geometallurgical variable, historically inputted in the spatial mining blocks as a constant mean value of the processing plant’s efficiency. As geometallurgy knowledge progressed, it has been clarified that geometallurgical variables are related to the interaction among geological/physical/mineralogical/chemical properties and industrial processes (Lishchuk et al. 2020). Hence, estimation of metal recovery based on chemical assays or quantitative mineralogical information became more common (Lishchuk and Pettersson 2021). A function which relates the process variable with primary-geological variables is a regression model. The grade-recovery regression plot is often used to display the metallurgical grade-recovery relationship (Dunham and Vann 2007).
A geometallurgical study of flotation performance in supergene and hypogene zones of Sungun copper deposit
Published in Mineral Processing and Extractive Metallurgy, 2021
Ataallah Bahrami, Yosef Ghorbani, Jafar Abdollahi Sharif, Fatemeh Kazemi, Morteza Abdollahi, Abbas Salahshur, Abolfazl Danesh
Geometallurgy combines both geological and metallurgical information to create spatially based predictive model for mineral processing plants (Lamberg 2011). Although geometallurgy is not a new science, it has newly caught the attention of mineral processing industry (Walters and Kojovic 2006). Geometallurgy should be considered as a powerful tool for efficient utilisation of resources and proper risk management, e.g. adaptation of the process to variations of ore, ‘what-if’ analysis of alternative production strategies, forecasting of financial results, and assessment of environmental impact. The reason for that is laying in acknowledgment of ore body heterogeneity and proper knowledge of the variability of ore body properties over the deposit (Korolev et al. 2017). The use of geometallurgy applications will result in optimisation of production planning and risk reduction in mineral processing operations. Studies have shown that the biggest factor in the failure of mineral processing projects is the mistakably identification and estimation of resource, and the second factor is the lack of proper design of the processing and metallurgical plant for the intended mineral deposit. The statistics indicate that inappropriate geological studies, metallurgical risks, and the wrong design and the mistaken selection of processing equipment, are the reasons for 76% of project failures (Lund and Lamberg 2014).