RESUMO
In this study, batch flotation tests were conducted to investigate the flotation of oxidized copper ore from the supergene zone of the Bounhas deposit. Utilizing the Response Surface Methodology (RSM) anchored on the Box-Behnken Design (BBD), the effects of parameters such as collector dosage, sulfidizing agent, copper oxide and iron oxide grades, and particle size on copper recovery were explored. ANOVA and response surface plots elucidated the complex behavior of copper flotation. Mineralogical analyses of the initial feed and postflotation tails were performed on an industrial scale using advanced Zeiss microscopy. The quadratic model with an R 2 of 94.49% accurately captured the impact of the process parameters on copper recovery. Among the key findings, the sulfidizing agent exhibited the most substantial quadratic effect, while the copper oxide grade had the most substantial linear impact on recovery. A pivotal observation was the link between copper losses in tailings and specific iron oxide-copper associations. Optimization identified conditions leading to a maximum copper recovery rate of 71.49%. When validated industrially, the model demonstrated robust applicability for analogous mineral processing contexts. The study's mineralogical insights enhance our understanding of the supergene zone mineralization and offer a solid foundation for subsequent in-depth mineral processing studies.
RESUMO
Underground mining operations present critical safety hazards due to limited visibility and blind areas, which can lead to collisions between mobile machines and vehicles or persons, causing accidents and fatalities. This paper aims to survey the existing literature on anti-collision systems based on computer vision for pedestrian detection in underground mines, categorize them based on the types of sensors used, and evaluate their effectiveness in deep underground environments. A systematic review of the literature was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify relevant research work on anti-collision systems for underground mining. The selected studies were analyzed and categorized based on the types of sensors used and their advantages and limitations in deep underground environments. This study provides an overview of the anti-collision systems used in underground mining, including cameras and lidar sensors, and their effectiveness in detecting pedestrians in deep underground environments. Anti-collision systems based on computer vision are effective in reducing accidents and fatalities in underground mining operations. However, their performance is influenced by factors, such as lighting conditions, sensor placement, and sensor range. The findings of this study have significant implications for the mining industry and could help improve safety in underground mining operations. This review and analysis of existing anti-collision systems can guide mining companies in selecting the most suitable system for their specific needs, ultimately reducing the risk of accidents and fatalities.
