Conveyor belts are critical equipment in mining operations, where continuous and reliable material transport is essential for production efficiency. This systematic review aims to analyze computer vision-based techniques applied to conveyor belt condition monitoring. Following PRISMA guidelines, a search was conducted in the Scopus and Web of Science databases, and 80 studies were selected after applying predefined eligibility criteria. These studies were synthesized through quantitative bibliometric methods and structured qualitative thematic categorization. The findings reveal a significant increase in scientific output after 2020, as well as its geographic distribution and potentially the most influential contributions. The main research lines focus on damage detection, deviation detection, and foreign object detection. A clear transition is also observed from traditional image processing methods—such as filtering, segmentation, and geometric analysis—toward deep learning models, including YOLO, CenterNet, and hybrid architectures, with improvements in precision, speed, and stability. Nevertheless, challenges remain related to datasets representativeness, the heterogeneity of evaluation protocols, and variability in operational conditions. Finally, opportunities for advancement are identified through multimodal datasets, adaptive models, and lightweight solutions that facilitate integration into asset management systems and support scalable industrial adoption.

The increasing scarcity of freshwater in mining regions of Chile has promoted the use of low-quality water as an alternative in flotation processes, significantly modifying their operating conditions. In particular, high salt concentrations and the presence of dissolved ionic species may interfere with the adsorption of collectors on chalcopyrite, thereby reducing its hydrophobicity. In this context, the present study analyzes the adsorption characteristics and the mechanistic role of selected representative ionic species on the chalcopyrite surface. To this end, simulations based on density functional theory (DFT) were employed to describe the interaction between the chalcopyrite (112) surface and Na⁺, Ca²⁺, Mg²⁺, and OH⁻ ions. After geometric convergence of the optimized structures was achieved, adsorption energies, charge redistribution based on Mulliken population analysis, and the final structural configurations were evaluated for each case. The results revealed clearly differentiated behaviors among the ionic species considered. The OH⁻ ion exhibited a localized and specific interaction with metal-centered sites. By contrast, Ca²⁺ and Mg²⁺ show stable adsorption near sulfur atoms, indicating a higher affinity that may lead to the occupation or blocking of active surface sites. Meanwhile, Na⁺ displays a weak interaction without inducing significant structural modifications. Overall, these findings provide an atomistic-level interpretation of how ionic species present in low-quality water can influence the surface reactivity of chalcopyrite under flotation operating conditions.