Gabriel País

Artificial intelligence in the mining industry is key to improving safety, optimizing resources, and ensuring sustainable operations in complex environments. The main objective of this research is to develop an autonomous mobile station equipped with artificial vision and artificial intelligence to identify and track equipment, people, and animals in critical areas of mining operations, issuing real-time alerts to reduce occupational risks and improve operational control. The research is applied with an experimental approach, designed to validate the effectiveness of the proposed system in real open-pit mining environments. The proposed methodology consisted of five stages: (i) Selection of data collection equipment, (ii) Definition of the positioning scheme, (iii) Incorporation of the communication system, (iv) Data processing and transformation, and (v) Equipment identification and tracking. The results showed an average accuracy of 98% in the validation and 95% in the test, achieving perfect performance (100%) in key categories such as excavators and drills, highlighting the potential of this technology to transform mining towards safer and more efficient standards.

A pivotal aspect in the evaluation of mining projects is the classification of mineral resources, which directly influences the definition of mineral reserves and significantly impacts mine planning and operational stages. However, the current classification methodologies often need specificity regarding the methods and parameters employed and heavily rely on the qualified/competent person’s judgment. This study addresses these gaps by proposing a pioneering fuzzy approach to assess grade and tonnage uncertainties. By allowing for overlapping resource categories and directly incorporating economic, geological, metallurgical, environmental, and operational criteria, we aim to provide tools for decision-making and for the final classification and public disclosure of mineral resources and reserves. We demonstrate the potential of our proposed methodology through an application to an iron ore deposit case study and through a detailed discussion on its uses, contributions, strengths, weaknesses, and on whether it complies with international reporting codes.