Por: Luis Álvarez Mendoza y Carlos Siccha Maco, Compañía Minera Antamina.

Abstract

This technical paper embarks on an in-depth exploration of the intersection between cutting-edge technology and the mining industry, with a critical look at how a robust data architecture can serve as a powerful catalyst for the effective use of Machine Learning (ML) and Artificial Intelligence (AI). Through a focused approach to the Antamina mining operation, this report articulates a detailed understanding of the challenges and opportunities present in the mining data management landscape. 

The report recognizes that standardization in data formats and definitions, the ability to analyze large volumes of data from various sources, data sharing and access, information security, and the use of advanced analysis techniques are some of the critical challenges faced by the mining sector today. Through a comprehensive review of technical papers, expert interviews and analysis of collected data, it is postulated that a robust data architecture can effectively address these challenges. 

This report provides not only a meticulous analysis of existing data and a theoretical overview, but also a tangible and concrete proposal for the implementation of a data architecture at Antamina. A strategic roadmap is developed that sets the path for the adoption of a robust data architecture, while illustrating how ML and AI technologies can improve operational efficiency, safety and decision making in the mining industry. 

Finally, a vision for the future of mining is outlined, in which a well-structured data architecture and advanced data analysis techniques, such as ML and AI, play a crucial role. It is argued that the adoption of these technologies has the potential to redefine mining operations, boosting productivity, reducing risks and creating a more sustainable environment. This technical paper seeks to open the door to a future in which mining is driven by innovation and intelligence, which could ultimately lead to Mining 4.0.