Centinela Mine, part of the Antofagasta Minerals Group, is located in the Antofagasta region, 15 km from Sierra Gorda and 60 km south of the city of Calama. The deposit is located at an average altitude of 2,300 meters above sea level. The mining system used in the Centinela mining district is open pit.

Currently, mining operations generate a growing and valuable volume of data from heavy equipment such as CAEX, shovels, and drills, as well as from the processing plant. However, the potential of this information is not yet fully exploited, as it is scattered across multiple systems, making integration and efficient historical analysis difficult.

This situation represents a strategic opportunity to evolve toward intelligent data management, implementing solutions that allow existing information to be consolidated, analyzed, and transformed into clear operational indicators, predictive models, and precise action plans.

Through this integration, it is possible to strengthen decision- making, optimize maintenance planning, and generate sustainable value throughout the asset lifecycle, driving more efficient, safer operations based on quality information.

Pilots have been conducted with data analysis and condition monitoring, but today, mature AI solutions are sought that allow real-time data to be integrated with management systems, anticipating failures and optimizing resources. The company requires technologies that transform existing data into prescriptive decisions, reducing reactivity and stabilizing the maintenance process. The specific challenge is to implement intelligent systems that, through accessible sensorization and predictive models, enable:

• Anticipating decisions in a context of long and critical maintenance cycles.
• Focusing interventions based on actual condition rather than calendar time.
• Reducing losses from unplanned downtime and optimizing the use of spare parts.
• Compiling historical information for easy data access.

Objectives

Implement Artificial Intelligence solutions that transform Minera Centinela’s mine maintenance model, migrating from a reactive approach to a predictive and prescriptive one, maximizing the availability of critical equipment and optimizing maintenance resources, task automation, intelligent assistants, or decision support systems, etc.

Scope

AI solutions applied to historical and real-time data from mining equipment. Covers everything from anomaly detection to prescriptive maintenance. Priority is given to scalable tools that can be integrated with existing systems. The solution must focus on mine maintenance (corrective, preventive, predictive, administration and control, resource management, and technical support).

Companies, startups, institutes, universities, research centers, and other legally established entities in accordance with the laws of their country of origin are eligible to apply. Natural persons (individual applicants) are excluded from this call.

We extend this invitation to foster collaboration and leverage technological and/or business capabilities among companies, entrepreneurs, and other entities. For teams or consortia, the lead institution must be clearly identified in the application.

En esta ocasión no se considerarán consultorías, asesorías y estudios de ingeniería.

The proposed solution must meet the following requirements:

• Mature solutions, tested in a mining environment, TLR ≥7.
• Pilot implementation times must be less than 4 months.
• The solution must ensure that it poses no risk to people, equipment, or the environment.
• The technological solution must have verifiable scalability.
• It is desirable that the technological solution be autonomous, requiring no personnel intervention during operation, and that it also be versatile, demonstrating robustness and adaptability to different conditions typical of mining operations (wind, solar radiation, dust, limited connectivity).
• It is desirable that the solution include an estimate of the impact of its implementation on the process.