530 DIGITAL TRANSFORMATION OF THE MINING DRILLING PROCESS: MULTIBRAND DATA INTEGRATION, REAL-TIME MONITORING, TARGETED REPORTING, AND ARTIFICIAL INTELLIGENCE APPLICATION L.A. Messa1, F.J. Guerra2 1OT-TICAR Department Cuajone Mine – Southern Peru Copper Corporation ABSTRACT The growing global demand for strategic minerals required by the energy transition challenges the mining industry to deliver resources faster, with greater operational intelligence and under increasingly stringent standards of responsibility and transparency. Drilling represents a critical leverage point within the mining value chain, as it directly influences fragmentation quality, downstream comminution energy consumption, and overall operational stability. Yet in many large-scale operations, high-resolution control system data remains underutilized due to interoperability barriers, technological heterogeneity, and field connectivity constraints. This paper presents the design and implementation of an interoperable and scalable digital model for production drilling in a large-scale copper operation, integrating heterogeneous controllers and multivendor fleets through open industrial standards and an edge-computing architecture incorporating store-andforward mechanisms to ensure data integrity during communication disruptions. The proposed architecture enables real-time acquisition of operational variables, centralized storage, online monitoring, per-hole traceability, and advanced analytics focused on productivity and energy efficiency. A locally deployed AI-assisted query layer is introduced to enable natural-language interaction with historical and real-time operational data while maintaining industrial cybersecurity requirements. Results demonstrate improved data availability, reduced decision latency, and enhanced ability to correlate specific energy per drilled meter with geological domains and operational performance. The principal contribution is a replicable framework that transforms raw control signals into actionable operational intelligence, strengthening data governance, technical efficiency, and operational transparency. Such capabilities constitute essential enablers for a more productive, predictable, and responsible mining sector capable of meeting the global challenge of supplying critical minerals. KEYWORDS Digital Mining, Drilling Optimization, Industrial Interoperability, Real-Time Operational Intelligence, Edge Computing, Artificial Intelligence Applications 1. INTRODUCTION The accelerating global demand for critical minerals, essential for electrification, renewable energy systems, and large-scale decarbonization, poses a structural challenge to the mining industry: how to expand production while simultaneously advancing efficiency, transparency, and environmental stewardship. Copper and other strategic minerals stand at the core of the energy transition. Yet increasing their supply is no longer driven solely by new discoveries or capital-intensive expansion projects. It increasingly depends on the industry’s capacity to reduce operational variability, enhance process predictability, and transform the vast volumes of operational data generated across the value chain into actionable intelligence. Within this context, production drilling constitutes one of the most influential technical leverage points in open-pit mining operations. Drilling performance directly shapes blast outcomes, determines rock
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