532 Recent research supports the deployment of private LTE networks as a robust alternative for industrial environments [5], [6]. Calle and Chero [5] proposed a management model for private LTE service architectures in mining operations, while Chau [6] analyzed LTE performance in harsh environments, demonstrating low-latency communication capabilities suitable for real-time control systems. These findings align with industry practices increasingly adopting private LTE infrastructures to support drilling telemetry and fleet integration. 2.3 Data Integration and Interoperability Despite the proliferation of sensing and connectivity technologies, interoperability challenges remain a major barrier to realizing digital drilling’s full potential. Mining fleets often comprise equipment from multiple manufacturers, each with proprietary control systems, data protocols, and heterogeneous PLC architectures. This fragmentation inhibits comprehensive data integration and cross-rig analytics. Standards such as OPC DA, OPC UA, and OPC HDA have emerged as key enablers of interoperability [7]. The OPC Foundation specification [1] formalizes mechanisms for accessing real-time and historical industrial data. Gonzales and Córdova [7] further discuss the standardization of OPC interfaces for SCADA interoperability, highlighting its importance for multi-platform environments. 2.4 Advanced Analytics and Machine Learning The accumulation of integrated, high-resolution drilling data has opened opportunities for advanced analytics and machine learning. A growing body of research focuses on predicting drilling performance, optimizing bit selection, estimating ground hardness, and detecting anomalies indicative of equipment degradation or geotechnical variability. Although standardized benchmarking methodologies are still evolving, energy-intensity-based models have demonstrated potential for improving drill-and-blast alignment and reducing comminution energy consumption [3], [9]. These approaches align with Mining 4.0 frameworks that seek to transform raw telemetry into predictive and prescriptive intelligence. 2.5 Digital Decision Support and Artificial Intelligence Beyond analytics, recent trends emphasize the integration of artificial intelligence (AI) into operational decision support systems. Conversational AI, natural language querying, and augmented analytics platforms facilitate rapid access to operational intelligence for technical and managerial stakeholders. AI-based dashboards and virtual assistants enable drilling supervisors to query performance indicators, investigate anomalies, and receive proactive recommendations without extensive statistical expertise. Automation platforms such as n8n [10] and AI-driven workflow engines facilitate the orchestration of industrial data pipelines and conversational analytics. However, most implementations remain at early maturity levels, with limited large-scale case studies demonstrating full integration of AI into operational drilling environments.
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