Application of Digital Twin

Abstract

A digital twin in mining is a dynamic, real-time virtual representation of physical assets and operational processes that supports predictive modeling, optimization, and decision-making. This chapter examines the architecture, data integration strategies, modeling approaches, and practical applications of digital twins in geotechnical monitoring within mining environments. It outlines the concept of digital twins and their key advantages, including improved operational efficiency, enhanced safety, and increased sustainability. Core components of a digital twin include data layers such as sensor streams, geospatial information systems, and engineering models alongside integration frameworks based on application programming interfaces and modular services, as well as visualization tools such as 3D environments, dashboards, and immersive technologies. Data ingestion encompasses both real-time monitoring sources, including advanced sensing systems, and historical datasets. Modeling approaches are categorized into physics-based methods, data-driven techniques, and hybrid models that combine both paradigms. Digital twins enable a range of use cases, including predictive analysis of slope stability, monitoring of tailings storage facilities, optimization of ventilation systems, tracking of infrastructure deformation, and support for emergency response planning. This chapter addresses real-time analytics pipelines that incorporate streaming architectures, edge computing, and operationalized machine learning, and covers key considerations for model validation, uncertainty quantification, and data governance. Illustrative examples demonstrate practical implementation concepts, supported by comparative analyses of system architectures, latency considerations, and sensor network configurations. Case studies from mining operations highlight applications such as slope failure monitoring, tailings surveillance, integrated mine-wide digital twins, and emergency response scenarios. This chapter also includes implementation checklists and governance guidelines, with a focus on technical rigor and alignment with current best practices. It concludes with a comprehensive list of references.