338 DIGITAL TWIN DESIGN FRAMEWORK FOR ADVANCED TAILINGS MANAGEMENT IN MINING OPERATIONS *Y.P. Carrasco1 1Department of Engineering, Pontificia Universidad Católica del Perú, Perú, (*Presenting author: yannick.carrasco@pucp.pe) KEYWORDS Digital Twin; Tailings Storage Facilities; Geotechnical Risk; Mining Digital Transformation; Sustainable Mining EXTENDED ABSTRACT Tailings Storage Facilities (TSFs) are among the highest-consequence assets in modern mining because their failure modes can result in rapid loss of life, large-scale environmental contamination, and long-duration liabilities. Following major global tailings incidents, the Global Industry Standard on Tailings Management (GISTM) elevated expectations for integrated knowledge bases, risk-based monitoring, governance accountability, emergency preparedness, and public disclosure across the full TSF lifecycle. Yet, in many operations, tailings management still relies on fragmented data environments, instrumentation telemetry, inspection logs, geotechnical models, water balance records, and operational controls managed in partial isolation. This fragmentation constrains early problem recognition, weakens scenario-based risk assessment, and reduces the traceability of decisions required for assurance and ESG reporting. This research presents a governance-oriented Digital Twin (DT) design framework for TSFs, positioning the DT as an operational risk-governance instrument rather than a visualization dashboard. The framework integrates multi-source sensing, interoperable data governance, coupled geotechnical–hydrogeological modelling, AI-assisted anomaly detection and forecasting, and decision-support workflows that preserve evidence trails. Building on a qualitative synthesis of the state of the art in geotechnical digital twins, TSF monitoring technologies, and DT+ML early-warning architectures, the research articulates design contracts for each layer, proposes a pilot validation roadmap, and defines performance indicators that enable quantitative evaluation without overstating maturity. The contribution is an implementation-oriented reference architecture and validation plan to support safer, more transparent, and more responsible tailings management in Mining 4.0 contexts. The mining sector faces a structural challenge: to deliver minerals required for societal development while meeting higher standards of safety, environmental protection, and social legitimacy. Tailings management sits at the center of this challenge because TSFs are large, continuously evolving earth structures that store fine-grained residues and process water over long horizons. The technical complexity of TSFs arises from multi-physics interactions, including seepage and pore-pressure evolution, consolidation and desiccation, cyclic loading and seismic demand, deposition sequencing and beach geometry, and climate-driven variability in water balance. These drivers evolve over time and can interact in non-linear ways. Consequently, effective TSF management requires continuous situational awareness, disciplined engineering interpretation, and governance processes capable of acting on weak signals before they escalate. Industry 4.0 technologies such as IoT, big data, 5G connectivity, cloud/edge computing, and artificial intelligence, are accelerating the transition toward smart mines and data-driven operational governance. In smart mines, multidisciplinary datasets (geology, exploration, geometallurgy, environment, and survey) increasingly function as mining big data with high volume, variety, velocity, veracity, and value.
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