340 Digital twins in mineral processing also demonstrate relevant principles: process DTs can close control loops under variable ore conditions (e.g., adaptive dosing control informed by soft sensing), which matters for TSFs because tailings properties are influenced by upstream variability in ore characteristics and processing decisions (grain size distribution, density, chemistry). Therefore, where feasible, TSF digital twins should connect to mine-to-mill information flows rather than operate as isolated geotechnical replicas. This research proposes a layered DT design framework explicitly tailored to TSF risk governance. The DT is positioned as an operational risk‑governance instrument that supports continuous monitoring, scenario analysis, and evidence‑based decision‑making with traceability. The innovation is not simply the use of digital models, but the integration of (i) multi‑source sensing and data governance, (ii) coupled modelling aligned to monitoring, (iii) analytics that translate evidence streams into actionable risk signals, and (iv) workflows that embed decisions and actions into a persistent evidence trail. The framework is designed to be implementable as an engineered system: it defines functional layers, input/output contracts, update logic, and governance integration points. It is also designed to be empirically testable: it includes a pilot validation roadmap and performance indicators that can be measured in real deployments without making undocumented claims. This directly responds to specialist feedback that high innovation and strategic relevance must be complemented with empirical validation and measurable performance evidence. Layer 1: Sensing and Data Acquisition. The sensing layer consolidates geotechnical instrumentation (pore pressure, deformation, settlement), hydrogeological and water-management measurements, survey and surface deformation data, inspection logs, and where applicable remote sensing such as UAV photogrammetry. The design requirement is to ensure data completeness, timestamp integrity, calibration metadata, and context tagging. Data acquisition should also include operational state variables that influence TSF behavior, such as deposition schedules, pond elevation, reclaim rates, and relevant construction activities. Layer 2: Integration and Data Governance. This layer provides the “single source of truth” across heterogeneous datasets by enforcing standardized schemas, metadata, QA/QC checks, and interoperability services. It supports lifecycle information management, enabling longitudinal analysis and ensuring that evidence used for decisions is retrievable and auditable. Integration should support both time-series and spatial data, linking sensor readings to GIS/BIM representations where appropriate, and linking inspection findings to specific TSF zones. Layer 3: Modelling and Simulation. The modelling layer maintains coupled geotechnical and hydrogeological models that represent TSF behavior and support scenario analysis, including seepage and consolidation representations, stability analyses, and deformation simulations. The design principle is to align models with monitoring: boundary conditions and parameter sets should be updateable using monitored data, while preserving model versioning and assumptions for traceability. Layer 4: Intelligence and Analytics. This layer applies statistical and machine-learning methods for anomaly detection, forecasting, and probabilistic risk inference. Analytics are designed to support early problem recognition and reduce cognitive load; they should not replace engineering judgment. Outputs should include uncertainty bounds and explainability artifacts where feasible. Layer 5: Visualization and Decision Support. This layer provides role-based dashboards, spatial maps, and scenario views that communicate current state, trends, and risk signals. Crucially, it embeds governance workflows: escalation protocols, action assignment, corrective action tracking, and documentation of decisions with linked evidence. The DT thus preserves an evidence trail supporting assurance and disclosure requirements.
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