Interoperability requires consistent metadata conventions (units, coordinate systems, timestamps), governed vocabularies for inspection observations, and standardized interfaces for ingesting data into the DT repository. Integration services should preserve raw data and generate curated datasets for analytics, maintaining provenance links. Lifecycle information management is crucial because TSF governance extends into closure and post-closure; the repository should preserve historical states and model versions to support longitudinal analysis, reproducibility, and assurance. The operational impact of a TSF digital twin depends on socio-technical integration: People: interdisciplinary collaboration; clear roles for triage, interpretation, escalation, and closure. Process: embedded governance workflows, escalation thresholds, response protocols, documentation requirements, review cadences. Technology: reliability, cybersecurity, integration with legacy systems; staged adoption from data integration to model synchronization to analytics. These considerations reflect the specialist guidance: high innovation and strategic relevance require empirical validation and operational integration to achieve demonstrable impact. Limitations must be recognized. This research is architecture-oriented and does not present quantified operational improvements from a live TSF deployment. TSF systems are inherently uncertain; sensor networks may be incomplete; models may require calibration; computational constraints can limit real-time simulation; and organizational readiness can constrain adoption. Future research should prioritize empirical pilots and case studies to quantify detection lead time, false alarms, data latency, and decision traceability. Additional directions include uncertainty-aware modelling, standards for DT data models, integration of remote sensing with internal state estimation, and evaluation of DT contributions to governance conformance and auditable ESG reporting. In conclusion, this research provides an expanded and governance-oriented DT design framework for advanced TSF management. The framework integrates multi-source sensing, interoperable data governance, coupled geotechnical–hydrogeological modelling, AI-assisted analytics, and decision workflows that preserve evidence trails. It is aligned with modern tailings governance expectations and provides a pilot validation roadmap and metric suite to support future empirical evaluation and scalable adoption. REFERENCES Bhambare, P. S., Kaulage, A., Darade, M. M., Murali, G., Dixit, S. M., Masthan Vali, P. S. N., Chougule, S. M., Kurhade, A. S. and Mali, C. N. (2025). Artificial intelligence for sustainable environmental management in the mining sector: A review. Applied Chemical Engineering. 8(3).
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