Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent

• Published in: Scientific reports Vol. 15; no. 1; pp. 26121 - 17
• Main Authors: Wang, Dawei, Li, Yifei, Gong, Cheng, Li, Tianle, Wang, Fang, Luo, Shanna, Li, Jun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/4073/4071; 639/166/4073/4099; 639/166/4073/4100; Air flow; Alternative energy sources; Battery energy storage; Blasting; Carbon; Costs; Dewatering; Distributionally robust optimization; Dynamic programming; Embedding; Emission standards; Emissions; Energy consumption; Energy coupling; Energy demand; Energy management; Energy requirements; Energy resources; Energy storage; Geology; Greenhouse gases; Humanities and Social Sciences; Hybrid energy management; Hydraulic mining; Innovations; Mining; Mining accidents & safety; multidisciplinary; Objective function; Optimization techniques; Process coupling; Renewable energy; Renewable energy integration; Renewable resources; Safety; Safety regulations; Science; Science (multidisciplinary); Sensitivity analysis; Simulation; Smart mining systems; Timing; Tunnel construction; Ventilation; Low-carbon mining; Process coupling; Battery energy storage; Distributionally robust optimization; Smart mining systems; Renewable energy integration; Hybrid energy management; Safety-constrained scheduling
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-11013-x

This study presents a process-centric hybrid energy management framework tailored for large-scale smart mining operations. The framework addresses three major challenges: (i) multi-source uncertainty propagation, (ii) cross-process energy coupling, and (iii) time-varying, safety-critical operational constraints. The energy scheduling problem is formulated as a process-constrained, multi-period optimization under uncertainty, explicitly modeling the spatio-temporal correlations among renewable power generation, ventilation loads, dewatering demands, and blasting energy requirements. To tackle high-dimensional uncertainties with non-Gaussian distributions, a Wasserstein metric-based distributionally robust optimization (DRO) model is constructed. The ambiguity set is dynamically refined through adaptive scenario generation and clustering, capturing worst-case energy supply-demand mismatches. The objective function jointly minimizes total energy cost, carbon emissions, and process-specific operational risks, subject to nonlinear thermodynamic process constraints, piecewise convex ventilation characteristics, and interdependent hydraulic-ventilation-thermal (HVT) processes. Mining safety regulations are integrated via chance constraints, embedding safety-critical margins related to pressure, airflow, and gas concentration. To alleviate the computational burden caused by nested risk formulations, a Primal-Dual Reformulated Distributionally Robust Process Scheduling (PDR-DRPS) algorithm is proposed. This method recursively updates process-coupled dual variables, enabling fast convergence within joint physical-energy feasible subspaces. The proposed framework is validated using a synthetic open-pit mining benchmark incorporating real-world meteorological data, empirical process dynamics, and regulatory thresholds. Numerical results indicate a 25.4% reduction in operational costs, a 31.2% cut in carbon emissions, and consistent adherence to safety constraints within a 3% tolerance under all uncertainty scenarios. Sensitivity analysis further highlights that process inertia and time delays significantly amplify uncertainty propagation, underscoring the necessity of process-aware robust energy scheduling in safety-critical industrial systems. The framework offers a generalizable paradigm applicable to smart mining, tunnel construction, and underground industrial infrastructures.