Deep-sea polymetallic nodules are characterized by their high grade and enormous reserves, and their commercial development is of great significance in addressing the shortage of terrestrial resources. Locomotion technology of mining vehicles is a core component of mining systems, and it directly determines the efficiency and productivity of collection operations. The feasibility verification stage remains in most existing research and technologies, and a large-scale, intelligent, and highly reliable locomotion technology system has not yet been established. Based on the basic dynamic characteristics of mining vehicles, this review systematically consolidates the research on structural optimization of vehicles, motion control, and dynamic coupling with trajectory control systems, and provides an integrated perspective connecting these domains with recent advances in path planning and intelligent navigation for deep-sea polymetallic nodule collection. It further examines path planning and navigation control in extreme environments by analyzing the inherent mechanisms of traction failure, along with existing prediction models and classical algorithms. The study identified theoretical gaps in the multibody dynamics analysis of deep-sea mining vehicles, outlined emerging trends in trajectory control, and clarified the challenges related to reliability and intelligence under extreme operating conditions. This integrative approach highlights the systemic interactions overlooked in previous studies, and proposes a novel framework for the development of intelligent mining vehicle in the future. In addition, some key future research directions aimed at laying a solid foundation for the commercial development of deep-sea polymetallic nodules were identified.

Coal remains the foundation of Chinese energy infrastructure, with production expected to reach 4.78 billion tons in 2024. However, underground mining operations continually face fire hazards, particularly from conveyor-belt systems, which correspond to the primary mechanical transport methods in coal mines. This study systematically examines the mechanisms of combustion, dynamics of smoke dispersion, and advanced strategies for mitigating conveyor belt fires. Moreover, a dual-parameter monitoring framework that integrated equipment diagnostics with environmental surveillance was proposed. Additionally, wireless sensor monitoring and AI-based (Artificial Intelligence, AI for short) visual recognition technologies have been identified as essential tools for fire management. The key findings highlight the urgent need to develop flame-retardant materials, large-scale testing platforms, and wireless sensor networks to improve mining safety standards.

Copper and molybdenum are critical strategic metals essential to emerging industries and national defense. They are primarily extracted from chalcopyrite (CuFeS₂) and molybdenite (MoS₂), respectively. Efficient and selective flotation of Cu–Mo sulfide ores-a key step in developing porphyry deposits-relies on the targeted modification of chalcopyrite and molybdenite surface properties by depressants. These depressants are generally classified as either inorganic or organic. To clarify the fundamental mechanisms of flotation and depression, this review examines the surface physicochemical properties of chalcopyrite and molybdenite. It also critically evaluates recent advances in depressant development, including inorganic agents, organic agents, and eco-friendly alternatives such as polysaccharides. The active components of current depressants are categorized, and a comprehensive analysis of their separation performance, toxicity, and cost in Cu–Mo sulfide ore flotation is presented. Finally, this review proposes a forward-looking framework for designing next-generation depressants through a multidisciplinary approach grounded in green chemistry. This perspective addresses current challenges while promoting sustainable and efficient exploitation of Cu–Mo resources.

Using cement as a filling cementitious material is challenging because of high energy consumption, high cost, and considerable carbon emissions. Thus, a new type of filling cementitious material, which involved soda residue (SR), calcium carbide slag (CS), ground granulated blast-furnace slag (GGBS), and fly ash (FA) as raw materials, was proposed in this study to replace conventional Portland cement. Response surface tests were conducted on test specimens to determine the optimal mixture proportion. The hydration mechanism of the developed filling cementitious material was investigated using low-field nuclear magnetic resonance, scanning electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The results indicated that the optimal mixture proportion of the developed cementitious material was as follows: SR : CS : FA : GGBS = 15:7.41:37.27:40.32. The 28-d unconfined compressive strength (UCS) of the composite filling material was 23.01 MPa. The test results indicated a 28-d UCS of 24.14 MPa and a fluidity of 164 mm. Response surface methodology was used to optimize the performance of the filling cementitious material. The CS concentration of the material exhibited the most significant influence on the UCS, followed by FA and SR. Microstructural analyses indicated that the main hydration products of the new material were Friedel’s salt (FS), ettringite, hydrotalcite, C–(A)–S–H gel, and hemicarbonate, among which the C–(A)–S–H gel and FS were the dominant sources of strength in the material. The developed cementitious material can effectively reduce carbon emissions by 92% relative to cement-based binders. It can be used as a substitute for cement in mine filling with excellent economic and environmental benefits.

Tailings waste backfill treatment has become a crucial approach for sustainable mining and green mine construction. Conventional backfill involves problems such as unstable operation, reliance on manual equipment, low efficiency, and major safety hazards. Recent advances in intelligence have provided opportunities to advance backfill operations. This paper introduces an intelligent backfill operation and control system. It integrates sensing, decision-making, and execution to achieve efficient, safe, and adaptive backfill management. The system adopts a layered architecture. Onsite sensors collect real-time data. These are subsequently processed by a programmable logic controller (PLC) and integrated into a system. Intelligent operation and control strategies were implemented across three key stages of the backfill process: tailing thickening, slurry mixing and preparation, and pipeline transportation. During the thickening of tailings, mathematical models were established to regulate the solid mass fraction (SMF) of the tailing-feeding slurry and the flow rate of the flocculant dosage. This ensured an optimal SMF in the deep cone thickener and safe control of the rake torque. During slurry mixing and preparation, the backfill control system automatically adjusts the amount of cementitious material added, flow rate of dilution water, and pinch valve operation based on real-time monitoring. This improves the slurry quality and stability. For pipeline transportation, real-time pressure monitoring can rapidly detect obstructions, ruptures, and incompletely filled conditions, and automatically initiate corrective actions to prevent system failures. Additionally, the backfill control system adopts an intelligent water circulation strategy to conduct closed-loop monitoring and distribution of process water. This significantly improves the circulation efficiency and reduces the overall resource consumption.

The mechanical properties of coal-rock masses are strongly affected by discontinuous structural defects such as joints and fractures. Identifying the dominant fractures allows for the simplification of complex fracture networks while preserving the mechanical behavior of the fractured rock mass and enhancing the computational efficiency of numerical models. This study reconstructed a numerical model of a coal specimen with complex fracture networks by integrating computed tomography (CT) scanning technology and numerical simulations and investigated the influence of the size ratio on the mechanical properties and failure characteristics. The dominant fracture size was determined. An equivalent discrete fracture network (DFN) model was developed in MATLAB by fitting planes to the three-dimensional coordinates of the fracture endpoints. The study results show the following: (1) The sensitivities of the mechanical parameters to fracture size are ranked in the order of tensile crack initiation stress > uniaxial compressive strength > peak strain > shear crack initiation stress > elastic modulus. (2) As the size ratio increases, the relationship between the crack number and dip angle shifts from exponential to linear growth. The failure characteristics consistently exhibit mixed tensile–shear failure. The macroscopic failure pattern is increasingly governed by larger fractures. (3) The mechanical parameter errors for the original fractured coal specimen, coal specimen with dominant-size fractures, and equivalent DFN models range from 5.04% to 18.89%. The models exhibit mixed tensile–shear failure dominated by shear fractures, although the specific failure pattern varies. The findings of this study establish a foundation for future studies on the transparent analysis of discontinuous structures and evolution of the multi-physics field.

Surface passivation is a promising technique to control acid mine drainage (AMD) generation. The results of our previous study revealed that food waste compost mitigates pyrite oxidation by serving as a source of passivators and alkalies. This study evaluated the long-term efficacy of the compost and compost water extract (CWE) in suppressing pyrite oxidation via 70-week kinetic column leaching (KCL) tests using fine coal refuse (CR) samples with low and high pyrite levels (9.3wt% versus 12.7wt% of Fe). The KCL tests involved weekly watering of the CR and compost-amended CR by deionized water and the calcite-amended CR by CWE. The results indicated that with the exception of AMD generation after week 47 in the compost-amended high-pyritic CR, the compost or CWE treatments effectively suppressed AMD generation and potentially toxic element release throughout the study. Distinct Fe–S–Al–Si–O-enriched and aggregate-like coating layers were observed on pyrite surfaces in the compost- or CWE-treated columns, although the coverage and thickness are heterogeneous. The performance of the compost amendment method could be further improved by increasing compost dosage or adding extra alkalies. Overall, compost mitigates pyrite oxidation and AMD generation in the low-pyritic CR by inducing the formation of the passivation layer via organo-mineral interactions and serving in a neutralization capacity, respectively.

Tailings dams are a critical infrastructure for mining enterprises, and their safety directly affects production security and environmental protection. However, owing to the loose nature of dam materials and their unique geological structures, traditional slope stability assessment models have limited applicability to tailings dams. Dam displacement serves as a key indicator for evaluating the stability and identifying potential developmental issues during operation, making it essential for safety monitoring. Therefore, developing reliable displacement prediction methods is crucial for early warning and mitigation of disasters. This study proposes a “feature derivation–decomposition forecasting–model optimization” approach for predicting displacements in tailings dams. First, the IDBO–VMD (Improved Dung Beetle Optimizer–Variational Mode Decomposition) decomposition algorithm is employed to separate dam displacement into the trend and periodic components. Subsequently, the trend and periodic displacements are predicted using the DBN (Deep Belief Network) and IDBO–TCN (Temporal Convolutional Network)–BiGRU (Bidirectional Gated Recurrent Unit)–self-attention models, respectively, with linear weighting applied to enhance feature representation. The final displacement prediction is obtained by superimposing the predicted components. The method was validated using the tailings reservoir of the Dayi Company in Lueyang County. The results showed that the predicted cumulative landslide displacement closely matched the measured values, achieving a correlation coefficient of 0.995 and a mean absolute error (MAE) of 0.092 mm. Specifically, the trend component prediction yielded an R value of 0.996 and MAE of 0.065 mm, whereas the multi-algorithm coupled IDBO–TCN–BiGRU–self-attention model achieved higher overall precision for the periodic component, with an MAE of 0.132 mm and R of 0.984. These results demonstrate that the proposed model provides a novel framework for intelligent early warning of tailings dams and can accurately predict stagewise variations in displacement.

The mining industry generates vast quantities of mining solid wastes (MSWs), posing significant environmental and ecological challenges. Alkali-activated materials (AAMs) offer a sustainable solution by converting MSWs into value-added cementitious products, providing an alternative to ordinary Portland cement while reducing carbon emissions. This review comprehensively examines recent advancements in utilizing MSWs as precursors for AAMs. Key topics include fundamental reaction mechanisms, the role of precursor composition in gel formation, and the effects of activator selection on mechanical properties and durability. The effectiveness of various pretreatment strategies, such as mechanical activation, thermal processing, and alkali fusion, in enhancing MSWs reactivity is critically assessed. Additionally, challenges related to precursor variability, energy consumption, and long-term performance are identified. This review provides valuable insights into the sustainable valorization of MSWs in AAMs, contributing to circular economy initiatives and the development of low-carbon construction materials.