A sulfonic-functionalized Zr-type metal–organic framework (UiO-66-SO3H) was synthesized, and UiO-66-SO3Li was obtained via subsequent lithiation treatment. The UiO-66-SO3H framework was incorporated into a polymer matrix as a functional filler to obtain high-performance solid composite electrolytes (SCEs). Structural and morphological analyses, as well as various electrochemical tests, were performed. Owing to well-defined porous channels, abundant lithium-ion transport sites, and effective regulation of polymer crystallinity by SO 3 − moieties at 60°C, the optimized SCE-2 (10wt
High-volume fly ash (HVFA) binders are widely utilized as a mature method for cemented paste backfill in green mining, yet their performance remains highly sensitive to mix design. The fundamental coupling mechanism between the water-to-binder mass ratio (W/B) and sodium lignosulfonate (SL) content in pozzolan-rich HVFA systems remains insufficiently understood. In this study, HVFA pastes with varying SL contents (0–0.9wt
Accurate groutability prediction is essential not only for mine water-inrush prevention, but also for reducing excessive cement consumption and the associated carbon footprint of grouting operations. However, field geological datasets are often small, which limits the reliability and generalization of data-driven models. A geology-informed prediction framework is presented for predicting unit grouting amount (uga) from three routinely measured borehole variables: water inflow rate, hydraulic pressure, and groundwater level. A generative augmentation strategy was employed to expand the training data from 44 to 880 samples, and the input variables were organized into a physically informed feature sequence. Based on this representation, a hybrid deep-learning model integrating a bidirectional temporal convolutional network, a bidirectional gated recurrent unit, and an attention mechanism was developed, with its hyperparameters optimized using the Crested Porcupine Optimizer. The results demonstrate that data augmentation improved the test performance, with the coefficient of determination (R2) increasing from 0.8851 to 0.9293 and the root mean square error (RMSE) decreasing by 21.6
Tailings thickening is a key unit operation in mineral processing, paste backfill, and tailings management systems, exerting a direct influence on water recovery efficiency, slurry transport behavior, and the stability of downstream dewatering and disposal processes. Throughout the entire thickening workflow, spanning free settling, compression settling, and high-concentration discharge, the evolution of rheological properties governs the formation of particle networks, the development of yield stress, and the resistance to flow and consolidation. These rheological responses are strongly coupled with particle size distribution, mineral composition, surface physicochemical characteristics, and operating conditions, leading to complex, stage-dependent thickening behavior. This review adopts a rheology-oriented perspective to analyze the thickening process across its distinct zones—clarification, free settling, hindered settling, and compression. Rheological behavior evolves from Newtonian to non-Newtonian and viscoplastic as solid concentration increases, affecting particle aggregation, settling, and network consolidation. Key parameters—shear yield stress, compressive yield stress, viscosity, and viscoelastic moduli—are central to understanding flocculation (e.g., Derjagin–Landau–Verwey–Overbeek, adsorption–bridging), settling, and compression. Integrating rheology with macroscopic models (e.g., Coe–Clevenger, Kynch, Buscall–White) and microscopic theories provides a unified framework for interpreting thickening mechanisms and guiding optimization. Advances in rheometry and online monitoring enable accurate slurry characterization, while numerical simulations incorporating rheology support the prediction of flow fields and solid–liquid separation. This review underscores the necessity of a rheology-based approach for designing flocculant dosing, optimizing equipment, and diagnosing failures. Future thickening technologies will rely on rheology combined with multi-scale modeling, intelligent monitoring, and artificial intelligence (Al)-based control for real-time regulation and sustainable tailings management.
With the increasing mining depth, heat hazards have become a critical challenge in deep underground operations. This study explores the incorporation of polyvinyl chloride (PVC) powder as a partial cement replacement in cemented backfill to improve thermal insulation and promote sustainable waste utilization. Five mix designs were prepared with 0, 5wt
Large-scale artificial intelligence (AI) models are increasingly shaping safety, efficiency, and sustainability in the mining industry. This paper reviews the development, applications, and challenges of domain-specific large AI models in coal mining. These models integrate heterogeneous multimodal data—text, images, video, audio, design data, point clouds, and time series—within multi-layered architectures encompassing infrastructure, data resources, algorithms, application services, and security. Application platforms supporting knowledge services, visual analysis, and intelligent scheduling demonstrate practical improvements in operational decision-making. Despite these advances, deployment faces challenges including fragmented data, limited labeled datasets, few-/zero-shot scenarios, industry-specific adaptation, robustness and interpretability, weak causal reasoning, edge computing limitations, cost–benefit trade-offs, and compatibility issues. Overcoming these barriers requires coordinated progress in data governance, model design, and industry standardization.
The acoustic emission (AE) Kaiser effect method is widely used for in-situ stress measurements because of its nondestructive nature, operational efficiency, and low cost. A key step in this method is the identification of the Kaiser point. However, traditional manual approaches require further improvement, indicating the importance of developing intelligent identification methods. In this study, an intelligent Kaiser point identification method was proposed based on a dual-branch gated recurrent unit (GRU) deep learning framework and phase-space reconstruction (PSR). In the proposed framework, AE waveform data was processed via PSR and principal component analysis to generate chaotic feature representations, which are then fused with the original waveform data through a dual-branch GRU architecture for classification. The classification results were then used for Kaiser point identification. The proposed model achieved an accuracy of 90.7
The extractive metallurgy sector is undergoing rapid digital transformation driven by Industry 4.0, advanced sensing, and artificial intelligence (AI). While machine learning has been widely adopted for predictive control and optimization, the role of generative artificial intelligence in metallurgical engineering remains inadequately characterized in the literature. This paper critically reviews the state of generative AI for extractive metallurgy, focusing on practical industrial applications rather than purely theoretical AI methods. We synthesize peer-reviewed research, industrial case studies, and emerging applications across comminution, flotation, hydrometallurgy, pyrometallurgy, ore sorting, and plant reliability. The review identifies five key generative AI models applicable to metallurgy: generative diffusion models, flow-based models, variational autoencoders, generative pre-trained transformers, and generative adversarial networks. Generative AI presents a transformative opportunity for extractive metallurgy, offering solutions for optimized process control, enhanced mineral recovery, predictive maintenance, and improved sustainability. While generative AI offers significant potential, its deployment requires rigorous validation, physics-informed modeling, and hybrid human AI workflows in metallurgical plants.
Backfilling and grouting in the goaf are effective methods that can efficiently dispose of solid wastes including coal gangue (CG) and coal gasification slag (CGS). When backfilling is solely for solid waste disposal, the strength requirement for backfill materials is low. In view of this, a gangue and coal gasification slag-based backfill material (GCBM) was prepared, using a low content of alkali-activated slag (AAS) to adjust its mechanical properties. Considering three influencing factors (solid content, CGS content, and AAS content), single-factor experiments and optimization experiments based on response surface methodology (RSM) were conducted, with fluidity and strength as the optimization objectives. Finally, the hardening mechanism and microstructure of GCBM were analyzed. Test results show that the fluidity of GCBM is negatively correlated with solid content, CGS content, and AAS content; the strength is positively correlated with solid content (in a certain range) and AAS content, and first increases and then decreases with the increment of CGS content. The optimal mix-proportions obtained via RSM were as follows: 75.35wt
Backfill–rock composite structures (BRCSs) are crucial for the stability of underground mining areas. However, during the mining and backfilling cycles, they are subjected to coupled dynamic-static loading. Herein, to systematically investigate the mechanical properties of BRCSs under in situ mining and filling stress loading, true triaxial dynamic-static tests were conducted. First, the effects of the depth, cement–tailings (C/T) ratio by mass, and interfacial angle (IA) on the composite strength, deformation characteristics, and failure modes were systematically investigated. Subsequently, the evolution of acoustic emission (AE) signal parameters during BRCS failure was analyzed. Finally, a damage constitutive model was established based on the AE energy analysis. With increasing depth, C/T ratio, and IA, the peak strength and elastic modulus of the BRCS exhibited an upward trend, and the strain during the loading–unloading disturbance stages correspondingly increased. At a C/T ratio of 1:8, the specimens exhibited a rock-dominated load-carrying capacity with distinct brittle failure. Conversely, at a C/T ratio of 1:4, the specimens demonstrated a coupled backfill–rock load-carrying capacity, exhibiting ductile failure in the shallow regions and a transition to brittle failure in the deeper zones. AE signals were concentrated during loading–unloading disturbance, plastic yielding, and failure stages. The dominant failure mode was tensile-shear composite fracture, with the proportion of shear cracks gradually increasing with depth. The damage evolution process of a BRCS can be divided into three stages: initial, accelerated, and ultimate failures. This study provides an important theoretical basis and practical guidance for optimizing C/T ratio and enhancing stability assessment in backfilled mine designs.
Kimberlite beneficiation is constrained by high circulating loads, inefficient liberation, and substantial fine waste generation. This study evaluates the effectiveness of microwave pre-treatment in improving comminution and dense media separation performance by subjecting coarse (16–31.5 mm) and overall (6.7–31.5 mm) feed classes to controlled microwave irradiation using an industrial 2.45 GHz, 15 kW system. Treated samples were subsequently crushed in a single-roll crusher and processed through a laboratory dense media separation workflow to quantify changes in product size distribution, concentrate recovery, circulating load, and waste generation. Microwave pre-treatment produced consistent improvements in downstream performance. At low energy inputs, concentrate mass fraction increased by up to 75
The initial bearing age of a backfill is primarily determined by the roof collapse time, which critically affects its performance during the hydration stage. In this study, 96 h uniaxial compression creep and permeability tests were conducted to investigate the creep behavior of cemented backfill at different stress levels and initial bearing ages and their effects on strength and permeability. The results indicated that with an increase in the stress level, the cumulative axial strain of 28 d backfill increases from 0.962
In conventional cemented paste backfill (CPB), ordinary Portland cement (OPC) is the primary binder; however, it has drawbacks such as high costs and carbon emissions, and low durability. Granulated blast-furnace slag, a byproduct of ironmaking, has emerged as a promising sustainable additive. In this review, three slag-based binders—slag–cement blends (SCB), alkali-activated slag (AAS), and alkali-sulfate-activated slag (ASAS)—are discussed, focusing on their hydration mechanisms, rheological characteristics, mechanical properties, microstructure, sulfate resistance, and heavy metal solidification capabilities. SCB–CPB exhibits enhanced fluidity and late-stage strength compared to OPC–CPB, albeit with reduced early-stage strength. Although AAS exhibits superior comprehensive properties, its application is hindered by the high cost and corrosiveness of alkali activators. In contrast, ASAS emerges as a balanced solution, offering early- and late-age strength, second only to AAS, while being the most cost-effective and lowest-carbon option. Moreover, the future prospects of slag-based binders in CPB are discussed, providing valuable guidance for their formulation and application. These findings offer valuable insights for the further development and implementation of cost-effective and environmentally friendly slag-based binders in CPB applications.
Intelligent green mining is emerging as a new paradigm for safer,cleaner,and more efficient mineral resource devel-opment. By integrating green mining principles with digital-ization,automation,and artificial intelligence,it offers a pathway to reduce environmental impacts,improve resource efficiency,and support the low-carbon transformation of the mining industry. Against this background,we present a spe-cial issue focusing on intelligent green mining. This special issue presents 23 papers covering green backfilling and mine solid waste utilization,low-carbon materials and sustainable mineral processing,artificial intelligence and digital techno-logies,and intelligent prediction for mining safety and rock mechanics.
Driven by the global energy transition and industrial intelligence, the mining industry is evolving towards smarter and more efficient methods. In mineral processing, particularly flotation, traditional techniques rely heavily on human experience, facing challenges due to complexity and variability. This study proposes an intelligent control system based on machine vision for spodumene flotation. It introduces an improved YOLOv11-M model with real-time foam detection and decision optimization, enhancing flotation efficiency. The research utilizes a dataset of over 100000 foam images and deep learning to detect foam states. Innovations include using EfficientNetV2 for feature extraction, the C3k2_LGP module for enhanced frequency perception, and the Saga-PIoU loss function for better robustness under complex conditions. Experimental results show improvements in mean average precision (mAP) (by 1.9 https://github.com/users/ytyyty368-arch .
The sustainable management of coal-based solid waste and effective CO2 sequestration are critical challenges for the mining industry. To address this, a novel aluminum nanoparticle-modified CO2-carbonated backfill (ANCB) material was developed that synergistically enhances mechanical properties with carbon capture functionality. The effects of varying aluminum nanoparticles (Al-NPs) concentrations (0.02wt
The microbial-induced carbonate precipitation (MICP) is a cementation and solidification method for sand with environmental advantages. However, the bonding performance of MICP for tailings with different particle sizes, as well as its applicability within conventional backfilling processes under varying cementing solution concentrations and bacterial addition levels, remains insufficiently understood. In this study, a uniform proportioning experiment considering the influence of cement–sand ratio (CSR), cementing solution concentration (CSC), and the volumetric ratio of bacterial solution to cementing solution (VRBC) on the uniaxial compressive strength (UCS) was conducted, and a series of microscopic analyses were used to demonstrate microbial mineralization behavior. Results show that the UCS of microbial blended tailings backfill (MBTB) exhibits a general trend of increasing and subsequently decreasing with rising CSC and VRBC. The UCS of optimally proportioned MBTB exceeds that of conventional backfill without microbial addition and maintains stable long-term strength. Comparative analysis indicates that a CSC of 0.5 mol/L and a VRBC of 1:1 yield the most effective microbial bonding performance. Although the cementing solution alone suppresses UCS, the subsequent incorporation of microbes significantly enhances strength, confirming the critical role of microbial mineralization and cementation within the backfill. The optimal UCS values for MBTB prepared with coarse and fine tailings are 2.44 and 1.55 MPa, representing increases of 49.69
The prediction of rock failure, a key fundamental research for addressing mining safety issues (such as mine slope stability and rockburst), faces challenges with traditional methods due to their complex generalization and computational processes that struggle to describe the entire failure process. Consequently, 12 prediction models integrating ensemble learning and optimization algorithms were established to predict rock peak stress and failure time using strain, elastic modulus, density, mass, and confining pressure as inputs. Five-fold cross-validation was used to optimize hyperparameters, significantly improving the model’s generalization ability, robustness, and stability. Dataset was established through rock mechanics experiments, with strain increments configured at 0.008‰, 0.01‰, and 0.012‰ in the test set. The cross-validation optimized particle swarm optimization eXtreme gradient boosting (CV-PSO-XGBoost) model performed best under a strain increment of 0.01‰, and its stress prediction achieved coefficient of determination R2 = 0.904, mean absolute error (MAE) = 4.315, and root mean square error (RMSE) = 5.435; while the failure time prediction demonstrated R2 = 0.811, mean absolute percentage error (MAPE) = 7.842
The synergistic CO2 sequestration via solid waste backfilling in goafs can simultaneously address the issues of CO2 emissions, accumulation of coal-based solid wastes, and safety hazards in goafs under China’s coal-dominated energy structure. In this study, a modified magnesium-coal-based all-solid-waste carbon-sequestering backfill material (MFCC, prepared from modified magnesium slag (MMS), fly ash (FA), coal gangue (CG), and coal gasification slag (CGS)) was fabricated. The fluidity of the fresh slurry was characterized using the mini slump test, and its carbonation curing performance was investigated via uniaxial compressive strength (UCS), carbonation depth (CD), X-ray diffraction (XRD), scanning electron microscopy (SEM), thermogravimetry-differential thermogravimetry (TG-DTG), and computed tomography (CT) tests, aiming to achieve the synergistic goals of high-value utilization of solid wastes and CO2 sequestration. The results indicate that the fresh MFCC slurry exhibits excellent fluidity with a mini slump ranging from 121.5 to 135 mm. The fluidity increases with the rise in CGS content, which fully meets the requirements for industrial pipeline pumping. During the carbonation curing process, the UCS of the material increases continuously with the extension of curing age, with the 28-d UCS ranging from 7.36 to 8.71 MPa, which fully meets the strength design requirements for coal mine backfilling engineering. Microscopic analyses reveal that the filling and cementation effects of hydration and carbonation products on pores render the material’s microstructure denser, significantly reducing pore volume and connectivity, which is the key reason for the strength improvement. After 28 d of carbonation curing, when the CGS content is 20wt
