Natural Resources Research最新文献

Flyrock, the unintended projection of rocks during mining blasts, poses significant safety risks and potential damage. Predicting flyrock is essential for implementing safety measures, minimizing injuries, preventing equipment and structural damage, optimizing blast plans, reducing downtime, and saving costs. Accurate predictions mitigate hazards, improve operational efficiency, and ensure the safety of workers and surrounding infrastructure. This study explored and developed hybrid methods for predicting flyrock using the group method of data handling (GMDH). Four swarm-based algorithms—particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO), and whale optimization algorithm (WOA)—were combined with GMDH to enhance prediction accuracy. Additionally, a k-fold cross-validation method was applied to the datasets to improve reliability. The accuracy of these methods was evaluated using various statistical functions, such as Nash–Sutcliffe coefficient and Willmott's index, along with R-squared correlation (R²) graphs, half-violin plots, and quantile–quantile plots. The R² values for the WOA–GMDH, ACO–GMDH, ABC–GMDH, and PSO–GMDH models were 0.99, 0.97, 0.96, and 0.96, respectively. The WOA–GMDH method yielded the most accurate results, demonstrating superior performance when combined with GMDH. Furthermore, the performance of the WOA–GMDH model was compared with models developed in the literature using the same database, confirming its effectiveness. Sensitivity analysis identified that, in WOA–GMDH modeling, the powder factor as the most significant parameter while the spacing parameter was the least significant. The ACO–GMDH method exhibited the narrowest uncertainty band; whereas, the PSO–GMDH method had the widest, indicating the highest level of uncertainty.

Flyrock from blasting in open-pit mining is one of the most dangerous occurrences that can cause accidents to workers, damage to machinery and equipment and even fatalities. Therefore, quick and reliable prediction of blast-induced flyrock distance (BIFRD) in open-pit mines is very crucial to ensure the safety of the surrounding environment. In this study, unmanned aerial vehicle (UAV) technology combined with advanced artificial intelligence techniques was used to predict BIFRD in open-pit mines and improve safety. UAV was used to record blasting operations and the resulting flyrock. The distance of the flyrock was then measured from the recorded video footage and was analyzed using the ProAnalyst software. Then, various metaheuristics-optimized ANFIS (adaptive neuro-fuzzy inference system) was developed to predict BIFRD. These networks were optimized using adaptive differential evolution with optional external archive (JADE), genetic algorithm (GA), fireworks algorithm (FWA), and artificial bee colony (ABC) algorithms and resulted to JADE–ANFIS, GA–ANFIS, FWA–ANFIS, and ABC–ANFIS models. A dataset with 204 blasting events was gathered and analyzed, and finally, only four input variables were used for developing these models, including spacing, weight charge, stemming, and powder factor. The results showed that JADE–ANFIS is the best with high accuracy (97.8%), good generalizability (MAPE of 1.1%), and reasonable training time for predicting BIFRD in this study. The other models performed poorly with accuracy ranging from 88.7 to 96.5% and MAPE ranging from 1.4 to 3.0%. Sensitivity analysis also showed that the length of stemming is the most affecting factor to flyrock distance in blasting and thus careful consideration should be given in designing blast patterns to control flyrock distance in open-pit mines.

The flow of shale oil in nano-scale rock pores follows the slip flow regime, in which the flow velocity at the nanopore walls is not zero. The nano-scale effect of the boundary layer renders the slip flow effect in the nanopores non-negligible. In this study, the slip flow mechanism of shale oil in nanopores was reviewed. The nano-scale effect of the boundary layer renders the slip flow effect in the nanopores non-negligible. The slip length and flow enhancement factor are the primary parameters used to evaluate the slip effect. The main factors influencing the slip effect were then analyzed, including the fluid properties, nanopore properties, pressure gradient, and temperature. Additionally, three slip flow models for shale oil in circular, elliptical and slit nanopores were reviewed. Moreover, a modification method for the shape factor is introduced to evaluate the slip effect of irregular nanopores. The general conclusions regarding the mechanism and models of slip flow in shale oil are summarized as follows: (1) Slip flow of shale oil occurs predominantly in nanopores due to scale effects and stronger internal interaction forces among alkane molecules. (2) The influence of slip flow is more pronounced in organic nanopores than in inorganic nanopores. (3) Significant slip flow effects are observed with larger slip lengths and flow enhancement factors. (4) Our analytical models indicated that slip flow effects are more pronounced with smaller hydraulic diameters. (5) The effects of slip flow are more pronounced in nanopores with irregular geometric shapes. Lastly, recommendations for future research are proposed.

An energy criterion model of coal and gas outbursts was established to study the energy conversion mechanism. Ideal gas law was utilized to establish a correlation between dissipated energy (i.e., energy dissipated during outbursts) and accumulated energy (potential energy leading to outbursts) in gas-containing briquettes. This relationship, along with the expression for the energy criterion, was derived from the deformation of briquettes under load, which led to instability and eventual failure expulsion. Hence, physical simulation experiments on coal and gas outbursts were conducted to analyze the energy conversion mechanism and to determine the change law of the initiation energy criterion index for outbursts. Besides, energy conditions were verified for the initiation of coal and gas outbursts. Potential energy includes the expansive deformation energy of adsorbed gas desorption, the expansive deformation energy of free gas, the elastic potential energy of gas-bearing briquettes under the stored load, and the gravitational potential energy work of unstable coal. Besides, the dissipation energy of outbursts included coal-crushing energy and coal-throwing power. The potential energy-to-dissipation energy ratios of outbursts were 1.07 and 1.04 in two groups of experiments. These values greater than 1 surpassed the threshold of the activation energy criterion, resulting in coal and gas outbursts. The firmness coefficient and Poisson's ratio of coal were negatively correlated with the energy criterion index, while the elasticity modulus, density, and initial velocity of gas emissions were positively correlated with the energy criterion index. The five experimental parameters (i.e., initial gas pressure, coal amount, maximum principal stress, average velocity of coal outbursts, and falling height of unstable coal) were positively correlated with the energy criterion index. The findings provide further insight into the mechanism of coal and gas outbursts, establishing a basis for their control, prevention, and dynamic warning systems.

The Han River-to-Wei River Water Transfer (HWWT) Project not only brings evident economic benefits to the water-receiving areas but it also generates ecological flow value (EFV) by indirectly supplementing river flows. However, the EFV resulting from water transfer is often overlooked due to its indirect nature, and traditional methods tend to overestimate it due to a lack of consideration of value growth thresholds. This paper proposes a research system to address these issues, scientifically quantifying the EFV increment after water transfer. Taking the Xianyang–Lintong river segment in the water-receiving area of the HWWT as an example, we present a holistic approach guided by river ecological issues to determine the suitable ecological flow (SEF) for the river, using it as the growth threshold for EFV. Subsequently, based on water resource allocation, changes in river flow and their relative percentages to SEF (SEF satisfaction) before and after water transfer were analyzed. Finally, an ecological value model based on SEF was employed to estimate changes in river EFV. The results indicate that the distribution of SEF varied throughout the year, correlating with the monthly water requirements of key ecological functions in the river. After water transfer, SEF satisfaction notably improved across all months except excessively wet periods. In drier years, river EFV increased significantly, reaching 31.31% at 95% flow frequency. The water purification, hydrologic cycle, sediment transport and biological diversity, contributed the most to EFV. This study provided new insights and methodologies for assessing EFV increments and formulating ecological compensation standards in the water-receiving areas after water transfer.

The identification and mapping of geochemical anomaly patterns have emerged as a more precise and efficient approach for mineral exploration, with deep learning algorithms being extensively employed in this realm. However, existing methodologies require further investigation regarding model interpretability and correlation with established mineral control factors. This paper proposes a regional geochemical anomaly identification method based on the memory-augmented autoencoder (MemAE), incorporating geological controlling factors. Firstly, the MemAE model is introduced to address the excessive generalization capability of the traditional autoencoder (AE) model. Secondly, utilizing multifractal singularity theory, a nonlinear functional relationship between faults and mineral deposits is established. This relationship reveals the controlling effect of faults on mineralization and it is incorporated as a constraint term in the MemAE's loss function. Finally, the constructed geochemical anomaly identification model is employed to delineate prospective mineralization areas, with comparative studies conducted on AE, MemAE, and geologically constrained MemAE models. The results demonstrate that the geologically constrained MemAE exhibits superior performance, achieving an AUC of 0.802. The eight delineated mineralization prospective areas show strong concordance with actual distributions. The proposed method, which considers geological controlling factors, effectively enhances model interpretability and demonstrates excellent geochemical anomaly identification capabilities. Consequently, this approach can be considered a viable methodology for mineral exploration.

Copper price forecasting is crucial for both investors and governments due to its significant economic impact. Recently, machine learning techniques have been widely employed to construct copper price forecasting models, demonstrating high forecasting accuracy. However, there are two main limitations in these models: (1) the lack of ability to capture the non-Euclidean relationships among numerous features; and (2) using purely data-driven algorithms, which lack tractability and physical effectiveness. To address these challenges, this study proposes a multi-view graph transformer (MVGT) model for 1-month ahead copper price forecasting. MVGT integrates a parametric fractional Brownian motion module, which provides conditional expectations of future copper prices for data augmentation. Moreover, to comprehensively capture the non-Euclidean structure of copper features, MVGT introduces five graph generation methods. Furthermore, a multi-view graph transformers model is designed to provide structural copper feature embeddings, and an attention-based multi-view fusion mechanism is developed to enable the MVGT to comprehensively understand market trends while focusing on the most influential views. Experimental results on the COMEX and LME datasets demonstrate that MVGT outperforms baseline models in terms of training efficiency, forecasting accuracy, and generalization.

Productivity in opencast mining, particularly in drill-blast (DB) and surface miner (SM) operations, is crucial for optimizing efficiency and reducing costs. These operations are directly affected by fragmentation, which in turn impacts equipment utilization, loading cycle times, and downstream operations. This study analyzed field data such as rock properties, machine parameters, blast design results, and post-blast fragmentation size (0.15–0.82 m), with 0.45 m identified as the optimal fragmentation size for a 12 m³ shovel bucket. Traditional productivity assessments often use simplistic models that fail to capture the complexities of mining operations. To address this, an explainable machine learning (ML) model was developed, integrating fragmentation size, rock and machine parameters, and geometric factors to evaluate DB and SM operations in opencast coal mines. Various ML techniques, such as artificial neural network (ANN), random forest regression (RFR), gradient boosting regressor (GBT), and support vector regression (SVR), were employed to analyze these parameters. Among these, the RFR model demonstrated the highest accuracy, with a coefficients of determination (R²) of 99.5% for training and 99.2% for testing in DB datasets, and 99.9% for training and 99.5% for testing in SM datasets. Furthermore, the RFR model had the lowest root mean square error, mean absolute error, and mean absolute percentage error of 10.35, 4.788, and 2.1% for DB training datasets, and 5.53, 1.75, and 1.5% for SM training datasets, respectively, underscoring its superior performance. Using SHAP (Shapley Additive exPlanations), the study identified key productivity drivers: SM cycle time, diesel consumption, and coal face length. Fragmentation size, resulting from blasting, was also found to influence shovel efficiency and overall productivity significantly. This paper highlights the effectiveness of ensemble ML models in predicting and analyzing complex productivity dynamics in opencast mining.

Low-permeability coal seams are widely distributed in China, with significant differences in coal ranks and properties. Identifying an effective method for nitrogen fracturing is an urgent challenge. To study the impact of coal ranks on fracturing, lignite, bituminous coal, and anthracite were used in liquid nitrogen freeze–thaw experiments. Low-field nuclear magnetic resonance was used to measure T₂ curves, porosity, and pore throat distribution during the freeze–thaw process. The fractal characteristics of pore microstructure and the dynamic evolution of unfrozen water were analyzed. The results indicate that liquid nitrogen freeze–thaw promotes pore development in coal of all ranks. Lignite, with its high moisture content and abundant pore structure, showed the most significant transformation effect, followed by bituminous coal and anthracite. After a single freezing–thawing cycle, the pore growth rates of lignite, bituminous coal, and anthracite are 135.98%, 104.17%, and 53.65%, respectively. Additionally, the transformation effect on different types of pores shows different characteristics. The distribution of adsorption pore throats slightly decreases, while the increase in distribution of permeable pore throats follows the order: lignite > bituminous coal > anthracite. The fractal dimension D_A of adsorption pores is less than 2, indicating no fractal characteristics, while the fractal dimension D_S of permeable pores is greater than 2.9, showing significant fractal characteristics. During the freezing process, lignite exhibits the greatest decrease in unfrozen water content, while during the thawing process, all three coal samples show a sudden increase in unfrozen water content, with bituminous coal showing the smallest increase, only 1836.49.

Research on the effects of acidic and alkaline solutions and stress on coal’s pore structure has traditionally focused on larger scales, leaving a gap in understanding nanoscale impacts. This study utilized a self-developed small-angle X-ray scattering (SAXS) miniature loading system and in situ synchrotron SAXS to investigate nanopore evolution under varying pH conditions and external stress. By analyzing the scattering data obtained, we investigated the changes in the internal nanopore structures of coal soaked in solutions with different pH values and subjected to external loading. The results showed that all coal samples exhibited negative Porod deviations. The degree of negative Porod deviation decreased after the coal samples were soaked in acidic solutions, while it increased after soaking in alkaline solutions. Negative Porod deviations increased notably under destructive loading. There are significant differences in the changes of internal nanopore structures in coal samples treated with chemical solutions of different pH values. The porosity and specific surface area of coal samples decreased significantly after soaking in acidic solutions, while coal samples treated with alkaline solutions showed substantial increases in both parameters. During subsequent loading, the samples soaked in acidic solutions exhibited minimal changes, whereas those treated with alkaline solutions experienced notable alterations. Chemically treated coal samples also showed increased sensitivity to external stress, especially in smaller nanopores. The study identifies three stages of nanopore evolution under stress: minor damage, compression, and rupture.