Search Results (1,158)

This study aims to solve the problem of the recovery efficiency of natural gas hydrate being affected by a large amount of mud and sand in the solid fluidization mining of natural gas hydrate. Therefore, a new method for the separation of in situ sediment and natural gas hydrate in a spiral-swirling downhole is proposed, and the corresponding numerical simulation model is established to realize the analysis and verification of key flow field parameters such as flow velocity, pressure, sediment, and natural gas hydrate phase distribution. The results show that the flow velocity field of the mixed slurry presents an ‘M’-shaped symmetrical distribution, and the slurry near the wall of the separator can obtain a larger flow velocity, which is beneficial to the separation of mud–sand and natural gas hydrate. The static pressure field shows an axisymmetric distribution that decreases first and then increases, indicating that the pressure of the mixed slurry increases with the increase in the radial position, and the closer to the wall, the greater the static pressure of the mixed slurry. Near the wall of the separator, the volume fraction of the sediment phase reaches the maximum. In contrast, the volume fraction of the natural gas hydrate phase reaches the minimum, which confirms the separation effect of the sediment and the natural gas hydrate. The results show that the separation of sediments and natural gas hydrate can be realized, thereby improving the exploitation efficiency of natural gas hydrate. The designed spiral cyclone coupling separator provides a new solution to solving the problem of sand removal in natural gas hydrate exploitation. Full article

Egypt’s average share of global carbon dioxide emissions has been rising from mid-1990s to date. This motivates the present study to identify industries that drive carbon dioxide emissions (as direct emitters and as total emitters with high emission multiplier effects). Environmental input–output analysis is applied to Egypt’s 2017–2018 input–output table to measure sectoral emissions. The industries identified as high emitters are linked to Egypt’s achievement of Sustainable Development Goals, namely, Goals 7, 8, 9, 12, and 13. The findings indicate that ten industries qualify as environmentally degrading (dirty), having the highest emission multiplier effects (in descending order): electricity, gas, and water; non-metallic mineral products; basic metals; rubber and plastic products; chemicals and chemical products; paper and paper products; food products; hotels and restaurants; transportation and storage; and textiles. Eight of these industries also have high output multiplier effects. This underscores that although potential investment in and the growth of these industries will generate output multiplier effects, they will also be coupled with emission multiplier effects. Five other industries had high emission multipliers, as follows: water and sewerage; beverages; coke and refined petroleum products; extraction of crude petroleum; and mining of metal ores. The growth of these industries would not be in favor of the achievement of SDGs. Policy measures are recommended. Full article

Accurate prediction of the height of water-conducting fissure zone (HWCFZ) is an important issue in coal water control and a prerequisite for ensuring the safe production of coal mines. At present, the prediction model of HWCFZ has some issues such as poor prediction accuracy. Based on the widely collected measured data of the HWCFZ in different coal mines in northern Shaanxi Province, China, the HWCFZ in shallow-buried coal seams is categorized into two types, i.e., typical shallow-buried coal seams and near-shallow-buried seams, according to the different depths of burial and base-loading ratios. On the basis of summarizing the research results of the previous researchers, three factors, namely, mining thickness, coal seam depth, and working length, were selected, and the data of the height of the water-conducting fissure zone in the study area were analyzed by using a multivariate nonlinear regression method. Subsequently, each group of the data was randomly divided into training data and validation data with a ratio of 70:30. Then, the training data were used to build a neural network model (BP), random forest model (RF), a hybrid integration of particle swarm optimization and the support vector machine model (PSO-SVR), and a hybrid integration of genetic algorithm optimization and the support vector machine model (GA-SVR). Finally, the test samples were used to test the model accuracy and evaluate the generalization ability. Accordingly, the optimal prediction model for the typical shallow-buried area and near-shallow-buried area of Jurassic coal seams in northern Shaanxi was established. The results show that the HWCFZ for the typical shallow-buried coal seam is suitable to be determined by the multivariate nonlinear regression method, with an accuracy of 0.64; the HWCFZ for near-shallow-buried coal seams is suitable to be predicted by the two-factor PSO-SVR computational model of mining thickness and the burial depth, with a prediction accuracy of 0.84; and machine learning methods are more suitable for near-shallow-buried areas, dealing with small-scale data and discrete data. Full article

Coal and gas outbursts pose significant threats to underground personnel, making the development of accurate prediction models crucial for reducing casualties. By addressing the challenges of highly nonlinear relationships among predictive parameters, poor interpretability of models, and limited sample data in existing studies, this paper proposes an interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine (AFT-Transformer-SVM) model with high predictive accuracy. The Ali Baba and the Forty Thieves (AFT) algorithm is employed to optimise a Transformer-based feature extraction, thereby reducing the degree of nonlinearity among sample data. A Transformer-SVM model is constructed, wherein the Support Vector Machine (SVM) model provides negative feedback to refine the Transformer feature extraction, enhancing the prediction accuracy of coal and gas outbursts. Various classification assessment methods, such as TP, TN, FP, FN tables, and SHAP analysis, are utilised to improve the interpretability of the model. Additionally, the permutation feature importance (PFI) method is applied to conduct a sensitivity analysis, elucidating the relationship between the sample data and outburst risks. Through a comparative analysis with algorithms such as eXtreme gradient boosting (XGBoost), k-nearest neighbour (KNN), radial basis function networks (RBFNs), and Bayesian classifiers, the proposed method demonstrates superior accuracy and effectively predicts coal and gas outburst risks, achieving 100% accuracy in the sample dataset. The influence of parameters on the model is analysed, highlighting that the coal seam gas content is the primary factor driving the outburst risks. The proposed approach provides technical support for coal and gas outburst predictions across different mines, enhancing emergency response and prevention capabilities for underground mining operations. Full article

Deep coal mining is essential for energy use and sustainable development. In a situation where coal–rock–gas dynamic disasters are prone to occur in coal seam variation areas affected by different degrees of roof angle during deep coal seam mining, a disaster energy equation considering the influence of roof elastic energy is established, and the disaster energy criterion considering the influence of roof elastic energy is derived and introduced into COMSOL6.1 software for numerical simulation. The results show that, compared with the simple change of coal thickness and coal strength, the stress concentration degree of a thick coal belt with small structure is higher, and the maximum horizontal stress can reach 47.6 MPa. There is a short rise area of gas pressure in front of the working face, and the maximum gas pressure reaches 0.82 MPa. The plastic deformation of the coal body in a small-structure thick coal belt is the largest, and the maximum value is 18.04 m³. The simulated elastic energy of rock mass is about one third of that of coal mass, and the influence of the elastic energy of roof rock on a disaster cannot be ignored. When the coal seam is excavated from thin to thick with a small-structural thick coal belt, the peak value of the energy criterion in front of the excavation face is the largest, and the maximum value is 1.42, indicating that a dynamic disaster can occur and the harm degree will be the greatest. It is easy to cause a coal and gas outburst accident when the excavation face enters a soft coal seam from a hard coal seam and a small-structural thick coal belt from a thin coal belt. Practice shows that holistic prevention and control measures based on high-pressure water jet slit drilling technology make it possible to increase the average pure volume of gas extracted from the drilled holes by 4.5 times, and the stress peak is shifted to the deeper part of the coal wall. At the same time, the use of encrypted drilling in local small tectonic thick coal zones can effectively attenuate the concentrated stress in the coal seam and reduce the expansion energy of gas. This study enriches our understanding of the mechanism of coal–rock–gas dynamic disaster, provides methods and a basis for the prevention and control of dynamic disaster in deep coal seam variation areas, and promotes the sustainable development of energy. Full article

To address the issues of low efficiency in manual processing and lack of accuracy in judgment within traditional mine gas safety inspections, this paper designs and implements the Intelligent Mine Gas State Decision-Making System based on large language models (LLMs) and a multi-agent system. The system aims to enhance the accuracy of gas over-limit alarms and improve the efficiency of generating judgment reports. The system integrates the reasoning capabilities of LLMs and optimizes task allocation and execution efficiency of agents through the study of the hybrid multi-agent orchestration algorithm. Furthermore, the system establishes a comprehensive gas risk assessment knowledge base, encompassing historical alarm data, real-time monitoring data, alarm judgment criteria, treatment methods, and relevant policies and regulations. Additionally, the system incorporates several technologies, including retrieval-augmented generation based on human feedback mechanisms, tool management, prompt engineering, and asynchronous processing, which further enhance the application performance of the LLM in the gas status judgment system. Experimental results indicate that the system effectively improves the efficiency of gas alarm processing and the quality of judgment reports in coal mines, providing solid technical support for accident prevention and management in mining operations. Full article

To investigate the molecular structure characteristics and chemical reaction mechanisms of gas coal from the Hong II coal mine of the Ningxia Hongdunzi Coal Industry, this study explores its elemental composition, structural features, and methods for constructing and optimizing molecular models. The basic properties of the coal were determined through proximate and elemental analyses. The carbon structure was characterized using ¹³C-NMR nuclear magnetic resonance, the N and S chemical states were analyzed with XPS, and the distribution of hydroxyl, aliphatic hydrocarbons, aromatic rings, and oxygen-containing functional groups was characterized by FT-IR. Based on the analysis results, a molecular structure model of Hongdunzi gas coal was constructed with the molecular formula C₂₀₄H₁₁₇O₁₇NS, and the calculated results of the model showed high consistency with the experimental spectra of ¹³C-NMR. The macromolecular model of gas coal was constructed using the Materials Studio 2020 software, and its structure was optimized through geometric optimization and dynamic simulations. After optimization, the total energy of the model was significantly reduced from 8525.12 kcal·mol⁻¹ to 3966.16 kcal·mol⁻¹, highlighting the enhanced stability of the coal molecular structure. This optimization indicates that torsional energy plays a dominant role in molecular stability, while van der Waals forces and electrostatic interactions were significantly improved during the optimization process. Full article

Traditional coal mine gas risk assessment relies on manual operations, leading to inefficiencies, incomplete information integration, and insufficient evaluation accuracy, ultimately affecting safety oversight. This paper proposes an intelligent gas risk assessment and report generation framework (IGRARG) based on fine-tuning a Generative Language Model (GLM) to address these challenges. The framework integrates multi-source sensor data with the reasoning capabilities of large language models (LLMs). It constructs a gas risk dataset for coal mine safety scenarios, fine-tuned with GLM. Incorporating industry regulations and a domain-specific knowledge base enhanced with a Retrieval-Augmented Generation (RAG) mechanism, the framework automates alarm judgment, suggestion generation, and report creation via a hierarchical graph structure. Real-time human feedback further refines decision making. Experimental results show an evaluation accuracy of 85–93%, with over 300 field tests achieving a 94.46% alarm judgment accuracy and reducing weekly report generation from 90 min to 2–3 min. This framework significantly enhances the intelligence and efficiency of gas risk assessment, providing robust decision support for coal mine safety management. Full article

Due to changes in the German government’s energy concept, the amount of gypsum produced in flue gas desulfurisation plants (FGD gypsum) will fall from 5 million tons per year to 1 million tons or less by 2038 at the latest. As of 2016, FGD gypsum accounts for 55% of German gypsum mix. The resulting raw material gap must be closed through innovative recycling concepts, such as the processing of existing mine dumps. The process development aims to achieve a calcium sulfate dihydrate content of 85% and a reduction in the stockpile volume by 50%. The main components of the stockpiles are calcium sulfate in the form of gypsum stone as well as clay minerals and organic matter. Successful laboratory tests were transferred to a pilot scale jigging machine with dewatering screening. The process water is circulated throughout the entire process. The gypsum content in the heavy fraction is 76% when measured with ICP OES and 87% when measured via thermogravimetric methods. Furthermore, pilot-scale dry screening on the stockpile took place, and up to 1500 tons of material could be processed. Due to fluctuating weather conditions, the screening quality was subject to significant variations. Under optimal conditions, up to 60% of the feed could be recovered as gypsum stone; however, the screening process was nearly impossible during rain; therefore, a process combination of screening and a downstream jigging machine is recommended. Full article

In this study, in a three-phase reactor with a rectangular cross-section, the effects of liquid circulation rates and solid particle concentration on gas holdup and bubble size distribution (BSD) were investigated. Air, water, and glass beads were used as the gas, liquid, and solid phases, respectively. Different liquid circulation velocities and different solid loads were applied. The results demonstrate that increasing solid content from 0% to 6% can decrease gas holdup by 50% (due to increased slurry phase viscosity and promotion of bubble coalescence). Also, increasing the liquid circulation rate showed a weak effect on gas holdup, although a slight incremental effect was observed due to the promotion of bubble breakup and the extension of bubble residence time. The gas holdup in counter-current slurry bubble columns (CCSBCs) was predicted using a novel correlation that took into account the combined effects of solid concentration and liquid circulation rate. These findings are crucial for the design and optimization of the three-phase reactors used in industries such as mining and wastewater treatment. Full article

Developing a green energy strategy for municipalities requires creating a framework to support the local production, storage, and use of renewable energy and green hydrogen. This framework should cover essential components for small-scale applications, including energy sources, infrastructure, potential uses, policy backing, and collaborative partnerships. It is deployed as a small-scale renewable and green hydrogen unit in a municipality or building demands meticulous planning and considering multiple elements. Municipality can promote renewable energy and green hydrogen by adopting policies such as providing financial incentives like property tax reductions, grants, and subsidies for solar, wind, and hydrogen initiatives. They can also streamline approval processes for renewable energy installations, invest in hydrogen refueling stations and community energy projects, and collaborate with provinces and neighboring municipalities to develop hydrogen corridors and large-scale renewable projects. Renewable energy and clean hydrogen have significant potential to enhance sustainability in the transportation, building, and mining sectors by replacing fossil fuels. In Canada, where heating accounts for 80% of building energy use, blending hydrogen with LPG can reduce emissions. This study proposes a comprehensive approach integrating renewable energy and green hydrogen to support small-scale applications. The study examines many scenarios in a building as a case study, focusing on economic and greenhouse gas (GHG) emission impacts. The optimum scenario uses a hybrid renewable energy system to meet two distinct electrical needs, with 53% designated for lighting and 10% for equipment with annual saving CAD$ 87,026.33. The second scenario explores utilizing a hydrogen-LPG blend as fuel for thermal loads, covering 40% and 60% of the total demand, respectively. This approach reduces greenhouse gas emissions from 540 to 324 tCO₂/year, resulting in an annual savings of CAD$ 251,406. This innovative approach demonstrates the transformative potential of renewable energy and green hydrogen in enhancing energy efficiency and sustainability across sectors, including transportation, buildings, and mining. Full article

Investigation of the critical metal elements in coal and coal-bearing strata has become one of the hottest research topics in coal geology and coal industry. Coal-hosted Ga-Al-Li-REE deposits have been discovered in the Jungar and Daqingshan Coalfields of Inner Mongolia, China. Gallium, Al, and Li in the Jungar coals have been successfully extracted and utilized. This paper reviews the discovery history of coal-hosted Ga-Al-Li-REE deposits, including contents, modes of occurrence, and enrichment origin of critical metals in each coal mine, including Heidaigou, Harewusu, and Guanbanwusu Mines in the Jungar Coalfield and the Adaohai Coal Mine in the Daqingshan Coalfield, as well as the recently reported Lao Sangou Mine. Gallium and Al in the coals investigated mainly occur in kaolinite, boehmite, diaspore, and gorceixite; REEs are mainly hosted by gorceixite and kaolinite; and Li is mainly hosted by cholorite. Gallium, Al, and REEs are mainly derived from the sediment-source region, i.e., weathered bauxite in the Benxi Formation. In addition, REE enrichment is also attributed to the intra-seam parting leaching by groundwater. Lithium enrichment in the coals is of hydrothermal fluid input. The content of Al₂O₃ and Ga in coal combustions (e.g., fly ash) is higher than 50% and ~100 µg/g, respectively; concentrations of Li in these coals also reach the cut-off grade for industrial recovery (for example, Li concentration in the Haerwusu coals is ~116 µg/g). Investigations of the content, distribution, and mineralization of critical elements in coal not only provide important references for the potential discovery of similar deposits but also offer significant coal geochemical and coal mineralogical evidence for revealing the geological genesis of coal seams, coal seam correlation, the formation and post-depositional modification of coal basins, regional geological evolution, and geological events. Meanwhile, such investigation also has an important practical significance for the economic circular development of the coal industry, environmental protection during coal utilization, and the security of critical metal resources. Full article

Mine gas emission is one of the main causes of gas disasters. In order to achieve the accurate prediction of gas emission, a gas emission prediction model based on the random forest (RF) method was proposed in combination with the analysis of its influencing factors. The prediction results were compared with the support vector regression (SVR) and BP neural network (BPNN) methods, and then they were verified and analyzed through the Dongqu coal mine. The results show that the gas emission prediction model based on random forest has strong generalization and robustness, and RF has a wide range of parameter adaptation during the modeling process. When the number of trees (n_tree) exceeds 100, its training error tends to stabilize, and changes in n_tree have no substantial impact on the prediction performance. The SVR prediction model has significant bias in both the training and testing stages. Meanwhile, the BPNN model has excellent prediction results in the training phase, but there is a large error in the testing stage, which indicates that there is an “overfitting” phenomenon in the training stage, resulting in weak generalization. The evaluation of variable importance shows that the extraction rate, coal seam depth, daily production, gas content in adjacent layers, and coal seam thickness have a significant impact on gas emission. Meanwhile, through application analysis, it is further demonstrated that the random forest method has high accuracy, strong stability, and universality, and it can achieve good predictive performance without the need for complex parameter settings and optimization, making it is very suitable for predicting gas emission. Full article

Accurate prediction of methane concentration in mine roadways is crucial for ensuring miner safety and enhancing the economic benefits of mining enterprises in the field of coal mine safety. Taking the Buertai Coal Mine as an example, this study employs laser methane concentration monitoring sensors to conduct precise real-time measurements of methane concentration in coal mine roadways. A prediction model for methane concentration in coal mine roadways, based on an Improved Black Kite Algorithm (IBKA) coupled with Informer-BiLSTM, is proposed. Initially, the traditional Black Kite Algorithm (BKA) is enhanced by introducing Tent chaotic mapping, integrating dynamic convex lens imaging, and adopting a Fraunhofer diffraction search strategy. Experimental results demonstrate that the proposed improvements effectively enhance the algorithm’s performance, resulting in the IBKA exhibiting higher search accuracy, faster convergence speed, and robust practicality. Subsequently, seven hyperparameters in the Informer-BiLSTM prediction model are optimized to further refine the model’s predictive accuracy. Finally, the prediction results of the IBKA-Informer-BiLSTM model are compared with those of six reference models. The research findings indicate that the coupled model achieves Mean Absolute Errors (MAE) of 0.00067624 and 0.0005971 for the training and test sets, respectively, Root Mean Square Errors (RMSE) of 0.00088187 and 0.0008005, and Coefficient of Determination (R²) values of 0.9769 and 0.9589. These results are significantly superior to those of the other compared models. Furthermore, when applied to additional methane concentration datasets from the Buertai Coal Mine roadways, the model demonstrates R² values exceeding 0.95 for both the training and test sets, validating its excellent generalization ability, predictive performance, and potential for practical applications. Full article

Transmission muography is an imaging technique that allows us to obtain two-dimensional and three-dimensional average-target density images by measuring the transmission of atmospheric muons. Through this technique, it is possible to observe density anomalies inside a target volume and locate them three-dimensionally. In this work, the potential of the technique will be illustrated through the description of the results of two measurements carried out in the tourist path of the Temperino mine (Livorno, Italy) in an area where a higher concentration of Radon gas is measured. This section of the gallery, located at a depth of about 50 m and dating back to the Etruscan period, might contain ancient cavities not yet discovered that could represent preferential conduits into which Radon gas is released into the tourist route. The muographic results are illustrated, focusing on the search for low-density anomalies attributable to cavities. The measurements are part of the MIMA-SITES project aimed at ensuring the safety of specific zones within the Temperino mine. Full article