Search Results (43)

The idea of this research is to develop a method of airflow velocity measurement in underground mines having a network of long-distance crossing tunnels, where inspections of the ventilation system are required. Currently, this time-consuming procedure is conducted manually, but it has great importance when the mine configuration is subjected to changes. The method is based on the measurements of UAV trajectory deviation when it crosses the lateral air streams while moving along the tunnel. The signals of the gyroscope from the Inertial Measurement Unit (IMU) are used as indicators. The calibration of the proposed method has been conducted in laboratory conditions similar to real conditions. The minimal sensitivity of 0.3 m/s required by regulations is achievable for small drones, and the error is less than 5%. The maximum measured airflow velocity depends on the UAV model and its stabilization system. Recommendations are formulated for method implementation in practice. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

As global mineral resources at shallow depths continue to deplete, thermal hazards have emerged as a critical challenge in deep mining operations. Conventional localized cooling systems suffer from a fundamental inefficiency where their cooling capacity is rapidly dissipated by the main ventilation airstream. This study introduces the innovative concept of a “microclimatic circulation zone” implemented through a convection–radiation cooling system. The design incorporates a synergistic arrangement of dual fans and flow-guiding baffles that creates a semi-enclosed air circulation field surrounding the modular convection–radiation cooling apparatus, effectively preventing cooling capacity loss to the primary ventilation flow. The research develops comprehensive theoretical models characterizing both internal and external heat transfer mechanisms of the modular convection–radiation cooling system. Using Fluent computational fluid dynamics software, we constructed an integrated heat–moisture–flow coupled numerical model that identified optimal operating parameters: refrigerant velocity of 0.2 m/s, inlet airflow velocity of 0.45 m/s, and outlet aperture height of 70 mm. Performance evaluation conducted at a mining operation in Yunnan Province utilized the Wet Bulb Globe Temperature (WBGT) index as the assessment criterion. Results demonstrate that the enhanced microclimatic circulation system exhibits superior cooling retention capabilities, with a 19.83% increase in refrigeration power and merely 3% cooling capacity dissipation at a 7 m distance, compared to 19.23% in the conventional system. Thermal field analysis confirms that the improved configuration successfully establishes a stable microclimatic circulation zone with significantly more concentrated low-temperature regions. This effectively addresses the principal limitation of conventional systems where conditioned air is readily dispersed by the main ventilation current. The approach presented offers a novel technological pathway for localized thermal environment management in deep mining operations affected by heat stress conditions. Full article

This study reports a CFD investigation of spray-based dust suppression strategies in mining tunnels, focusing on the dynamic operation of roadheaders, onboard spraying systems, and water curtains. The simulations assess how these systems affect airflow patterns, velocity distributions, and pressure variations under various operating conditions. The results indicate that cutterhead sprays produce conical dispersion patterns directed toward the rear of the tunnel under forced ventilation, while transfer point sprays establish localized zones of extended residence time, with stable droplet distributions achieved in 3.5 s. Spray activation markedly increases local air velocity, with peak values near the cutterhead rising from 0.88 m/s to 32.29 m/s. Meanwhile, water curtains, modeled as porous media, induce stepwise pressure drops from 186.89 Pa to 91.15 Pa. These findings underscore the distinct effects of spraying and water curtain systems on tunnel ventilation and offer valuable insights for the design and optimization of airflow control and dust suppression in underground mining environments. Full article

This paper addresses the numerical simulation of unsteady, non-isothermal ventilation in a dead-end mine working of a potash mine excavated using a borer miner. During its operations, airflow can become unsteady due to the variable operating modes of the borer miner, the switching on and off of its motor cooling fans, and the movement of a shuttle car transporting ore. While steady ventilation in a dead-end working with a borer miner has been previously studied, the specific features of air microclimate parameter distribution in more complex and realistic unsteady scenarios remain unexplored. Our experimental studies reveal that over time, air velocity and, particularly, air temperature experience significant fluctuations. In this study, we develop and parameterize a mathematical model and perform a series of numerical simulations of unsteady heat and mass transfer in a dead-end working. These simulations account for the switching on and off of the borer miner’s fans and the movement of the shuttle car. The numerical model is calibrated using data from our experiments conducted in a potash mine. The analysis of the first factor is carried out by examining two extreme scenarios under steady-state ventilation conditions, while the second factor is analyzed within a fully unsteady framework using a dynamic mesh approach in the ANSYS Fluent 2021 R2. The numerical results demonstrate that the borer miner’s operating mode notably impacts the velocity and temperature fields, with a twofold decrease in maximum velocity near the cabin after the shuttle car departed and a temperature difference of about 1–1.5 °C between extreme scenarios in the case of forcing ventilation. The unsteady simulations using the dynamic mesh approach revealed that temperature variations were primarily caused by the borer miner’s cooling system, while the moving shuttle car generated short-term aerodynamic oscillations. Full article

This study addresses the abnormal emission of pressure-relieved methane under high-intensity mining conditions by integrating geostatistical inversion, FLAC^3D-COMSOL coupled numerical simulations, and stable carbon–hydrogen isotopic tracing. Focusing on the 12023 working face at Wangxingzhuang Coal Mine, we established a heterogeneous methane reservoir model to analyze the mechanical responses of surrounding rock, permeability evolution, and gas migration patterns under mining intensities of 2–6 m/d. Key findings include the following: (1) When the working face advanced 180 m, vertical stress in concentration zones increased significantly with mining intensity, peaking at 12.89% higher under 6 m/d compared to 2 m/d. (2) Higher mining intensities exacerbated plastic failure in floor strata, with a maximum depth of 47.9 m at 6 m/d, enhancing permeability to 223 times the original coal seam. (3) Isotopic fingerprinting and multi-method validation identified adjacent seams as the dominant gas source, contributing 77.88% of total emissions. (4) Implementing targeted long directional drainage boreholes in floor strata achieved pressure-relief gas extraction efficiencies of 34.80–40.95%, reducing ventilation air methane by ≥61.79% and maintaining return airflow methane concentration below 0.45%. This research provides theoretical and technical foundations for adaptive gas control in rapidly advancing faces through stress–permeability coupling optimization, enabling the efficient interception and resource utilization of pressure-relieved methane. The outcomes support safe, sustainable coal mining practices and advance China’s Carbon Peak and Neutrality goals. Full article

A PtPd/Al₂O₃ catalyst developed for the complete oxidation of methane from the ventilation air of underground coal mines is compared against a model PdO/Al₂O₃ catalyst. Although the PtPd/Al₂O₃ catalyst is substantially more active and stable than the model catalyst, the nature of active sites between the two catalysts is deemed to be fundamentally the same based on their response to different feed gas compositions and the evolution of surface CO adsorption complexes during time-resolved CO adsorption DRIFTS experiment. For both catalysts, coordinatively unsaturated Pd sites are considered the active centers for methane activation and the subsequent oxidation reaction. H₂O competes with CH₄ for the same active sites, resulting in severe inhibition. Additionally, the CH₄ oxidation reaction also causes self-inhibition. Taking both inhibition effects into consideration, a relatively simple kinetic model is developed. The model provides a good fit of the 72 sets of kinetic data collected on the PtPd/Al₂O₃ catalyst under practically relevant reaction conditions with CH₄ concentration in the range of 0.05–0.4%, H₂O concentration of 1.0–5.0%, and reaction temperatures of 450–700 °C. Kinetic parameters based on the model suggest that the CH₄ activation energy on the PtPd/Al₂O₃ catalyst is 96.7 kJ/mol, and the H₂O adsorption energy is −31.0 kJ/mol. Both values are consistent with the parameters reported in the literature. The model can be used to develop catalyst sizing guidelines and be incorporated into the control algorithm of the catalytic system. Full article

In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety. Full article

Effective fire prevention in mine workings and tunnels requires a thorough theoretical analysis of the heat and mass transfer processes within these structures. This involves using established models to calculate non-isothermal air flow dynamics in long tunnels and mine workings. While the ventilation of tunnels has been extensively studied, significant challenges persist regarding mine ventilation systems, particularly due to their complex and branched topology. This study aimed to address these challenges and gaps in mine ventilation. We designed a laboratory bench to simulate an inclined mine working with a heat source (fire) and validated a mathematical model of heat and mass transfer in such settings. Using experimental measurements, we verified the model’s accuracy. It is important to note that our experimental and theoretical analyses focused solely on the thermal effects of a fire, without considering the release of harmful impurities. Using the validated model, we conducted multiparameter simulations to identify the conditions leading to the formation of a thermal slug in an inclined mine working and the subsequent reversal of air flow. The simulation data enabled us to determine the dependency of the critical heat release rate on the aerodynamic parameters of the mine working. Additionally, we evaluated the changes in average air density within a mine working at the critical heat release rate. These findings are crucial for the further development of a network-based method to analyze air flow stability in mine ventilation networks during fires. Full article

Erosion damage can occur in fans and blowers during industrial processes, cooling, and mine ventilation. This study focuses on investigating erosion caused by particulate air flows in a centrifugal fan with forward-inclined blades. This type of fan is particularly vulnerable to erosion due to its radial flow component and flow recirculation. The flow field was solved separately, and the data transferred to the particle trajectory and erosion code. This in-house code implements the Lagrangian approach and the random walk algorithm, including statistical descriptions of particle sizes, release positions, and restitution factors. The study involved two types of dust particles, with a concentration between 100 and 500 μg/m³: The first type is the Saharan (North Africa) dust, which has a finer size between 0.1 and 100 microns. The second type is the Coarse Arizona Road Dust, also known as AC-coarse dust, which has a larger size ranging from 1 to 200 microns. The complex flow conditions within the impeller and scroll, as well as the concentration and size distribution of particles, are shown to affect the paths, impact conditions, and erosion patterns. The outer wall of the scroll is most heavily eroded due to high-impact velocities by particles exiting the impeller. Erosion is more pronounced on the pressure side of the full blades compared to the splitters and casing plate. The large non-uniformities of erosion patterns indicate a strong dependence with the blade position around the scroll. Therefore, the computed eroded mass is cumulated and averaged for all the surfaces of components. These results provide useful insights for monitoring erosion wear in centrifugal fans and selecting appropriate coatings to extend the lifespan. Full article

The mining industry faces persistent challenges related to hazardous gas emissions. Diesel engine-powered wheeled vehicles are commonly used during work shifts and are a primary source of nitrogen oxides (NOx) in underground mines. Despite diesel engine manufacturers providing gas generation data, mining companies need to predict NOx emissions from numerous load-haul-dumping (LHD) vehicles operating under dynamic conditions and not always equipped with gas sensors. This study focused on two ensemble methods: bootstrap aggregation (bagging) and least-square boosting (boosting) to predict NOx emissions. These approaches combine multiple weaker statistical models to yield a robust result. The innovation of this research is in the statistical analysis and selection of LHD vehicles’ working parameters, which are most suitable for NOx emission prediction; development of the procedure of source data cleaning and processing, model building and analyzing factors, which may influence the accuracy; and the comparison of two ensemble methods and showing their advantages and limitations for this specific engineering application, which was not previously reported in the literature. For datasets obtained from the same LHD vehicle and different operators, the more efficient bagging method gave a coefficient of determination R² > 0.79 and the RMSE (root mean square error) was under 30 ppm, which is comparable with the measurement accuracy for transient regimes of physical NOx sensors available in the market. The obtained insights can be utilized as input for mine ventilation systems, enhancing mining transport management, reducing workplace air pollution, improving work planning, and enhancing personnel safety. Full article

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process. Full article

The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine’s climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature $T_{f_{out}}$ and humidity ${RH}_{out}$ at the end of the water trickling roadway. The importance scores (ISs) indicate that $T_{f_{out}}$ is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While ${RH}_{out}$ is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control. Full article

This paper analyzes the airflow requirements of underground operations and the accurate assessment of future conditions so as to effectively adjust ventilation parameters. More particularly, ML techniques are utilized to capture patterns or prevailing conditions and to be able to generalize/predict future conditions managed via the ventilation system. The case examined is about underground bauxite mining operations, the ventilation characteristics and requirements of which have been firstly developed and modelled into a validated digital twin. With this twin model, several scenarios are developed and evaluated and more importantly data are gathered, allowing for the training of the ML algorithms used to assess and predict the required ventilation airflow, taking into account air quality data, the number of workers, and machine fleet. Full article

Air pressure monitoring is the basis of mining-intelligent ventilation. In order to optimize the coverage of monitoring nodes, the node importance in the ventilation network was taken as the optimization basis in this study. Two evaluation indexes of the extent of node coverage and the influence degree of nodes were obtained by analyzing the influence degree of node air pressure. The entropy weight method (EWM) was used to weigh the evaluation indexes to obtain the importance of all nodes in the ventilation network. A node layout method with node importance as the optimization of air pressure-monitoring nodes was proposed. The minimum distance correlation between the limited monitoring nodes and the monitored nodes was set as the constraint condition, and any air pressure monitoring node could only monitor its adjacent nodes. The nodes with high node importance were selected as air pressure-monitoring nodes in turn until the coverage of air pressure-monitoring nodes in the ventilation network was maximized. By applying the entropy weight method (EWM) and the clustering algorithm (CA) to the case mine, the research results show that the application of the entropy weight method (EWM) to optimize the air pressure-monitoring nodes was more feasible than the clustering algorithm (CA). The coverage rate was 81.6% at different constraint values, and the maximum coverage rate was 92.1%, which meets the needs of arranging the least air pressure-monitoring nodes to monitor the maximum range of air pressure changes and can carry out full coverage monitoring of mine air pressure. Full article