Coal Mine Production Research Articles

Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environments

Abstract Fault diagnosis of belt conveyors is crucial for coal mine production, but audio-based fault diagnosis in underground coal mines remains challenging due to the strong noise environment. To address this problem, a method for audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional networks in a strong noise environment is proposed. Firstly, the improved beluga whale optimization is designed by introducing the non-linear balance factor and non-linear probability and combining them with the proposed cyclical shock factor to optimize the variational modal decomposition parameters to achieve noise reduction and signal reconstruction. Secondly, an improved adaptive noise reduction convolutional network is developed using an adaptive threshold activation function and an improved loss function to enhance noise robustness and fault diagnosis accuracy. Finally, the proposed method's effectiveness is evaluated in low and strong noise environments, with experimental results demonstrating superior fault diagnosis performance. In low noise environments, the fault diagnosis accuracy is 98.61%, and in strong noise environments, it is 98.96%, outperforming existing fault diagnosis methods.

Study on Variation Law of Hazardous Areas in Goaf Based on Coal Oxidation Kinetics Theory

ABSTRACT Regarding various problems on safety production in coal mines, residual coal in goaf’s spontaneous combustion is extensive, determining its hazardous areas is critical to extinguish or prevent fires in goaf. Taking production practices at the 010803-working face of Wang Wa Coal Mine as background, a combined approach involving on-site experiments, laboratory tests, and numerical simulations is adopted to investigate “three-zone” spontaneous combustion as well as the distribution patterns of temperature fields in shallowly buried coal seams. The mathematical and physical models of spontaneous combustion in goaf are constructed. Upon oxidation of coal at low temperatures, experimental data about the intensity of release of heat and the rate of consumption of oxygen inform the construction of a kinetic model for coal oxidation in goaf. Utilizing field-measured data on air leakage, a distribution model for permeability within the working face’s goaf is constructed. To study combustion “three-zone” and temperature field distribution patterns in goaf under various air supply volumes, as well as to quantitatively analyze the relationship between the width and area of oxidation zones, the area of high-temperature areas, and the distribution of high-temperature sites in the goaf, with relation to the working face’s supply volumes of air. The results show that the maximum simulated air leakage in the goaf of the working face is 148.03 m3/min, with a relative error of just 1.4% compared to the measured value. Furthermore, validation confirms that the simulated oxygen concentration variation pattern closely aligns with the measured oxygen concentration. As the airflow rate at the working face increases, the oxidation zone width at the intake side gradually grows. While on the return side, it gradually decreases, resulting in a maximum width difference of 22.6 m. The goaf area’s concentration of oxygen exhibits an “S” shape distribution pattern. The overall temperature change range within the high-temperature zone of the goaf is 4.5 K, gradually migrating toward the deeper areas. As the air supply rate rises, oxidation zone area gradually decreases, exhibiting an overall sinusoidal distribution. Concurrently, the high-temperature zone area progressively expands, conforming to a Boltzmann function distribution. When the Air supply rate is 800 m3/min to 1120 m3/min, the areas of the two are linearly correlated with the air supply volumes. Under varying air supply volumes, the position of the high-temperature point within the goaf changes in both strike and dip directions, and the relative distance with the intake air corner follows an exponential function in both strike and dip directions. Through comprehensive analysis, it is concluded that the actual air supply volume on-site should ideally range between 800 m3/min and 1120 m3/min. This range effectively prevents and controls the hazardous area of spontaneous combustion in the goaf and optimizes the ventilation scheme.

A Digital Twin-Based Adaptive Height Control for a Shearer

The shearer is an important component of the smart mining workface, and its effective control is a key aspect that guarantees safe and high-efficient production in coal mines. To address the issue of autonomous height adjustment during the shearer’s cutting process, a self-adaptive speed control method driven by digital twin technology is proposed. A digital twin-based control architecture for the shearer is first established, which consists of physical and the corresponding virtual entities, as well as reality–virtual interaction between them. Based on the mathematic model formulated for height adjustment system of the shearer, an adaptive fuzzy sliding mode controller (AFSMC) with the displacement estimation is designed for the virtual entity, with the purpose of guiding the operation of the corresponding physical entity. Simulation experiments on MATLAB compares the control performance among the proposed method and four comparative ones, including PID controller, integral sliding mode controller (ISMC), feedback linearization controller (FLC), and fuzzy sliding mode controller (FSMC). The experimental results confirm the effectiveness of the proposed AFSMC. More specifically, its steady-state error is 0.024, the maximum absolute control input is 8.43, and the settling time is 1.74 s. This also proves that the digital twin-based control method enables the precise adaptive height adjustment of the shearer, providing potential reference for the intelligent development of a smart mining workface.

Low-carbon development path based on carbon emission accounting and carbon emission performance evaluation: a case study of Chinese coal production enterprises.

Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11-2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect.

Experimental study on the mechanical characteristics of weakly cemented mudstone under different loading rates

With the gradual shift of coal mining to the western coal mining region of China, floor heave in weakly cemented mudstone roadways has become an issue affecting the safety and efficiency of coal mine production. Additionally, different mining rates can lead to fluctuating support stresses on the roof and floor of weakly cemented mudstone roadways. Therefore, obtaining a comprehensive understanding of the mechanical properties of weakly cemented mudstone at different loading rates is conducive to improving the issue of floor heave in such roadways and provides a theoretical basis for further study. In this context, a series of uniaxial mechanical tests with concurrent acoustic emission monitoring were conducted on specimens of weakly cemented mudstone under various loading rates (0.005, 0.01, 0.05, and 0.1 mm/s). The stress‒strain and acoustic emission response curves were obtained to effectively characterize the strength, deformation, damage, macroscale instability, and crack propagation characteristics of the mudstone under the influence of loading rate effects. The research results support the following findings: (1) With increasing loading rate, the peak strength and elastic modulus of weakly cemented mudstone significantly increase, while the peak axial strain and peak radial deformation significantly decrease. (2) With increasing loading rate, the stress required to trigger the expansion of weakly cemented mudstone gradually increases, and a significant power-law relationship arises between the strain of the mudstone at the start of expansion and the loading rate. (3) With increasing loading rate, the acoustic emission ringing count of weakly cemented mudstone increases: The failure of weakly cemented mudstone changes from small-range progressive failure to sudden failure, and the failure mode transitions from shear failure to tensile‒shear composite failure. (4) The studied mudstone damage variables increase with increasing loading rate, following an approximate exponential function. The conclusions obtained in this work can provide a theoretical basis for the evolution mechanism and control of floor heave in deep roadway mining.

Effect of low blockage ratio obstacle on explosion characteristic in methane/air mixture

Methane/air explosion is one of the common hazards in process and mining industries. In this study, the methane explosion propagation is studied, and two-dimensional configuration is considered. The gas phase equations are solved using an OpenFOAM code for compressible reacting flow, EXiFOAM. The effects of the low blockage ratio (LBR) obstacles are investigated. The results show that the flame propagation speed, flame structure, shock wave propagation, and gas flow are considerably affected by LBR obstacles. Specifically, the flame propagation speed first increases and then decreases. As the initial pressure increases, the flame propagation speed gradually increases. The maximum speed is up to 500 m/s, when the initial pressure is 1.5 MPa. If the flame propagation is prevented, resulting in the flame deformation. Moreover, the flame disruptive phenomenon is captured due to the reflected waves. In front of the obstacle, the cellular structures of the high-pressure distribution are formed. Note that the tiny cellular structures are produced in the wake of the leading shock. Furthermore, a high-speed flow region and two low-speed flow regions are produced around the LBR obstacles. It is found that as the initial pressure increases, the explosion pressure is larger, while the temperature change is not obvious. These research findings have implications for enhancing safety production in coal mines.

Research on the optimal design of roadway support for fully mechanized coal seam working face

Abstract For the optimal design of surrounding rock deformation and support in the shallow buried coal seam of large-scale fully mechanized mining face, this paper took the surrounding rock of the roadway as the research object, used theoretical calculation and numerical simulation to establish the mechanical model of the deformation of the surrounding rock structure, and proposed and analyzed the reasonable roadway support scheme of the shallow buried coal seam. The numerical simulation results showed that the top anchor rod with Φ18×2200 mm and the inter-row distance of 1100×1000 mm, the anchor cable with Φ15.24×6300 mm and the inter-row spacing of 2200×2000 mm, the upper part anchor of Φ16×2200 mm, and the C30 concrete with a thickness of 200 mm for laying the bottom plate are selected, which was certified as the optimal support scheme. In practical engineering applications, the deformation of surrounding rock has been effectively controlled, and the supporting effect of surrounding rock was obvious, which was of great significance to ensure the safe production of coal mines.

Experimental study on classification and identification method for fault signals of mining steel wire ropes

Abstract In the coal mining production process, wire ropes play a crucial role as the main tool for transporting personnel and materials in the entire coal mine hoisting system, and their fault identification is particularly critical. To address the problem of fault identification of mining wire ropes, a recognition model design combining K Singular Value Decomposition (SVD) with Genetic Algorithm (GA) optimized Support Vector Machine (SVM) is proposed. Firstly, K Singular Value Decomposition is used to denoise the collected wire rope damage signals. Feature vectors are extracted from the denoised and reconstructed signals to form the feature set of wire rope damage. Genetic Algorithm is utilized to select the optimal parameters of the Support Vector Machine and train it to obtain the optimal identification model for wire rope damage. Simulation and experimental results show that compared with wavelet thresholding and Empirical Mode Decomposition (EMD) denoising algorithms, the method using K Singular Value Decomposition for denoising achieves a signal-to-noise ratio of 25.166dB and a root mean square error of 0.805. Compared with Particle Swarm Optimization (PSO) optimized Support Vector Machine identification model, the identification rate of the Support Vector Machine identification model optimized by Genetic Algorithm is higher, reaching 96.67%. This method realizes accurate identification of different damage signals of wire ropes, which is conducive to ensuring the safe and efficient production of coal mines.

Оценка хозяйственных рисков при реализации технологий угледобычи в современных условиях недропользования

Introduction. This paper presents research in the field of methodology of assessment and monitoring of associated under ground coalmining risks in order to level their impact on the resulting technical and economic indicators of production and economic activities in modern conditions of subsoiluse. The functioning of technological systems of coal mining enterprises at the present stage of subsurface use occurs in a highly dynamic and highly variable functional environment with a fair degree of uncertainty, which is generated by a significant variety of parameters and characteristics of the initial mining, geological and mining engineering environment, which, in turn, generates the emergence of various groups and risk factors for subsurface use. The established procedures and methods of risk forecasting currently do not give satisfactory results due to the globalization of world markets and high volatility of prices for coal products. Leveling the negative degree of influence of the risk component on the resulting indicators – indicators of technical and economic activity can be achieved through the introduction of an operational system for assessing and monitoring various groups of risk factors. Materials and methods. For the purpose of evaluation, it is proposed to use the methodological and methodological foundations of the method called “vector norms”, which involves the use of Affine space, the constituent areas of compromises and the introduction of utility estimates. A complete model representation of the assessment and monitoring of the impact of various risk factors of subsurface use, taking into account their clustering and ranking, is presented. The algorithmic support of this method is reduced to the following components-iterations: 1. Formation of criteria-based assessment matrices of various factors of subsurface use risk groups. 2. Assignment of optima to evaluation criteria. 3. In order to ensure the legality of the use and implementation of mathematical procedures and operations, the reduction of multi-dimensional evaluation criteria to a relative form, taking into account the assigned optima. 4. Formation of the objective function of the “norm of the vector” (integral indicator) and the development of a procedure for selecting the optimal option. Since the use of the methodological and methodological foundations of the method called “vector norms” allows us to form quantitative estimates of the presented factors-types of risk of subsurface use, this makes it possible to implement the clustering procedure or ranking of a selected set of coal mining enterprises in conjunction with existing risk areas. Results. Quite high values of correlation coefficients were obtained with a significant closeness of the relationship of inversely proportional dependencies of generalizing integral indicators of the degree of risk and the level of achievement of planned indicators and development of production capacity with the level of operational profitability from the conduct of production and economic activities of coal mining enterprises with a directly proportional dependence on the operating costs of mining 1t of coal. Discussion. Сomplete model representation of the assessment and monitoring of the impact of various risk factors of subsurface use, taking into account their clustering and ranking, is presented. As a practical component of the research, variable series of areas of integral degree of risk in the functional environment of Kuzbass coal mines are presented. The use of software modules for correlation and regression analysis, taking into account three-year retrospective dynamics, made it possible to establish the legality and objectivity of the proposed methodological apparatus for integrated risk assessment of the implementation of coal mining technologies in modern conditions of subsurface use. Conclusion. The results of the implementation of the theoretical components of the methodology and model representations of the developed conceptual foundations for the integrated risk assessment of the implementation of coal mining technologies in modern conditions of subsurface use, taking into account five-year retrospective dynamics, showed that in the Kuznetsk basin there was a steady tendency to reduce the cluster of coal mines, which is characterized by high risks of carrying out production and economic activities in the presence of a disproportion of resource potential (the emergent part). The accompanying processes of formation of this trend are manifested in a quantitative increase in the functional units of the rating cluster of group 2B with an average resource potential and inherent moderate risk. It is within this cluster that strategies for prioritizing investments for the development and renewal of technological systems and structures of coal mining are being formed, since only they are able to provide the fastest possible return on invested funds. Resume. The article presents the results of research in the field of methodology for assessing and monitoring the risks associated with underground coal mining in order to level out their impact on the resulting technical and economic indicators of production and economic activity in modern conditions of subsoil use. The research results can be useful in the implementation of coal mining technology in modern conditions of subsoil use.

Analysis of the Gas Safety Situation Based on the Schweizer-Sklar Rule.

In the coal mining process, a large amount of harmful gases will be produced, which we call "gas". The main component of gas is methane. After the methane concentration reaches a certain limit, an explosion will occur, seriously affecting the safety of the production of coal mines. Gas safety situational awareness is an important basis for gas early warning. In order to take appropriate measures for different levels of risk, the safety situation level is classified into five levels, which can be attributed to the classification problem in machine learning. We propose a gas security situation analysis model based on the Schweizer-Sklar rule (GSSAM-SSR) using the multiweighted Schweizer-Sklar triangular norm array combination rule (SSR). First, the raw data are preprocessed. The model uses K-means clustering with feature preference to determine the label column of the data set and principal component analysis to reduce the dimensionality of the collected high-dimensional gas-related data. Ten basic classification models are constructed, and the best performing models are selected as the basic classifier set by using the preorder method. Then, the SSR combination rule is proposed to combine the basic classifiers. Finally, considering the dynamic and quasi dynamic data of the mine, the basic classifier set and SSR combination rule are combined to construct the GSSAM-SSR model and applied to the gas security situation analysis. Experimental results show that the SSR combination rule achieves the best classification performance, exhibiting an accuracy of 94.43%, which is 2.51% higher than the accuracy of the gradient boosting decision tree model, which was the highest performing basic classifier and higher than the accuracies of the voting method and Bayesian combination rule.

Full-scale study on the coupling settling behavior of droplets and PM10 particles based on the CLSVOF method and wetting experiments

Coal mines produce large quantities of dust during production and transportation of coal, especially respirable particulate matter of diameter < 10 µm (PM10). This not only jeopardizes the health of workers but also pollutes the environment. Currently, spraying is the most commonly used dust reduction technology. Traditional spraying techniques use pure water; however, the wetting and condensing of dust by pure water droplets is poor, resulting in low dust reduction efficiency. To address this issue, we used orthogonal tests to determine the optimal ratio of composite dust-reducing agents. Using the coupled level set and volume of fluid (CLSVOF) method, we investigated the dynamic wetting process of PM10 when using solutions of dust-reducing agents. We found that a particle size ratio of ≥ 2 and an initial velocity of droplets of 20–40 m/s resulted in optimal wetting and encapsulation of dust particles by droplets. We then conducted macroscopic experiments and found that the settling efficiency of PM10 reached 88.98 % when using a 1.5-mm caliber fine atomizing nozzle and a spray pressure of 6 MPa. Our findings reveal the dynamic wetting law of droplets and dust during the process of spray dust reduction, which could lead to improved efficiency of dust-reducing agents and better management of respirable dust. These findings will help to improve clean production in coal mines and the occupational health of coal miners.

InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China

Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas.

Mine water inrush source discrimination model based on KPCA-ISSA-KELM.

In order to ensure the safety of coal mine production, a mine water source identification model is proposed to improve the accuracy of mine water inrush source identification and effectively prevent water inrush accidents based on kernel principal component analysis (KPCA) and improved sparrow search algorithm (ISSA) optimized kernel extreme learning machine (KELM). Taking Zhaogezhuang mine as the research object, firstly, Na+, Ca2+, Mg2+, Cl-, SO2- 4 and HCO- 3 were selected as evaluation indexes, and their correlation was analyzed by SPSS27 software, with reducing the dimension of the original data by KPCA. Secondly, the Sine Chaotic Mapping, dynamic adaptive weights, and Cauchy Variation and Reverse Learning were introduced to improve the Sparrow Search Algorithm (SSA) to strengthen global search ability and stability. Meanwhile, the ISSA was used to optimize the kernel parameters and regularization coefficients in the KELM to establish a mine water inrush source discrimination model based on the KPCA-ISSA-KELM. Then, the mine water source data are input into the model for discrimination in compared with discrimination results of KPCA-SSA-KELM, KPCA-KELM, ISSA-KELM, SSA-KELM and KELM models. The results of the study show as follows: The discrimination results of the KPCA-ISSA-KELM model are in agreement with the actual results. Compared with the other models, the accuracy of the KPCA-ISSA-KELM model is improved by 8.33%, 12.5%, 4.17%, 21.83%, and 25%, respectively. Finally, when these models were applied to discriminate water sources in a coal mine in Shanxi, and the misjudgment rates of each model were 28.57%, 19.05%, 14.29%, 23.81%, 9.52% and 4.76%, respectively. From this, the KPCA-ISSA-KLEM model is the most accurate about discrimination and significantly better than other models in other evaluation indicators, verifying the universality and stability of the model. It can be effectively applied to the discrimination of inrush water sources in mines, providing important guarantees for mine safety production.

Design of unattended gas pumping station system based on multi-source data fusion

Abstract Aiming at the problems that some data has been built and distributed in different subsystems during the transformation of the unattended system of an underground gas pumping pump station in Shanxi Tiandi Wangpo Coal industry, such as low data sharing and comprehensive utilization rate due to data isolation, and weak linkage execution ability, a method combining site-level data fusion and platform-level data fusion is proposed. The advantages and disadvantages of the fusion method are analyzed, and the PLC program and WinCC program are written for the corresponding fusion method. The practical application of the underground gas pumping pump station shows that the system has strong fusion ability, which can meet the integration of extraction parameters, environmental parameters, electric parameters, pipeline parameters, working conditions parameters, and other data required by the unattended system, improve the data utilization rate of the unattended system, and realize the complex control functions of the gas pumping system such as one-key start-stop and one-key rotation vacuum pump. The use of data-driven execution instead of manual operation effectively reduces the duty personnel of the underground gas pumping station, provides a guarantee for the less manned or unmanned operation of the gas pumping pump station, reduces the unplanned downtime of the pumping system, improves the efficiency of gas pumping, and ensures the safety of coal mine production.

Solid-liquid particle flow sealing mucus (PFSM) in enhancing coalbed methane (CBM) recovery: Multiple-perspectives analysis and mechanism insights

The paper presents an in-depth investigation of solid-liquid two-phase particle flow sealing mucus (PFSM) for enhanced coalbed methane (CBM) recovery. PFSM is composed of solid waste particles such as fly ash and rubber particles. The PFSM is assessed using multiple-perspectives analysis, which includes measurements of water retention, pressure expansion, rheological characteristics, adhesion, wettability, microscopy and field case studies. Single-factor and multi-factor studies are used to optimize slurry material ratios. The results show that PFSM has outstanding water retention characteristics, with over 80 % retention for 30 days at 30 °C, and impressive expansion performance, with 9.52 % at 0.6 MPa. The shear thinning curve conforms to the Cross model. After 30 s, the final contact angle is 75.17 °, which is about 6 ° smaller than that of cement based materials. Field experiments show that, after applying PFSM, the drainage gas concentration can be improved at least 60 % comparing to other sealing materials. This research findings could offer novel insights into enhancing CBM extraction, indicating potential for safer and more efficient coal mine production.

Selective adsorption of coalbed methane on multilayer defective graphene nanostructures and their Mn-modified nanostructures: A DFT study

Coalbed methane (CBM) is an unconventional natural gas with CH4 as the main component. It is found in coal seams and surrounding rocks. The development and exploitation of CBM is a promising approach to ensure the safety of coal mine production, reduce greenhouse effects, and increase clean energy resources. Based on the first-principles calculations in conjunction with a grand canonical Monte Carlo (GCMC) simulation, this paper aims to examine the effects of interlayer spacing and Mn modification on CBM and CO2 adsorption. We assess the CH4 storage performance in multilayered defective graphene nanostructures (MDGNs). Using density functional theory in conjunction with the Grimm correction method (DFT-D approach), both the adsorption energy and charge distribution between CBM components and extended line defects (ELD) are appropriately evaluated in the presence or lack of Mn modification (Mn-ELD). The GCMC simulation is implemented to investigate CH4 adsorption isotherms of MDGN and Mn-MDGN configurations at 298.15 K. The selective adsorption capabilities of MDGNs and Mn-MDGNs for CO2, N2, and CH4 are also systematically analyzed. The obtained results reveal that an Mn-MDGN configuration with an interlayer spacing of dGL=1.02 nm is effective in sequestering CO2 from CBM. In contrast, MDGN configurations with interlayer spacings of dGL=0.68 and 1.02 nm are more effective for the storage of purified CBM. In continuing, the CH4 purification mechanism in Mn-MDGNs and the CH4 storage mechanism in MDGNs are also methodically explored at the molecular level. The research work reported here can serve as a crucial guide for the future development and utilization of CBM.

A new IPCA-CPSO-BP Model for Predicting Gas Emission in Underground Coal Mines

Gas has become the primary factor restricting the safe and efficient production of coal mines. Gas emission prediction model is very important for mine gas emission prediction and gas disaster prevention. The improved principal component analysis (IPCA) was used to reduce the dimension of 13 influencing factors of gas emission, and the CPSO-BP neural network prediction model was built with MATLAB software to predict the absolute gas emission of Zhongtai Mining.The results show that the cumulative contribution rate of principal components is not less than 95%, and the number of principal components after improvement is reduced from the original 6 to 3, which effectively solves the problem of excessive principal components and data redundancy caused by the correlation difference between various influencing factors and gas emission amount. At the same time, the optimized particle swarm optimization algorithm alleviates the trouble that the particle swarm optimization algorithm is prone to fall into the local optimal solution. By coupling the improved particle swarm optimization algorithm with BP neural network, the problem of BP neural network's over-dependence on the initial value of weights and thresholds is solved, and the optimal initial weights and thresholds are provided for BP neural network, improving the accuracy of model prediction results. The average relative error of the original BP neural network is 1.6638%, the normalized mean square error is 0.3155, and the regression correlation index is 0.8157. The IPCA-CPSO-BP prediction model decreased to 0.5749%, 0.1143 and 0.9758, respectively. IPCA improves the data dimensionality reduction ability of principal component analysis, IPCA-CPSO-BP enhances the stability of particle swarm optimization algorithm, and significantly improves the prediction accuracy of BP model. The prediction trend is highly consistent with the actual value, which verifies the reliability of the model and provides strong data support for mine gas prevention and control.

Height Prediction of Water-Conducting Fracture Zone in Jurassic Coalfield of Ordos Basin Based on Improved Radial Movement Optimization Algorithm Back-Propagation Neural Network

The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4−2216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas.

Prediction of the creeping of AFC based on fuzzy reasoning and Bi-LSTM fusion iteration

The creeping of Armoured Face Conveyor (AFC) is an engineering problem that needs to be avoided in coal mining production process. In this paper, a method for predicting the creeping accident of AFC based on fuzzy reasoning and Bi-directional Long Short-Term Memory (Bi-LSTM) fusion iteration is put forward. Firstly, through the force analysis of the AFC and the fuzzy correlation analysis method in the actual operation process, the reasons for the creeping of AFC are analyzed; Secondly, according to the propulsion characteristics of the AFC in the time sequence development, the method of the AFC running track based on Bi-LSTM neural network is proposed; Then, on the basis of the virtual transformation of the prediction results, a judgment mechanism for the extent of the creeping of the AFC based on fuzzy evidence reasoning based on fuzzy comprehensive evaluation method and Dempster-Shafer evidence theory (D-S evidence theory) is established; Finally, the analysis on the creeping of 9711 full-mechanized mining face in Kaiyuan Mine under virtual environment after 6 cycles of continuous advancement shows that the extent of creeping of AFC is relatively high and coal mining accidents are likely to occur.

Nonlinear evolution characteristics and seepage mechanical model of fluids in broken rock mass based on the bifurcation theory

With the deep extension of coal mining in China, fault water inrush has become one of the major disasters threatening the safety production of coal mine. Based on the control equations of steady state and non-Darcy seepage in fractured rock mass, the multi-parameter nonlinear dynamic seepage equations of fractured rock mass are established in this paper. Based on the nonlinear dynamics theory, the function of the state variable in the system is derived, and the influence of the gradual change of non-Darcy flow factors on the structural stability of seepage system is studied. The research achievements show that there are three branches in the equilibrium state of the seepage system. Specifically, the stability of the equilibrium state changes abruptly near the limit parameter. The seepage dynamic system of fractured rock mass has the delayed bifurcation, and the coal mine disaster such as fault water inrush occurs easily at the bifurcation point. The research results are of great significance to enrich the theory of fault water inrush in coal mine, and to reveal the disastrous mechanism of fault water inrush and guide its prevention and control technology in coal mine, which can provide the theoretical reference for predicting the water seepage stability in fractured rock mass.