Abstract:In order to improve the prediction precision and accuracy of oil-type gas content in mines,we propose an accurate determ-ination method based on multivariate data fusion.Based on the main factors affecting the oil-type gas content,and 27 sets of actual measurement data from the mine were collected,and the XGBoost algorithm was used to screen out buried depth,roof and floor li-thology,fold and porosity as the key features which were standardized to ensure that the data with different magnitudes could be reasonably fused in the modeling process.Four classical machine learning algorithms,namely,Kriging interpolation,least squares support vector machine,multilayer perception and gradient boosted regression tree,were used for preliminary prediction,and com-parative analysis was carried out for the regression problem of oil-type gas content.The results show that the gradient boosting re-gression tree algorithm performs best in terms of prediction performance,with a coefficient of determination of 0.987,a normalized mean square error between 0.001 and 0.010,and a total information criterion between 0.019 and 0.046.The prediction accuracy is further improved by combining the Stacking algorithm.The Stacking method fuses the prediction results of multiple base learners as new features by using an improved whale optimization algorithm to optimize the weights of each base learner.In order to further im-prove the prediction ability of the model,a bidirectional long and short-term memory network is introduced,and the final fusion model is constructed through a meta-learning mechanism to deeply learn the prediction results of the base learners in order to cap-ture more complex nonlinear relationships and temporal information.The fusion model significantly outperforms the traditional single algorithm on the test set.The average absolute error of model prediction is 0.116 m3/t,the average value of the normalized mean square error is 0.006,the average value of the total information criterion is 0.004,and the coefficient of determination is higher than 0.98,which shows its high accuracy and stability in the prediction of oil-type gas content in mines.

Abstract:Under the condition of slicing recovering in giant thick coal seams,the intense repeated mining-induced disturbance causes the roof to be extremely fragmented.This leads to water gushing from the upper layer and the shallow working face,which then seeps downward and accumulates in the lower layer and the deep working face.The mechanism of this leakage process is com-plex and does not follow the traditional seepage theory.Instead,it exhibits distinct characteristics of fracture flow or pipe flow,mak-ing it difficult to accurately predict the water inflow at the working face under such conditions.Taking the 1412 working face of Barapukuria Coal Mine in Bangladesh as an example,the deficiencies and inapplicability of the traditional water inflow prediction method based on seepage theory under such complex conditions were systematically analyzed;subsequently,a new method for pre-dicting water inflow based on the quantitative estimation of leakage volume through the spatial position relationship of the working face,the mining sequence,and the water attenuation degree of adjacent working faces was proposed;at the same time,a groundwa-ter numerical model was constructed based on the ground water math simulation(GMS)software,and the water inflow during the mining of the 1412 working face was predicted using the numerical simulation method,and the evolution law of the groundwater flow field was revealed.The research results show that the numerical simulation based on the GMS Drain module has a fitting de-gree of 96%for the water inflow volume,with a relative error of 4%.The water volume reduction factor range established based on the leakage volume estimation method is 57%to 80%,and the fitting degree of the predicted water inflow volume is 97.8%,with a relative error of only 2.2%.Both the leakage volume estimation method and the numerical simulation method can accurately predict the water inflow volume under this complex working condition,providing an effective reference for the prediction of water inflow volume slicing recovering in similar conditions of slicing recovering in giant thick coal seams with strong disturbance or multiple coal seams superimposed mining.

Abstract:In view of the characteristics of slow subsidence and deformation,long deformation period,and strong disaster threat caused by collapse in the hanging goaf of Shenmu Coal Mine,taking the typical shallow coal seam mining in Shenbei Mining Area as an example,based on the synthetic aperture radar(SAR)remote sensing monitoring rapid scanning settlement deformation analys-is technology is mainly adopted,supported by fine exploration technologies such as drilling,peeping through the hole and lidar scan-ning through the hole.The long-term accurate monitoring and stability analysis of the surface deformation of large hanging roof goaf in coal mine are realized.The results show that:the maximum cumulative deformation amount of the mine is about-200 mm in the northern concentrated deformation area from December 2006 to January 2011,about-140 mm in the three central concentrated de-formation areas from December 2014 to July 2015,and about-970 mm in the three concentrated deformation areas from July 2015 to July 2023.The surface deformation in the north of the mine field is mainly due to the goaf collapse of coal 2-2,the control of sur-face fire area and the influence of coal 3-1 mining,while the surface deformation in the south is mainly due to the subsidence and compaction of backfill layer in the control of surface fire area.In the south of the mine field,the 3-1 coal strip type hanging roof goaf is generally stable.

Abstract:Character recognition on digital displays of coal mine equipment is one of the research hotspots in the intelligent construc-tion of coal mines.However,this field still faces two significant problems:poor recognition effect due to interference factors such as the small effective recognition area,complex underground lighting conditions,and low image quality;due to the constraints of the underground working environment in coal mines,the collection of sample data is limited,resulting in insufficient generalization abil-ity of the model.Aiming at the existing problems mentioned above,a character recognition algorithm for digital displays in coal mines based on PP-OCRv3(a practical ultra light weight optical character recognition,PP-OCR)transfer learning is proposed.Firstly,PP-OCRv3 is adopted as the pre-training model to improve the expression ability of general text features and enhance the ac-curacy of character detection and recognition in the complex environment of coal mines.Secondly,driven by the public data set for text recognition,the self-made digital display character data set,and the real and simulated coal mine digital display character data sets respectively,the PP-OCRv3 model was gradually migrated multiple times to drive the model to adaptively transform from the general scene to the special scene of the coal mine,achieving the improvement of the cross-scene generalization.The experimental verification shows that in the anti-interference ability test,the average accuracy of the migration optimization model reaches 78.83%(an increase of 17.29%),among which the improvement in the interference block scenario is particularly significant,reaching as high as 79.73%(an increase of 29.32%).The real-time evaluation shows that the average inference frame rate has increased by 27.295 fps,among which the increase in the fuzzy scene is as high as 57.67 fps.The PP-OCRv3 model after multiple migrations effectively re-duces the dependence on labeled data samples while having better recognition accuracy and recognition speed than the comparison models.

Abstract:To improve the prediction accuracy of the spontaneous combustion temperature of coal in goafs and accurately identify the combustion risks during the heating stage of the coal body,a prediction model(PLO-GBDT)for the spontaneous combustion temperature of coal in mined-out areas based on multi-parameter fusion is proposed.Programmed temperature experiments were con-ducted to collect data on the volume fractions of key indicator gases O2,CO,CO2,CH4,C2H6 and C2H4 as temperature increased,and a multi-parameter dataset for coal spontaneous combustion in goafs was constructed.Through the analysis of feature importance,the indicator gases that significantly affect the ignition temperature of coal spontaneous combustion were identified.O2,CO,CO2,CH4,C2H6 and C2H4 were selected as the input variables of the model.The polar light optimization(PLO)algorithm was used to optimize the parameters of the gradient boosting decision tree(GBDT)model.A PLO-GBDT model for predicting the ignition temperature of coal spontaneous combustion was constructed.The model used 70%of the sample data as the training set and 30%as the test set.The prediction performance of the model was evaluated using evaluation indicators such as MSE,RMSE,MAE,MAPE and R2.The results show that the MSE,RMSE,MAE,MAPE and R2 of the PLO-GBDT model prediction are 0.000 03,0.005 50,0.003 70,0.010 70 and 0.979 40 respectively.The established model has high fitting accuracy and stability.To verify the superiority of the model,it is compared with five other models.The results show that the R2 values of the PLO-GBDT model compared with the refer-ence models PLO-MLP,PLO-CNN,PLO-GRU,PLO-Informer and PLO-XGBoost are 0.967 7,0.659 7,0.785 3,0.846 1,0.895 6 and 0.948 5 respectively.

Abstract:To address the issues of frequent communication and limited concurrency in underground coal mine ultra-wideband(UWB)positioning systems using double-sided two-way ranging(DS-TWR)for one-dimensional localization,which traditionally re-quires two exchanges between devices to measure distance,this study proposes a rapid localization method that accomplishes net-work access and ranging within a single exchange.Firstly,in terms of communication mechanism,a random delay network access strategy is introduced to reduce the potential signal conflicts that may occur when multiple tag devices simultaneously request net-work access.At the same time,a channel sniffing function is added at the transmitter end,and the network access request is sent only when the channel is detected to be idle,rather than being sent directly as in traditional methods.This improvement can effectively avoid signal collisions when multiple devices simultaneously send network access requests,which could otherwise lead to access failure,thereby enhancing the stability of distance measurement.Further,by combining with the dual-antenna time-division multi-plexing technology,the polling efficiency and processing throughput of the reader in multi-tag scenarios are significantly improved.Secondly,in the ranging process,the separated access frame and ranging frame in the traditional method are integrated into a multi-functional ranging frame,enabling the system to simultaneously complete the access confirmation and distance measurement in a single signal interaction.By combining the timestamp information of the current round and historical rounds,a complete timestamp set for calculating distance is constructed,achieving the ranging effect equivalent to the traditional two-round interaction in a single interaction,effectively reducing communication overhead and ranging delay,and without the need for additional hardware resources,the concurrent access capability of the system has been significantly enhanced.Finally,the concurrent positioning performance of the system was verified at Wuhai Energy Laoshandan Coal Mine.The results showed that,while maintaining the same positioning accur-acy as the DS TWR,this method reduced the single measurement cycle to 3.3 ms,enabling concurrent ranging for 100 identification cards per second,and the ranging success rate reached as high as 99%.

Abstract:For the"long corridor"and"high dynamic"scenarios in underground coal mines,where the topology of the wireless sensor network(WSN)for gas inspection frequently changes,energy consumption and latency are difficult to balance,and abnormal alarms need to be forwarded deterministically,an energy-aware multi-objective dynamic scheduling method suitable for multiple business loads has been constructed.A network model based on the energy consumption of the radio transceiver as the cost was es-tablished.The two-segment path losses in free space and multipath,the signal-to-interference-plus-noise ratio(SINR)threshold,the listening energy consumption and queuing delay introduced by carrier sensing/binary exponential backoff in the medium access con-trol(MAC)layer,as well as three types of service constraints(periodic reporting,event-triggering,and abnormal verification)were explicitly taken into account.Based on this,a"coarse-fine"two-level collaborative optimization framework combining differential evolution(DE)and improved particle swarm optimization(IPSO)is proposed.The outer layer uses differential evolution to conduct global search in discrete dimensions(time slots,channels,inter-cluster concurrent combinations),while the inner layer employs im-proved particle swarm optimization with linearly decreasing inertia weight and contraction factor to achieve fine-grained conver-gence in continuous dimensions(transmission power,duty cycle,aggregation length).At the same time,a fuzzy weight adaptive mechanism with the average remaining energy and the length of the alarm queue as inputs was introduced,enabling the trade-off among"energy saving-reliability-delay"to switch online according to the network status.In a 100 m×600 m mining strip area,the number of nodes is 200.The base stations are deployed at the end of the simulation environment and on the 80 m"long corridor"physical platform.The proposed DE-IPSO cooperative scheduling(differential evolution-improved particle swarm optimization co-operative dynamic scheduling,DEIPSO-DS)is compared with the quantum-behaved particle swarm optimization with fuzzy logic baseline(QPSOFL)and the wireless sensor network adaptive differential evolution baseline(WSNADE).The evaluation indicators include the first node,half node,and final node death rounds(FND,HND,LND),the average energy consumption per round of the network,the variance of residual energy,the data packet delivery ratio(PDR),and the average end-to-end delay.In a scenario with an abnormality rate of approximately 5%,the FND of DEIPSO-DS is 1 763 rounds and the LND is 2 458 rounds,which are respect-ively 15.82%and 49.64%higher than those of QPSOFL and WSNADE.The average energy consumption per round is 0.118 J,which is 13.84%and 27.27%lower than the two baselines respectively;the variance of residual energy is 0.041 J2,and the PDR reaches 85.73%.The average latency is 168.4 ms,which is 17.15%and 30.36%shorter than the two baselines.Under approximately 10%and approximately 15%abnormal loads,the relative improvement of FND remains at 17.37%and 16.61%(compared to QPSOFL)and 41.17%and 43.15%(compared to WSNADE),energy consumption is reduced by approximately 15%and 27%,PDR is increased by approximately 12%and 30%,and latency is shortened by approximately 16%and 28%.The real measurement trend of 80 m physical platform is consistent with the simulation trend.The measured energy consumption is approximately 5%to 9%higher than the simulation,PDR is approximately 2%to 3%lower,and latency is approximately 7 to 11 ms higher.The dynamic scheduling framework based on energy consumption fine modeling and the two-level collaboration of DE-IPSO can achieve online reconfiguration of"nodes-channels-time slots-power"under the circumstances of topological migration and abnormal load fluctu-ations caused by tunneling advancement.It can jointly improve network lifetime,reliability and delay,and maintain stable gains un-der different abnormal densities.Subsequently,engineering expansion can be carried out in the directions of multi-convergence col-laboration,online parameter self-adaptation and more fine-grained MAC/physical layer joint optimization.

Abstract:Aiming at the problems of large demand and high cost of fly ash in traditional coal mine paste filling materials,the mech-anical properties and acid resistance of limestone powder instead of part of fly ash as admixture of paste filling materials were stud-ied.The optimal ratio of limestone powder paste filling material and its effect on acid resistance were determined by mixed orthogon-al test and limit test.The results show that the strength of filling body is the best when the paste ratio is 25%-50%of limestone powder replacement,25-38 μm of limestone powder fineness,190 kg/m3 of cement content and 82%of mass fraction.In the acid mine water,the limestone powder paste has both hydration reaction and decomposition reaction,and the compressive strength in-creases first and then decreases.Its acid resistance is positively correlated with the fineness of limestone powder and negatively cor-related with the substitution amount of limestone powder.When the substitution amount of limestone powder is 25%and the fine-ness of limestone powder is 25 μm,the paste has better acid corrosion resistance.

Abstract:Metal elements in the environment have a certain impact on key transformation processes in microbial metabolic path-ways.In order to study the effect of trace metal elements on the metabolism of microorganisms,the methane-oxidizing bacterial communities were screened from nature,and four representative metal elements,Cu2+,Fe2+,Mn2+,and Zn2+were selected.The meth-ane oxidation laws of microbial communities under different mass concentration conditions were obtained through experiments.The research results showed that Cu2+and Fe2+had a significant impact on the methane oxidation efficiency of bacterial communities.When the mass concentration of Cu2+was 0.03 g/L,the bacterial communities achieved their best CH4 degradation effect,with the highest CH4 degradation amount reaching 33.43 mL within 6 days;compared with Cu2+,the effect of Fe2+was more significant,with the increase of Fe2+mass concentration(0-0.6 g/L),the methane oxidation efficiency of the microbial community was gradually en-hanced,and the amount of CH4 degradation could be significantly increased from 13.02 mL to 37.28 mL within 6 days;Mn2+and Zn2+had a weak effect on the methane degradation efficiency of the microbial communities.When the mass concentrations of the two ions were 0.003 and 0.010 g/L,respectively,they had the best promoting effect on the methane oxidation efficiency of microbial communities,and the methane oxidation ability was weakened by elevating or decreasing the concentrations.

Abstract:Mine high-voltage cables play a crucial role in the power supply system of coal mines.The stable operation of mine high-voltage cables directly affects the normal operation of all underground electrical equipment in the coal mine.The insulation level of mine cables is the main influencing factor.During the long-term continuous operation of the cables,due to environmental and elec-trical factors,their insulation performance gradually deteriorates until insulation breakdown occurs,posing a threat to the safe pro-duction of coal mines.Partial discharge(PD)is one of the main means to monitor the insulation status of mine high-voltage cables.However,the monitoring of PD signals at the coal mine site is easily interfered by noise.To address this issue,this study proposes a Rime-ice Optimization Algorithm(RIME)to perform denoising processing on PD signals by optimizing the variational mode decom-position(VMD)combined with the singular value decomposition(SVD)method.Firstly,the VMD is optimized by the rime-ice op-timization algorithm(RIME),and the optimal parameters(K,α)are obtained through the minimum envelope entropy(Min-EE).Then,the intrinsic mode function(IMF)components are obtained through VMD.The nature of the IMF is determined by using the fuzzy dispersion entropy(FuzzyDispEn),so as to distinguish the effective signal components and the noise components.The noise-dominated components after classification are denoised by the SVD method,while the effective signal components are retained.Fi-nally,the noise-dominated signal components denoised by SVD and the effective signal components are reconstructed to complete the denoising process.Comparison denoising experiments are carried out with similar algorithms,such as the butterfly optimization algorithm(BOA)and the grey wolf optimizer(GWO).Through simulation experiments and on-site partial discharge experiments,the denoising effect diagrams are compared,and the signal-to-noise ratio(SNR),normalized cross-correlation coefficient(NCC),and root mean square error(RMSE)of the denoised signals are calculated.These are used to judge the advantages and disadvantages of the denoising effects of the three methods,namely RIME-VMD-SVD,GWO-VMD-SVD,and BOA-VMD-SVD.This proves the ef-fectiveness of the method proposed in this paper in denoising noisy PD signals.It can remove the noise components in the PD sig-nals of mine high-voltage cables,which has practical significance for ensuring the safe and stable operation of the power supply sys-tem in coal mines.

Abstract:Mining-induced roadway in large dip angle coal seam affected by the factors such as goaf and coal pillars,the failure laws of surrounding rock and the characteristics of mining pressure are complex and vary greatly,making support and maintenance diffi-cult.In response to this issue,taking the 3123 return airway of the large dip angle coal seam in Lyushuidong Coal Mine as the engin-eering background,a comprehensive research approach using theoretical analysis,numerical analysis,and field experiments was em-ployed to analyze the failure patterns of the surrounding rock in large dip angle coal seam roadway.The study revealed the stress characteristics of the surrounding rock at different locations and the deformation and failure characteristics of the roadway under these stresses.The results indicate that under the effect of large dip angle and mining,the maximum principal stress and the principal stress difference around the roadway are relatively large,resulting in the plastic zone of the roadway showing characteristics such as non-uniformity and the maximum failure depth deviation,moreover,the location of the roadway determines the distribution range and shape of the plastic zone of the surrounding rock;as the roadway gradually moves away from the upper working face,the im-pact of mining decreases,when the roadway is located 12 m or more below the upper goaf along the coal seam,the maximum prin-cipal stress and the principal stress difference remain stable at 15-20 MPa and 5-10 MPa respectively,under this stress environment,the size and shape of the plastic zone of the surrounding rock around the roadway do not change significantly.Based on key influen-cing factors such as the deformation and failure laws of mining roadways in large dip angle coal seams,the geometric characteristics of roadway sections,and the structural characteristics of coal and rock,the non-uniform active-passive support parameters of anchor rods(cables)were designed,and an industrial test was conducted in the 3123 return airway,and the monitoring of the deep displace-ment of the roadway roof was carried out,during the roadway excavation process,the total deformation of the roadway roof was con-trolled within 100 mm,and the main deformation was concentrated in the soft coal layer of the roadway roof,and the control effect of the surrounding rock of the roadway was good.

Abstract:In order to enhance the accuracy of ground subsidence monitoring in mining areas using SBAS-InSAR technology,based on the Sentinel-1A image data,a certain mining area in Jining City was selected as the research area.The ridge estimation method was used to calculate the SBAS-InSAR deformation model constructed.The accuracy of the results obtained by the ridge regression(RR)estimation and the least squares(LS)estimation methods was verified and compared through the leveling measurement data.The results show that the temporal and spatial evolution patterns of ground subsidence in the mining area obtained by the two meth-ods are consistent,but the maximum cumulative subsidence amounts measured are-128.1 mm and-227.8 mm respectively,indicat-ing a certain difference;the data fitting effect of the RR method is significantly better than that of the LS method,and the average in-ternal consistency accuracies of the two methods are 2.77 mm and 77.66 mm respectively;the relative error between the solution res-ults of the RR method and the leveling data is small,with an average RMSE of 10.5 mm;the time series monitoring results are con-sistent with the leveling data,while the solution accuracy of the LS method is lower,with an average RMSE of 21.7 mm,and the fluctuation of the time series subsidence trend is relatively large.

Abstract:To explore the influence of grouting materials on strengthening coal and rock mass structure,controlling coal and rock de-formation and preventing coal and gas outburst,high performance grouting material(HGC),micro expansion and high performance grouting material(EHGC),ordinary portl and cement(OPC)and sulphoaluminate cement(SAC)were used to prepare four different grout-coal composite samples.Through uniaxial compression and electron microscope scanning tests,the strength characteristics of different grout-coal samples and the microstructure characteristics of grout-coal interface were analyzed.The results show that the compressive strength of HGC sample is the highest,and the peak strength of EHGC sample is 10.98%lower than that of HGC.The failure modes of OPC and SAC specimens are tensile shear failure,HGC specimens are tensile failure,and EHGC specimens are tensile splitting failure,with the smallest degree of failure.The scanning electron microscope(SEM)results show that the cementa-tion form of HGC,OPC and SAC with coal rock is covered,and there are cracks in the interface area of grout-coal.The cementation form of EHGC and coal rock is embedded type.The micro-porosity of OPC sample is the largest,followed by SAC,and HGC is the smallest.The micro-porosity of EHGC is 6.93%higher than that of HGC.

Abstract:To enhance the safety and real-time performance of underground gas inspection in coal mines and to overcome the low ef-ficiency and high risk associated with manual inspection,a networked intelligent control approach integrating perception and de-cision-making was developed.Relying on multi-agent reinforcement learning(MARL),the framework constructs an embedded dual-module system that combines object recognition with cooperative path planning.The perception module utilizes the lightweight YOLO-Float detection network,which is optimized through structural pruning and 8-bit quantization to balance computational effi-ciency and accuracy.Furthermore,by integrating multi-scale feature fusion and attention mechanisms,the model achieves high-pre-cision recognition of gas pipelines,valves,and dynamic obstacles.The decision-making layer introduced local optimal guidance,crossover mutation and information self-adaptive update mechanism based on the ant colony optimization algorithm,and formed an improved ant colony optimization(IACO)algorithm to complete the path planning.On the Gazebo three-dimensional mine simula-tion dataset,the constructed model was compared with the reference model.A 24-hour small-scale continuous inspection test was also conducted in the real mine environment.The test indicators covered detection accuracy,root mean square error(RMSE),path planning success rate,convergence generations,system failure rate,energy consumption,and data packet loss rate,etc.The simula-tion results show that after 50 iterations,the path fitness of the model reached 0.92,the root mean square error was 0.15,the detec-tion accuracy was 88.3%,the average accuracy mean was 90.1%,and the training time with 800 frames of data was 1.8 seconds,and the processing delay was 3.2 seconds.Compared with the traditional model,it was shortened by 28%and 20%respectively.In the actual mine tests,the detection accuracy remained at 87.9%,the comprehensive failure rate was 1.8%,the communication interrup-tion rate was 0.6%,the path planning failure rate was 0.7%,the average response delay was 2.8 seconds,the energy consumption was 14.6 Wh,and the data packet loss rate was 0.4%.Even under weak illumination and high humidity conditions,the detection accur-acy still remained above 85%,and the path planning success rate was around 95%.The research results show that this method has achieved high-precision and low-latency autonomous gas inspection in coal mines.

Abstract:The identification of concealed water-conducting channels in coal seam floor and the simulation and evaluation of seep-age field are the key to the prevention and control of mine water inrush.Geophysics exploration techniques can identify the spatial structure distribution of concealed water-conducting channels,and the inversion results need to be converted into hydrogeological parameters before they can be used for seepage field simulation of floor aquifers,however,the simulated values of water pressure are not in good agreement with the measured values.In hydraulic tomography,the spatial non-uniform distribution of hydrogeological parameters in the aquifer is directly inverted by the water pressure monitoring value to identify the water-conducting channel,which ensures the accuracy of seepage field simulation.Therefore,the sand box test was used to study the characteristics of water pressure fluctuation in karst aquifer with water passage based on borehole slug test,and the hydraulic tomography inversion identification of water passage and the prediction and comparison of seepage field were carried out according to the monitoring data,finally,numeric-al experiments are used to study the influence of the number of monitoring holes,the number of excitation sources,and the prior geo-logical information of geophysical prospecting on the identification accuracy.The results show that the water pressure fluctuation curve inside the water-conducting channel presents the characteristic of a rapid increase followed by oscillatory attenuation,while the water pressure fluctuation curve outside the water conducting channel presents the unimodal distribution form of rise and then slow decline.The closer the distance to the source is,the larger the peak value of water pressure fluctuation is.The farther the distance is,the smaller the peak value of water pressure fluctuation is and the time of water pressure fluctuation is delayed.Compared with the inversion results of hydraulic conductivity which can roughly describe the structural characteristics of water channel,the spatial res-olution of the inversion results of water storage coefficient is lower;compared with increasing the number of monitoring holes,the spatial resolution of the inversion results of water storage coefficient is lower,increasing the number of excitation sources can signi-ficantly improve the identification accuracy of water channel.If the geophysical results are quite different from the structure of the real water channel,the wrong geophysical results can be used as prior geological information,and it will significantly reduce the ac-curacy of hydraulic tomography identification.

Abstract:To address the oxygen supply issues of traditional coal mine isolated compressed oxygen self-rescuers which provide a fixed oxygen supply rate of 1.2 L/min,causing oxygen shortage during escape(oxygen demand 2.1 L/min)and oxygen waste during seated waiting(oxygen demand 0.5 L/min),as well as problems like easy failure of mechanical pressure indicators,inconvenience in replacing CO2 absorbents,and unreliable materials for key components,a novel compressed oxygen self-rescuer meeting the GB 24502-2023 Self-rescuer for Coal Mine standard has been designed.By optimizing the flow channel topology of pressure redu-cer,the oxygen supply rate can be dynamically adjusted between 0.5 L/min and 2.4 L/min to meet the oxygen demands of both es-cape and seated waiting modes,significantly enhancing the protection time and oxygen supply stability during seated waiting.A low-power and intrinsically safe digital pressure indicator for mining applications has been developed using a diffused silicon pressure sensor,resolving issues of elastic fatigue,nonlinear drift,and high failure rates associated with mechanical indicators.A modular and replaceable canister has been designed to enable the rapid replacement of CO2 absorbent within 10 seconds.A mathematical model for determining the required amount of CO2 absorbent was established based on the chemical reaction kinetics of Ca(OH)2 absorbing CO2.Additionally,key components such as the shell,oxygen cylinder,and breathing bag have been carefully selected.Experimental results demonstrate that under the rated protection time of 45 minutes,the self-rescuer achieves a seated protection time of over 135 minutes,with an O2 volume fraction of at least 46.5%,a peak CO2 volume fraction of 1.46%,a peak inhalation temperature of 41.1℃,and a total breathing resistance of no more than 1.2 kPa.In terms of oxygen supply performance,when the working pres-sure of oxygen cylinder is between 3 MPa and 20 MPa,the self-rescuer provides a constant oxygen supply of 2.3 L/min at a breath-ing rate of 35 L/min and 0.7 L/min at 10 L/min.Both automatic and manual supplemental oxygen supplies reach 70 L/min,with oxy-gen supply fluctuations not exceeding 0.1 L/min.The research findings have been applied in multiple self-rescuer manufacturing en-terprises and have passed the safety certification inspection based on GB 24502-2023 Self-rescuer for Coal Mine.

Abstract:The problems such as poor wettability of ultrafine coal dust(mass fraction of coal dust with a particle size less than 5 μm is not less than 50%)have seriously restricted the effect of mine dust control.Based on this,a new type of highly efficient and biode-gradable dust suppressant was studied and designed.Fourteen common surfactants were selected.The surface tension tester,contact angle tester and sedimentation test were used to test the 14 surfactants,and three single surfactants were seleted.The single surfact-ants seleted were compounded in pairs.Three compounded solutions were selected for laboratory spray dust suppression simulation tests,five-day biochemical oxygen demand(BOD5)and chemical oxygen demand(COD)tests to determine the final dust suppres-sion agent formula.Infrared spectroscopy was used to characterize the changes of oxygen-containing functional groups on the sur-face of coal dust after treatment with dust suppressant,as well as the changes of surface potential of coal before and after the action of dust suppressant.The results show that water has poor wettability for ultrafine coal dust,while the dust suppressant can improve the wettability of ultrafine coal dust.The comprehensive determination of the dust suppressant formula is a mixture of coconut oil di-ethanolamide(CDEA)and secondary alkyl sodium sulfonate(SAS-60),with the two in a 4∶1 mass ratio(mass fraction is 0.05%).The biodegradability value(BOD5/COD)of the dust suppressant reaches 0.4,and it belongs to the biodegradable type,meeting the standards for coal mine wastewater treatment.The COD also meets the requirements of anaerobic fermentation technology,thereby promoting the release of organic matter and providing raw materials for biological manufacturing.The dust suppression agent has a dust suppression rate of over 80%for ultrafine coal dust,and the maximum dust suppression efficiency can reach 90%.After being treated with the dust suppressant,the oxygen-containing functional groups on the surface of coal dust increase,the hydrophilicity is enhanced,and the electronegativity is weakened,indicating that the dust suppressant can adsorb on the surface of coal dust,thereby achieving the dust suppression effect.

Abstract:Aiming at the problem of frequent occurrence of rock burst disaster in deep coal mine roadway and unclear dynamic re-sponse mechanism of anchor bolt support,we adopt numerical simulation and field test to systematically analyze the mechanical re-sponse characteristics and damage law of anchor bolts with different impact toughness under dynamic load;based on LS-DYNA software,the impact model of anchor bolts was established,and the dynamic response of anchor bolts with yield strength of 500 MPa and impact absorbed work of 15-94 J under 20 ms isosceles triangle load was simulated.By analyzing the Mises stress distribution,fracture characteristics,displacement change and energy absorption law of the anchor bolts,the mechanism of impact toughness on the damage mode of the anchor bolts was revealed,and applied to the design of anchor bolt support in Tangkou Coal Mine 7307 track roadway.The results show that with the increase of impact toughness,the Mises stress at anchor bolt fracture increases,and the fracture necking phenomenon is significantly enhanced;the higher the impact absorption work of the anchor bolt under the same im-pact load,the higher the displacement at fracture,to cause an anchor bolt with high impact toughness to break within a fixed impact time,high impact loads need to be applied.The higher the impact load the greater the deformation rate of the anchor bolt in the plastic deformation stage,resulting in the anchor bolt breaking without giving full play to the deformation capacity,there will be a high impact toughness and low elongation of the anchor bolt.The deformation of the anchor bolt increases with the decrease of dis-tance from the impact load application,but it does not change much in the range of 1.5 m from the fixed end,the study further re-veals the local effect of anchor bolt deformation,in the range of 1.5-2.0 m from the impact point,the deformation increases 50%compared with the section of 1.0-1.5 m,and the plastic strain firstly occurs in the fixed end and extends to the impact point,and fi-nally destroys centrally in the threaded section.

Abstract:In order to study the spatial and temporal changes of coal fires in Wugong Coal Mine in Xinjiang,and to provide help for the restoration and management of coal fires in mines.The Landsat8-9 OIL/TIRS C2 L2 remote sensing data from 2014 to 2023 were selected and divided into three phases,namely,mining period,shutdown period and control period,and the surface temperature was inverted using the split-window algorithm,and the inversion accuracy was verified by using the Pearson correlation analysis,and then combined with the field survey to classify the mine area into five grades of areas by using robust statistical method,namely,low-temperature zone,sub-low-temperature zone,sub high temperature area,sub-low temperature area,high temperature area,and coal fire area,using raster statistics,standard deviation ellipse and the introduction of land use transfer matrix,from the area,spatial change and the transfer of the rank area three dimensions to analyze the high temperature area of the mining area and the coal fire area,to get the rule of change of the coal fire in space and time.The results show that:the area of high-temperature area and coal fire area increased twice in the mining period and shutdown period,the coal fire was the most serious in 2017,and the area of coal fire began to decrease in the control period;the overall scope of coal fire from the mining period to the control period was reduced,shif-ted to the east-west direction,and the distribution of coal fire area was concentrated in the east-west direction;the transfer out and in-flow objects of coal fire area in the three stages were high-temperature area,and the high-temperature area was the most serious in the mining period,and the transfer out in the mining period and the transfer out in the control period.The transfer out of the high-temperature zone in the mining period and the inflow object in the control period are mainly coal fire zones.The study result indic-ates that the control of high-temperature areas and coal fire areas is the key to the ecological restoration of mines.

Abstract:Combustion metamorphic rocks(CMR)are one of the main concealed disaster-causing factors in coal mines.Magnetic de-tection is the main method for delineating the distribution range of combustion metamorphic rocks,but accurately identifying the boundaries is a problem that needs to be solved.In order to accurately determine the boundaries of metamorphic rocks in coal fields using magnetic anomaly data,theoretical analysis,model calculation and field experiments were employed to conduct identification studies on the boundaries of single-layer and double-layer metamorphic rocks.Four boundary identification algorithms were selec-ted to process the positive evolution results of the single-layer model,and the boundary identification effects were compared and ana-lyzed;the secondary processing results of various combinations were analyzed,and the combination identification algorithms with better identification effects were selected;the two preferred combination identification algorithms were applied to the double-layer model to determine the optimal combination identification algorithm;the reliability of the boundary identification algorithms was verified through field experiments.The results show that the total horizontal derivative(THDR),vertical derivative(VDR),analytic-al signal amplitude(ASM),and gradient tilt angle(Tilt)based on the Reduction to the Pole magnetic anomaly(RTP)data can all dis-play the boundaries of the single-layer model to varying degrees.Among them,the total horizontal gradient modulus has higher clar-ity relying on using the maximum value for identification.The comparison of the results of multiple combinations of secondary pro-cessing shows that the result of RTP-THDR-Tilt can clearly identify the boundary of the single-layer and has good continuity of the recognition signal.Further tests on the double-layer model revealed that the results of various combinations of secondary treatment using RTP-THDR-Tilt could effectively determine the boundaries of the superimposed metamorphic rocks;the RTP-THDR-Tilt res-ults obtained from the on-site test data of the coal mine showed that the distribution of the boundary strips was in line with the geolo-gical characteristics of the mining area,and the two-layer boundaries of the metamorphic rocks determined by this method were con-sistent with the drilling and exposure results.