Mine Production Research Articles

Study on Ratio Optimization and Diffusion-Gelation Process of Polymer Grouting Materials for Fracture Filling in Underground Mines.

The existence of fissures poses a serious threat to the safe production of underground mines, and this paper investigates a polymer grouting material for filling fissures in underground mines. To optimise the ratio of polymer grouting materials, this paper designed 16 test groups using the orthogonal test method to find the most reasonable slurry ratio. In order to study the gel diffusion process of polymer slurry in the fissure and to explore the changes of various parameters of the slurry after injection, simulated grouting tests were carried out, and the distribution laws of viscosity, pressure, and diffusion distance of the slurry were discussed. The findings indicate that when the proportion of ethylenediamine polypropylene oxide tetrol: glycerol polyether: catalyst: foam stabiliser is 10:8:0.5:0.4, the polymer grouting material has excellent compressive strength, and the maximum compressive strength can reach 12.31 MPa. Prior to reaching the gel time point, the viscosity of the polymer slurry was nearly constant, which is basically maintained at 0.772 Pa·s under normal temperature and pressure, but after reaching the gel time point, it abruptly rose. As the slurry mass increased, so did the penetration distance and pressure; in the simulated grouting test, when the slurry mass was 400 g, the maximum diffusion distance of the slurry reached 39 cm. Conversely, as the fracture pore size increased, the diffusion distance and pressure of the slurry decreased. Along the diffusion path, the slurry pressure progressively drops, but this change is not synchronised with the diffusion distance's change. This work can serve as a reference for the configuration of polymer slurry and aid in comprehending the diffusion law of the slurry within the fissure.

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.

Integration of ground geophysical methods to characterize near‐surface aquifer zones within an active mine

AbstractUnderstanding near‐surface groundwater storage, flow patterns, surface and groundwater interactions in mining areas can assist in making mining more efficient and profitable. This is especially important in opencast mines affected by water inflows that may negatively affect production and increase mining costs. We map and characterize the near‐surface aquifer zones at the opencast site of Tharisa Minerals, located in the southwestern region of the Bushveld Complex (South Africa). The main goal is to infer pit water inflow at the mine site and determine how it may be better controlled. The Bushveld Complex hosts partially connected and unconfined alluvial, shallow‐weathered and crystalline bedrock aquifers, which are often connected by small‐scale permeable zones. Seismic refraction tomography, multichannel analysis of surface waves, electrical resistivity tomography and borehole data are used to map and understand the different aquifer zones in the vicinity of the mine, as well as infer their relation to water inflow in the mine pits. The geophysical surveys map the overburden, weathered bedrock aquifer zone, and the top of the crystalline aquifer rock zone reasonably well. They reveal extensive and deep weathering, and possible high hydraulic conductivity in the vicinity of the mine. The results provide a better understanding of the mine's near‐surface environment, which could be used to implement effective and targeted dewatering techniques, thus enabling better pit inflow water control to improve mine working conditions and production.

Renewable exergy return on investment (RExROI) in energy systems. The case of silicon photovoltaic panels

The ongoing energy transition towards renewable sources faces reliance on fossil fuels throughout their life cycle, primarily due to mining and material production for infrastructure. This study proposes the Renewable Exergy Return on Investment (RExROI) metric, which quantifies the renewable exergy obtained from each unit of non-renewable exergy invested in energy systems. It can be seen as a Renewation Index, applicable to any energy production system, indicating the degree of renewability of such technologies. Focused on silicon photovoltaic panels, the study explores five material intensities, nine scenarios based on the capacity factor and lifespan, and two alternatives for electricity used in the manufacture. Results show that material intensity increases RExROI from −0.6 MJ/MJ (non-renewable exergy is higher than electricity produced) to 5.7 MJ/MJ, increasing until 19 MJ/MJ with the best location and lifespan, and reaching 34 MJ/MJ if renewable electricity is used in manufacturing. Thus, carbon intensity can range from 734 to 7 gCO2eq/kWh. Furthermore, some strategies to enhance RExROI are discussed based on the (i) energy sources, (ii) materials, and (iii) production stages of photovoltaic panels. Thus, this study demonstrates the usefulness of RExROI in evaluating the energy-material-emission nexus of energy systems through exergy in the context of energy transition.

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.

Optimization of cut-off grades and depletion rates by means of dynamic programming

The determination of cut-off grades is critical for mine appraisal. Because of the time value of money, an optimum schedule of cut-off grades must be used instead of determination of a single cut-off grade for the life of a mine. In order to achieve the maximum net present value, a decreasing order of cut-off grades schedule must be used because of the opportunity cost. In the optimization of cut-off grades by dynamic programming, the depletion rates to be searched are actually limiting the search area. The optimum can be found from among the alternative depletion rates. Selling prices of mining products are volatile and future values cannot be known. This paper describes the use of dynamic programming in cut-off grades optimization and further extension of the method to price volatility in selling prices of mining products. The author introduces the general background of use of dynamic programming in cut-off grades optimization and the case in uncertain market conditions. Software developed in this subject handles to find the optimum sequence of cut-off grades and depletion rates for mineral deposits under market uncertainty. Dynamic programming method can be used as a means of cut-off grades optimization in this context very robustly.

Identification of tin species released in solution during the water leaching of solids from the alkaline smelting (NaOH-KOH) of stanniferous ores

Tin species were studied Makundju ores in DRCongo. Identification of tin species released in aqueous solution during leaching of solids from the alkaline smelting (NaOH-KOH) was done to prepare added-value mining products on tin ores mined in the eastern DR Congo. Prior to alkaline fusion to have the smelting products (SP), the initial sample (IS) was processed for tests and characterization of tin species. Alkaline fusion in molten KOH-NaOH system was used and leached solution was considered for further analyses. Various physicochemical techniques including X-ray fluorescence spectrometry (XRF), scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA) and FTIR spectroscopy were used to characterize the initial ore and the molten cake. Water leached cake solution at a solid/liquid ratio of 1:10 was used to identify and characterize tin species. ICP-OES analysis of the pH 11.9 solution revealed a content of 4506.3 ppm, which is equivalent to a 95.87% leaching yield. Such yield attests to the stable Sn(OH)&lt;sup&gt;2-&lt;/sup&gt;&lt;sub&gt;6(aq)&lt;/sub&gt; ion as confirmed by UV-Vis. Crystallizations of M&lt;sub&gt;2&lt;/sub&gt;Sn(OH)&lt;sub&gt;6(s)&lt;/sub&gt; (M = Na and/or K) was studied using XRD, FTIR and TGA. As pH decreases below 9, Sn(OH)&lt;sup&gt;2-&lt;/sup&gt;&lt;sub&gt;6(aq)&lt;/sub&gt; ion changes to other tin species such as Sn(OH)&lt;sup&gt;-&lt;/sup&gt;&lt;sub&gt;5(aq)&lt;/sub&gt;, Sn(OH)&lt;sub&gt;4(S)&lt;/sub&gt;, Sn(OH)&lt;sup&gt;+&lt;/sup&gt;&lt;sub&gt;3(aq)&lt;/sub&gt;, Sn(OH)&lt;sup&gt;2+&lt;/sup&gt;&lt;sub&gt;2(aq)&lt;/sub&gt;, Sn(OH)&lt;sup&gt;3+&lt;/sup&gt;&lt;sub&gt;(aq)&lt;/sub&gt; and Sn(OH)&lt;sup&gt;4+&lt;/sup&gt;&lt;sub&gt;(aq)&lt;/sub&gt;. The stability of each the tin species was determined at different pH values in aqueous solution. Hence, alkaline hexahydroxostannate nanoparticles, used in many electronic applications, can be produced from tin ores.

Exploration and Frontier of Coal Spontaneous Combustion Fire Prevention Materials

Mine fires have always been one of the disasters that restrict coal mining in China and endanger the life safety of underground workers. The research and development of new fire prevention materials are undoubtedly important to ensure the safe and efficient production of modern mines. At present, the main inhibiting materials used are grout material, inert gas, retarding agent, foam, gel, and so on. In order to explore the current situation of coal spontaneous combustion (CSC) fire prevention, the existing fire prevention materials were reviewed and prospected from three aspects: physical, chemical, and physicochemical inhibition. The results show that, at present, most of the methods of physicochemical inhibition are used to inhibit CSC. Antioxidants have become popular chemical inhibitors in recent years. In terms of physical inhibition, emerging biomass-based green materials, including foams, gels, and gel foams, are used to inhibit CSC. In addition, CSC fire-fighting materials also have shortcomings, including incomplete research on the mechanism of material action, poor stability of inhibitory properties, low efficiency, and economic and environmental protection to be improved. The future research direction of fire-fighting materials will be based on theoretical experiments and numerical simulation to study the mechanism and characteristics of CSC and develop new directional suppression materials with physicochemical synergies. These findings have extremely important implications for improving materials designed to prevent CSC.

Tracing China’s agrochemical complex

Chinese firms, both private and state-owned, have transformed the global agrochemical complex. China dominates exports of pesticides (particularly herbicides) and various kinds of nitrogen- and phosphorous-based fertilisers. And yet the precise nature of this transformation is poorly understood: where are Chinese agrochemicals made and by whom; in what ways is domestic production changing; which actors are active in the export market; and what is flowing where? In this article we begin to flesh out some of the detail of China’s massive agrochemical complex and its internal dynamics by examining key actors and flows in China’s agrochemical export industry. Our analysis is framed by insights drawn from the Global China literature and from pesticide studies. We find two distinct industries, both of which are in flux. Domestic environmental regulations and industry upgrading are reconfiguring the landscape for mining and chemical production, resulting in a scaling back of pesticide and fertiliser production, while mergers and acquisitions are concentrating production and export capacity in particular firms. Export flows are nonetheless growing in a generally favourable export environment. Concealed in the generic categories ‘pesticide’ and ‘fertiliser’ is the fact that different products go to different destinations and do so in patterns that complicate North-South categories. Our analysis highlights the need for new commodity geographies of Chinese-made agrochemicals that can account for production, circulation, and use.

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.

Dynamics of backfill compressive strength obtained from enrichment tails for the circular waste management

At the current stage of development, the transition to a circular economy is a single option to achieve sustainable mining. Purpose was to identify a direction for mining tailings usage based on the circular waste management. The novelty of the author's approach lies in regulation of mechanical treatment of slag to optimize the backfill composition. The methodology involved a study of mining waste (slag) obtained from major (eight) metallurgical plants in Russia. For mathematical processing primary data, smoothing and deterministic three-dimensional interpolation methods were used. As a result, for the first time it has been established that the ultimate uniaxial compressive strength after mechanical treatment of slag increases by logarithmic laws. As the curing time increases from 10 days to 90 days (with the addition of Ca or Si) the strength increases by 21 % (7 % and 23 %). The possibility of completely replacing the traditional cement binder with metallurgical slag in the backfill composite has been proven. It has been established that the use of activation treatment (both mechanical and chemical) makes it possible to increase the strength characteristics of backfill samples after their curing. The principle of organizing mining production has been implemented, which provides for the use of intermediate products (blast furnace granulated slag), previously classified as technogenic waste, in a closed production cycle. The introduction of this principle will eliminate (minimize) the formation of man-made waste. The scientific merit of replacing traditional cement binder with technogenic waste (blast furnace granulated slag) allows implementation of circular economy in mining.

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.

STUDYING THE THERMOCHEMICAL CHARACTERISTICS OF SINTERING WITH ALKALI CONCENTRATE FROM WASTE COPPER TAILINGS

A long period of deposit development leads to a reduction in the volume of balance reserves of ores, while the volume of accumulation of waste (tailings) from mining and processing production increases. In connection with this, an urgent problem is the processing of waste tailings, which can serve as an additional source of commercial products of non-ferrous metals. The set objectives can be achieved by implementing comprehensive extraction of valuable components, reducing fuel, and energy costs at the stage of low-temperature sintering to produce white soot (grade BS-100). In order to determine the amount of heat of chemical reactions during sintering of rough copper concentrate with sodium hydroxide, thermochemical calculations were carried out. For the research, we used rough copper concentrate with a copper content of 4.40% and silver of 77.03 g/t. An equation has been obtained for calculating the heat of chemical reactions of rough concentrate sintering. According to this equation, the greatest contribution to the heat release is provided by the iron content (60%), three times less and equally (19%) - copper and silicon and about 0.2% is absorbed by aluminum. The thermal effect of sintering will be -920.45 kW·h/t of concentrate. From the heat balance of sintering it follows that with an alkali consumption of up to 200% of the mass of the concentrate. The heat of chemical reactions will be sufficient for the autogeneity of the process. Research has been carried out on sintering the concentrate with alkali in the temperature range of 250-500 °C and obtaining white soot from a productive leaching solution with the extraction of 60% silicon. White soot (mSiO2·nH2O) was isolated by two-stage carbonization of a silicate solution with carbon dioxide in a recirculation system with further washing of the precipitate with a solution of sulfuric acid. For citation: Karimova L.M., Kairalapov Ye.T., Makascheva G.K., Kharchenko Ye.M. Studying the thermochemical characteristics of sintering with alkali concentrate from waste copper tailings. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 7. P. 72-79. DOI: 10.6060/ivkkt.20246707.7004.

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.

Brown seaweeds fishery and copper mining production: Two distant economic industries connected by socioecological impacts in Northern Chile

Northern Chile is the scenario of two main activities from a social and economic point of view the: brown seaweeds fishery and cooper mining industry. Chilean brown seaweeds of economic importance, commonly known as kelps, constitute a multi-specific fishery with a workforce of 15,000 people. Four species of kelps are harvested from natural populations by artisanal fishers and exported to world markets as raw material for alginate extraction. On the other hand, copper mining production, contributes 36 % of GDP and it is the major Chilean export commodity. Due to the non-existence of entry barriers to seaweed extraction, this activity has become the sink for unemployment in northern Chile mainly from mining activity. In this context, our results show that when the price of copper is in a downward cycle, it negatively affects the abundance of brown seaweeds, due to the mobility of the workforce to the coastline, increasing the illegal harvest of these marine resources. In contrast, the upward cycle of copper decreases the harvesting pressure, removing illegal harvesters from the coastline and returning them to a mining industry and its suppliers with better wages. These drivers would generate a temporary window for recovery and restoration of brown algae populations, as well as the numerous ecosystem services provided by these engineering organisms.

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.

Study on Support Technology of Truss Anchor Cable Beam in Deep Soft Rock Roadway Excavation

Aiming at the difficult problem of soft rock roadway support in deep mine, the support mode of "truss anchor beam" is proposed, and the support mode technology of truss anchor beam in deep soft rock roadway driving is studied by FLAC simulation software. Combined with the measured displacement of coal and rock around underground roadway under this support mode, it is concluded that the truss anchor beam support mode can introduce the high stress of shallow coal and rock around roadway into deep area, thus effectively controlling the deformation of coal and rock around roadway and ensuring safe and efficient production of mine.

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.