Mine Water Inflow Research Articles

Characteristics of roof rock failure during coal seam mining and prediction techniques for mine water inflow in exposed karst areas

Characteristics of roof rock failure during coal seam mining and prediction techniques for mine water inflow in exposed karst areas

Time series prediction model using LSTM-Transformer neural network for mine water inflow

Mine flooding accidents have occurred frequently in recent years, and the predicting of mine water inflow is one of the most crucial flood warning indicators. Further, the mine water inflow is characterized by non-linearity and instability, making it difficult to predict. Accordingly, we propose a time series prediction model based on the fusion of the Transformer algorithm, which relies on self-attention, and the LSTM algorithm, which captures long-term dependencies. In this paper, Baotailong mine water inflow in Heilongjiang Province is used as sample data, and the sample data is divided into different ratios of the training set and test set in order to obtain optimal prediction results. In this study, we demonstrate that the LSTM-Transformer model exhibits the highest training accuracy when the ratio is 7:3. To improve the efficiency of search, the combination of random search and Bayesian optimization is used to determine the network model parameters and regularization parameters. Finally, in order to verify the accuracy of the LSTM-Transformer model, the LSTM-Transformer model is compared with LSTM, CNN, Transformer and CNN–LSTM models. The results prove that LSTM-Transformer has the highest prediction accuracy, and all the indicators of its model are well improved.

Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach

Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.

Prediction technology of mine water inflow based on entropy weight method and multiple nonlinear regression theory and its application

Mine water inflow is an important basis for the formulation of mining plans and the utilization of groundwater resources. The mine water inflow is the result of the combined influence of many factors. The weight value of the influencing factors is calculated by the entropy method, and the order of importance of the factors is: precipitation > mining depth > cumulative mined-out area > aquifer thickness > mining area > mining height. The optimal univariate nonlinear regression model of mine water inflow to each influencing factor is obtained by factor scatter analysis and Matlab function programming. On this basis, combined with the weight values of factors, a multivariate nonlinear regression prediction model of mine water inflow based on weighting is innovatively established, which overcomes the defect that the traditional water inflow prediction method that cannot reflect the relative importance differences of various influencing factors. The multivariate weighted nonlinear regression model is used to predict the mine water inflow of typical coal mines, and the prediction results are compared with the linear regression model and the measured value. The results show that the prediction model of mine water inflow based on weighted multivariate nonlinear regression is accurate higher, with higher practical application value.

Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China

As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities.

Water-Filling Characteristics and Water Source of Weakly Rich Water and Weakly Conducting Water Aquifers in the Changxing Formation after Mining Damage

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

Impact of long-term mining activity on groundwater dynamics in a mining district in Xinjiang coal Mine Base, Northwest China: insight from geochemical fingerprint and machine learning.

Long-term coal mining could lead to a serious of geo-environmental problems. However, less comprehensive identification of factors controlling the groundwater dynamics were involved in previous studies. This study focused on 68 groundwater samples collected before and after mining activities, Self-Organizing Maps (SOM) combining with Principal Component Analysis (PCA) derived that the groundwater samples were classified into five clusters. Clusters 1-5 (C1-C5) represented the groundwater quality affected by different hydrochemical processes, mainly including mineral (carbonate and evaporite) dissolution and cation exchange, which were controlled by the hydrochemical environment at different stages of mining activities. Combining with the time-series data, the Extreme Gradient Boosting Decision Trees (XGBoost) derived that the mine water inflow (feature relative importance of 40.0%) and unit goaf area (feature relative importance of 29.2%) were dominant factors affecting the confined groundwater level, but had less or lagged impact on phreatic groundwater level. This was closely related to the height of the water flow fractured zone and hydraulic connection between aquifers. The results of this study on the coupled evolution of groundwater dynamics could enhance our understanding of the effects of mining on aquifer systems and contribute to the prevention of water hazards in the coalfields.

FUZZY CONTROL OF POWER CONSUMPTION OF ELECTROMECHANICAL EQUIPMENT OF THE MAIN DRAINAGE COMPLEX AT IRON ORE MINE

The paper presents a fuzzy control system for the consumption of electric power by pumps of the main drainage complex of a mine based on the Mamdani fuzzy inference algorithm. The proposed system has a structure with two inputs, such as water inflow and electricity cost, and one output, the power of pumping units. The choice of the Mamdani method for building a fuzzy system is due to the fact that all these inputs and outputs are subject to preliminary expert evaluation. All quantities are fuzzificated by smooth membership functions - Gaussian and sigmoid. The input values of the system are represented by linguistic variables, each of which includes five fuzzy sets that define the membership in the very low, low, medium, high, and very high levels of the corresponding input value. The output of the system contains ten fuzzy sets. During the fuzzification, it was assumed that in order to avoid overloading the pump motors, only one option is considered: their operation at maximum power when there is a very high level of mine water inflow during heavy rains or for other reasons. In all other cases, the operation of at least one of the pumps with the highest permissible power is excluded. A base of 25 fuzzy rules was formed. When defining the rules, it is assumed that in all cases when there is a high water inflow into the mine, all three pumps must be put into operation, regardless of the electricity cost in force in the current period of time. This set of rules is of the highest importance due to the need to prevent flooding of the mine. For other values of water inflow, the output power consumption of drainage pumping units depends on the current cost. This set of rules has a lower priority. We simulated the operation of the dewatering pumping system with and without a fuzzy power consumption control system. The test signal for the "water inflow" input was represented as a random variable with a normal distribution. The test signal for the "electricity cost" input was formed on the basis of real data on the price determined by the operator for the day ahead market for 12/24/2023. It was found that the use of a fuzzy system can reduce the cost of purchasing electricity by 1.24% per day. It should also be noted that the economic effect is achieved not by reducing the level of power consumption, but by rationalizing the operation of the drainage complex, which ensures maximum groundwater drainage performance when the cost of electricity is high, and minimum when it is low.

The effects and solidification characteristics of municipal solid waste incineration fly ash-slag-tailing based backfill blocks in underground mine condition

Currently, there is a limited understanding of the material performance effects and hydration mechanisms of solidified waste-based binder filling materials containing municipal solid waste incineration fly ash in the actual underground filling environment. This study utilized the Circulating Fluidized Bed (CFB) and Grate Furnace (GF) techniques to produce quadri-component uncalcined binder fill materials from municipal solid waste incineration fly ash (MSWI FA), blast furnace slag (BFS), flue gas desulfurization (FGD) gypsum, and tailings. The solidified test blocks were then cured in-situ in a void space of a certain iron underground mine in Hebei with conditions (temperature: 25-30℃, humidity: 18-19%, mine water influx: annual average of 150m3/h). The research results indicate that the mechanical performance and heavy metal solidification outcomes of these blocks surpassed those samples cured under laboratory conditions (temperature: (20±1)℃, humidity: not less than 90%), demonstrating the feasibility of using MSWI FA for underground binding and backfilling in mines. Building upon preliminary research, a binder mix proportion was established: 20wt.% MSWI FA, 70wt.% BFS, and 10wt.% FGD gypsum. Test blocks were fabricated using lead-zinc tailings sand and iron mine tailings as aggregates. All blocks met the 28-day strength requirements for typical mine backfills ranging from 1 to 6.5MPa, with the strength of the XB-M group reaching 24MPa (when cured underground). The leaching concentrations of heavy metals were all below the limits set for class III groundwater standards, with Pb, Zn, Cr, and As achieving a solidification rate of over 98% at 28 days. Microscopic hydration mechanisms indicate that the primary hydration products in the solidified body were ettringite, Friedel's salt, and C-S-H gel. The high content of reactive Al2O3 in the CFB fly ash enables the [AlO4]5- to substitute [SiO4]4-, leading to the formation of a long-chain, high polymerization C-A-S-H phase. Additionally, the abundant Cl- in GF fly ash readily reacted with [Al(OH)6]3−, forming Friedel's salt. The hydration products in the blocks cured under mine conditions significantly increased in their degree of polymerization. This provides a practical application approach for the future treatment and reuse of MSWI FA. Environmental implicationMSWI FA is classified as hazardous waste (HW18) due to its content of harmful components such as chlorides, dioxins, and soluble heavy metals, which pose potential environmental pollution risks. This study, for the first time, solidified test specimens were sent to an underground mining void in a certain iron mine in Hebei, China, for on-site curing (temperature range: 25-30°C, humidity: 18-19%, annual average mine water inflow rate: 150m3/h). The mechanical properties and the solidification performance of heavy metals in the test specimens were superior to those under laboratory curing conditions (temperature: (20±1)°C, humidity not less than 90%).

Modeling abrupt changes in mine water inflow trends: A CEEMDAN-based multi-model prediction approach

The global impact of coal mining on groundwater resources is increasingly prominent. Constructing waterproof curtains can mitigate the destruction caused by open-pit coal mining on groundwater resources. However, the construction of these curtains alters the hydrogeological conditions, leading to abrupt changes in mine water inflow that are challenging to predict. Traditional models like Long Short-Term Memory Neural Network (LSTM) and Gated Recurrent Unit Neural Network (GRU) demonstrate poor performance when predicting abrupt changes in data trends. This study proposes a multi-model prediction approach and a Self-Attention-based prediction model to address this issue. The proposed Approach, based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), leverages the strengths of multiple models for prediction. The research results found that the proposed Self-Attention model excels at forecasting abrupt shifts in data trends. Comparatively, the multi-model predictions show a 26.12 % improvement in Mean Absolute Error (MAE) and a 33.47 % improvement in Root Mean Square Error (RMSE) when compared to single-model predictions. This study not only unveils the potential of Self-Attention in predicting abrupt trend changes but also integrates it with traditional LSTM and Support Vector Machine (SVM) models. The multi-model prediction framework proposed in this study integrates the strengths of multiple models, achieving higher accuracy than a single model. Accurate prediction of mine water inflow is essential, as it can provide valuable insights for decision-making by coal mining enterprises and local regulatory authorities, offering a scientific basis for effective management.

Numerical simulation of the interaction between mine water drainage and recharge: A case study of Wutongzhuang coal mine in Heibei Province, China

Water inflow poses a critical safety concern in coal mining. The pressing challenge in current mine water inflow research is the need to enhance the prediction of dynamic changes in mine water inflow across different mining stages. In this study, the hydrogeological conditions and aquifer structural characteristics of the Wutongzhuang Coal Mine in Hebei Province, China were analyzed, and the results were combined with pumping test and drilling data. A numerical model of groundwater flow in the study area was established using Visual MODFLOW and the model was calibrated and verified by comparing its results with the measured values to ensure that it realistically reflected the state of groundwater movement in the study area. The results of numerical simulation indicated that: (1) The water level of the Ordovician aquifer varies significantly by season. A large exposed area of Ordovician limestone in the western mountainous region receives a large amount of rainfall infiltration recharge in the summer rainy period that raises the water level of the Ordovician aquifer in the study area. (2) The groundwater system in the study area is in an equilibrium state, with the water inflow approximately equal to the outflow. (3) The recharge of mine drainage water has a slight positive effect on the Ordovician water level and the floor water inflow, with the impact on the water level of the aquifer decreasing as the distance from the recharge well and the import of Ordovician limestone water increase. In cases in which there is mine water recharge, the water level of the Ordovician aquifer is approximately 0.33 to 1.37 m higher than in cases without recharge, resulting in an increase in water inflow of approximately 10.6 m3/d. (4) In cases in which there is recharge of all drainage water, the maximum water inflow into the fifth mining area is 3,491.3 m3/d, which is 12.5 m3/d higher than in the case without recharge. These results provide important technical references for the formulation of mining schemes and water prevention measures in mining areas.

Analysis of the Impact of Hydrological Parameters on Mine Water Inflow Prediction in Environmental Impact Assessment

Analysis of the Impact of Hydrological Parameters on Mine Water Inflow Prediction in Environmental Impact Assessment

Characteristics of strata movement and method for runoff disaster management for shallow multiseam mining in gully regions: A case study

Multiseam mining in gully regions has resulted in severe and complex disaster chains, including crack development, mountain landslides, river blockage, and intensified mine water inflow. To prevent and control water inrush disasters, it is imperative to investigate the characteristics and mechanizations of overburden failure under the combined effect of gully terrain and repeated mining in coal seams. This study takes multiseam mining within the gully region of the Xiqu Coal Mine as a case study. The comprehensive analytical method that integrates surface exploration, Interferometric Synthetic Aperture Radar (InSAR) measurement, numerical simulation, and rainfall-runoff analysis, is used to anatomize the entire process of overburden failure induced by downward multiseam mining and the accumulation process of runoff in the gully region. The results reveal that compared to single-seam mining activities, repeated mining and the occurrence of key stratum (KS) structure failure triggered the comprehensive subsidence of the interburden, producing a cutting-type fracture and stepped subsidence, while the overburden exhibited severe damage. Compared to gentle surface topography, gully slopes are prone to shear sliding under the effects of mining subsidence and gravity, resulting in the connection of the downward cracks of the slope and the upward cracks of the overburden. In addition, if the accumulation formed by gully landslides due to repeated mining blocks the river channel and forms a barrier lake during the flood season, it may cause a surge in underground water. High-precision terrain synthesized by UAV tilt photogrammetry is used to simulate the rainfall inundation range and time of different durations during the “100-year return period” rainstorm in the study area. This study proposes a zoning prevention and control method for surface runoff disasters based upon the characteristics of surface deformation and surface inundation time; this method involves filling surface cracks in runoff areas and submerged and crack development areas, implementing slope cutting and control in potential landslide areas, and strengthening the riverbed in submerged and maximum settlement areas.

Prediction of the Mine Water Inflow of Coal-Bearing Rock Series Based on Well Group Pumping

Previous scholarly investigations have mainly concentrated on examining water intake, particularly within the specific domain of coal mines. Nevertheless, the scholarly discourse lacks significant research on predicting water inflow in environments with complex multi-layer mineral distributions. The Yanlong mining area is a complex mine containing coal and bauxite. Forecasting the water inflow of bauxite deposits is crucial for designing mining drainage and formulating a mining plan in a coal-bearing rock series mining area. The water inflow on the roof and floor of bauxite was studied with various numerical simulation and analytical methods (such as the big well method). The hydrogeological conceptual and numerical model of the mining area was established by the MODFLOW module in Groundwater Modeling System (GMS (7.1)) software, and the measured groundwater level was identified and verified in the model. The results show that the model average values of R2, Ens, and PBIAS are 0.86, 0.81 and 2.71, respectively, indicating that the established numerical simulation model can accurately forecast water inflow into the aquifer. Taking No. XII orebody in the eastern Songshan Mining area as an example, a virtual well group consisting of 12 wells was set up, and the numerical model forecast a water inflow of 71,500 m3/d from the Taiyuan Formation aquifer in the bauxite ore roof, which was lower than the value predicted by the large well method (72,786.66 m3/d). The numerical method predicted an average water inflow of 59,000 m3/d and a maximum water inflow of 82,600 m3/d from the Majiagou Formation in the bauxite ore floor. A dependence has been established that the numerical method estimates water inflow with accuracy. Additionally, the model predicts future mining water inflow, and also provides a standard framework for estimating inflow in similar mining conditions.

Response Characteristics and Water Inflow Prediction of Complex Groundwater Systems under High-Intensity Coal Seam Mining Conditions

With the gradual improvement in coal mining efficiency, the disturbance of groundwater systems caused by high-intensity mining also increases, leading to challenges in maintaining mine safety and protecting water resources in mining areas. How to accurately describe the dynamic changes in the groundwater system under mining and quantitatively predict mine water inflow are currently major problems to be addressed. Based on a full analysis of the response characteristics of a groundwater system to the extraction disturbance, this paper presents a new method to establish a mine hydrogeological conceptual model that can accurately represent the water inrush process. The unstructured-grid package of MODFLOW is used to accurately characterize the formation structure and finally make accurate water inflow predictions. Taking the Caojiatan coal mine in Shaanxi Province, China, as an example, a numerical model of unstructured water inflow is established, and the changes in the water inflow source and intensity are quantitatively evaluated. Compared with the traditional water inflow prediction method, the prediction accuracy of the new model is improved by 12–17%, which is achieved by detailing the response of the complex groundwater system under high-intensity mining conditions. The method presented in this paper has great significance and applicatory value for obtaining a comprehensive understanding of the disturbance characteristics of human underground engineering activities (e.g., coal mining) on groundwater systems, as well as accurately predicting water inflow.

Characteristics of an underground stope channel supplied by atmospheric precipitation and its water disaster prevention in the karst mining areas of Guizhou

Atmospheric precipitation floods mining areas, which seriously affects the safe production of coal mines. However, research on the mechanism underlying precipitation supplying coal mines, particularly in karst landform areas, remains far from sufficient. Based on the collection of a large amount of geological and hydrogeological mining data and some data related to mine atmospheric precipitation and mine water inflow, the channels of atmospheric precipitation supplying mines in the main coal-producing areas of Guizhou, China, are systematically analysed and studied. They are divided into weathering zone fractures, mining fractures, water diversion faults, water diversion collapse columns and karst channels. Recharge channels have the characteristics of surface infiltration, pipeline flow and layered recharge, as well as self-healing after being filled by surface loess and other materials. The supply of atmospheric precipitation to the coal mine stope is seasonal. The mine water inflow in the rainy season is 1.2 ~ 12 times that in the dry season, with an average of 1.9 times. The supply has hysteresis. The lag time of surface infiltration, pipeline flow and layered flow is 2 ~ 4 days, within 24 h and more than 2 days, respectively. The recharge is affected by the burial depth of the coal seam and the characteristics of the combined upper roof slate. Among the mines affected by atmospheric precipitation and water disasters, some mines have carried out research on the comprehensive treatment of water disasters, implemented supplementary exploration projects such as surface hydrogeological drilling and geophysical exploration, or carried out hydrochemical research. Some mines have adopted water prevention and control projects, such as blocking ground water diversion cracks, constructing water diversion projects, adjusting the mining time of the working face, transforming the drainage system and improving the drainage capacity, to ensure the safe production of mines. This research achievement may provide a theoretical basis and practical experience for the prevention and control of atmospheric precipitation infiltration in coal mines in karst areas.

Evaluation of mine water inflow quality based on multiple methods

To reduce the negative impact of mine water gushing on the environment, it is necessary to evaluate the water quality. Taking the Pingdingshan coalfield as the research object, six components – namely, chroma, turbidity, total dissolved solids, total hardness, chloride (Cl−) and sulfate (SO4 2−) – were selected as index factors. The composite weight was calculated using the variable-weight theory, and the water quality of mine gushing water was evaluated using the matter-element extension model, fuzzy variable set model, Bayesian theory and Nemerow index method. The deviation of the four evaluation methods was calculated based on the specially constructed mathematical model. The research results show that the order of the deviation of the evaluation method from small to large is as follows: fuzzy variable set method (5.5) < matter-element extension method (8) < Bayesian statistical method (10) < Nemerow index method (17). The fuzzy variable set method is more suitable for Pingdingshan coalfield mine gushing water quality assessment. The authors hope that this research can provide a certain reference value for the evaluation of mine water quality in the future.

Study on the failure characteristics of overburden and the evolution law of seepage field in deep buried thick coal seam under aquifers

Water inrush at roof area seriously affects the safety of coal mines. The characteristics of aquifer and aquiclude at Wutongzhuang Mine are analyzed. Considering the effect of seepage field, a formula for calculating the height of water-conducting fractured zone (HWCFZ) in deep buried thick coal seam mining is derived. A damage-seepage coupling model with rock porosity and damage factor as independent variables is established. FLAC3D is re-developed by using FISH language, and the fluid–solid coupling calculation model of deep buried thick coal seam mining is established. The evolution law of the plastic zone, seepage field and water-conducting fractured zone (WCFZ)of the overburden in the gob with the advancement of the working face is analyzed, the main conclusions are as follows: With the continuous advancement of the working face, the distribution shape of the plastic zone and seepage field has changed from a trapezoidal to a saddle shape; when the working face reaches full mining, the maximum heights of the caving zone, fractured zone and HWCFZ are 24 m, 113 m, and 123 m, respectively; The 50 m-thick sandy shale aquifer is penetrated by the WCFZ, and the WCFZ on the side of the working face above the gob is the main water channel when the working face is advanced to 220 m. The on-site monitoring results showed that mine water inflow is not affected by surface rainfall and the 50 m-thick sandy shale is successively connected by the WCFZ. The results of comprehensive research showed that the HWCFZ cannot be calculated by traditional formulas when mining deep buried thick coal seams.

Construction and application of mine water inflow prediction model based on multi-factor weighted regression: Wulunshan Coal Mine case

Construction and application of mine water inflow prediction model based on multi-factor weighted regression: Wulunshan Coal Mine case

Ecological water security impact of large coal base development and its protection

The two main factors of groundwater system disturbance in coal mines are mine water inflow caused by overburden destruction and advance drainage projects as the main means of mine water hazard control. An urgent problem to be solved in the ecologically fragile area is how to minimize the impact of advance drainage on the groundwater ecological environment while reducing mine water inflow rate to ensure safe production. This paper proposes an optimal advance drainage technology based on multi-objective groundwater management. Based on the analysis of the respective relationship between the drainage groundwater level and the capture supply and mine water inflow rate, the concept and mathematical expression of the drainage threshold of the mine and the ecological environment are put forward for the first time. Based on the numerical simulation model, this technology fully describes the dynamic changes of the groundwater system and establishes a groundwater management model with multi-objective constraints such as mine production safety, ecological water resources protection and efficient drainage. The optimal drainage scheme for the boreholes location, the drainage volume and the time distribution is obtained through objective programming. While reducing drainage volume and ensuring mine production safety, the impact on the locally important phreatic groundwater is significantly reduced, effectively resolving the contradiction between ecological water resource protection and mine safety production. In the example of a coal mine in Northwest China, by comparing the traditional equal drainage scheme, the optimized advance drainage technology fully considers the water-richness characteristics and the dynamic changes of the groundwater system during the drainage process. The number of boreholes in the optimization scheme is reduced by 50 %, and the total drainage volume is reduced by 40.60 %. More importantly, under the condition that the decrease of mine water inflow rate in the two schemes is basically the same, the average increase of Quaternary leakage in the optimization scheme decreases by 53.36 %. Through this technology, the ecological critical value in the study area is evaluated. When the regional Quaternary groundwater level decreases by more than 0.73 m, the impact range of the drawdown funnel and the drawdown rate are obviously accelerated. It provides a scientific foundation for groundwater resource management and low-carbon mining in ecologically fragile areas.