Mechanism,prevention,and control of mining-induced dynamic disasters in underground metal mines in China:Challenges and solutions

Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.

Application of open-pit and underground mining technology for residual coal of end slopes

Given the conditions of residual coal from the boundary of a flat dipping open-pit mine,which uses strip areas mining and inner dumping with slope-covering,we propose an open-pit and underground integrated mining technology for residual coal of end slopes.In the proposal a conveyance road and ventilation conveyance near the slope are built,corresponding to the pit mining area and the surface coal mine dump,as well as an interval haulage tunnel and air-inlet tunnel.The outcome shows that such mining method may reduce the effect to slope stability from underground mining,it does not affect the dumping advance and has a high recovery rate of residual coal resources.The working face is timbered by single hydraulic props,transported by a scraper conveyor and supported by coal walls.This method of mining is one of layered top coal caving,with high resource recovery,low production cost where positive economic benefit can be realized.

Fire behavior of mining vehicles in underground hard rock mines

The results from a number of investigations and fire experiments are presented and analyzed in order to characterize the fire behavior of mining vehicles in underground hard rock mines. The analysis also includes fire safety and fire protection measures with respect to the mining vehicle fire behavior.Earlier studies on fires in underground hard rock mines have shown that vehicles or mobile equipment are the dominant sources of fire. A better knowledge about the fire behavior of vehicles in underground hard rock mines is therefore needed. During the analysis the direction and flow rate of the ventilation in a drift was found to have a significant impact on the fire behavior, causing for example flame tilt with rapid fire spread. The shielded sections of a vehicle will be less affected by the ventilation flow resulting in for example a decreased flame spread. It was also found that spray fires may result in considerable heat release rate but are generally of shorter duration and will not make any significant contributions to the overall heat release rate of the fully developed vehicle fire. The fire duration of a loader tire from a full-scale fire experiment was found to be at least 200 min and will largely determine the total fire duration of the vehicle. A different scenario with different conditions with for example a slower flame spread resulted in an even longer fire duration. The radiative and convective fraction will be a key factor when determining the heat transfer mechanisms involved in a fire and will vary from material to material.Calculations show that the radiative fraction of the tire fires on two mining vehicles is significantly lower than found in earlier experiments. The design and construction of the mining vehicle will have an important impact on the fire behavior and could possibly mitigate the consequences of a fire and allow fire personnel to extinguish a fire that otherwise would have had a too high heat release rate.

Artificial ground freezing of underground mines in cold regions using thermosyphons with air insulation

Current practice of underground artificial ground freezing(AGF)typically involves huge refrigeration systems of large economic and environmental costs.In this study,a novel AGF technique is proposed deploying available cold wind in cold regions.This is achieved by a static heat transfer device called thermosyphon equipped with an air insulation layer.A refrigeration unit can be optionally integrated to meet additional cooling requirements.The introduction of air insulation isolates the thermosyphon from ground zones where freezing is not needed,resulting in:(1)steering the cooling resources(cold wind or refrigeration)towards zones of interest;and(2)minimizing refrigeration load.This design is demonstrated using well-validated mathematical models from our previous work based on two-phase enthalpy method of the ground coupled with a thermal resistance network for the thermosyphon.Two Canadian mines are considered:the Cigar Lake Mine and the Giant Mine.The results show that our proposed design can speed the freezing time by 30%at the Giant Mine and by two months at the Cigar Lake Mine.Further,a cooling load of 2.4 GWh can be saved at the Cigar Lake Mine.Overall,this study provides mining practitioners with sustainable solutions of underground AGF.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines

One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations.As a result,a reliable roof fall prediction model is essential to tackle such challenges.Different parameters that substantially impact roof falls are ill-defined and intangible,making this an uncertain and challenging research issue.The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls.Data acquired for 37 mines is limited due to several restrictions,which increased the likelihood of incompleteness.Fuzzy logic is a technique for coping with ambiguity,incompleteness,and uncertainty.Therefore,In this paper,the fuzzy inference method is presented,which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters.The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error,Mean-Absolute-Error,and coefficient of determination(R_(2)).Based on these criteria,the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.

Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.

Numerical simulation study of the influence on stability of slope by underground mining under opencast coal mine slope

In view of the study on mining transferred from open-pit to underground, the research on the problem of the stabil- ity of slope is less. This article combined the actual situation of the Gaohai Coal Mine in Fuxin City and set up a three-dimensional model of the part of Huizhou open-pit slope by the finite difference software. Through the three-dimensional numerical simulation study of the influence on the stability of slope by underground mining, the basic characteristics of the open-pit slope deformation and the situation of basic stability were discussed. The simulation results of the mining slope of the displacement and deformation analysis of the state for mining provide a reference to the slope stability research.

Evaluation of the impact of commodity price change on mine plan of underground mining

Fluctuations in commodity prices should influence mining operations to continually update and adjust their mine plans in order to capture additional value under new market conditions. One of the adjustments is the change in production sequencing. This paper seeks to present a method for quantifying the net present value(NPV) that may be directly attributed to the change in commodity prices. The evaluation is conducted across ten copper price scenarios. Discrete event simulation combined with mixed integer programming was used to attain a viable production strategy and to generate optimal mine plans. The analysis indicates that an increase in prices results in an increased in the NPV from$96.57M to $755.65M. In an environment where mining operations must be striving to gain as much value as possible from the rights to exploit a finite resource, it is not appropriate to keep operating under the same mine plan if commodity prices alter during the course of operations.

Solution to the Horizontal Crossing Problem of Transportation System at Key Points in Open-Pit Mines

In order to reduce the horizontal crossing transportation problems between coal trucks and stripping trucks,large and small vehicles,and transport trucks and belt conveyors at key points of open pit mine in production,the separate transportation mode of underpass bridge and overpass steel trestle is proposed to optimize the open pit development transportation system,so as to solve the practical problems that the horizontal cross of transport vehicles causes vehicle blockage,affects production schedule and production safety.The results show that the horizontal crossing road can be changed into a separate type of overpass steel trestle,which can realize the classified transportation of large and small vehicles,reduce the traffic density,make vehicles with different functions go their own way,eliminate the hidden danger of traffic accidents,and improve the production efficiency.

Study on the Law of the Movement and Damage to Slope with the Combination of Underground Mining and Open-Pit Mining

Under circumstances in which both underground mining and open-pit mining are employed, the mining effects of two approaches will be superposed and the mining slope will receive several induced stress fields, which makes the sliding mechanism and deformation law of slope rock mass more complicated. This paper, targeting at the east slope of Antaibao Mine with the joint employment of underground mining and open-pit mining, aims to study the moving law of the slope rock mass and the damage mechanism to the overburden of the goaf by numerical simulation. It is supposed that models of possible damage to the slope could be explored for guidance to safety-production of the mine.

Evaluation of Convolutional-Long Short-Term Memory Model in Predicting Surface Displacement of Underground Mines

Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise the adverse effects of surface displacement, it is vital to monitor and accurately predict them and unravel their mechanisms. In recent years, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have proven effective in predicting complex problems. However, CNN neglects the dynamic dependency of the input in the temporal dimension, which affects surface displacement features. The Convolutional-LSTM model can dynamically learn the temporal dependency among input features via the feedback connections in the LSTM to improve accurate captures of surface displacement features. This study focused on evaluating the C-LSTM model in predicting surface displacement of underground mines and assessed the predictive capabilities and generalisation strength of using hybridised ANN models. Geodetic and geotechnical data gathered from a Gold Mine in Ghana was used. The three models were tested on experimental data collected at Monitoring Scan Point 3. It was observed from the prediction output that all the methods could provide applicable and practical results. However, using indicators like root mean square error (RMSE) and correlation coefficient (R) in assessing the output of the prediction, the C-LSTM had the best prediction output. This study contributes to the advancement of accurate and efficient prediction of surface displacement of underground mines, ultimately enhancing and assisting safety operations.

Smelting,Underground Mining,Smoking,and Lung Cancer:A Case-Control Study in a Tin Mine Area

An increased risk of lung cancer in the Dachang tin mine of Guangxi has been reported.To investigate the factors contributing to the excessive risk of lung cancer,we conducted a matched case-control study in the mine area and analyzed the effects of multiple factors,such as living and housing conditions,occupational exposure,and smoking,with the methods of conditional logistic regression.The case series consisted of 69 patients with primary bronchial cancer,55 of whom had died.The control series consisted of 138 individuals,55 of whom were deceased.The results showed that the factors contributing to the excessive risk of lung cancer in the mine area related mainly to occupational exposure.The significant risk factors were time of exposure to smelting,time of exposure to underground mining,and age at which underground mining was begun.In study model II where living controls were used,daily consumption of cigarettes was an additional risk factor.Furthermore,there was a synergistic action among these factors.The relation of the risk factors to lung cancer is discussed.1989 Academic Press.Inc.

Failure characteristics and its influencing factors of talus-derived rock mass during open-pit mining

The failure characteristic of talus-derived rock mass continues to challenge quantitative hazard assessments in open-pit mining. Physical model test was used to assess the failure modes and mechanisms on talus-derived rock mass. The different types of failure modes of the talus-derived rock mass were introduced and a possible failure mechanism relation between the failure zone and the structure of the talus-derived rock mass was also shown. The physical model test results indicate that the rainfall has significant influence on the stability and failure modes of talus-derived rock mass during open-pit mining. The development of the seepage area caused by rainfall initiates the localized failure in that particular area, and the initiation of localized instability is mainly induced by stress changes concentrated in the seepage area.

A review of the health effects and exposure-responsible relationship of diesel particulate matter for underground mines

The increasing use of diesel-powered equipment in confined spaces(underground mines) has the potential to over expose underground miners under the threat of diesel particulate matter(DPM). Miners in underground mines can be exposed to DPM concentrations far more than works in other industries. A great number of animal and epidemiological studies have shown that both short-term and long-term DPM exposure have adverse health effect. Based on reviews of related studies, especially some recent evidence, this paper investigated the long and short-term health effects based on animal studies and epidemiological studies. The exposure-response relationship studies were also explored and compared to the current DPM regulation or standards in some countries. This paper found that the DPM health effect studies specifically for miners are not sufficient to draw solid conclusions, and a recommendation limit of DPM concentration can be put in place for better protection of miners from DPM health risk. Current animal studies lack the use of species that have similar lung functions as human for understanding the cancer mode of action in human. And finally, the DPM health hazard will continue to be a challenging topic before the mode of action and reliable exposure-response relationship are established.

Spatial context in the calculation of gas emissions for underground coal mines

The prediction of gas emissions arising from underground coal mining has been the subject of extensive research for several decades, however calculation techniques remain empirically based and are hence limited to the origin of calculation in both application and resolution. Quantification and management of risk associated with sudden gas release during mining(outbursts) and accumulation of noxious or combustible gases within the mining environment is reliant on such predictions, and unexplained variation correctly requires conservative management practices in response to risk. Over 2500 gas core samples from two southern Sydney basin mines producing metallurgical coal from the Bulli seam have been analysed in various geospatial context including relationships to hydrological features and geological structures. The results suggest variability and limitations associated with the present traditional approaches to gas emission prediction and design of gas management practices may be addressed using predictions derived from improved spatial datasets, and analysis techniques incorporating fundamental physical and energy related principles.

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing rapidly.The coal mines have to pace up the mining operations with large production to meet the energy demand.This requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a few.Therefore,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(i.e.multi-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS models.Accordingly,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile strength.The MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground mines.The results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

Stability of boundary pillars in transition from open pit to underground mining

Based on the height of back-filled materials, thickness of ore body, height of boundary pillar and dipping angle of ore body and water pressure, the safety factors of all the pillars are calculated with the limit equilibrium method. The calculation results present that the safety factors of pillars in Sections 19, 20, 24, 28 are less than 1.3, and those of unstable sections are identified preliminarily. Further, a numerical investigation in Sections 18, 20, 22, 24, 25 and 28 implemented with numerical code RFPA20 is employed to further validate the pillar performance and the stability of stopes. The numerical results show the pillars in Sections 18, 22 and 24 are stable and the designed pillar size is suitable. The width of the ore body near Section 28 averages 20 m, failure occurs in the left stope, but the boundary pillars near Section 28 maintain good performance. The pillars in Sections 20 and 25 are unstable which are mainly affected by the Faults F8 and F18. The existence of faults alters the stress distribution, failure mode and water inrush pathway. This work provides a meaningful standard for boundary pillar and stope design in a mine as it transitions from an open pit to underground.

Optimization model of unascertained measurement for underground mining method selection and its application

An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.

Quantitative hazard assessment for Zonguldak Coal Basin underground mines

Underground coal mining is one of the most dangerous occupations throughout the world.The reasons behind an underground occupational accident are too complex to analyze mainly due to many uncertainties which may arise from geological,operational conditions of the mine or individual characteristics of employees.This study proposes implementing a quantitative methodology for the analysis and assessment of hazards associated with occupational accidents.The application of the proposed approach is performed on the mines of Turkish Hard Coal Enterprises(TTK).The accidents in TTK between the years 2000 and 2014 are firstly statistically analyzed with respect to the number,type and location of accidents,age,experience,education level and main duty of the casualties and also injuries resulting from such accidents.The hazards are classified as individual,operational and locational hazards and quantified using contingency tables,conditional and total probability theorems.Lower and upper boundaries of hazards are determined and event trees for each hazard class are prepared.Total hazard evaluation results show that Armutcuk,Karadon and Uzulmez mines have relatively high hazard levels while Amasra and Kozlu mines have relatively lower hazard values.