Construction and application of an intelligent prediction model for the coal pillar width of a fully mechanized caving face based on the fusion of multiple physical parameters

The scientific and reasonable width of coal pillars is of great significance to ensure safe and sustainable mining in the western mining area of China. To achieve a precise analysis of the reasonable width of coal pillars in fully mechanized caving face sections of gently inclined coal seams in western China, this paper analyzes and studies various factors that affect the retention of coal pillars in the section, and calculates the correlation coefficients between these influencing factors. We selected parameters with good universality and established a data set of gently inclined coal seams based on 106 collected engineering cases. We used the LSTM algorithm loaded with a simulated annealing algorithm for training, and constructed a coal pillar width prediction model. Compared with other prediction algorithms such as the original LSTM algorithm, the residual sum of squares and root mean square error were reduced by 27.2% and 24.2%, respectively, and the correlation coefficient was increased by 12.6%. An engineering case analysis was conducted using the W1123 working face of the Kuangou Coal Mine. The engineering verification showed that the SA-CNN-LSTM coal pillar width prediction model established in this paper has good stability and accuracy for multi-parameter nonlinear coupling prediction results. We have established an effective solution for achieving the accurate reservation of coal pillar widths in the fully mechanized caving faces of gently inclined coal seams

Research on key technologies of intelligent fully mechanized mining on working face with large mining height

In order to solve problems of large number of workers and high labor intensity on fully mechanized mining face with large mining height, according to actual situation of No.2 Coal Mine of Huangling Mining Group Co., Ltd., technical difficulties such as precise control of rib spalling, soft bottom frame and equipment reliability, precision of perception and poor coordination in intelligent fully mechanized mining on working face with large mining height were analyzed. Technology route of visual remote intervention were used to realize intelligent normal mining of fully mechanized mining face with large mining height. Efficient coal mining technology, control technology of anti-rib spalling, intelligent control technology with soft bottom and broken roof were research focus. Mining efficiency of triangular coal is increased by 30% through mining process innovation; different control methods are used in different stages of rib spalling to control, and precise control of guard plate is achieved; intelligent processing under weak condition of soft bottom is completed through simulation of manual operation of descending of hydraulic support for several times, and the problem of coal piling in front of the support is solved; intelligent treatment under the condition of broken roof is completed by pulling support advanced to ensure that the roof broken area can be mined intelligently. The practice of No.2 Coal Mine shows that after adopting intelligent fully mechanized mining technology, intelligent production mode is realized which mainly based on intelligent operation of working face equipments and with the help of remote intervention control of monitoring center, as well as the “7+2” operation mode of 7 people underground and 2 people on the ground, so achieves the purpose of reducing staff and improving efficiency

Research on Safety Subregion Partition Method and Characterization for Coal Mine Ventilation System

The research analyzes and improves upon the concept of ventilation safety subregions in coal mines and proposes a partition method based on the breadth-first search algorithm for assessing the quality of the ventilation. This involves the analysis of the function of ventilation subregions and of ventilation gas-monitoring data. Then, using the so-called ventilation sensitivity matrix as the analysis method, we confirm the consistency of considering safety subregions, which are associated with air consumption places as the core concept and the objective positioning of the safety subregions within the ventilation analysis. This allows us to establish the validity and practicability of employing regional characteristic information of mine ventilation and gas concentrations in the mine’s air. Finally, based on the characteristics of the ventilation subregions, ventilation air quantity network maps with safety subregions are proposed and their application is demonstrated. The advantages and characteristics of using these maps to replace ventilation network maps for ventilation analysis are demonstrated

Innovation progress and prospect on key technologies of intelligent coal mining

Innovation and practice were summarized overall about intelligent fully mechanized coal mining in thin coal seam and thinner coal seam, intelligent fully mechanized coal mining of large mining height and super large mining height in thick coal seam, and intelligentization technology of fully-mechanized top coal mining in super thick coal seam, and shortcomings of the above technologies were analyzed. Five key technologies were proposed for fully mechanized coal mining equipments to adapt to surrounding rock movement and dynamic environment variety, which were intelligent height adjustment control of shearer, intelligent coupling self-adaptive control of hydraulic support units and surrounding rock, intelligent alignment control of working face, cooperative control based on multi-information fusion and intelligent control of advance support and assistant operation. The key technologies lay technical base for intelligent mining progressing to senior stage of self-learning, self-decision-making and self-adjustment from current initial stag. Technical development directions and targets of coal industry in the short term, medium term and long term were proposed, namely intelligent mining, limited unmanned mining and fluidized mining, and development route, key technologies and development direction were also prospected

Study on Key Parameters of Directional Long Borehole Layout in High-Gas Working Face

This research aims to obtain a new way based on the directional long borehole layout to investigate the gas migration behavior of coal bed methane. In view of the study of the oil-type gas reservoir and the influence range of mining on the working face in the surrounding rock, the paper puts forward the optimization method of the directional long borehole layout of the surrounding rock and the method of the borehole layout with comprehensive consideration of the influence of mining on the failure of the roof and floor and the distribution of surrounding rock gas-bearing reservoirs. The test results showed that the layout horizon of the directional long boreholes is determined, and the layout was within the height range of 20–40 meters, clarified the relationship between the drainage data and the exposure level of the borehole and the layout of the borehole, compared the gas concentration in the upper corner of the working face before and after the drainage, and constructed a three-dimensional comprehensive drainage mode for the high-gassy working face, which provided a worth-promoting method that supports surrounding rock oil-type gas and gas treatment in the high-gas mining area

A Spark Streaming-Based Early Warning Model for Gas Concentration Prediction

The prediction and early warning efficiency of mine gas concentrations are important for intelligent monitoring of daily gas concentrations in coal mines. It is used as an important means for ensuring the safe and stable operation of coal mines. This study proposes an early warning model for gas concentration prediction involving the Spark Streaming framework (SSF). The model incorporates a particle swarm optimisation algorithm (PSO) and a gated recurrent unit (GRU) model in the SSF, and further experimental analysis is carried out on the basis of optimising the model parameters. The operational efficiency of the model is validated using a control variable approach, and the prediction and warning errors is verified using MAE, RMSE and R2. The results show that the model is able to predict and warn of the gas concentration with high efficiency and high accuracy. It also features fast data processing and fault tolerance, which provides a new idea to continue improving the gas concentration prediction and warning efficiency and some theoretical and technical support for intelligent gas monitoring in coal mines

Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines

A Gas Concentration Prediction Method Driven by a Spark Streaming Framework

In the traditional coal-mine gas-concentration prediction process, problems such as low timeliness of data and low efficiency of the prediction model in learning data features result in low accuracy of the final prediction. To solve these problems, a gas-concentration prediction method driven by the Spark Streaming framework is proposed. In this research study, the Spark Streaming framework, autoregressive integrated moving average (ARIMA) model and support vector machine (SVM) model are used to construct a new prediction model called the SPARS model. The Spark Streaming framework is used to process large batches of real-time streaming data in a short period of time, and the model can be used to intermittently update and optimize the prediction model so that the model can fully learn the characteristics of the data. At the same time, the advantages of the ARIMA model and SVM model for processing linear data and nonlinear data are combined to improve the model’s prediction efficiency and fully reflect the timeliness of gas prediction. Finally, the proposed prediction model is verified using gas data collected on site. The optimal learning time for the SPARS model in predicting this set of data is determined, and a comparative analysis of the prediction results obtained from the ARIMA, SVM and other models fully confirms that high-precision prediction results can be obtained using the SPARS model. The proposed model can be used to realize scientific and accurate real-time prediction and analyses of coal-mine gas concentrations and provides a new idea for realizing real-time and accurate gas prediction in coal mines

Ultra-Thin/Wide-Band Polarization Conversion Metasurface and Its Applications in Anomalous Reflection and RCS Reduction

In this paper, the design of an ultra-wideband polarizer based on a metasurface with high-performance is reported and demonstrated. The polarizer is composed of a dielectric substrate with double semicircular gap patches and a metal film. Multiple strong resonance points enable the design to convert the incident linearly polarized waves into cross-polarized waves in the 14.8–28.0 GHz range, with a fractional bandwidth of 61.7% and a corresponding polarization conversion rate (PCR) above 95%. Further simulated results show that the PCR remains above 87% in the 14.37–24.75 GHz range when the incident angle of the electromagnetic (EM) waves is between 0–30°, and the physical mechanism is explained by the surface current distribution. In addition, the gradient metasurface is designed according to the Pancharatnam–Berry phase principle to achieve anomalous reflection, and the 1-bit metasurface is coded to reduce the Radar Cross Section (RCS). The EM waves reach an anomalous reflection of −23° at 15 GHz normal incidence, and the RCS is reduced by 10 dB in the range of 15.3–28.0 GHz. These findings have potential application value in stealth and antenna design