Raw Gangue Filling Mining under Construction —A Case Study in China

In order to recover the strip pillar coal resources, reduce the amount of gangue mountain and realize remediation of the goaf environment in the old mining area, the raw gangue filling mining technology was proposed. According to the previous practical experience, the feasibility of the implementation of raw gangue filling mining technology in the coal-pressed area was analyzed. Through the filling gangue compaction test, the deformation under different loading stages was obtained. Further, a reasonable prediction of the deformation beyond the experimental limited loading load was made based on the experimental results. Through the deformation source analysis of the whole process of gangue filling, the key factors for controlling deformation before, during, and after filling were determined. Additionally, the proportion of deformation during different stages was quantified. Considering the protection of surface buildings, mining fullness of the working face and mining technology, the production parameters of 1209 and 1210 filling working faces were preliminarily determined. Through numerical simulation, the rationality of mining scheme was verified. Based on the practice of 1209 working face and the key factors to control the deformation of gangue filling, the mining system and process in 1210 working face were optimized. According to the measured surface rock movement, raw gangue filling mining technology can meet the requirements of surface building protection level. Especially, this paper provides a method to quantitatively calculate the equivalent mining height (EMH) of raw gangue filling and its mining deformation, which has reference significance for old mining areas.

Mechanics criterion and factors affecting overburden stability in solid dense filling mining

The effect of controlling strata movement in solid filling mining depends on the filling rate of the goal. However, the mechanical property of the overburden in the backfill stope and the designed size of the backfill mining workface should also be considered. In this study, we established a main roof strata model with loads in accordance with the theory of key strata to investigate the stability of the overburden in solid dense filling mining. We analyzed the stress distribution law of the main roof strata based on elastic thin plate theory. The results show that the position of the long side midpoint of the main roof strata failed more easily because of tensile yield, indicating that this position is the area where failure is likely to occur more easily. We also deduced the stability mechanics criterion of the main roof strata based on tensile yield criterion. The factors affecting the stability of the overburden in solid dense filling mining were also analyzed, including the thickness and elasticity modulus of the main roof strata, overlying strata loads, advanced distance and length of workface, and elastic foundation coefficient of backfill body. The research achievements can provide an important theoretical basis for determining the designed size of the solid dense filling mining workface.

Development and prospect on fully mechanized mining in Chinese coal mines

Fully mechanized mining(FMM)technology has been applied in Chinese coal mines for more than 40 years.At present,the output of a FMM face has reached 10-million tons with Chinese-made equipment.In this study,the new developments in FMM technology and equipment in Chinese coal mines during past decades are introduced.The automatic FMM technology for thin seams,complete sets of FMM technology with ultra large shear height of 7 m for thick seams,complete sets of fully mechanized top coal caving technology with large shear height for ultra-thick seams of 20 m,complete sets of FMM technology for complex and difficult seams,including steeply inclined seams,soft coal seams with large inclination angle,and the mechanized filling mining technology and equipment are presented.Some typical case studies are also introduced.Finally,the existing problems with the FMM technology are discussed,and prospect of FMM technology and equipment applied in Chinese coal mines is put forward.

Identifying Metabolite and Protein Biomarkers in Unstable Angina In-patients by Feature Selection Based Data Mining Method

Unstable angina（UA） is the most dangerous type of Coronary Heart Disease（CHD） to cause more and more mortal and morbid world wide. Identification of biomarkers for UA at the level of proteomics and metabolomics is a better avenue to understand the inner mechanism of it. Feature selection based data mining method is better suited to identify biomarkers of UA. In this study, we carried out clinical epidemiology to collect plasmas of UA in-patients and controls. Proteomics and metabolomics data were obtained via two-dimensional difference gel electrophoresis and gas chromatography techniques. We presented a novel computational strategy to select biomarkers as few as possible for UA in the two groups of data. Firstly, decision tree was used to select biomarkers for UA and 3-fold cross validation was used to evaluate computational performanees for the three methods. Alternatively, we combined inde- pendent t test and classification based data mining method as well as backward elimination technique to select, as few as possible, protein and metabolite biomarkers with best classification performances. By the method, we selected 6 proteins and 5 metabolites for UA. The novel method presented here provides a better insight into the pathology of a disease.

Stress field evolution law of mining environment reconstructing structure with change of filling height

For improving global stability of mining environment reconstructing structure,the stress field evolution law of the structure with the filling height change of low-grade backfill was studied by ADINA finite element analysis code.Three kinds of filling schemes were designed and calculated,in which the filling heights were 2,4,and 7 m,separately.The results show that there are some rules in the stress field with the increase of the filling height as follows:(1) the maximum value of tension stress of the roof decreases gradually,and stress conditions are improved gradually;(2) the tension stress status in the vertical pillar is transformed into the compressive stress status,and the carrying capacity is improved gradually;however,when the filling height is beyond 2.8 m,the carrying capacity of the vertical pillar grows very slowly,so,there is little significance to continue to fill the low-grade backfill;(3) the bottom pillar suffers the squeezing action from the vertical pillars at first and then the gravity action of the low-grade backfill,and the maximum value of tension stress of the bottom pillar firstly increases and then decreases.Considering the economic factor,security and other factors,the low-grade backfill has the most reasonable height(2.8 m) in the scope of all filling height.

Sediment settlement rate and consolidation time of flling reclamation in coal mining subsidence land

With the continuous growth of the population and the continuous reduction of cultivated land,China’s food security is greatly threatened.In addition,China’s coal mining has been mainly underground mining,causing land subsidence and damaging existing cultivated land.This efect intensifes the contradiction between the growth of the risk population and the reduction of cultivated land.The reclamation of mining subsidence land with Yellow River sediment is often used as an efective way to improve the recovery rate of cultivated land.Shortening the reclamation time and realizing continuous flling are signifcant issues.The work presented in this paper studied the sediment settlement rate and consolidation time by combining theory,feld flling and reclamation tests and numerical simulations.A feld flling test study was carried out in the lowlands of Jibeiwang Village,Qihe County,Shandong Province,China.By calculating the drainage consolidation time,the consolidation factor of 0.015656 m^(2)/d,and the time factor for sediment consolidation of 0.575 were determined.The sediment consolidation time for this test was 9.18 days.The calculation of sediment deposition rate and consolidation time is of great practical signifcance to guide the Yellow River sediment flling,realize continuous flling,and save reclamation time and cost.

Selection and evaluation of suitable tree species in dry and dusty mining areas of Northwest China

To select drought-resistant and dust-tolerant native species suitable for use in the rehabilitation of major coal bases in northwest China,nine tree species were identified for growth rates,biomass,harm index,and physiological indices under drought and high dust stress conditions.The results showed that,in the dust resistance index system,the order was Caragana korshinskii>Amorpha fruticosa>Sabina vulgaris>Hedysarum scoparium>Tamarix chinensis>Ammopiptanthus mongolicus>Ulmus pumila>C aryopteris mongholica>Elaeagnus angustifolia.In a comprehensive drought and dust resistance index system,14 indices(such as shoot length,stomatal conductance,and peroxidase)had the larger weight indices.The drought and dust resistance order of the tree species was C aragana korshinskii>Ulmus pumila>Amorpha fruticosa>Sabina vulgaris>Caryopteris mongholica>Ammopiptanthus mongolicus>H edysarum scoparium>Tamarix chinensis>Elaeagnus angustifolia.This study provides effective strategies and references for selecting suitable tree species for arid mining sites in China,and also for the revegetation of coal mining sites worldwide.

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and researches.Data mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the data.At the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational sector.Recently,artificial intelligence(AI)techniques can be used for mining a large amount of data.At the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization algorithms.With this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education sector.The presented IEAFSS-NFC model involves data pre-processing,feature selection,and classification.Besides,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification results.Then,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education data.The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository.The simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

URL Phishing Detection Using Particle Swarm Optimization and Data Mining

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

Engineering and Biological Measures of Reclamation for Coal Mining Subsided Land with High Phreatic Level in East China: A Case Study

In east China , a great amount of water-logged land is caused by coal mining subsidence,which results in arduous reclamation tasks. Taking Hanqiao Coal Mine in Xuzhou as an example, this paper introduces a new reclamation method suitable for suhsided land caused by coal mining with high phreatic level in east China. The new method consists of two respects: 1 ) engineering reclamation measure is a non-filled method which mainly applies dredging approach ; 2 ) biological reclamation measure is a high beneficial pattern, which mainly makes use of dikes and ponds. The new engincering and biological measures of reclamation have been used widespreadly in Xuzhou mining area. Considerable economic benefits and social and environmental effects have been obtained.

Safe mining technology of undersea metal mine

Xinli district of Sanshandao Gold Mine is the first subsea metal mine in China.To achieve 6 kt/d production capacity under the premise of safe mining,high-intensity mining might destroy the in-situ stress filed and the stability of rockmass.According to sampling and testing of ore-rock and backfill and in-situ stress field measurement,safety factor method calculation model based on stress-strain strength reduction at arbitrary points and Mohr-Coulomb yield criterion was established and limit displacement subsidence values under the safety factor of different limit stoping steps were calculated.The results from three years in-situ mining and strata movement monitoring using multi-point displacements meter showed that the lower settlement frame stope hierarchical level filling mining method,mining sequence are reasonable and rockmass stability evaluation using safety factor method,in-situ real-time monitoring can provide the technical foundation for the safety of seabed mining.

Acupoint Selection Standards of Auricular Therapy in the Treatment of Maintenance Hemodialysis Insomnia Based on Data Mining

Background:Insomnia is a high-incidence complication in patients undergoing maintenance hemodialysis(MHD).Auricular therapy can effectively improve sleep with few adverse effects.Acupoint selection affects the impact of auricular therapy.However,there is currently a lack of analysis on the standards of acupoint selection.Our study used data mining technology to investigate the acupoint selection principles and characteristics of auricular therapy for the treatment of MHD-related insomnia.Objective:The objective of the study is to explore the standards of acupoint selection in auricular therapy for the treatment of MHD-related insomnia through data mining technology.Materials and Methods:We searched three English(PubMed,WOS,and Embase)and four Chinese(CNKI,VIP,Wangfang,and CBM)databases for studies on auricular therapy for MHD-related insomnia from self-establishment to November 14,2022.Results:Eighty-one publications were involved,which included 33 acupoints.The most common auricular points in patients with MHD-related insomnia were the Shenmen,heart,and kidney points.More applications involved the visceral,nervous system,and specific acupoints.Five effective clusters and two clusters were obtained through cluster analysis,including specific auricular points for insomnia,such as the multi-dream area,neurasthenia area,deep sleep point,and anterior ear lobe.Complex network analysis showed that the core auricular acupoint combinations for the intervention of MHD-related insomnia were Shenmen with kidney,Shenmen with heart,heart with kidney,heart with Shenmen,and heart and Shenmen with subcortex.Conclusions:The selection of auricular points for the treatment of MHD-related insomnia was guided by the heart theory of traditional Chinese medicine.Clinical treatment attaches importance to the use of the multi-dream area,neurasthenia area,and other acupoints.

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

An investigation of surface deformation after fully mechanized,solid back fill mining

The surface deformation after fully mechanized back filling mining was analyzed.The surface deformation for different backfill materials was predicted by an equivalent mining height model and numerical simulations.The results suggest that:(1) As the elastic modulus,E,of the backfill material increases the surface subsidence decreases.The rate of subsidence decrease drops after E is larger than 5 GPa;(2) Fully mechanized back fill mining technology can effectively control surface deformation.The resulting surface deformation is within the specification grade I,which means surface maintenance is not needed.A site survey showed that the equivalent mining height model is capable of predicting and analyzing surface deformation and that the model is conservative enough for engineering safety.Finally,the significance of establishing a complete error correction system based on error analysis and correction is discussed.

Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method

One of the most critical and complicated steps in mine design is a selection of suitable mining method based upon geological,geotechnical,geographical,safety and economical parameters.The aim of this study is developing a Monte Carlo simulation to selection the optimum mining method by using effective and major criteria and at the same time,taking subjective judgments of decision makers into consideration.Proposed approach is based on the combination of Monte Carlo simulation with conventional Analytic Hierarchy Process(AHP).Monte Carlo simulation is used to determine the confdence level of each alternative’s score,is calculated by AHP,with the respect to the variance of decision makers’opinion.The proposed method is applied for Jajarm Bauxite Mine in Iran and eventually the most appropriate mining methods for this mine are ranked.

Data Mining for Mesoscopic Simulation of Electron Beam Selective Melting

In the electron beam selective melting(EBSM)process,the quality of each deposited melt track has an effect on the properties of the manufactured component.However,the formation of the melt track is governed by various physical phenomena and influenced by various process parameters,and the correlation of these parameters is complicated and difficult to establish experimentally.The mesoscopic modeling technique was recently introduced as a means of simulating the electron beam(EB)melting process and revealing the formation mechanisms of specific melt track morphologies.However,the correlation between the process parameters and the melt track features has not yet been quantitatively understood.This paper investigates the morphological features of the melt track from the results of mesoscopic simulation,while introducing key descriptive indexes such as melt track width and height in order to numerically assess the deposition quality.The effects of various processing parameters are also quantitatively investigated,and the correlation between the processing conditions and the melt track features is thereby derived.Finally,a simulation-driven optimization framework consisting of mesoscopic modeling and data mining is proposed,and its potential and limitations are discussed.

Developing selective mining capability for longwall shearers using thermal infrared-based seam tracking

Longwall mining continues to remain the most efficient method for underground coal recovery. A key aspect in achieving safe and productive longwall mining is to ensure that the shearer is always correctly positioned within the coal seam. At present, this machine positioning task is the role of longwall personnel who must simultaneously monitor the longwall coal face and the shearer's cutting drum position to infer the geological trends of the coal seam. This is a labour intensive task which has negative impacts on the consistency and quality of coal production. As a solution to this problem, this paper presents a sensing method to automatically track geological coal seam features on the longwall face, known as marker bands, using thermal infrared imaging. These non-visible marker bands are geological features that link strongly to the horizontal trends present in layered coal seams. Tracking these line-like features allows the generation of a vertical datum that can be used to maintain the shearer in a position for optimal coal extraction. Details on the theory of thermal infrared imaging are given, as well as practical aspects associated with machine-based implementation underground. The feature detection and tracking tasks are given with real measurements to demonstrate the efficacy of the approach. The outcome is important as it represents a new selective mining capability to help address a long-standing limitation in longwall mining operations.

Optimum mining method selection using fuzzy analytical hierarchy process–Qapiliq salt mine,Iran

Mining method selection is the first and the most critical problem in mine design and depends on some parameters such as geotechnical and geological features and economic and geographic factors. In this paper, the factors affecting mining method selection are determined. These factors include shape, thick- ness, depth, slope, RMR and RSS of the orebody, RMR and RSS of the hanging wall and footwall. Then, the priorities of these factors are calculated. In order to calculate the priorities of factors and select the best mining method for Qapiliq salt mine, Iran, based on these priorities, fuzzy analytical hierarchy process （AHP） technique is used. For this purpose, a questionnaire was prepared and was given to the associated experts. Finally, after a comparison carried out based on the effective factors, between the four mining methods including area mining, room and pillar, cut and fill and stope and pillar methods, the stope and nillar mining method was selected as the most suitable method to this mine.

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.