新中国的煤炭工业

煤炭工业是我国重要的能源支柱产业之一,我国原煤产量现已位居世界第一。因此,煤炭工业题材成为新中国邮票的重要内容之一。 建国初期,煤矿工人的形象就跃人方寸。普6-5“煤矿工人”邮票(图1),画面为两位矿工紧握电钻正在奋力开采煤炭。普8-1“矿工”邮票,画面是矿工手握凤镐的特写。特13-3“煤矿”邮票(图2),画面为井下矿工在操作联合采煤机,标志着我国采煤技术正向机械化迈进。纪70-2“举国欢庆”

Coal bursts in the deep longwall mines of the United States

Coal bursts involve the sudden, violent ejection of coal or rock into the mine workings. They are a particular hazard because they typically occur without warning. During the past 2 years three US coal miners were killed in two coal bursts, following a 6-year period during which there were zero burst fatalities. This paper puts the US experience in the context of worldwide research into coal bursts. It focuses on two major longwall mining coalfields which have struggled with bursts for decades. The Utah experience displays many of the ＂classic＂ burst characteristics, including deep cover, strong roof and floor rock, and a direct association between bursts and mining activity. In Colorado, the longwalls of the North Fork Valley （NFV） also work at great depth, but their roof and floor strengths are moderate, and most bursts have occurred during entry development or in headgates, bleeders, or other outby locations. The NFV bursts also are more likely to be associated with geologic structures and large magnitude seismic events. The paper provides a detailed case history to illustrate the experience in each of these coalfields. The paper closes with a brief discussion of how US longwalls have managed the burst risk.

Predictors of work injury in underground mines—an application of a logistic regression model

Mine accidents and injuries are complex and generally characterized by several factors starting from personal to technical, and technical to social characteristics.In this study, an attempt has been made to identify the various factors responsible for work related injuries in mines and to estimate the risk of work injury to mine workers.The prediction of work injury in mines was done by a step-by-step multivariate logistic regression modeling with an application to case study mines in India.In total, 18 variables were considered in this study.Most of the variables are not directly quantifiable.Instruments were developed to quantify them through a questionnaire type survey.Underground mine workers were randomly selected for the survey.Responses from 300 participants were used for the analysis.Four variables, age, negative affectivity, job dissatisfaction, and physical hazards, bear significant discriminating power for risk of injury to the workers, comparing between cases and controls in a multivariate situation while controlling all the personal and socio-technical variables.The analysis reveals that negatively affected workers are 2.54 times more prone to injuries than the less negatively affected workers and this factor is a more important risk factor for the case-study mines.Long term planning through identification of the negative individuals, proper counseling regarding the adverse effects of negative behaviors and special training is urgently required.Care should be taken for the aged and experienced workers in terms of their job responsibility and training requirements.Management should provide a friendly atmosphere during work to increase the confidence of the injury prone miners.

Causation mechanism of coal miners' human errors in the perspective of life events

In order to effectively decrease the safety accidents caused by coal miners’human errors,this paper probes into the causality between human errors and life events,coping,psychological stress,psychological function,physiological function based on life events’vital influence on human errors,establishing causation mechanism model of coal miners’human errors in the perspective of life events by the researching method of structural equation.The research findings show that life events have significantly positive influence on human errors,with a influential effect value of 0.7945 and a influential effect path of‘‘life events—psychological stress—psychological function—physiological function—human errors’’and‘‘life events—psychological stress—physiological function—human errors’’.

Void fill techniques for stabilizing roof conditions during longwall recovery

It has been proven that longwall faces can be moved safely and efficiently. However,abutment pressures and poor ground control conditions can halt operations and be hazardous to coal miners. Recently at a mine in Southwestern Pennsylvania,roof material collapsed above shields that created two large voids and caused major challenges for shield recovery. A unique,engineering solution was developed that utilized a modified concrete material to fill the voids,creating stability in the affected area. The many phases of this project included the construction phase,void pumping,cutting out,and bolting of the concrete material. This project eliminated the hazards associated with bolting the recovery face and removing shields in adverse conditions,making it possible for the mine operator to safely complete the longwall move.

Study on optimization control method based on artificial neural network

In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

Study on Filling Cross-Roadway in Fully-Mechanized Coal Faces with High Water-Content Material

A new method using high water content material to mechanically fill cross roadways to form artificial bottom for coal faces was introduced. The reasonable determination of filling range, the optimization of the compounding ratio of high water content material, and the filling technique were discussed in detail. This new method has been spread after industrial testing in Baodian Colliery. Compared with the traditional method, the manual wooden chock method, the new one decreases about 40% of the filling range and cost in dealing every one set of cross roadway in the testing condition.

An optimized control of ventilation in coal mines based on artificial neural network

According to the nonlinear and time dependent features of the ventilation systems for coal mines, a neural network method is applied to control the ventilator for coal mines in real time. The technical processes of coal mine ventilation system are introduced, and the principle of controlling a ventilation fan is also explained in detail. The artificial neutral network method is used to calculate the wind quantity needed by work spots in coal mine on the basis of the data collected by the system, including ventilation conditions, environmental temperatures, humidity, coal dust and the contents of all kinds of poisonous and harmful gases. Then the speed of ventilation fan is controlled according to the required wind which is determined by an overall integration of data. A neural network method is presented for overall optimized solution or the genetic algorithm of simulated annealing.

APPLICATION OF EXPERT SYSTEM BASED ON NEURAL NETWORK IN MINING THE GENTLE INCLINED THICK SEAM

This paper presents a new method for the correct selection of mining methods and pre-diction of main technological and economic indexes of the face in the gentle inclined thick seams with the application of the artificial neural network theory and the expert system. The theory anal- ysis and calculating results indicate that the method is reliable, practical and precise. This method has strongly capabilities of self-study and non-linear dynamic data process. It is expected to be widely applied in the policy decision and prediction of mining technology in coal mine.

Revisiting of the South African Mine Workers＇ Union --A Significant Factor in South African Mining History

Founded in 1902 as the Transvaal Miners＇ Association, the South African Mine Workers＇ Union （MWU）, as it was renamed in 1913, rose to prominence as a militant union for white miners, especially during the turbulent years of industrial strife and bloody strike action in the first two decades of the 20th century. Since the election victory of the Pact government in 1924 a program of pro-white protectionist legislation was introduced. As South Africa＇s most prominent white trade union of the 20th century, the MWU therefore became renowned for defending job reservation in the mining industry. After 1948, it enjoyed the National Party government＇s support in this regard. However, skilled labor shortages and changing labor conditions in the 1970s forced the government to introduce labor reform. Job reservation was scrapped and black unions were officially recognized. These initiatives put the MWU on a confrontational path with the government and the union aligned itself with right-wing political resistance and protest. But, the altered political and economic South African realities after 1994 forced the MWU to rethink its strategies. By 2002, it had reinvented and transformed itself into Solidarity, adequately equipped for addressing the labor challenges and demands of a post-apartheid South Africa. This article, based on the author＇s research of the South African labor movement in the early 20th century, traces the historical development of the union and its influence on South African political and labor history.

Study on neural network model for calculating subsidence factor

The major factors influencing subsidence factor were comprehensively analyzed. Then the artificial neural network model for calculating subsidence factor was set up with the theory of artificial neural network （ANN）. A large amount of data from observation stations in China was collected and used as learning and training samples to train and test the artificial neural network model. The calculated results of the ANN model and the observed values were compared and analyzed in this paper. The results demonstrate that many factors can be considered in this model and the result is more precise and closer to observed values to calculate the subsidence factor by the ANN model. It can satisfy the need of engineering.

Research on Feasibility of Top-Coal Caving Based on Neural Network Technique

Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.