Comparative Analysis of Coal Mining Disasters in Turkey

Use of coal mine is based on centuries, but the expansion of its usage area has been after the Industrial Revolution in parallel with other mines. With the effect of this revolution; The use of minerals such as coal, iron, copper and tin has increased. Among these mines, coal mine; It has a very important place because it can be used for industrialization, powering machines, generating electricity and heating. In order to provide the increasing need in these areas every year, coal enterprises were established in countries with rich coal deposits and served the energy sector. The share of coal in the energy resources consumed in the world has increased in a short time and has become a determining factor on the world economy. This situation has led to an increase in the number of coal mine enterprises, with developed countries turning to the mining sector for industrialization and economic progress. Accidents in coal mines have increased as the number of mines has grown. Despite the fact that numerous studies have been conducted to prevent these accidents, accidents still occur. Coal mining is regarded as a hazardous work environment due to the high frequency of accidents and their consequences. To minimize these risks and ensure a healthy working environment, the required infrastructure should be established, audit-oriented studies should be conducted, and flaws should be remedied based on the findings. Within the scope of the study, the way and types of coal mine accidents, which are common in Turkey, were investigated, as well as a literature analysis of the reasons of these accidents. The number of accidents and casualties between 2015-2020 were examined and a future situation analysis was made. Regression Analysis Method, one of the statistical analysis methods, was used in the situation analysis phase. The number of coal mine accidents and fatalities in countries around the world since 1902, has been studied. By using the same Analysis Method, the future situation analysis for the next 10 years was made in the light of the data between 1902-2020, and data on the number of accidents and casualties that may occur in the coal mining area were obtained.

Modified Stochastic Petri Net-Based Modeling and Optimization of Emergency Rescue Processes during Coal Mine Accidents

The coal mine accidents seriously affect the safety and efficiency of mining for coal mining enterprises. The reliable emergency rescue (ER) processes are explored to minimize the loss of accidents. This paper introduces the stochastic Petri net (SPN) and Markov chain (MC) models based on the system structure flow to analyze the ER processes of coal mine accidents. In addition, a triangle fuzzy strategy is presented to optimize the SPN model. The “9·28” major water inrush accident in Shanxi Fenxi Zhengsheng Coal Company of China is adopted to evaluate the time performance and accident data of the ER process. The MC model-based steady-state probabilities of the system under various states are used to calculate the average delay time of this system. The triangular fuzzy strategy is used to analyze the change value of the total time in the ER system at the unit transition speed when the firing rate of each transition is changed, which finds the most time-consuming key activities in the ER process. The results show that SPN and MC can reflect the dynamic behaviors of ER process, which provides a reference for the rescue operations of other coal mine accidents. The triangular fuzzy strategy can quickly find out the key activities affecting the ER time, which greatly decreases the calculations generated by analyzing the total time of the system changed at the unit transition speed.

Correlation in Causality: A Progressive Study of Hierarchical Relations within Human and Organizational Factors in Coal Mine Accidents

It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China’s coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing “trajectories” in HAFCS-CM. This study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.

Hazards identification and risk analysis in surface mines of Pakistan using fault tree analysis technique

Purpose. Technology has advanced significantly but still mining industry faces a higher number of accidents. The purpose of the research is to identify the common hazards and associated risk which are the root causes of accidents in surface mines of Pakistan and to suggest the preventive measures to enhance safety at workplace. Methods. Integrated approach used in this research work involves: collection of mine accidents data from related Government departments; occupational safety data collection from mine sites with questionnaire; fault tree analysis method applied based on three groups of factors/causes obtained from 3E’s Model i.e. Engineering, Education and Enforcement that causes accidents in mine; risk assessment and suggestion of preventive measures. Findings. In this study forty three root causes of accidents in surface mines are identified and presented as basic events and undeveloped events in the Fault Trees. A compressed picture of the root causes is revealed leading to accidents in mine. The main causes identified are human errors, unsafe operating procedure, lack of machinery, lack of personal protective equipment, environmental and haulage related hazards and violation of law. Originality.The root causes of accidents in surface mines have been identified. For the first time, the visual paths to accidents causation in surface mines of Pakistan are outlined through fault tree analysis technique. Practical implications. The identified causes of accidents along with the suggested preventive measures can be used to avoid/curtail the number and severity of accidents in surface mines and can save lives of workers and economy. Keywords: hazards identification, surface mine, accidents, fault tree analysis, risk assessment, preventive measures

Mathematical Modelling using Gray Markov SCGM(1,1)c of Zambia’s Fatal Mining Accidents Between 2001 and 2015

Most mine accidents are caused by human error. The effects of accidents either fatal or not are adverse and range from economical to social. In this paper, the amended Grey Markov model with double exponential smoothing has been used. Predicting fatal accidents will provide the basis of safety assessment and decision making and also help to plan for possible economic and social impacts generated by fatal accidents. The amended Grey Markov combines the advantages of the grey prediction model and the Markov chains and can, therefore, be used on data that is few, has little and stochastic fluctuations. The gray SCGM(1,1)c model is applied to imitate the development tendency of the mine safety accident, and adopt the amended model to improve prediction accuracy, while Markov prediction is used to predict the fluctuation along with the tendency. Finally, the model is applied to forecast the fatal mine accident deaths from 2001 to 2015 in Zambia, and, 2016 fatal mine accidents were predicted. The model predicted the fatal mine accidents results with a relative error of 0.06 and is classified as excellent in the precision test. The proposed model, therefore, possesses a stronger engineering application.