Prediction of Mine Dust Concentration Based on Grey Markov Model

Accurate quantitative analysis and prediction of dust concentration in mines play a vital role in avoiding pneumoconiosis to a certain extent, improving industrial production efficiency, and protecting the ecological environment. The research has far-reaching significance for the prediction of dust concentration in mines in the future. Aiming at the shortcomings of the grey GM (1, 1) model in forecasting the data sequence with large random fluctuation, a grey Markov chain forecasting model is established. Firstly, considering the timeliness of monitoring data, the new dust concentration data is supplemented by using the method of cubic spline interpolation in the original data sequence. Therefore, the GM (1, 1) model is established by the method of metabolism. Then, the GM (1, 1) model is optimized by the theory of the Markov chain model. According to the relative error range generated during the prediction, the state interval is divided. Subsequently, the corresponding state probability transition matrix is constructed to obtain the grey Markov prediction model. The model was applied to the prediction of mine dust concentration and compared with the prediction results of the BP neural network model, grey prediction model, and ARIMA (1, 2, 1) model. The results showed that the prediction accuracy of the grey Markov model was significantly improved compared with other traditional prediction models. Therefore, the rationality and accuracy of this model in the prediction of mine dust concentration were verified.

Research Status of Pathogenesis of Pneumoconiosis and Dust Control Technology in Mine—A Review

Pneumoconiosis has become one of the biggest threats to the occupational health and life safety of mining workers in China. The number of pneumoconiosis cases has continued to rise in recent years. The main task of occupational health development is to study the pathogenesis of pneumoconiosis and to develop mine dust prevention and control technology. Therefore, this paper summarizes the research progress of coal worker pneumoconiosis and dust prevention and control in mines. Firstly, the research progress of coal worker pneumoconiosis is analyzed from the aspects of pathogenesis, animal model and pathological changes of coal worker pneumoconiosis. Then, the existing basic theory and technology of dust prevention are described, including ventilation and dust removal, spray and dust suppression, and chemical dust suppression methods. Finally, based on the dust removal theory of wet shotcrete, the progress of shotcrete dust control technology and equipment used for shotcrete is summarized from the aspects of shotcrete technology process and shotcrete materials. At the same time, in view of the shortcomings of the existing research, the next research prospect is given in the pathogenesis of pneumoconiosis, intelligent dust prevention, jet spraying dust removal and so on. This paper provides theoretical support for realizing the separate source and efficient treatment of mine dust control and helps to improve the clean production level of mine, control and prevent pneumoconiosis.

Demonstration of Optical Microscopy and Image Processing to Classify Respirable Coal Mine Dust Particles

Respirable coal mine dust represents a serious health hazard for miners. Monitoring methods are needed that enable fractionation of dust into its primary components, and that do so in real time. Near the production face, a simple capability to monitor the coal versus mineral dust fractions would be highly valuable for tracking changes in dust sources—and supporting timely responses in terms of dust controls or other interventions to reduce exposures. In this work, the premise of dust monitoring with polarized light microscopy was explored. Using images of coal and representative mineral particles (kaolinite, crystalline silica, and limestone rock dust), a model was built to exploit birefringence of the mineral particles and effectively separate them from the coal. The model showed >95% accuracy on a test dataset with known particles. For composite samples containing both coal and minerals, the model also showed a very good agreement with results from the scanning electron microscopy classification, which was used as a reference method. Results could further the concept of a “cell phone microscope” type monitor for semi-continuous measurements in coal mines.

Respirable Coal Mine Dust: A Review of Respiratory Deposition, Regulations and Characterization

In the late 1990s, despite years of efforts to understand and reduce coal worker’s pneumoconiosis (CWP) prevalence from more than 30% in 1970 to less than 4.2%, the level of occurrence among the US coal miners increased unexpectedly. The recent resurgence of lung diseases has raised concerns in the scientific and regulatory communities. In 2014, the United States Mine Safety and Health Administration (MSHA) issued a new dust rule changing the respirable coal mine dust (RCMD) exposure limits, measurement technology, and sampling protocol. The analysis for probable causes for the substantial increase in the CWP incidence rate is rather complicated. This paper aims to conduct a review of RCMD respiratory deposition, health effects, monitoring, regulations, and particle characteristics. The primary sources of RCMD along with the health risks from potential exposure are highlighted, and the current RCMD exposure regulations of the major coal producer countries are compared. A summary of RCMD characterization studies from 1972 to the present is provided. A review of the literature revealed that numerous factors, including geological and mining parameters, advancements in mining practices, particle characteristics, and monitoring approaches are considered to contribute to the recent resurgence of RCMD lung diseases. However, the root causes of the problem are still unknown. The effectiveness of the new dust rules in the United States will probably take years to be correctly assessed. Therefore, future research is needed to understand the relationship between RCMD particle characteristics and lung deposition, and the efficacy of current monitoring practices to measure the true dose of RCMD exposure.