The recent resurgence of pneumoconiosis among coal miners in the United States has been linked to their exposure to excessive levels of coal dust.PDM3700 monitors are used in the mining industry to measure each miner&...The recent resurgence of pneumoconiosis among coal miners in the United States has been linked to their exposure to excessive levels of coal dust.PDM3700 monitors are used in the mining industry to measure each miner's coal dust exposure levels and control overexposure.However,the high cost of the PDM3700 hinders its use in measuring the exposure levels of all miners.Plantower PMS5003 low-cost particulate matter(PM)sensors can measure coal dust concentrations with high spatial resolution in real-time owing to their low cost and small size.However,these sensors require extensive calibration to ensure a high accuracy over long deployment periods.Because they have only been calibrated for mining-induced PM monitoring using linear regression models,the objective of this study was to leverage machine learning algorithms for calibration of coal-dust-monitoring sensors.Laboratory collocation tests were performed using the PDM3700 and aerodynamic particle sizer as reference monitors in a wind tunnel at a wide range of concentrations(0-3mg/m^(3)),temperatures(20-32℃),and relative humidities(23%-43%).The results revealed that nonlinear machine learning techniques significantly outperformed traditional linear regression models for low-cost sensor calibration.With the artificial neural network(ANN)being the strongest calibration model,Pearson's correlation of the PMS5003 sensors reached 0.98 and 0.97,those of the Airtrek sensors reached of 0.89 and 0.91,and those of the GasLab sensors reached 0.93 and 0.92.This shows a 2%-11%improvement in model performance over the linear regression model using ANN calibration.The success of the machine learning algorithms used in this study demonstrates the feasibility of deploying low-cost PM sensors for coal dust monitoring in mines.展开更多
Exposure to respirable coal mine dust(RCMD)can cause chronic and debilitating lung diseases.Real-time monitoring capabilities are sought which can enable a better understanding of dust components and sources.In many u...Exposure to respirable coal mine dust(RCMD)can cause chronic and debilitating lung diseases.Real-time monitoring capabilities are sought which can enable a better understanding of dust components and sources.In many underground mines,RCMD includes three primary components which can be loosely associated with three major dust sources:coal dust from the coal seam itself,silicates from the surrounding rock strata,and carbonates from the inert‘rock dust’products that are applied to mitigate explosion hazards.A monitor which can reliably partition RCMD between these three components could thus allow source apportionment.And tracking silicates,specifically,could be valuable since the most serious health risks are typically associated with this component-particularly if abundant in crystalline silica.Envisioning a monitoring concept based on field microscopy,and following up on prior research using polarized light,the aim of the current study was to build and test a model to classify respirable-sized particles as either coal,silicates,or carbonates.For model development,composite dust samples were generated in the laboratory by successively depositing dust from high-purity materials onto a sticky transparent substrate,and imaging after each deposition event such that the identity of each particle was known a priori.Model testing followed a similar approach,except that real geologic materials were used as the source for each dust component.Results showed that the model had an overall accuracy of 86.5%,indicating that a field-microscopy based moni-tor could support RCMD source apportionment and silicates tracking in some coal mines.展开更多
Mine dust is one of the main hazards in underground longwall mines worldwide.In order to solve the mine dust problem,a significant number of studies have been carried out regarding longwall mine dust control,both in C...Mine dust is one of the main hazards in underground longwall mines worldwide.In order to solve the mine dust problem,a significant number of studies have been carried out regarding longwall mine dust control,both in China and Australia.This paper presents a comparative study of dust control practices in Chinese and Australian longwall mines,with particular references to statutory limits,dust monitoring methods and dust management practices,followed by a brief discussion on the research status of longwall mine dust control in both countries.The study shows that water infusion,face ventilation controls,water sprays,and deep and wet cutting in longwall shearer operations are commonly practiced in almost all underground longwall mines and that both Chinese and Australian longwall mine dust control practices have their own advantages and disadvantages.It is concluded that there is a need for further development and innovative design of more effective dust mitigation products or systems despite the development of various dust control technologies.Based on the examinations and discussions,the authors have made some recommendations for further research and development in dust control in longwall mines.It is hoped that this comparative study will provide beneficial guidance for scholars and engineers who are engaging in longwall mine dust control research and practice.展开更多
基金funded by the National Science Foundation(No.2034198)the National Institute for Occupational Safety and Health(NIOSH)(No.75D30123C17714)
文摘The recent resurgence of pneumoconiosis among coal miners in the United States has been linked to their exposure to excessive levels of coal dust.PDM3700 monitors are used in the mining industry to measure each miner's coal dust exposure levels and control overexposure.However,the high cost of the PDM3700 hinders its use in measuring the exposure levels of all miners.Plantower PMS5003 low-cost particulate matter(PM)sensors can measure coal dust concentrations with high spatial resolution in real-time owing to their low cost and small size.However,these sensors require extensive calibration to ensure a high accuracy over long deployment periods.Because they have only been calibrated for mining-induced PM monitoring using linear regression models,the objective of this study was to leverage machine learning algorithms for calibration of coal-dust-monitoring sensors.Laboratory collocation tests were performed using the PDM3700 and aerodynamic particle sizer as reference monitors in a wind tunnel at a wide range of concentrations(0-3mg/m^(3)),temperatures(20-32℃),and relative humidities(23%-43%).The results revealed that nonlinear machine learning techniques significantly outperformed traditional linear regression models for low-cost sensor calibration.With the artificial neural network(ANN)being the strongest calibration model,Pearson's correlation of the PMS5003 sensors reached 0.98 and 0.97,those of the Airtrek sensors reached of 0.89 and 0.91,and those of the GasLab sensors reached 0.93 and 0.92.This shows a 2%-11%improvement in model performance over the linear regression model using ANN calibration.The success of the machine learning algorithms used in this study demonstrates the feasibility of deploying low-cost PM sensors for coal dust monitoring in mines.
基金supported by the Alpha Foundation for the Improvement of Mine Safety and Health,grant number AFC316FO-84.
文摘Exposure to respirable coal mine dust(RCMD)can cause chronic and debilitating lung diseases.Real-time monitoring capabilities are sought which can enable a better understanding of dust components and sources.In many underground mines,RCMD includes three primary components which can be loosely associated with three major dust sources:coal dust from the coal seam itself,silicates from the surrounding rock strata,and carbonates from the inert‘rock dust’products that are applied to mitigate explosion hazards.A monitor which can reliably partition RCMD between these three components could thus allow source apportionment.And tracking silicates,specifically,could be valuable since the most serious health risks are typically associated with this component-particularly if abundant in crystalline silica.Envisioning a monitoring concept based on field microscopy,and following up on prior research using polarized light,the aim of the current study was to build and test a model to classify respirable-sized particles as either coal,silicates,or carbonates.For model development,composite dust samples were generated in the laboratory by successively depositing dust from high-purity materials onto a sticky transparent substrate,and imaging after each deposition event such that the identity of each particle was known a priori.Model testing followed a similar approach,except that real geologic materials were used as the source for each dust component.Results showed that the model had an overall accuracy of 86.5%,indicating that a field-microscopy based moni-tor could support RCMD source apportionment and silicates tracking in some coal mines.
基金supported by the Program for New Century Excellent Talents in University of China(No.NCET-10-0770)the financial support provided by the China Scholarship Council(Nos.201306425002&201406425048)the University of Wollongong to pursue study at the University of Wollongong as undergraduate visiting students
文摘Mine dust is one of the main hazards in underground longwall mines worldwide.In order to solve the mine dust problem,a significant number of studies have been carried out regarding longwall mine dust control,both in China and Australia.This paper presents a comparative study of dust control practices in Chinese and Australian longwall mines,with particular references to statutory limits,dust monitoring methods and dust management practices,followed by a brief discussion on the research status of longwall mine dust control in both countries.The study shows that water infusion,face ventilation controls,water sprays,and deep and wet cutting in longwall shearer operations are commonly practiced in almost all underground longwall mines and that both Chinese and Australian longwall mine dust control practices have their own advantages and disadvantages.It is concluded that there is a need for further development and innovative design of more effective dust mitigation products or systems despite the development of various dust control technologies.Based on the examinations and discussions,the authors have made some recommendations for further research and development in dust control in longwall mines.It is hoped that this comparative study will provide beneficial guidance for scholars and engineers who are engaging in longwall mine dust control research and practice.
