Smart Sensors, Smart Calibration: Applications in Machine Learning for Coal Dust Monitoring

Details

• Journal Article:
  Green and Smart Mining Engineering
• Personal Author:
  Amoh-Asante, Nana A. A. ; Zaid, Mirza Muhammad ; Du, Xiaosong ; Wang, Yang ; Xu, Guang
• Description:
  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 (0-3 mg/m³), temperatures (20-32 degrees C), 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. [Description provided by NIOSH]
• Subjects:
  Coal Coal Mining Dust Machine Learning Mining Occupational Health Particulate Matter Safety
• Keywords:
  Author Keywords: Smart Sensors; Smart Calibration; Machine Learning; Coal Dust Monitoring; Artificial Neural Network Mining Industry; Coal Dust; Sensors; Machine Learning; Dust Measurement; Monitors; Particulate Sampling Methods; Neural Networks; Mining Engineering;
• Source:
  Green Smart Min Eng 2025 Sep; 2(3):301-312
• ISSN:
  2950-5550
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Atlanta Region [OSHA] ; Birmingham Region [OSHA] ; Florida ; Kansas City Region [OSHA] ; Missouri
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  12 pdf pages
• Volume:
  2
• NIOSHTIC Number:
  nn:20071626
• Contact Point Address:
  Guang Xu, Department of Mining Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA
• Email:
  guang.xu@mst.edu
• Federal Fiscal Year:
  2025
• Performing Organization:
  Missouri University of Science and Technology
• Peer Reviewed:
  True
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:219cf3a45398173c16d7673dc4419a3b1be81e02613b232f3102830310631c0305f4afe3cbbd40a88c6d8144dcf142d5bd93280824f3426a543857bde09509d2
• Download URL:
  https://stacks.cdc.gov/view/cdc/259401/cdc_259401_DS1.pdf
• File Type:
  [PDF - 8.10 MB ]