Research status and development trends of dust concentration monitoring technology
This paper introduces the measurement principles of various domestic and international dust concentration continuous monitoring technologies, including the filter weighing method, β-ray method, light scattering method, charge induction method, and micro-oscillating balance method. It compares and an...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2024-12-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024100076 |
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| author | ZHANG Yongqi WANG Jie ZHOU Yuhao YANG Junni DENG Bin |
| author_facet | ZHANG Yongqi WANG Jie ZHOU Yuhao YANG Junni DENG Bin |
| author_sort | ZHANG Yongqi |
| collection | DOAJ |
| description | This paper introduces the measurement principles of various domestic and international dust concentration continuous monitoring technologies, including the filter weighing method, β-ray method, light scattering method, charge induction method, and micro-oscillating balance method. It compares and analyzes the advantages and limitations of these monitoring technologies in terms of accuracy, sensitivity, and real-time performance. The paper also delves into the continuous separation technologies and standards for respirable dust particles on a global scale and systematically examines the challenges that current dust concentration continuous monitoring technologies face in terms of instrumental measurement precision, reliability, stability, environmental adaptability, intelligent automatic calibration, and power consumption optimization. The discussion covers the development trends in dust concentration monitoring technology: the shift from traditional single total dust concentration monitoring to a combined monitoring of total and respirable dust, and rapid transition from point monitoring to area monitoring and regional monitoring. It is proposed that future efforts should be dedicated to integrating dust concentration monitoring technologies with emerging technologies such as machine learning, deep learning, computer vision, and big data analysis and prediction. This integration will facilitate the integration and application of intelligent detection technologies with dust-related occupational hazard monitoring and early warning systems and provide reference for intelligent and automated dust control in future industrial scenarios. |
| format | Article |
| id | doaj-art-76d8170b2e934553b52f84fc4cc1c59b |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-76d8170b2e934553b52f84fc4cc1c59b2025-01-23T02:17:44ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2024-12-015012111119, 16510.13272/j.issn.1671-251x.2024100076Research status and development trends of dust concentration monitoring technologyZHANG YongqiWANG JieZHOU YuhaoYANG JunniDENG Bin0School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaThis paper introduces the measurement principles of various domestic and international dust concentration continuous monitoring technologies, including the filter weighing method, β-ray method, light scattering method, charge induction method, and micro-oscillating balance method. It compares and analyzes the advantages and limitations of these monitoring technologies in terms of accuracy, sensitivity, and real-time performance. The paper also delves into the continuous separation technologies and standards for respirable dust particles on a global scale and systematically examines the challenges that current dust concentration continuous monitoring technologies face in terms of instrumental measurement precision, reliability, stability, environmental adaptability, intelligent automatic calibration, and power consumption optimization. The discussion covers the development trends in dust concentration monitoring technology: the shift from traditional single total dust concentration monitoring to a combined monitoring of total and respirable dust, and rapid transition from point monitoring to area monitoring and regional monitoring. It is proposed that future efforts should be dedicated to integrating dust concentration monitoring technologies with emerging technologies such as machine learning, deep learning, computer vision, and big data analysis and prediction. This integration will facilitate the integration and application of intelligent detection technologies with dust-related occupational hazard monitoring and early warning systems and provide reference for intelligent and automated dust control in future industrial scenarios.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024100076dust continuous monitoringrespirable dustpneumoconiosisparticulate matter separationdeep learning |
| spellingShingle | ZHANG Yongqi WANG Jie ZHOU Yuhao YANG Junni DENG Bin Research status and development trends of dust concentration monitoring technology Gong-kuang zidonghua dust continuous monitoring respirable dust pneumoconiosis particulate matter separation deep learning |
| title | Research status and development trends of dust concentration monitoring technology |
| title_full | Research status and development trends of dust concentration monitoring technology |
| title_fullStr | Research status and development trends of dust concentration monitoring technology |
| title_full_unstemmed | Research status and development trends of dust concentration monitoring technology |
| title_short | Research status and development trends of dust concentration monitoring technology |
| title_sort | research status and development trends of dust concentration monitoring technology |
| topic | dust continuous monitoring respirable dust pneumoconiosis particulate matter separation deep learning |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024100076 |
| work_keys_str_mv | AT zhangyongqi researchstatusanddevelopmenttrendsofdustconcentrationmonitoringtechnology AT wangjie researchstatusanddevelopmenttrendsofdustconcentrationmonitoringtechnology AT zhouyuhao researchstatusanddevelopmenttrendsofdustconcentrationmonitoringtechnology AT yangjunni researchstatusanddevelopmenttrendsofdustconcentrationmonitoringtechnology AT dengbin researchstatusanddevelopmenttrendsofdustconcentrationmonitoringtechnology |