基于康达效应的长压短抽通风除尘系统优化设计及性能评价。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2025-04-03 eCollection Date: 2025-01-01 DOI:10.3389/frai.2025.1565889
Xinguo Wang, Jinbo Zhao, Yufu Li, Zhibin Li
{"title":"基于康达效应的长压短抽通风除尘系统优化设计及性能评价。","authors":"Xinguo Wang, Jinbo Zhao, Yufu Li, Zhibin Li","doi":"10.3389/frai.2025.1565889","DOIUrl":null,"url":null,"abstract":"<p><p>Mine ventilation and dust control systems are crucial for ensuring occupational safety and health during underground mining operations. Traditional long-pressure short-suction systems face challenges such as inefficient airflow organization, formation of vortex dead zones, high energy consumption, and inadequate adaptability to dynamic conditions in mining faces. This study addresses these limitations by proposing an optimized long-pressure short-suction ventilation and dust control system leveraging the Coandă effect. Through numerical simulations, experimental validation, and machine learning techniques, the study develops a comprehensive system to enhance dust control performance. The Coandă effect was employed to optimize the structural design of ventilation ducts, ensuring airflow attachment to tunnel surfaces, reducing dust dispersion, and achieving high-efficiency airflow with lower power consumption. The key parameters optimized include the spacing between the air supply and exhaust ducts, the pressure-to-suction ratio, and the height of the ventilation duct. The optimal pressure-to-suction ratio was found to be 2:3, which minimizes dust concentration at both the mining machine and downstream locations. Numerical simulations and experimental results demonstrated that the optimized system achieved dust concentration reductions of up to 84.12% in high initial dust conditions (800 mg/m<sup>3</sup>). These findings provide a solid foundation for intelligent and energy-efficient ventilation and dust control in mining operations, ensuring both safety and energy savings.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1565889"},"PeriodicalIF":3.0000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004412/pdf/","citationCount":"0","resultStr":"{\"title\":\"Optimized design and performance evaluation of long-pressure-short-extraction ventilation and dust removal system based on the Coanda effect.\",\"authors\":\"Xinguo Wang, Jinbo Zhao, Yufu Li, Zhibin Li\",\"doi\":\"10.3389/frai.2025.1565889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Mine ventilation and dust control systems are crucial for ensuring occupational safety and health during underground mining operations. Traditional long-pressure short-suction systems face challenges such as inefficient airflow organization, formation of vortex dead zones, high energy consumption, and inadequate adaptability to dynamic conditions in mining faces. This study addresses these limitations by proposing an optimized long-pressure short-suction ventilation and dust control system leveraging the Coandă effect. Through numerical simulations, experimental validation, and machine learning techniques, the study develops a comprehensive system to enhance dust control performance. The Coandă effect was employed to optimize the structural design of ventilation ducts, ensuring airflow attachment to tunnel surfaces, reducing dust dispersion, and achieving high-efficiency airflow with lower power consumption. The key parameters optimized include the spacing between the air supply and exhaust ducts, the pressure-to-suction ratio, and the height of the ventilation duct. The optimal pressure-to-suction ratio was found to be 2:3, which minimizes dust concentration at both the mining machine and downstream locations. Numerical simulations and experimental results demonstrated that the optimized system achieved dust concentration reductions of up to 84.12% in high initial dust conditions (800 mg/m<sup>3</sup>). These findings provide a solid foundation for intelligent and energy-efficient ventilation and dust control in mining operations, ensuring both safety and energy savings.</p>\",\"PeriodicalId\":33315,\"journal\":{\"name\":\"Frontiers in Artificial Intelligence\",\"volume\":\"8 \",\"pages\":\"1565889\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004412/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frai.2025.1565889\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2025.1565889","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

矿井通风和粉尘控制系统是保障井下作业职业安全与健康的关键。传统的长压短吸系统面临着气流组织效率低、涡流死区形成、能耗高、对采煤工作面动态条件适应性不足等挑战。本研究通过提出一种利用科安德效应的优化的长压短吸通风和粉尘控制系统来解决这些局限性。通过数值模拟、实验验证和机器学习技术,研究开发了一个全面的系统来提高粉尘控制性能。利用科安德效应优化通风管道结构设计,保证气流附着在隧道表面,减少粉尘分散,以更低功耗实现高效气流。优化的关键参数包括送风和排风管间距、压吸比和风管高度。最佳压吸比为2:3,使矿机和下游位置的粉尘浓度最小。数值模拟和实验结果表明,优化后的系统在高初始粉尘条件下(800 mg/m3),粉尘浓度降低率高达84.12%。这些研究结果为采矿作业中智能节能通风和粉尘控制提供了坚实的基础,确保了安全和节能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized design and performance evaluation of long-pressure-short-extraction ventilation and dust removal system based on the Coanda effect.

Mine ventilation and dust control systems are crucial for ensuring occupational safety and health during underground mining operations. Traditional long-pressure short-suction systems face challenges such as inefficient airflow organization, formation of vortex dead zones, high energy consumption, and inadequate adaptability to dynamic conditions in mining faces. This study addresses these limitations by proposing an optimized long-pressure short-suction ventilation and dust control system leveraging the Coandă effect. Through numerical simulations, experimental validation, and machine learning techniques, the study develops a comprehensive system to enhance dust control performance. The Coandă effect was employed to optimize the structural design of ventilation ducts, ensuring airflow attachment to tunnel surfaces, reducing dust dispersion, and achieving high-efficiency airflow with lower power consumption. The key parameters optimized include the spacing between the air supply and exhaust ducts, the pressure-to-suction ratio, and the height of the ventilation duct. The optimal pressure-to-suction ratio was found to be 2:3, which minimizes dust concentration at both the mining machine and downstream locations. Numerical simulations and experimental results demonstrated that the optimized system achieved dust concentration reductions of up to 84.12% in high initial dust conditions (800 mg/m3). These findings provide a solid foundation for intelligent and energy-efficient ventilation and dust control in mining operations, ensuring both safety and energy savings.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信