• 中文核心期刊
  • 中国科技核心期刊
  • RCCSE中国核心学术期刊

矿井智能通风关键科学技术问题综述

刘 剑

刘 剑. 矿井智能通风关键科学技术问题综述[J]. 煤矿安全, 2020, 51(10): 108-111,117.
引用本文: 刘 剑. 矿井智能通风关键科学技术问题综述[J]. 煤矿安全, 2020, 51(10): 108-111,117.
LIU Jian. Overview on Key Scientific and Technical Issues of Mine Intelligent Ventilation[J]. Safety in Coal Mines, 2020, 51(10): 108-111,117.
Citation: LIU Jian. Overview on Key Scientific and Technical Issues of Mine Intelligent Ventilation[J]. Safety in Coal Mines, 2020, 51(10): 108-111,117.

矿井智能通风关键科学技术问题综述

Overview on Key Scientific and Technical Issues of Mine Intelligent Ventilation

  • 摘要: 为实现矿井通风的真正智能化,从实时矿井通风网络解算、灾变时期智能控风、通风参数高精度快速测试、智能网络与智能装备等4个方面论述了智能通风亟待解决的基于热流耦合的矿井通风网络理论、通风系统非线性观测器构建、传感器布设优化、阻变型故障诊断、扰动识别、灾变时期风流状态、应急状态下致灾因子传播快速推演技术等关键科学技术问题。
    Abstract: To achieve the real intelligence of mine ventilation, this paper discusses the key scientific and technical issues that need to be solved urgently for intelligent ventilation which are the theory of mine ventilation network based on thermal-fluid coupling, ventilation system nonlinear observer construction, sensor layout optimization, resistance variant fault diagnosis, disturbance identification, airflow state during cataclysmic period, rapid deduction technology for the propagation of disaster-causing factors under emergency conditions and so on, from four aspects of real-time mine ventilation network calculation, intelligent airflow control of cataclysmic period, high precision and rapid test of ventilation parameters, intelligent network and intelligent equipment.
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  • 期刊类型引用(3)

    1. 高科,吕航宇,戚志鹏,刘玉姣. 基于PCA-BP神经网络的巷道通风摩擦阻力系数预测模型. 矿业安全与环保. 2024(01): 7-13 . 百度学术
    2. 梁军. 基于大数据的通风巷道摩擦阻力系数快速确定方法研究. 能源与环保. 2024(04): 51-55 . 百度学术
    3. 刘彦青. 基于巷道摩擦阻力系数BP神经网络预测模型的矿井风网风量预测研究. 矿业安全与环保. 2021(02): 101-106 . 百度学术

    其他类型引用(3)

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出版历程
  • 发布日期:  2020-10-19

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