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Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network.
Liu, Yujiao; Liu, Zeyi; Gao, Ke; Huang, Yuhan; Zhu, Chengyao.
Afiliación
  • Liu Y; College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China.
  • Liu Z; Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China.
  • Gao K; College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China.
  • Huang Y; Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China.
  • Zhu C; College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China.
Sensors (Basel) ; 22(6)2022 Mar 08.
Article en En | MEDLINE | ID: mdl-35336265
ABSTRACT
The accurate and reliable monitoring of ventilation parameters is key to intelligent ventilation systems. In order to realize the visualization of airflow, it is essential to solve the airflow reconstruction problem using few sensors. In this study, a new concept called independent cut set that depends on the structure of the underlying graph is presented to determine the minimum number and location of sensors. We evaluated its effectiveness in a coal mine owned by Jinmei Corporation Limited (Jinmei Co., Ltd., Shanghai, China). Our results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines). The results showed that the algorithm was feasible. The reconstructed air volume of the ventilation network using this algorithm was the same as the actual air volume. The algorithm provides theoretical support for flow reconstruction.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Respiración / Ventilación Tipo de estudio: Prognostic_studies País/Región como asunto: Asia Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Respiración / Ventilación Tipo de estudio: Prognostic_studies País/Región como asunto: Asia Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China