A novel in-depth intelligent evaluation approach for the gas drainage effect from point monitoring to surface to volume
Xia Tongqiang,
Li Diao,
Li Xiaolin,
Yan Xin and
Wang J.G.
Applied Energy, 2024, vol. 353, issue PB, No S0306261923015118
Abstract:
Developing a comprehensive and intelligent evaluation approach for dynamic gas extraction parameters is especially important for the operation and management of gas drainage in coal mines. However, existing approaches lack an in-depth intelligent evaluation approach for acquiring surface parameters of gas flow and volume characteristic parameters of the entire drainage area from the finite monitoring data. To fill this gap, a novel Field-Zone-Network model as a unique approach were presented to overcome the above-mentioned challenges, with innovative multi-parameter solution and inversion models. It utilized advanced mathematical algorithms for parameter estimation of gas drainage, by integrating the finite monitoring points data of pipe network and physical characteristic parameters of pipe network and coal seam. The model's accuracy and reliability were validated through rigorous comparisons with field data collected from coal mines and numerical simulations conducted in simulated scenarios. It realized the sufficient mining of finite monitoring data from points (monitoring points) to surface (network) to volume (coal seam). Further, the newly comprehensive software was developed to enable real-time monitoring, analysis, and evaluation of gas drainage effect, providing valuable insights for optimizing extraction operations in coal mines. This research contributes to the advancement of gas drainage in coal mines by presenting a novel Field-Zone-Network model and comprehensive software, empowering efficient and intelligent gas extraction operations with enhanced monitoring and evaluation capabilities.
Keywords: Intelligent gas drainage; Field-zone-network model; Monitoring data; In-depth mining; Inversion model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:353:y:2024:i:pb:s0306261923015118
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DOI: 10.1016/j.apenergy.2023.122147
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