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<p>This study presents a novel approach to forecasting psychrometric conditions of air in the lower section of a near-surface mine shafts/inclines using readily available surface data. We employ a prediction model, which incorporates the thermal flywheel effect observed in shallow mine intake airway. The model utilizes MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) dataset as input for surface air psychrometric properties, offering a potentially scalable, approximate estimation tool for predicting underground climate conditions. The research was conducted at a coal mine in eastern India, with psychrometric assessments performed over 13 months. The model’s performance was evaluated by comparing predictions to actual measurements at an underground monitoring station 55&#xa0;m below the surface. Results demonstrate the model’s effectiveness, with the majority of predictions for dry-bulb temperature (T<sub>d</sub>), wet-bulb temperature (T<sub>w</sub>), and moisture content (X) falling within ± 5% of measured values. Average RMSE values of 0.70&#xa0;°C for T<sub>d</sub>, 0.38&#xa0;°C for T<sub>w</sub>, and 0.40&#xa0;g/kgda for X indicate good prediction accuracy across various conditions. This study highlights the potential of integrating MERRA-2 dataset with advanced modeling techniques to provide a fast, approximate method for estimating mine climate. The approach offers particular value for remote or inaccessible mining operations, potentially streamlining ventilation planning and monitoring processes while improving safety and efficiency in underground mining operations worldwide.</p>

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MERRA-2 Dataset Driven Environment Modeling for Shallow Mine Ventilation: Case Study in Jharia Coalfields, India

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Aditya Pandey,
  • Tanmay Dasgupta,
  • Chinmay Mondal

摘要

This study presents a novel approach to forecasting psychrometric conditions of air in the lower section of a near-surface mine shafts/inclines using readily available surface data. We employ a prediction model, which incorporates the thermal flywheel effect observed in shallow mine intake airway. The model utilizes MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) dataset as input for surface air psychrometric properties, offering a potentially scalable, approximate estimation tool for predicting underground climate conditions. The research was conducted at a coal mine in eastern India, with psychrometric assessments performed over 13 months. The model’s performance was evaluated by comparing predictions to actual measurements at an underground monitoring station 55 m below the surface. Results demonstrate the model’s effectiveness, with the majority of predictions for dry-bulb temperature (Td), wet-bulb temperature (Tw), and moisture content (X) falling within ± 5% of measured values. Average RMSE values of 0.70 °C for Td, 0.38 °C for Tw, and 0.40 g/kgda for X indicate good prediction accuracy across various conditions. This study highlights the potential of integrating MERRA-2 dataset with advanced modeling techniques to provide a fast, approximate method for estimating mine climate. The approach offers particular value for remote or inaccessible mining operations, potentially streamlining ventilation planning and monitoring processes while improving safety and efficiency in underground mining operations worldwide.