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Application of an artificial neural network to the prediction of firedamp emissions in coal mines

Abstract : Coal extraction at great depths and high output faces lead to more and more elevated and irregular firedamp emissions. To respect safety conditions, coal production must be programmed to limit methane emissions in airways. In this context, the prediction of firedamp emissions is an interesting tool to optimize safety and production. But mathematical modelling of firedamp desorption and gas circulation physical processes involve non-linear physical laws and a high number of hardly accessible parameters. So artificial neural networks have been developed to model firedamp emissions : artificial neural networks as universal approximators are able to learn from examples and generalize in unknown situations. Artificial neural networks need a large amount of data sufficiently representative to learn the physical processes. Data relative to mining ventilation, such as methane concentration and air velocity in airways, are monitored and can be used to model firedamp emissions. The model based on artificial neural networks has been calibrated and validated using data from coal faces recently exploited in Lorraine Coalfield (East of France). The model reliability has been appreciated on the results of a posteriori forecasts. The model is used to forecast methane concentration values in airways as a function of coal production.
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  • HAL Id : ineris-00972184, version 1
  • INERIS : PU-1999-072



Jean-Christophe Couillet, Marc Prince. Application of an artificial neural network to the prediction of firedamp emissions in coal mines. APCOM Symposium 1999, Oct 1999, Golden, United States. pp.933-940. ⟨ineris-00972184⟩



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