基于煤矿瓦斯浓度动态监测的多点气体检测技术优化研究

    Optimization Research on Multi-point Gas Detection Technology for Dynamic Monitoring of Coal Mine Gas Concentration

    • 摘要: 本文面向煤矿瓦斯浓度动态监测的需求,提出了一种基于多点气体检测技术的优化方案。该优化方案采用高精度MEMS气体传感器阵列,提高了检测的空间分辨率和数据采集质量;进而,运用改进的LSTM深度学习算法构建瓦斯浓度预测模型,实现了高精度的浓度预测;最后,通过这2项技术的有机结合,构建了一套高精度、实时、可靠的多点气体检测系统。

       

      Abstract: In order to meet the demand of dynamic monitoring of coal mine gas concentration, an optimization scheme based on multi-point gas detection technology is proposed in this paper. The optimized scheme uses high-precision MEMS gas sensor array to improve the spatial resolution of detection and the quality of data acquisition. Then, the improved LSTM deep learning algorithm is used to construct the prediction model of gas concentration, which realizes the high precision prediction of gas concentration. Finally, through the organic combination of these two technologies, a set of high-precision, real-time and reliable multi-point gas detection system is constructed.

       

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