针对低浓度瓦斯直流（direct current, DC）氧化系统主管瓦斯浓度预测实时性有限，以及在甲烷浓度等传感器下的适应性不足等问题，提出了低浓度瓦斯直流氧化系统主管瓦斯浓度自适应预测方法。首先基于灰色关联度分析（grey relational analysis, GRA）选择与主管瓦斯浓度相关性高的特征指标建立规范化数据集，以增强模型的适应性；其次构建主管瓦斯浓度预测模型对主管瓦斯浓度进行预测；进一步对模型的适应性和预测实时性从均方误差和样本运行时间进行对比分析；最后将模型集成于基于人工智能与软件界面开发的直流氧化系统主管瓦斯浓度预测平台。实验结果表明：主管瓦斯浓度预测模型的均方误差为0.061 1，运行时间为4 023 s，对主管瓦斯浓度的预测效果优于（long short-term memory, LSTM）等模型，在直流氧化系统主管瓦斯预测平台的预测效果接近监测值，为系统安全运行提供技术保障。

Aiming at the problems of limited real-time prediction of main gas concentration in low-concentration gas direct current(DC) oxidation systems and insufficient adaptability under sensors such as methane concentration, an adaptive prediction method for main gas concentration in low-concentration gas DC oxidation system was proposed. Firstly, based on grey relational analysis(GRA), characteristic indicators with high correlation with main gas concentration were selected to establish a normalized dataset to enhance the adaptability of the model. Secondly, the main gas concentration prediction model was constructed to predict the main gas concentration. Further, the adaptability and prediction real-time of the model were compared and analyzed from the mean square error and sample running time. Finally, the model was integrated into the main gas concentration prediction platform of DC oxidation system developed based on artificial intelligence and software. The experimental results show that the mean square error of the main gas concentration prediction model is 0.061 1, and the running time is 4 023 s. The prediction effect of the main gas concentration is better than that of long short-term memory (LSTM) and other models. The prediction effect of the main gas concentration prediction platform of the DC oxidation system is close to the monitoring value, which provides technical support for the safe operation of the system.