火焰自由基成像和极限学习机在NOx排放预测中的研究

Research on Flame Radical Imaging and Extreme Learning Machine to Prediction of NOx Emissions

火焰自由基对深入了解燃烧机理起着重要作用。通过数字成像研究火焰自由基光谱特征,并利用该特征参数建立极限学习机(ELM,Extreme Learning Machine)模型,以试验数据与数字仿真相结合的方法实现在生物质燃烧中对NOx的排放在线预测。用电子倍增CCD(EMCCD)相机采集火焰中的四种自由基OH*,CN*,CH*和C_2*的数字图像,采用模糊C均值聚类法(FCM,Fuzzy C-Means)进行图像分割并提取特征值,结合燃烧火焰温度,利用极限学习机进行NOx排放预测建模。采用燃气燃烧试验炉上的试验数据验证了预测模型的有效性。

Flame radicals are crucial for an in-depth understanding of the combustion mechanisms. The spectral characteristics of flame radicals were studied based on digital imaging and feature extraction techniques. The information obtained was used to establish the extreme learning machine（ELM） model which can be used to predict the NOx emissions based on the experimental data and digital simulation from a biomass-gas-air combustion process. The digital images of four flame radicals, i.e., OH＊, CN＊, CH＊ and C2＊, were collected using an EMCCD（Electron Multiplying Charge Coupled Device） camera. The image segmentation was performed using the fuzzy C-means（FCM） algorithm, and image features were extracted. Finally, the ELM model was built for the prediction of NOx emissions based on the radical features and flame temperture. The experimental data on a gas-biomass combustion test rig demonstrate the validity of the proposed ELM model.