基于低分辨率红外发射光谱采集技术,耦合深度学习计算方法,提出了一种烟气温度和CO2浓度在线检测方法。利用气体光谱辐射模型计算训练数据,基于多层感知器(MLP)神经网络反演火焰烟气温度和CO2浓度的分布,结果表明,MLP神经网络模型对温度、CO2和H2O体积分数的反演误差均低于1%,预测精度均大于94.5%,具有良好的泛化能力和预测能力。建立了一套基于深度学习与发射光谱耦合的烟气温度和CO2浓度在线检测装置,并对乙烯扩散火焰和C2H4/NH3部分预混火焰展开了研究。乙烯扩散火焰烟气温度和CO2体积分数的测量结果与模拟火焰结果相一致,验证了基于深度学习与发射光谱耦合的在线检测法的可行性。改变部分预混火焰的掺氨比例,分析火焰中轴上方不同高度处气体的温度和CO2浓度变化,结果表明,同一高度处的烟气温度会随着掺入氨气的增加而增大,而CO2体积分数会呈先增大后急剧减少的趋势。所提出的方法可以较灵敏的检测温度和CO2浓度的变化,用于多种火焰的燃烧诊断研究,在燃煤电厂碳排放在线检测上也有一定的应用前景。

Based on low-resolution infrared spectroscopy acquisitionand deep learning computational methods, an online detection methodfor flue gas temperature and CO concentration is proposed. The gas spectral radiation model was used to calculate the training data, thedistribution of flame flue gas temperature and CO concentration was inverted based on a multi-layer perceptron (MLP) neural network.Results show that the inversion errors of the MLP neural network model for temperature and CO and HO volume fractions are less than1%, and the prediction accuracies are all greater than 94.5%, which has good generalization and prediction capabilities. A set of on-linedetection device for flue gas temperature and CO concentration based on deep learning coupled with emission spectroscopy was established, and the ethylene diffusion flame and CH / NH partially premixed flame were investigated. The measurement results of flue gastemperature and CO volume fraction for the ethylene diffusion flame were consistent with the simulated flame results, which verified thefeasibility of the online detection method based on deep learning coupled with emission spectroscopy. Changing the ammonia doping ratio ofthe partially premixed flame and analyzing the temperature and CO concentration changes of the gas at different heights above the centralaxis of the flame, results show that the flue gas temperature at the same height increases with the increase of the doped ammonia, and theCO volume fraction shows a tendency to increase and then decrease sharply. The proposed method can detect the changes of temperatureand CO more sensitively, which can be used for combustion diagnostic studies of many kinds of flames, and also has some potential applications in the online detection of carbon emissions in power plants.