燃烧排放监测对于优化燃烧质量、提高燃烧效率具有重要意义.为了实现燃烧NOx排放的精确预测,本研究提出了一种基于火焰图像的半监督学习模型.在该模型中,火焰图像的深层特征首先由卷积自编码(Convolutional Autoencoder,CAE)提取,然后送至高斯过程回归(Gaussian Process Regression,GPR)进行分析,得到燃烧NOx浓度.在重油燃烧炉膛上开展实验研究,利用不同工况下的火焰图像测试CAE-GPR性能.结果证实,CAE可以自动提取火焰图像的关键信息,GPR能够提供NOx点预测及置信区间.
Prediction of Combustion NOx Emissions through Flame Image and Semi-supervised Learning Model
Combustion emissions monitoring is crucial for optimizing combustion quality and improving combustion efficiency.To achieve accurate prediction of combustion NOx emission,a semi-supervised learning model is established based on the flame image.In this model,the deep features of the flame im-age are first extracted by the Convolutional Autoencoder(CAE),and then analyzed by the Gaussian Process Regression(GPR)to determine the NOx concentration.Experimental research was conducted on a heavy-oil combustion furnace,and the flame images under different operating conditions were captured to verify the CAE-GPR performance.Results confirmed that the CAE can automatically extract key im-age information,and the GPR can provide reliable NOx point prediction and confidence intervals.
NOx emissionflame imagedeep featureadversarial convolutional autoencodergaussian process
NETL
NSTL
万方数据
