Flame Images for Oxygen Content Prediction of Combustion Systems Using DBN

As an increasingly popular method in the machine learning field, deep learning is applied to industrial combustion processes in this work. Using easily available color flame images obtained by the charge-coupled device (CCD), a soft sensor system based on deep learning is proposed to predict the outlet oxygen content online. Unlike the traditional principal component analysis which only extracts linear features, a multilayer deep belief network (DBN) is designed to extract the nonlinear features for a better description of the important trends in a combustion process. With the DBN-based multilevel representation of the CCD flame images, more useful information about the physical properties of a flame can be characterized. Sequentially, in a supervised fine-tuning stage, two DBN-based regression models are simply constructed to obtain the nonlinear relationship between the flame images and the outlet oxygen content. The advantages of the proposed deep learning-based analyzing and modeling method are demonstrated via on-site tests in a real combustion system.