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Article summary:
1. This article proposes a soft sensor system based on deep learning to predict the outlet oxygen content online using easily available color flame images obtained by the charge-coupled device (CCD).
2. A multilayer deep belief network (DBN) is designed to extract nonlinear features for a better description of important trends in a combustion process.
3. The advantages of the proposed deep learning-based analyzing and modeling method are demonstrated via on-site tests in a real combustion system.
Article analysis:
The article “Flame Images for Oxygen Content Prediction of Combustion Systems Using DBN | Energy & Fuels” is an informative and reliable source of information about the use of deep learning for industrial combustion processes. The authors provide evidence from on-site tests in a real combustion system, demonstrating the advantages of their proposed deep learning-based analyzing and modeling method. The article also provides detailed descriptions of the methods used, such as the use of a multilayer deep belief network (DBN) to extract nonlinear features for better description of important trends in a combustion process.
The article does not appear to have any biases or one-sided reporting, as it presents both sides equally and does not make any unsupported claims or missing points of consideration. It also does not contain any promotional content or partiality, and all possible risks are noted throughout the article. Furthermore, all evidence provided is supported by research and experiments conducted by the authors themselves, making it a trustworthy source of information about this topic.