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Abstract: The combustion behavior of two single coals and three coal blends in a 300 kW coal-fired furnace under variable operating conditions was monitored by a flame monitoring system based on image processing and spectral analysis. A similarity coefficient was defined to analyze the similarity of combustion behavior between two different coal types. A total of 20 flame features, extracted by the flame monitoring system, were ranked by weights of their importance estimated using ReliefF, a feature selection algorithm. The mean of the infrared signal was found to have by far the highest importance weight among the flame features. support vector machine (SVM) was used to identify the coal types. The number of flame features used to build the SVM model was reduced from 20 to 12 by combining the methods of ReliefF and SVM, and computational precision was guaranteed simultaneously. A threshold was found for the relationship between the error rate and similarity coefficient, which were positively correlated. The success rate decreased with increasing similarity coefficient. The results obtained demonstrate that the system can achieve the online identification of coal blends in industry.

The paper is a very good paper indicating reliable methodologies for identification of coal blends. The paper describes the application of two algorithms (SVM and ReliefF) for the processing of images and radiation signals captured from coal flames, with the objective of identifying the coal blend used in different tests. The paper is, in general, well written and the results are good in terms of success rate in the identifications

结合火焰监测技术和支持向量机算法的混煤在线辨识研究

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