Support vector machine based online coal identification through advanced flame monitoring

This paper presents a new on-line coal identification system based on support vector machine (SVM) to achieve on-line coal identification under variable combustion conditions. Four different coals were burnt in a 0.3 MW coal combustion furnace with different coal feed rates, total air flow rates and flow rate ratios of primary air and secondary air. The flame monitoring system was installed at the exit of the burner to acquire the coal flame images and light intensity signals. Spatial and temporal flame features were extracted for coal identification. The averaged prediction accuracy is 99.1%. The mean value of the infrared signal has the most significant influence on prediction accuracy. For “unstudied” operation cases, the prediction accuracy is 94.7%.