Assessing slagging propensity of coal from their slagging indices

Energy production by coal combustion is the most commonly used energy technology. At this time, the correlation between the existing coal slagging indices and the actual observations made in most conventional boilers is poor. Some of the conventional test results and empirical ratios frequently offer misleading information, especially, when their use is extended to other coals or blends. For better understanding of the coal properties related to slagging problems, here a multi-variable regression (MR) analysis equation to predict slagging propensity and new models based on multi-resolution wavelet neural network (MWNN) and vague sets are proposed. Coal samples collected from a wide range of Chinese power plants are evaluated. The results of predictions correlate well with the reported field performance of the coals and the new models offer better predictive capability for understanding the field slagging observations than the conventional indices. The methods proposed here provide an encouraging development towards the search for a generic technique of assessing the slagging potential of pulverized coals/blends in boilers.