Grey Clustering Prediction for Slagging Potential of Coal Blends Combustion

The ash behavior and slagging characteristics of several individual coals, different in rank and ash content, and of their two-component blends, were studied by measuring chemical composition, ash fusion temperature, viscosity, and mineral transformation. The experimental results indicate that ash fusion temperatures for various percentages of two different coal blends illustrate non-arithmetic averaging. The fusion slag of base coals affects the viscosity of coal blends. Temperature and atmosphere affect mineral species vaporization. Considering the limitation of single slagging index for prediction, an overall grey clustering model that takes into account the main related parameters such as ash characteristics, mineral transformation, and combustion parameters is proposed to predict the slagging propensity of the blends. The results suggest that the slagging propensity of coal blends from two coals of similar reactivity and slagging potential was similar to that of the individual coals. However, when one coal was blended with another coal with widely different reactivity or slagging potential, the slagging grade of the coal blends changed significantly. The results of slagging experiments in a combustion test furnace were in agreement with those from model prediction. It implies that the proposed overall grey clustering model can predict slagging tendency more accurately than conventional index. Moreover, the predicted results are comparable with that from a practical boiler slagging test.