Study on the state of slag on coal-fired boilers based on symmetric fuzzy cross entropy and vague sets

A prediction model is built to predict the state of slag on coal-fired boilers based on the theory of symmetric fuzzy cross entropy and vague sets. 10 boilers were selected as known-slagging samples and 6 boilers as samples to be predicted. Softening temperature, SiO₂-Al₂O₃ ratio, Alkali-acid ratio, silicon percentage of value, the dimensionless average furnace temperature and the dimensionless inscribed circle diameter furnace were selected as input vectors. Firstly, the vague sets of known and unknown-slagging samples were built based on vague sets theory, and then the similarity degree was evaluated based on symmetric fuzzy cross entropy. The feasibility of this method was proved by the results of predicting the state of slag on the 6 coal-fired boilers. Besides, the impact of parameter p in the similarity formula was discussed.