Aiming at optimizing the soot blowing for boiler

a confidence rule base was established by data mining

on the basis of combustion characteristics and heat absorption characteristics of the boiler. By using mathematical statistics and the Bayesian theory algorithm combined with the experts' operation experiences

the soot blowing tactic and symptom set was extracted

the rules are expressed and the rule variables were set. Moreover

the rule base was tested. The results show that

this confidence rule base meets the actual demand of soot blowing with description of the experts' experience and knowledge representation

which basically realizes soot blowing optimization.