热力发电2017，Vol.46Issue(6)：113-118,6.DOI:10.3969/j.issn.1002-3364.2017.06.113
基于生产数据挖掘的吹灰需求度置信规则库研究
Confidence rule base for soot blowing demand based on production data mining
摘要
Abstract
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.关键词
锅炉/积灰结渣/吹灰优化/置信规则库/数据挖掘/贝叶斯理论/生产数据Key words
boiler/fouling and slagging/soot blowing optimization/belief rule base/data mining/Bayes theory/production data分类
能源科技引用本文复制引用
钱虹,宋亮,陈琪琪,陈纲..基于生产数据挖掘的吹灰需求度置信规则库研究[J].热力发电,2017,46(6):113-118,6.基金项目
上海市自然科学基金(15ZR1417500) Natural Science Foundation of Shanghai (15ZR1417500) （15ZR1417500）
