模糊神经网络在降低燃煤锅炉结渣风险中的应用研究  

Research on the Application of Fuzzy Neural Network in Reducing Slagging Risk of Coal-Fired Boiler

作　　者：曲默丰 辛炜[1] QU Mofeng;XIN Wei(Shanghai Boiler Works Co.,Ltd.,Shanghai 200245,China)

机构地区：[1]上海锅炉厂有限公司,上海200245

出　　处：《锅炉技术》2022年第6期23-27,共5页Boiler Technology

摘　　要：燃煤锅炉炉膛结渣风险始终是威胁机组安全经济运行的因素之一,开发一种高准确度的结渣预测模型尤为重要。综合选取7个炉膛结渣判别指标作为输入条件,采用模糊数学理论与BP神经网络相结合的方式构建结渣预测模型,经验证具有较高的预测准确率。针对某1000 MW超超临界燃煤锅炉进行实例预测分析,提出调整燃烧器摆角的方式降低炉膛结渣风险,为锅炉运行优化提供参考。The risk of slagging in the coal-fired boiler furnace is one of the factors threatening the safe and economic operation of the units.It is particularly important to develop a high accuracy slagging prediction model.Seven furnace slagging discrimination indexes are selected as input conditions,and the slagging prediction model is constructed by combining fuzzy mathematics theory and BP neural network in this paper.It is verified that it has high prediction accuracy.An example of a 1000 MW ultra-supercritical coal-fired boiler is analyzed,and the method of adjusting the swing angle of burner is put forward to reduce the risk of furnace slagging,which provides a reference for boiler operation optimization.

关 键 词：模糊算法 神经网络 结渣风险 

分 类 号：TK224[动力工程及工程热物理—动力机械及工程]