摘要
应用基于模糊C均值聚类算法预处理的支持向量机算法对锅炉结渣特性进行预测建模,将煤的软化温度、碱酸比、硅铝比和硅比以及无因次炉膛烟气平均温度和无因次实际切圆直径作为模型的输入变量,结渣程度作为输出变量,利用优化后的模型对10台锅炉的结渣特性进行评判.结果表明:该模型能够减小训练样本的过拟合度,具有较强的泛化能力;本试验中FCM-SVM预测模型预测结果的正确率为100%,可以实现对锅炉结渣特性的精确预测.
A model of support vector machine （SVM） forecasting system was built up for state prediction of slagging on coal-fired boilers based on fuzzy C-means clustering （FCM） data preprocessing, which takes the softening temperature, alkali-acid ratio, SiO2-Al2O3 ratio, percentage of silicon content, dimensionless average furnace temperature and dimensionless diameter of actual tangential flow circle as input variables, and the slagging degree as output variable. Slagging characteristics of 10 boilers were evaluated with the optimized model. Results show that the model is able to avoid over-fitting of training sample sets, and it has relatively strong generalization capability. The prediction accuracy with FCM-SVM model used in this experiment is 100%, indicating high prediction accuracy of boiler slagging characteristics.
出处
《动力工程学报》
CAS
CSCD
北大核心
2014年第2期91-96,共6页
Journal of Chinese Society of Power Engineering
关键词
燃煤锅炉
结渣
支持向量机
模糊C均值聚类算法
预测
coal-fired boiler
slagging
support vector machine
fuzzy C-means clustering
prediction
