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为利用长期积累的单一煤灰结渣指标数据直接预测煤灰结渣特性,以煤灰成分S iO2、A l2O3、Fe2O3、CaO、MgO、Na2O、K2O和TiO2作为输入变量,实际结渣程度作为输出变量,基于支持向量机建立了煤灰结渣特性诊断模型。通过对某热电厂锅炉煤灰样本的诊断,表明该诊断模型具有较高的评判准确率。
In order to directly predict the slagging characteristics of coal ash using long-term accumulative single slagging index data, the actual slagging degree is taken as the input variables by using soot components S iO2, A l2O3, Fe2O3, CaO, MgO, Na2O, K2O and TiO2 as input variables Output variables, based on the support vector machine to establish a slagging characteristic diagnostic model. Through the diagnosis of boiler coal ash samples in a thermal power plant, it shows that the diagnostic model has higher accuracy.