基于BP神经网络和最小二乘支持向量机的灰熔点预测和对比
摘要：为了预测燃煤锅炉受热面的结渣情况，以灰成分金属氧化物、煤灰SO3含量以及结渣评判指标为自变量，灰熔点变形温度(deformation temperature，DT)和软化温度(softening temperature，ST)为因变量，建立了BP神经网络(BP neural network，BPNN)和最小二乘支持向量机(least squares support vector machine，LSSVM)的灰熔点预测模型。回归分析和误差分析结果表明：针对样本量多的DT预测过程，2种模型精度接近，预测结果置信度均达到95%，相关系数均约为0.92，平均相对误差均约为3.4%；针对样本量较少的ST预测过程，LSSVM模型预测效果较优，相关系数为0.950 52，高于BPNN模型的0.904 26，平均相对误差为4.98%，并且大误差点个数少于BPNN模型。因此，LSSVM模型能够更准确预测飞灰的DT和ST。
Abstract:To predict the slagging on heating surface of coal-fired boilers, BP neural network (BPNN) and least squares support vector machine (LSSVM) prediction models were established to predict ash fusion temperature, deformation temperature (DT) and softening temperature (ST). The models take ash metal oxide, SO3 content of ash and slagging evaluation indexes as independent variables, and take DT and ST as dependent variables. Regression analysis and error analysis show that when predicting DT with a large number of samples, the prediction accuracy of the two models is similar, and the confidence of prediction is over 95%. The correlation coefficients are both about 0.92, and the average relative errors are about 3.4%. When predicting ST with less samples, LSSVM model is better with a correlation coefficient of 0.950 52, which is higher than 0.904 26 of BPNN model. The average relative error is 4.98%, and the number of large error points is less than the BPNN model. Therefore, LSSVM model can predict DT and ST of fly ash more accurately.
标题：基于BP神经网络和最小二乘支持向量机的灰熔点预测和对比
title:Prediction and Comparison of Ash Fusion Temperatures Based on BP Neural Network and Least Squares Support Vector Machine
作者：时浩, 肖海平, 刘彦鹏
authors:Hao SHI, Haiping XIAO, Yanpeng LIU
关键词：BP神经网络(BPNN),最小二乘支持向量机(LSSVM),灰熔点,灰成分,结渣评判指标,
keywords:BP neural network (BPNN),least squares support vector machine (LSSVM),ash fusion point,ash composition,slagging evaluation index,
发表日期：2022-02-28
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