沥青生产过程中软化点的SVR预测

Prediction on the softening point of bitumen in producing by using SVR

根据30组不同电阻和温度下的沥青软化点的实测数据集,应用基于粒子群算法(PSO)寻优的支持向量回归(SVR)方法,并结合留一交叉验证(LOOCV)法对沥青软化点进行了建模和预测研究,将其预测结果与多元线性回归(MLR)模型的计算结果进行了比较。SVR-LOOCV预测的最大误差为2.1℃,远比MLR模型计算的最大误差7.9℃要小得多。统计结果表明:基于SVR-LOOCV预测结果的均方根误差(RMSE=0.75℃)、平均绝对误差(MAE=0.32℃)和平均绝对百分误差(MAPE=0.28%)相应也比MLR回归模型的预测结果(RMSE=3.3℃,MAE=2.6℃和MAPE=2.34%)要小。因此,应用SVR实时预测沥青产品的软化点,可为生产优质沥青提供准确的科学指导。

According to an experimental dataset on the softening points of 30 bitumen samples under different resistances and temperatures,the support vector regression（SVR） approach combined with particle swarm optimization（PSO） for its parameter optimization is proposed to conduct leave-one-out cross validation（LOOCV） for modeling and predicting the softening point of bitumen,and its prediction result is compared with that of multivariate linear regression（MLR）.The maximum error 2.1 ℃ predicted by SVR is much less than 7.9 ℃ which is calculated by MLR modeling.The statistical results reveal that the root mean square error（RMSE=0.75 ℃）,mean absolute error（MAE=0.32 ℃） and mean absolute percentage error（MAPE=0.28%） achieved by SVR-LOOCV are all less than those（RMSE=3.3 ℃,MAE=2.6 ℃ and MAPE=2.34%） calculated via MLR model.This study suggests that the softening point of bitumen can be forecasted timely by SVR to provide an accurate guidance for producing of high-quality bitumen.