基于BP神经网络和多元线性回归的辛烷值预测

Octane number prediction based on BP neuralnetwork and multiple linear regression

为降低硫、烯烃含量及辛烷值损失,保证汽油清洁化生产,基于S Zorb装置运行积累的数据,首先利用Lasso算法初步筛选建模变量,并基于BP神经网络计算指标因子贡献度,进一步筛选出15个主要变量用于建立辛烷值损失预测模型;其次对比分析4种模型,得出BP神经网络预测精度更优,更适合作为辛烷值损失预测模型,并经过10折交叉验证得到均方误差(MSE)均值为0.027 193,R~2均值为0.904 87,验证了该模型的可靠性;最后在控制油品硫质量分数不大于5μg/g的前提下,结合多元线性回归对主要变量进行优化调控.结果表明,需同时改变多个变量才能使辛烷值损失降幅大于30%,多元线性回归模型预测精度较好,能按照一定比例对主要变量进行正反向调控.本文还可视化展示了优化过程中辛烷值和硫含量的变化轨迹.

In order to reduce the sulfur and olefin and the loss of octane number so as to promote the clean production of gasoline,an octane number loss prediction model is established based on data accumulated by the S Zorb device.First,the Lasso is used to screen out the modeling variables,then the index factor contributions are calculated by the BP neural network,based on which 15 main variables are screened out to build the model.Second,four modeling approaches are compared and analyzed,which shows that the BP neural network has better prediction accuracy thus is more suitable to model the octane number loss.The ten-fold cross-validation produces the average MSE value of 0.027193 and the average R 2 value of 0.90487,verifying the reliability of the model.Furthermore,the main variables are optimized and adjusted by multiple linear regression under the premise that the sulfur content is not greater than 5μg/g.The results show that multiple variables need to be adjusted simultaneously to reduce the octane number loss by more than 30%.The multiple linear regression model has good prediction accuracy and can adjust main variables positively or negatively according to a certain proportion.The trajectories of octane number and sulfur content are also visualized in the paper.