基于SSA-BP神经网络构建近红外光谱汽油辛烷值预测模型  

RESEARCH ON THE CONSTRUCTION OF GASOLINE OCTANE NUMBER MODEL BASED ON SSA-BP NEURAL NETWORK

作　　者：郑圣国 陆道礼[1] 陈斌[1] Zheng Shengguo;Lu Daoli;Chen Bin(School of Mechanical Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013)

机构地区：[1]江苏大学机械工程学院,江苏镇江212013

出　　处：《石油炼制与化工》2024年第11期149-154,共6页Petroleum Processing and Petrochemicals

基　　金：国家重点研发计划项目(31772056);江苏大学产学研项目(8411363013)。

摘　　要：基于100组汽油样品的近红外光谱分析数据,采用不同方法进行预处理,结合麻雀搜索算法(SSA)优化BP神经网络模型,构建了汽油辛烷值SSA-BP预测模型,对模型预测值与汽油研究法辛烷值(RON)测量值进行拟合,并与偏最小二乘法模型及BP神经网络模型的预测结果进行对比。结果表明:采用标准正态变量变换(SNV)方法进行光谱数据预处理后,所建SSA-BP模型的预测精度最高,验证集决定系数(R^(2))为0.9760,预测标准偏差(RMSEP)为0.326;3种汽油辛烷值预测模型中,SSA-BP神经网络模型预测准确度最好,且模型适用性和稳定性良好。说明利用SNV方法预处理光谱数据的SSA-BP神经网络模型,可以将近红外光谱分析技术更好地用于汽油辛烷值的预测,且预测结果具有良好的准确度,可以实现汽油辛烷值的快速无损检测。Based on the data of 100 groups of gasoline samples analyzed by near infrared spectroscopy,the prediction model of gasoline octane number(SSA-BP)was established by pre-processing with different methods and optimizing BP neural network model with the sparrow search algorithm(SSA).The predicted value of the model was fitted with the measured value of gasoline research octane number(RON)and compared with those results obtained by partial least squares model and BP neural network model.The results showed that the SSA-BP model had the highest prediction accuracy,with a validation set determination coefficient(R^(2))of 0.9760 and a prediction standard deviation(RMSEP)of 0.326 after the standard normal variate transformntion(SNV)data pre-processing.Among the three gasoline octane number prediction models,SSA-BP neural network model has the best prediction accuracy and good applicability and stability.It shows that the SSA-BP neural network model,which preprocesses the spectral data with SNV method,can be better used for the prediction of gasoline octane number.The prediction results have good accuracy,it can realize the rapid non-destructive detection of gasoline octane number.

关 键 词：汽油 辛烷值 麻雀搜索算法 BP神经网络 近红外光谱 偏最小二乘法 

分 类 号：TE626.21[石油与天然气工程—油气加工工程] TP183[自动化与计算机技术—控制理论与控制工程] O657.33[自动化与计算机技术—控制科学与工程]