摘要
首先采用聚类分析方法将某石油化工企业汽油样本进行适当分类,然后采用光谱库自动检索算法,从RIPP汽油库中有针对性地找到一定量的汽油样本作为校正集,建立汽油重要性质的分析模型。近红外分析方法结合偏最小二乘法具有测量快速、操作简单、无需预处理、重复性好等优点。建立的校正模型对该石油化工企业汽油样本的辛烷值、烯烃含量和芳烃含量的预测标准偏差分别为0.3、1.6%和1.0%,满足快速分析要求。
The petrochemical enterprise gasoline samples were properly classified by cluster analysis method,then the spectral library automatic retrieval algorithm was employed to find a certain amount of gasoline samples as calibration sets from RIPP gasoline library to establish the analysis model for the properties of gasoline.The calibration models of research octane number（RON）,olefin and aromatics content had been established by Partial least square（PLS）.The standard error of prediction（SEP）of the petrochemical enterprise gasoline samples on gasoline RON,olefin and aromatics mass fraction were 0.3,1.6% and 1.0%,respectively.The results predicted by this method were very close to those determined by standard methods.Compared with standards,PLS combined with near infra-red（NIR）method was provided with advantages such as high-speed,simplicity,no-pretreatment and good-repeatability.
出处
《石油学报（石油加工）》
EI
CAS
CSCD
北大核心
2017年第1期131-137,共7页
Acta Petrolei Sinica(Petroleum Processing Section)
