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作 者:付梅艳 周俊领[1] 岑为[1] 严立安[1] 施展 Fu Meiyan;Zhou Junling;Cen Wei;Yan Li’an;Shi Zhan(Sinopec Beijing Research Institute of Chemical Industry,Beijing 100013,China)
出 处:《石油化工》2022年第11期1317-1322,共6页Petrochemical Technology
摘 要:以聚丙烯催化剂中给电子体化合物邻苯二甲酸二正丁酯(DNBP)为研究对象,建立近红外定量测试DNBP含量的方法。采用偏最小二乘法将220个催化剂试样的近红外光谱与催化剂中DNBP含量的GC测量值关联建立数学模型,选取6个不同光谱预处理方法模型,采用内部交叉验证考察模型的稳定性。研究结果表明,选取3段光谱区间、使用二阶导数进行预处理的模型M5具有最低的均方根误差、最高的决定系数和残留预测偏差、较高的模型稳定性和预测准确性。模型M5的维数最多,但预估值相对偏差最小,不存在过度拟合。模型M5与GC测量值之间的偏差远小于HPLC与GC测量值之间的偏差。模型M5可减少不同测试方法引起的误差,所得数据平行性较高,采取多次测量取平均值的方式可得到更准确的DNBP定量分析结果。Taking the electron donor compound in polyolefin catalyst di-n-butyl phthalate(DNBP)as the research object,a near infrared spectroscopy method for quantitative determination of DNBP was established.The partial least square method was used to establish a mathematical model between the nearinfrared spectra of 220 catalyst samples and the GC determination values of DNBP content in the catalyst.Six models with different spectral pretreatment methods were selected to investigate the stability of the models by internal cross validation.The results show that model M5 pretreated by second derivative with 3 spectral intervals,has the lowest root mean square error,the highest determination coefficient and residual prediction deviation,higher model stability and prediction accuracy.Although the rank of M5 is the most,the predicted relative deviation of which is the smallest,over fitting does’t exist.It is proved that the deviation between the model M5 data and GC measured values is far less than that between HPLC and GC.Model M5 can reduce the error caused by different test methods,and the parallelism of the model M5 data is high,furthermore,more accurate quantitative determination result of DNBP content can be obtained by taking the average value of multiple determinations.
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