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首页> 外文期刊>Journal of near infrared spectroscopy >Diesel oil quality parameter determinations using support vector regression and near infrared spectroscopy for hydrotreating feedstock monitoring
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Diesel oil quality parameter determinations using support vector regression and near infrared spectroscopy for hydrotreating feedstock monitoring

机译：使用支持向量回归和近红外光谱法测定柴油的质量参数，以监测加氢处理原料

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摘要

Production monitoring and final quality control of diesel can be performed in refineries using near infrared (NIR) spectroscopy combined with regression algorithms. Partial least squares (PLS) is the multivariate regression approach commonly used for such purposes, but it is deficient for modelling complex data sets, such as found in diesel production at refineries. On the other hand, support vector regression (SVR) has demonstrated greater efficiency with high generalisation performance. The aim of this work was to develop regression models using SVR to improve the effectiveness of determining feedstock quality parameters monitored for hydrotreating process control refinery diesel production. SVR and PLS models were developed for the parameters aniline point, cetane index, density and temperature of distillation (initial boiling point and 50%, 85% and 90% recovered). The results indicate the superior modelling capability of SVR. SVR models predicted test set samples with root mean squares errors which were 21% to 54% lower than those predicted using PLS. The NIR determinations presented root mean square error lower than the reproducibility values specified by the established reference methods.
机译：柴油的生产监控和最终质量控制可以在炼油厂中使用近红外（NIR）光谱结合回归算法进行。偏最小二乘（PLS）是通常用于此目的的多元回归方法，但它不足以对复杂的数据集进行建模，例如在炼油厂的柴油生产中发现的那样。另一方面，支持向量回归（SVR）表现出更高的效率和较高的泛化性能。这项工作的目的是使用SVR开发回归模型，以提高确定监测加氢处理过程控制炼油厂柴油生产的原料质量参数的有效性。针对苯胺点，十六烷指数，蒸馏的密度和温度（初始沸点和回收率分别为50％，85％和90％）开发了SVR和PLS模型。结果表明，SVR具有出色的建模能力。 SVR模型预测的测试集样本的均方根误差比使用PLS预测的均方根误差低21％至54％。 NIR测定的均方根误差低于已建立的参考方法指定的重现性值。

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