4.7 Article

Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system

期刊

FUEL
卷 97, 期 -, 页码 710-717

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2012.03.016

关键词

Diesel; Near infrared spectroscopy; Support vector regression; In-line blending

资金

  1. CNPq
  2. CAPES
  3. Fapesp

向作者/读者索取更多资源

This work demonstrates the application of support vector regression (SVR) applied to near infrared spectroscopy (NIR) data to solve regression problems associated to determination of quality parameters of diesel oil for an in-line blending optimizer system in a petroleum refinery. The determination of flash point and cetane number was performed using SVR and the results were compared with those obtained by using the PLS algorithm. A parametric optimization using a genetic algorithm was carried out for choice of the parameters in the SVR regression models. The best models using SVR presented a RBF kernel and spectra preprocessed with baseline correction and mean centered data. The obtained values of RMSEP with the SVR models are 1.98 degrees C and 0.453 for flash point and cetane number, respectively. The SVR provided significantly better results when compared with PLS and in agreement with the specification of the ASTM reference method for both quality parameter determinations. (C) 2012 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据