Journal
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
Volume 117, Issue -, Pages 389-396Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.saa.2013.08.018
Keywords
Support vector machines; Classification; Near infrared spectroscopy; Diesel pool streams
Categories
Funding
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - CNPq
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - CAPES
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo - FAPESP
Ask authors/readers for more resources
The use of near infrared (NIR) spectroscopy combined with chemometric methods have been widely used in petroleum and petrochemical industry and provides suitable methods for process control and quality control. The algorithm support vector machines (SVM) has demonstrated to be a powerful chemometric tool for development of classification models due to its ability to nonlinear modeling and with high generalization capability and these characteristics can be especially important for treating near infrared (NIR) spectroscopy data of complex mixtures such as petroleum refinery streams. In this work, a study on the performance of the support vector machines algorithm for classification was carried out, using C-SVC and nu-SVC, applied to near infrared (NIR) spectroscopy data of different types of streams that make up the diesel pool in a petroleum refinery: light gas oil, heavy gas oil, hydrotreated diesel, kerosene, heavy naphtha and external diesel. In addition to these six streams, the diesel final blend produced in the refinery was added to complete the data set. C-SVC and nu-SVC classification models with 2, 4, 6 and 7 classes were developed for comparison between its results and also for comparison with the soft independent modeling of class analogy (SIMCA) models results. It is demonstrated the superior performance of SVC models especially using nu-SVC for development of classification models for 6 and 7 classes leading to an improvement of sensitivity on validation sample sets of 24% and 15%. respectively, when compared to SIMCA models, providing better identification of chemical compositions of different diesel pool refinery streams. (C)13 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available