Predicting Research and Motor Octane Numbers based on Near Infrared Spectroscopy: Models based on Partial Least Squares Regression and Artificial Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Leal, AL (Author) Other; Ribeiro, JC (Author) Other; Silva, AMS (Author) Other; Martins, FG (Author) FEUP

Conference proceedings International

Title: 28TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING

Pages: 187-192

28th European Symposium on Computer-Aided Process Engineering (ESCAPE)

Graz, AUSTRIA, JUN 10-13, 2018

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ISI Web of Knowledge - 0 Citations

Other information

Authenticus ID: P-00P-HJT

DOI: 10.1016/b978-0-444-64235-6.50034-6

Abstract (EN): This work focuses on the prediction of research and motor octane numbers of gasolines throughout multivariate statistical analysis, which may significantly increase the celerity of the quality control process. The aim of this work is to compare the performance of two different multivariate models, based on partial least squares regression and artificial networks. The results show that both models predict octane numbers with accuracy, presenting coefficients of determination above 0.95 for the calibration data set. For the test data set, partial least squares model is more accurate, which might indicate the existence of linear correlations between spectral data and octane numbers. The statistical parameters also show that the research octane number prediction is more accurate than the motor octane number.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6