Establishing structure–property correlations and classification of base oils using statistical techniques and artificial neural networks

• Published in: Analytica chimica acta 2004-03, Vol.506 (1), p.57-69
• Main Authors: Kapur, G.S, Sastry, M.I.S, Jaiswal, A.K, Sarpal, A.S
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Base oils; Chemistry; Classification; Exact sciences and technology; General, instrumentation; Neural networks; Property prediction; RBOT; Spectrometric and optical methods
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2003.10.074

The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I–III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using 1 H and 13 C nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their 1 H NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the ‘measured’ and ‘estimated’ RBOT values for both training ( R=0.859) and validation ( R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only 1 H NMR data) also showed high correlation between the ‘measured’ and ‘estimated’ RBOT values for training ( R=0.881), validation ( R=0.860) and test ( R=0.955) data sets.