Prediction of the Total Base Number (TBN) of Engine Oil by Means of FTIR Spectroscopy

Artur Wolak, Jarosław Molenda, Kamil Fijorek and Bartosz Łankiewicz
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Artur Wolak: Department of Quality and Safety of Industrial Products, Institute of Quality and Product Management Sciences, Cracow University of Economics, 27 Rakowicka St., 31-510 Cracow, Poland
Jarosław Molenda: Łukasiewicz Research Network—Institute for Sustainable Technologies, 6/10 Pułaskiego St., 26-600 Radom, Poland
Kamil Fijorek: Department of Statistics, College of Economics, Finance and Law, Cracow University of Economics, 27 Rakowicka St., 31-510 Cracow, Poland
Bartosz Łankiewicz: Department of Quality and Safety of Industrial Products, Institute of Quality and Product Management Sciences, Cracow University of Economics, 27 Rakowicka St., 31-510 Cracow, Poland

Energies, 2022, vol. 15, issue 8, 1-21

Abstract: The objective of this study is to develop a statistical model to accurately estimate the total base number (TBN) value of diesel engine oils on the basis of the Fourier transform infrared spectroscopy (FTIR) analysis. The research sample consisted of oils used in the course of 14,820 km. The samples were collected after each 1000 km and both FTIR and TBN measurements were performed. By applying the measured absorbance values, five statistical models aimed at predicting TBN values were elaborated with the use of the following information: aggregated values of measured absorbance in defined spectral ranges, extremes at wavenumbers, or the surface area of spectral bands related to the vibrations of specific molecular structures. The obtained models may be considered a continuation and an extension of previous studies of this type described in the literature on the subject. The results of the study and the analysis of the obtained data have led to the development of two models with high predictive capabilities (R 2 > 0.98, RMSE < 0.5). Another model, which had the smallest number of variables in comparison to other models, had markedly lower R 2 value (0.9496) and the highest RMSE (0.5596). Yet another model, where the dimensionality of the pre-processed full spectra was reduced to four aggregates through averaging, turned out to be slightly worse than the best one (R 2 = 0.9728). The study contributes to a more in-depth understanding of the FTIR-based TBN prediction tools that may be readily available to all interested parties.

Keywords: engine oil; total base number (TBN); FTIR; oil condition monitoring; chemometric analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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