FTIR Spectrometry with PLS Regression for Rapid TBN Determination of Worn Mineral Engine Oils

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dc.contributor.authorSejkorová, Mariecs
dc.contributor.authorŠarkan, Branislavcs
dc.contributor.authorVeselík, Petrcs
dc.contributor.authorHurtová, Ivanacs
dc.coverage.issue23cs
dc.coverage.volume13cs
dc.date.accessioned2021-01-12T15:55:00Z
dc.date.available2021-01-12T15:55:00Z
dc.date.issued2020-12-05cs
dc.description.abstractThe TBN (Total Base Number) parameter is generally recognized by both engine oil processors and engine manufacturers as a key factor of oil quality. This is especially true for lubricating oils used in diesel and gas engines, which are exposed to relatively high temperatures and, therefore, require more effective protection against degradation. The FTIR spectrometry method together with a multivariate statistical software helped to create a model for the determination of TBN of worn motor oil SAE 15W-40 ACEA: E5/E7, API: CI-4. The best results were provided using a model FTIR with Partial Least Squares (PLS) regression in an overall range of 4000–650 cm1 without the use of mathematical adjustments of the scanned spectra by derivation. Individual spectral information was condensed into nine principal components with linear combinations of the original absorbances at given wavenumbers that are mutually not correlated. A correlation coefficient (R) between values of TBN predicted by the FTIR-PLS model and values determined using a potentiometric titration in line with the CSN ISO 3771 standard reached a value of 0.93. The Root Mean Square Error of Calibration (RMSEC) was determined to be 0.171 mg KOH.g1, and the Root Mean Square Error of Prediction (RMSEP) was determined to be 0.140 mg KOH.g1. The main advantage of the proposed FTIR-PLS model can be seen in a rapid determination and elimination of the necessity to work with dangerous chemicals. FTIR-PLS is used mainly in areas of oil analysis where the speed of analysis is often more important than high accuracy.en
dc.formattextcs
dc.format.extent1-12cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationENERGIES. 2020, vol. 13, issue 23, p. 1-12.en
dc.identifier.doi10.3390/en13236438cs
dc.identifier.issn1996-1073cs
dc.identifier.other167502cs
dc.identifier.urihttp://hdl.handle.net/11012/195830
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofENERGIEScs
dc.relation.urihttps://www.mdpi.com/1996-1073/13/23/6438cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1996-1073/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectoil analysisen
dc.subjectengine oilen
dc.subjectlubricantsen
dc.subjectFTIR spectrometryen
dc.subjecttotal base number (TBN)en
dc.subjectpartial least squares (PLS)en
dc.titleFTIR Spectrometry with PLS Regression for Rapid TBN Determination of Worn Mineral Engine Oilsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-167502en
sync.item.dbtypeVAVen
sync.item.insts2021.05.03 12:54:23en
sync.item.modts2021.05.03 12:14:37en
thesis.grantorVysoké učení technické v Brně. Ústav soudního inženýrství. Odbor inženýrství rizikcs
thesis.grantorVysoké učení technické v Brně. . Univerzita Pardubicecs
thesis.grantorVysoké učení technické v Brně. . Žilinská univerzita v Žiliněcs
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