PEFTEC 2019

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Abstract Title: Near-Infrared Spectroscopy and Spectrofluorimetry combined with Chemometrics In Order to Determine The Performance Level of Gasoline Engine Oils
Abstract Type: Poster
Session Choice: Mathematical and Statistical Analysis techniques and applications
Presenter Name: Ms Maryam Hooshyari
Co-authors:Prof Monica Casale
Dr Paolo Oliveri
Prof Ricardo Leardi
Company/Organisation: University of Genova
Country: Italy

Abstract Information :

This study is focused on “Gasoline Engine Oil”, one of the Fluid Automotive Lubricant. Generally, most of the customers recognize quality of products based on signs of standard institutes and quality control organizations that operates worldwide certification scheme of products. They evaluate these lubricants by using simulation tests that require a lot of time and money. The aim of the present study is to find an alternative inexpensive and rapid solution in order to distinguish the type of base oil into lubricants. This also could help the formulators when developing a new or tailored lubricant targeting a given performance level. Since spectroscopy techniques are low-cost, green, non-destructive and fast, in order to reach this goal, the capabilities of near infrared (NIR) and excitation- emission florescence (EEF) spectroscopies coupled with chemometrics have been investigated. Engine oils are prepared by blending almost eighty present base stocks (a mixture of one or more base oil) and twenty present sums of different additives to meet each performance levels requirements. Since base stocks are the major parts of these blends, they have main role. Base oils are mineral oils, synthetic base oils, re-refinery base oils and vegetable oils. In this study we considered the classification of American Petroleum Institute (API) that divided base oils into five categories. Unfortunately, there are no reliable ways or methods to exactly distinguish the type of base oils in end lubricants. However, in laboratory it is possible to distinguish pure base oils just by looking at the combination of the physical properties such as: viscosity index, density, color, flash point, pour point, aniline point, thermal stability, etc. but, a mixture of synthetic and mineral oil become a very big analytical challenging study in the composition of base stock and additives. In this study, 24 base oil samples have been analyzed by means of NIR and fluorescence spectroscopy. NIR spectra were acquired with a FT-NIR spectrophotometer (Buchi NIRFlex N-500), in the 4000-10000 cm-1 range and 4 cm-1 resolution. All the experiments were performed at controlled temperature (35 ̊C). The Excitation–emission fluorescence measurements were performed at room temperature on a PerkinElmer LS 55 spectrometer. First of all, a D-optimal design (Lewis GA, 1999) was performed in order to optimize the configurable factors of the Fluorescence spectrometer while avoiding spectra saturation was our target. According to the results of the experimental design, the excitation spectra were recorded between 200 and 500 nm (each 10 nm), whereas the emission wavelengths ranged from 300 to 900 nm (each 0.5 nm)., the excitation and emission monochromator slit widths were set to 4.5 and 11.0 nm alternatively, excitation scan speed was 200 nm/min and gain has set at low. Although optimized amount of the both slit were sensitive to climate change. Principal Component Analysis (PCA) (I.T. Joliffe, 2002) was applied as a multivariate display method in order to visualize the data structure and some multivariate classification tools were investigated in order to distinguish pure base oil groups. Finally data fusion (Eva Borras, 2015) was performed in order to combine NIR and fluorescence data blocks for more comprehensive result. Preliminary results showed a separation between the base oil samples according to the five API categories. In conclusion, NIR and fluorescence spectroscopy appear to be rapid and non-destructive analytical methods capable of discriminating among different base oil groups and therefore seem to be promising for Gasoline Engine Oils' analysis. Acknowledgment: we thank Eni Co. from Milan, Italy, Bellini Co, from Bergamo, Italy and Afzoon Ravan Co. from Tehran, Iran for samples support.