Nashrieh Shimi va Mohandesi Shimi Iran

Nashrieh Shimi va Mohandesi Shimi Iran

Determination of Functional Groups Affecting the Viscosity Index of Motor Oils Using FT-IR and Multivariate Linear Regression Based on Genetic Algorithm

Document Type : Research Article

Authors
Samaneh Ehsani
Mani Varnosfaderani
Yadollah Yamini
Faculty of Sciences, Tarbiat Modares University, Tehran, I.R. IRAN
Abstract
Motor oils have different physicochemical properties, namely viscosity, viscosity index, flash point, pour point, etc. Viscosity is one of the important properties of motor oils since all the properties of industrial lubricants are referred to as their viscosities. The changes in viscosity with variation in temperature are regarded as the viscosity index. The greater the viscosity index, the lower the chances of the viscosity of motor oil with temperature and vice versa. According to the importance of viscosity index in lubricants and because the viscosity index of lubricants is dependent on the chemical composition of motor oils, thus in this study, a simple spectroscopic technique like Fourier Transform InfraRed (FT-IR) spectroscopy was used to analyze the Behran motor oils. The important wavenumbers that affect the viscosity indices were identified by using the Genetic Algorithm (GA) as a variable selection method. By using this method, some functional groups like Alkyl halides, Alkene, Nitro, Acid, Alkane, Alkyne, and Alcohol were recognized that affect the viscosity index of motor oils. Modeling the viscosity index of motor oils was done by Multivariate Linear Regression (MLR) method. Various data preprocessing techniques like Mean Centering and Auto-scaling were operated before the MLR and GA-MLR techniques. The results of modeling were evaluated by using different parameters like regression coefficients (R2) and Root Mean Square Error (RMSE). The values of R2 and RMSE, obtained by the GA-MLR were 0.998 and 0.954 respectively.
Keywords
Engine oils
Viscosity Index
FT-IR
Genetic Algorithm (GA)
Multivariate Linear Regression (MLR)

Subjects

[1] Souza L.M., Mitsutake H., Gontijio L., Neto W., Quantification of Residual Automotive Lubricant Oil as an Adulterant in Brazilian S-10 Diesel Using MIR Spectroscopy and PLS, Fuel, 130: 256-262(2014).
[2] Kreisberger G., Himmelsbach M., Buchberger W., Klampfl C.W., Identification and Semi-Quantitative Determination of Anti-Oxidants in Lubricants Employing Thin-Layer Chromatography Spray Mass Spectrometry, Journal of Chromatography A, 1383: 169-174 (2015).
[3] گودرزنیا, ایرج, سعیدی, عبدالرضا. بازیافت روغن موتور کارکرده به روش استخراج فوق بحرانی با کربن دی اکسید. نشریه شیمی و مهندسی شیمی ایران, (3)31: 39 تا 44 (1391).
[4] سعیدیان, میلاد, غضنفری, محمد حسین, مسیحی, محسن, خراط, ریاض. بررسی آزمایشگاهی اثر ویژگی های فیزیکی شکاف بر شکل گیری و توسعه انگشتی ها در مخازن نفت سنگین شکاف دار در شرایط تزریق امتزاجی. نشریه شیمی و مهندسی شیمی ایران, (4)30: 55 تا 65 (1390).
[5] Panchal T.M., Patel A., Chauhan D., Merlin T., Jigar V., A Methodological Review on Bio-Lubricants from Vegetable Oil Based Resources, Renewable and Sustainable Energy Reviews, 70: 65-70 (2017).
[6] Amer M.H., Gaberah A.S., Used Lubricating Oils Re-Fining by Solvent Extraction. American Journal of Environmental Engineering and Science, 1: 44-50 (2014).
[7] Ting C., Chen C.C., Viscosity and Working Efficiency Analysis of Soybean Oil Based Bio-Lubricants, Measurement, 44: 1337-1341 (2011).
[8] صمدی زاده, مرجانه, علیزاده, شیرین, کشاورز, شهریار. سنتز روی دی آلکیل دی تیو فسفات­ها به عنوان ماده افزودنی به روغن موتور و بررسی ویژگی­های آنتی اکسیدانی و ضد سایشی آن­ها، نشریه شیمی و مهندسی شیمی ایران، (4)36: 71 تا 76 (1396).
[9] Sejkorov́́a M., Application of FTIR Spectrometry Using Multivariate Analysis for prediction Fuel in Engine Oil, Acta Universitatis Agriculturae Et Silviculturae Mendelianae Brunensis, 65(3): 933-38 (2017).
[10]  Amat S., Braham Z., Dréau Y.L., Kister J., Dupuy N., Simulated Aging of Lubricant Oils by Chemometric Treatment of Infrared Spectra: Potential Antioxidant Properties of Sulfur Structures, Talanta, 107: 219-224 (2013).
[11] Al-Ghouti M.A., Al Degs Y.S., Amer M., Application of Chemometrics and FT-IR for Determination of Viscosity Index and Base Number of Motor Oils, Talanta, 81: 1096-1101 (2010).
[12] De Rivas B.L., Vivancos J.L., Mere J.O., Capuz-Rizo S.F., 2017. Determination of the Total Acid Number (TAN) of Used Mineral Oils in Aviation Engines by FTIR Using Regression Models, Chemometrics and Intelligent Laboratory Systems, 160: 32-39 (2017).
[13] Vrtiska D., Simack P., Prediction of HVO Content in HVO/Diesel Blends Using FTIR and Chemometric Methods, Fuel, 174: 225-234 (2016).
[14] Yousefinejad S., Hemmateenejad B., Chemometrics Tools in QSAR/QSPR Studies: A Historical Perspective, Chemometrics and Intelligent Laboratory Systems, 149: 177-204 (2015).
[15] Rebechi S.R., Velez M.A., Perotti M.C., Adulteration of Argentinean Milk Fats with Animal Fats: Detection by Fatty Acids Analysis and Multivariate Regression Techniques, Food Chemistry, 192- 1025-1032 (2016).
[16] Wang X., Sun Y., Wu L., Liu R., Liu L., Liu X., Xu J., Quantitative Structure-Affinity Relationship Study of Azo Dyes for Cellulose Fibers by Multiple Linear Regression and Artificial Neural Network, Chemometrics and Intelligent Laboratory Systems, 134: 1-9 (2014).
[17] Holland J.H., “Adaptation in Neural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”, University of Michigan Press, (1975).
[18] کیا، م، الگوریتم ژنتیک در MAT LAB، انتشارات دانشگاهی کیان، ص، 129 (1394).
[19] McCall J., Genetic Algorithms for Modelling and Optimization, Computational and Applied Mathematics, 184: 205-222 (2005).
[20] Li L., Ustin L., Riano D., Retrieval of Fresh Leaf Fuel Moisture Content Using Genetic Algorithm Partial Least Squares (GA-PLS) Modeling, IEEE Geoscience and Remote Sensing Letters, 4: 216-220 (2007).