Autoregressive modeling of near-IR spectra and MLR to predict RON values of gasolines

Fuel

Volumn 89, Issue 1, 2010, Pages 158-161

  Kardamakis, Andreas A ^a   Pasadakis, Nikos ^a  

^a TECHNICAL UNIVERSITY OF CRETE   (Greece)

Author keywords

Gasoline; Infrared spectroscopy; Linear Predictive Coding; RON

Indexed keywords

ACCURACY PREDICTION; AUTOREGRESSIVE MODELING; CALIBRATION METHOD; DATA SETS; ESTIMATION TECHNIQUES; GASOLINE FRACTIONS; GASOLINE SAMPLES; INFRARED SPECTRUM; INPUT VARIABLES; LINEAR PREDICTIVE CODING; MULTIPLE LINEAR REGRESSIONS; NEAR-IR; PRE-TREATMENT; PREDICTION ACCURACY; PREDICTOR VARIABLES; REGRESSION MODEL; RESEARCH OCTANE NUMBER; RON; ROOT MEAN SQUARED ERRORS; SPECTRAL FEATURE; SPECTRAL INFORMATION;

GASOLINE; MATHEMATICAL MODELS; REGRESSION ANALYSIS; SPECTROSCOPIC ANALYSIS;

INFRARED SPECTROSCOPY;

EID: 70349470954     PISSN: 00162361     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fuel.2009.08.029     Document Type: Article

References (22)

1. Workman Jr. J. A brief review of near infrared in petroleum product analysis. J Near Infrared Spectrosc 4 (1996) 69-74
2. Kelly J.J., Barlow C.H., Jinguji T.M., and Callis J.B. Prediction of gasoline octane numbers from near-infrared spectral features in the range, 660-1215 nm. Anal Chem 61 (1989) 313-320
3. Rashid H.A., Dekran S.B., Fakhri N.A., Aziz H.J., and Hamoudi N.A. Determination of several physical properties of light petroleum products using IR. Fuel Sci Technol Int 7 3 (1989) 237-250
4. Parisi A.F., Nogueiras L., and Prieto H. On line determination of fuel quality parameters using near-infrared spectrometry with fibre optics and multivariate calibration. Anal Chim Acta 238 (1990) 95-100
5. Asker N., and Kokot S. The application of NIR spectroscopy for the prediction of properties of Australian refined reformate. Appl Spectrosc 45 7 (1991) 1153-1157
6. Crawford N.R., Hellmuth W.W., Marcellus D.H., and Chou K.J. Predicting research octane number from near-infrared absorbance data with neural networks. Process Control Qual 4 (1992) 13-20
7. Andrade J.M., Muniategui S., Lopez P., and Prada D. Costs, laboratory safety, productivity and faster research octane number and motor octane number determinations in industrial chemical laboratories. Analyst 120 (1995) 249-253
8. Asfaha I., Ali M.A., Tawabini B.S., Anabtawi J.A., Ali S.A., and Al-Farayedhi A. Prediction of reformate research octane number by FT-IR spectroscopy. Fuel 74 2 (1995) 227-231
9. Cooper J.B., Wise K.L., Groves J., and Welch W.T. Determination of octane numbers and Reid vapor pressure of commercial petroleum fuels using FT-Raman spectroscopy and partial least-squares regression analysis. Anal Chem 67 (1995) 4096-4100
10. Fodor G.E., Kohl K.B., and Mason R.L. Analysis of gasolines by FT-IR spectroscopy. Anal Chem 68 (1996) 23-30
11. Litani-Barzilai I., Sela I., Bulatov V., Zilberman I., and Schechter I. On-line remote prediction of gasoline properties by combined optical methods. Anal Chim Acta 339 (1997) 193-199
12. Bohács G., Ovádi Z., and Salgó A. Prediction of gasoline properties with near infrared spectroscopy. J Near Infrared Spectrosc 6 (1998) 341-348
13. Pavoni B., Rado N., Piazza R., and Frignani S. FT-IR spectroscopy and chemometrics as a useful approach for determining chemical-physical properties of gasoline, by minimizing analytical times and sample handling. Annal Chim 94 (2004) 521-532
14. Chung H., Lee H., and Jun C.-H. Determination of research octane number using NIR spectral data and ridge regression. Bull Korean Chem Soc 22 1 (2001) 37-42
15. Felício C.C., Brás L.P., Lopes J.A., Cabrita L., and Menezes J.C. Comparison of PLS algorithms in gasoline and gas oil parameter monitoring with MIR and NIR. Chemom Intell Lab Syst 78 (2005) 74-80
16. Brudzewski K., Kesik A., Kolodziejczyk K., Zborowska U., and Ulaczyk J. Gasoline quality prediction using gas chromatography and FTIR spectroscopy. An artificial intelligence approach. Fuel 85 (2006) 553-558
17. Lee Y., Chung H., and Kim N. Spectral range optimization for the near-infrared quantitative analysis of petrochemical and petroleum products: naptha and gasoline. Appl Spectrosc 60 8 (2006) 892-897
18. Chung H., Cho S., Toyoda Y., Nakano K., and Maeda M. Moment combined partial least squares (MC-PLS) as an improved quantitative calibration method: application to the analyses of petroleum and petrochemical products. Analyst 131 (2006) 684-691
19. Di Foggio R. Examination of some misconceptions about near-infrared analysis. Appl Spectrosc 49 1 (1995) 67-75
20. Kardamakis A.A., Mouchtaris A., and Pasadakis N. Linear predictive spectral coding and independent component analysis in identifying gasoline constituents using infrared spectroscopy. Chemom Intell Lab Syst 89 1 (2007) 51-58
21. Haykin S. Adaptive filter theory (1996), Prentice Hall, New Jersey
22. Oppenheim A.V., and Shafer R.W. Discrete time signal processing (1989), Prentice Hall, New Jersey