Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system

Fuel

Volumn 97, Issue , 2012, Pages 710-717

  Alves, Julio Cesar L ^a   Henriques, Claudete B ^b   Poppi, Ronei J ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b PETROBRAS   (Brazil)

Author keywords

Diesel; In line blending; Near infrared spectroscopy; Support vector regression

Indexed keywords

BASELINE CORRECTION; BEST MODEL; DIESEL; DIESEL OIL; DIESEL QUALITY; FLASH POINTS; IN-LINE; OPTIMIZERS; PARAMETRIC OPTIMIZATION; QUALITY PARAMETERS; RBF KERNELS; REFERENCE METHOD; REGRESSION MODEL; REGRESSION PROBLEM; SUPPORT VECTOR REGRESSION (SVR);

BLENDING; C (PROGRAMMING LANGUAGE); DIESEL FUELS; ELECTRIC NETWORK PARAMETERS; NEAR INFRARED SPECTROSCOPY;

REGRESSION ANALYSIS;

EID: 84861183082     PISSN: 00162361     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fuel.2012.03.016     Document Type: Article

References (32)

1. M. Chèbre, Y. Creff, and N. Petit Feedback control and optimization for the production of commercial fuels by blending J Process Control 20 2010 441
2. A. Singh, J.F. Forbes, P.J. Vermeer, and S.S. Woo Model-based real-time optimization of automotive gasoline blending operations J Process Control 10 2000 43
3. J. Workman Jr., K.E. Creasy, S. Doherty, L. Bond, M. Koch, A. Ullman, and D.J. Veltkamp Process analytical chemisty Anal Chem 73 2001 2705
4. F.G. Shinskey Feedback controllers for the process industries 1994 McGraw-Hill New York
5. R.M. Balabin, and R.Z. Safieva Near-infrared (NIR) spectroscopy for biodiesel analysis: fractional composition, iodine value and cold filter plugging point from one vibrational spectrum Energy Fuels 25 2011 2373
6. R.M. Balabin, and S.V. Smirnov Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data Anal Chim Acta 692 2011 63
7. K. Brudzewski, A. Kesik, K. Kolodziejczyk, U. Zborowska, and J. Ulaczyk Gasoline quality prediction using gas chromatography and FTIR spectroscopy: an artificial intelligence approach Fuel 85 2006 553
8. R.M. Balabin, and E.I. Lomakina Support vector machine regression (SVR/LS-SVM) - an alternative to neural networks (ANNs) for analytical chemistry? comparison of nonlinear methods on near infrared (NIR) spectroscopy data Analyst 136 2011 1703
9. R.M. Balabin, and R.Z. Safieva Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data Anal Chim Acta 689 2011 190
10. R.M. Balabin, R.Z. Safieva, and E.I. Lomakina Near-infrared (NIR) spectroscopy for motor oil classification: from discriminant analysis to support vector machines Microchem J 98 2011 121
11. F. Si, C.E. Romero, Z. Yao, E. Schuster, Z. Xu, R.L. Morey, and B.N. Liebowitz Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms Fuel 88 2009 806
12. R.M. Balabin, E.I. Lomakina, and R.Z. Safieva Neural network (ANN) approach to biodiesel analysis: analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy Fuel 90 2011 2007
13. R.M. Balabin, R.Z. Safieva, and E.I. Lomakina Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques Anal Chim Acta 671 2010 27
14. R.M. Balabin, and R.Z. Safieva Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data Fuel 87 2008 2745
15. R.M. Balabin, and R.Z. Safieva Gasoline classification by source and type based on near infrared (NIR) spectroscopy data Fuel 87 2008 1096
16. R.M. Balabin, R.Z. Safieva, and E.I. Lomakina Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction Chemom Intell Lab Syst 88 2007 183
17. E. Bertran, M. Blanco, S. Maspoch, M.C. Ortiz, M.S. Sanchez, and L.A. Sarabia Handling intrinsic non-linearity in near-infrared reflectance spectroscopy Chemom Intell Lab Syst 49 1999 215
18. P. Geladi, and B.R. Kowalski Partial least squares regression: a tutorial Anal Chim Acta 185 1986 1
19. H. Li, Y. Liang, and Q. Xu Support vector machines and its application in chemistry Chemometr Intell Lab Syst 95 2009 188
20. U. Thissen, M. Pepers, B. Ustun, W.J. Melssen, and L.M.C. Buydens Comparing support vector machines to PLS for spectral regression applications Chemom Intel Lab Syst 73 2004 169
21. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines and other kernel-based learning methods 2000 Cambridge University Press Cambridge
22. V. Vapnik Statistical learning theory 1998 John Wiley & Sons New York
23. B. Ustun, W.J. Melssen, M. Oudenhuijzen, and L.M.C. Buydens Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization Anal Chim Acta 544 2005 292
24. A.J. Smola, and B. Scholkopf A tutorial on support vector regression Stat Comput 14 2004 199
25. A. Chalimourda, B. Scholkopf, and A.J. Smola Experimentally optimal ν in support vector regression for different noise models and parameter settings Neural Netw 17 2004 127
26. J.H. Holland Adaptation in natural and artificial systems 1975 University of Michigan Press
27. D.E. Goldberg Genetic algorithms in search, optimization and machine learning 1989 Addison-Wesley Longman Publishing Co., Inc. Boston
28. R. Wehrens, and L.M.C. Buydens Evolutionary optimisation: a tutorial Trends Anal Chem 17 1998 193
29. Chang CC, Lin CJ. LIBSVM: a library for support vector machines; 2001. Software < http://www.csie.ntu.edu.tw/~cjlin/libsvm >.
30. B.M. Wise, N.B. Gallagher, R. Bro, J.M. Shaver, W. Windig, and R.S. Koch PLS toolbox version 4.0 for use with Matlab 2006 Eigenvector research Inc. Wenatchee
31. M.B. Romia, and M.A. Bernardez Multivariate calibration for quantitative analysis, in: Infrared spectroscopy for food quality analysis and control 2009 Elsevier Inc.
32. J. Workman Jr., and L. Weyer Practical guide to interpretive near infrared spectroscopy 2008 CRC Press Boca Raton