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Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

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Abstract

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization (HDS) process was proposed. Therefore, an integrated approach using support vector regression (SVR) based on wavelet transform (WT) and principal component analysis (PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance (E AARE=0.058 and R 2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression (MLR), SVR and PCA-SVR.

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References

  1. BOLF N, GALINEC G, BAKSA G T. Development of soft sensor for diesel fuel quality estimation [J]. Chemical Engineering Technology, 2010, 33: 405–413.

    Article  Google Scholar 

  2. VAPNIK V N. The nature of statistical learning theory [M]. New York: Springer-Verlag, 1995: 93–110.

    Book  Google Scholar 

  3. BASAK D, PAL S, PATRANABIS D C. Support vector regression [J]. Neural Information Processing, 2007, 11: 203–225.

    Google Scholar 

  4. NIU P, ZHANG W. Model of turbine optimal initial pressure under off-design operation based on SVR and GA [J]. Neurocomputing, 2012, 78: 64–71.

    Article  Google Scholar 

  5. AMINGHAFARI M, CHEZE N, POGGI J. Multivariate denoising using wavelets and principal component analysis [J]. Journal of Computational Statistics and Data Analysis, 2006, 50: 2381–2398.

    Article  MATH  MathSciNet  Google Scholar 

  6. DIXON S J, BRERETON R G. Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure [J]. Chemometrics and Intelligent Laboratory Systems, 2009, 95: 1–17.

    Article  Google Scholar 

  7. SON H, KIM C, KIM C. Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables [J]. Automation in Construction, 2012, 27: 60–66.

    Article  Google Scholar 

  8. BABAOGLU I, FINDIK O, BAYRAK M. Effects of principle component analysis on assessment of coronary artery diseases using support vector machine [J]. Expert Systems with Applications, 2010, 37: 2182–2185.

    Article  Google Scholar 

  9. ANTANASIJEVIC D Z, RISTIC M D, GRUJIC A A P, POCAJT V V. Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis [J]. International Journal of Greenhouse Gas Control, 2014, 20: 244–253.

    Article  Google Scholar 

  10. JUHOS I, MAKRA L, TOTH B. Forecasting of traffic origin NO and NO2 concentrations by support vector machines and neural networks using principal component analysis [J]. Simulation Modelling Practice and Theory, 2008, 16: 1488–1502.

    Article  Google Scholar 

  11. JOLLIFFE I T. Principal component analysis [M]. 2nd ed. Berlin: Springer, 2002.

    Google Scholar 

  12. SARBU C, POP H F. Principal component analysis versus fuzzy principal component analysis. A case study: The quality of danube water (1985–1996) [J]. Talanta, 2005, 65: 1215–1220.

    Article  Google Scholar 

  13. WU J D, LIU C T. Finger-vein pattern identification using SVM and neural network technique [J]. Expert Systems with Applications, 2011, 38: 14284–14289.

    Google Scholar 

  14. LINDSAY I, SMITH A. A tutorial on principal components analysis [EB/OL]. [2014-04-22] http://kybele.psych.cornell.edu/~edelman/Psych-465-Spring-2003/PCA-tutorial 2002.

    Google Scholar 

  15. NARASIMHAN S, SHAH S L. Model identification and error covariance matrix estimation from noisy data using PCA [J]. Control Engineering Practice, 2008, 16: 146–155.

    Article  Google Scholar 

  16. SHRESTHA S, KAZAMA F. Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan [J]. Environmental Modelling and Software, 2007, 22: 464–475.

    Article  Google Scholar 

  17. DAUBECHIES I. Ten lectures on wavelets [M]. Philadelphia, Pennsylvania: SLAM, 1992: 17–53.

    Book  Google Scholar 

  18. HUANG H P, LUO KY. On-line wavelets filtering with application to linear dynamic data reconciliation [J]. Industrial & Engineering Chemistry Research, 2007, 46: 8746–8755.

    Article  Google Scholar 

  19. BALAFAR M A. Review of noise reducing algorithms for brain MRI images [J]. International Journal on Technical and Physical Problems of Engineering, 2012, 4: 54–59.

    Google Scholar 

  20. JIANG T, CHEN B, HE X. Industrial application of wavelet transform to the on-line prediction of side draws qualities of crude unit [J]. Computers & Chemicals Engineering, 2000, 24: 507–512.

    Article  Google Scholar 

  21. FLEMING F, WATZDORF R V, MARQUARDT W. Identification of trends in process measurements using the wavelet transforms [J]. Computers & Chemicals Engineering, 1998, 22: 491–496.

    Article  Google Scholar 

  22. NOUNOU M, BAKSHI B R. On-line multiscale filtering of random and gross errors without process models [J]. AIChE Journal, 1999, 45(5): 1041–1058.

    Article  Google Scholar 

  23. BENQLILOU C. Data reconciliation as a framework for chemical processes optimization and control [D]. Barcelona, Spain: Universitat Politecnica de Catalunya, 2004.

    Google Scholar 

  24. UNSER M, BLU T. Wavelet theory demystified, IEEE transaction on signal processing [J]. IEEE Transcations on Signal Processing 2003, 51(2), 470–483.

    Article  MathSciNet  Google Scholar 

  25. SI F, ROMERO C E, YA Z, XU Z, MOREY R L, LIEBOWITZ B N. Inferential sensor for on-line monitoring of ammonium bisulfate formation temperature in coal-fired power plants [J]. Fuel Processing Technology, 2009, 90: 56–66.

    Article  Google Scholar 

  26. CRISTIANINI N, SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learning methods [R]. Cambridge, UK: Cambridge University Press, 2000.

    Book  Google Scholar 

  27. TAO X, ZHOU Y, WEI Q, YU G, CUI Q, LIU J, LIU T. Effect of morphology properties of NiW catalysts on hydrodesulfurization for individual sulfur compounds in fluid catalytic cracking diesel [J]. Fuel Processing Technology, 2014, 118: 200–207.

    Article  Google Scholar 

  28. MEDINA E A, PAREDES J I P. Artificial neural network modeling techniques applied to the hydrodesulfurization process [J]. Mathematical and Computer Modelling, 2009, 49: 207–214.

    Article  MATH  MathSciNet  Google Scholar 

  29. WANG J, WU X, ZHANG C. Support vector machines based on K-means clustering for real-time business intelligence systems [J]. International Journal of Business Intelligence and Data Mining, 2005, 1(1): 54–64.

    Article  MathSciNet  Google Scholar 

  30. MOMMA M, BENNETT K P. A pattern search method for model selection of support vector regression [C]// Second SIAM International Conference on Data Mining, Arlington, USA: SIAM. 2002: 261–274.

    Google Scholar 

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Authors and Affiliations

  1. Department of Chemical Engineering, Iran University of Science and Technology (IUST), Tehran, 16765-163, Iran

    Saeid Shokri & Mohammad Taghi Sadeghi

  2. Process & Equipment Technology Development Division, Research Institute of Petroleum Industry (RIPI), Tehran, 14665-137, Iran

    Mahdi Ahmadi Marvast

  3. Department of Chemical Engineering, IIT Madras, Chennai, 600036, India

    Shankar Narasimhan

Authors
  1. Saeid Shokri
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  2. Mohammad Taghi Sadeghi
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  3. Mahdi Ahmadi Marvast
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  4. Shankar Narasimhan
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Correspondence to Mohammad Taghi Sadeghi.

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Shokri, S., Sadeghi, M.T., Marvast, M.A. et al. Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA. J. Cent. South Univ. 22, 511–521 (2015). https://doi.org/10.1007/s11771-015-2550-6

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  • DOI: https://doi.org/10.1007/s11771-015-2550-6

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