Development of a soft sensor for a batch distillation column using support vector regression techniques

Chemical Engineering Research and Design

Volumn 85, Issue 2 A, 2007, Pages 283-287

  Jain, P ^a   Rahman, Imran ^b   Kulkarni, B D ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^b NATIONAL CHEMICAL LABORATORY   (India)

Author keywords

Batch distillation; Composition estimation; Soft sensor; Suport vector regression

Indexed keywords

COMPUTER SIMULATION; MATHEMATICAL MODELS; POLYNOMIALS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; TEMPERATURE MEASUREMENT;

BATCH DISTILLATION; COMPOSITION ESTIMATION; SOFT SENSOR; SUPPORT VECTOR REGRESSION;

DISTILLATION COLUMNS;

COMPUTER SIMULATION; DISTILLATION COLUMNS; MATHEMATICAL MODELS; POLYNOMIALS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; TEMPERATURE MEASUREMENT;

EID: 33947266512     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1205/cherd05026     Document Type: Article

References (17)

1. Barolo, M. and Berto, F., 1998, Composition control in batch distillation: Binary and multicomponent mixtures, Ind Eng Chem Res, 37: 4689-4698.
2. Barolo, M., Pistillo, A. and Trotta, A., 2000, Issues in the development of a composition estimator for a middle-vessel batch column, in Camacho, E.F., Baslanez, L. and de la Puente, J.A. (eds), Advanced Control of Chemical Processes 2000-IFAC ADCHEM 2000, 923-928 (Elsevier, Oxford, UK).
3. Cherkassky, V. and Ma, Y., 2004, Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, 17(1): 113-126.
4. Edgar, T.F., 1996, Control of unconventional processes, J Proc Cont, 6: 99-110.
5. Gunn, S., 1997, Support Vector Machine for Classification and Regression, ISIS Technical Report, Dept. of Electronics and Computer Science, University of Southampton, UK.
6. Han, M. and Park, S., 2001, Profile position control of batch distillation based on a nonlinear wave model, Ind Eng Chem Res, 40: 4111-4120.
7. Jimenez, L., Basualdo, M.S., Gomez, J.C., Toselli, L. and Rosa, M., 2002, Nonlinear dynamic modeling of multicomponent batch distillation: A case study, Braz J Chem Eng, 19(3): 307-317.
8. Luyben, W.L., 1991, Process Modelling, Simulation and Control for Chemical Engineers, 2nd edition (McGraw Hill, New York, USA).
9. Mujtaba, I.M. and Macchietto, S., 1996, Simultaneous optimization of design and operation of multicomponent batch distillation column - single and multiple separation duties, J Proc Cont, 6(1): 27-36.
10. Oisiovici, R.M. and Cruz, S.L., 2000, State estimation of batch distillation columns using an extended Kalman filter, Chem Eng Sci, 55: 4667-4680.
11. Osiovici, R.M. and Cruz, S.L., 2001, Inferential control of high-purity multicomponent batch distillation columns using an extended Kalman filter, Ind Eng Chem Res, 40: 2628-2639.
12. Quintero-Marmol, E., Luyben, W.L. and Georgakis, C., 1991, Application of an extended Luenberger observer to the control of multicomponent batch distillation, Ind Eng Chem Res, 30: 1870-1880.
13. Quintero-Marmol, E. and Luyben, W.L., 1992, Inferential model based control of multicomponent batch distillation, Chem Eng Sci, 47: 887-898.
14. Thissen, U., Brakel, R., Weijer, A.P., Melssen and Buydens, L.M.C., 2003, Using support vector machines for time series prediction, Chemometrics and Intelligent Laboratory Systems, 69(1-2): 35-49.
15. Vapnik, V., 1998, Statistical Learning Theory (Springer, New York, USA).
16. Venkateswarlu, C. and Avantika, S., 2001, Optimal state estimation of multicomponent batch distillation, Chem Eng Sci, 56(20): 5771-5786.
17. Zamprogna, E., Barlo, M. and Seborg, D.E., 2004, Development of a soft sensor for a batch distillation column using linear and nonlinear PLS regression techniques, Control Engineering Practice, 12: 917-929.