Identification of semi-parametric hybrid process models

Computers and Chemical Engineering

Volumn 35, Issue 1, 2011, Pages 63-70

  Yang, Aidong ^a,b   Martin, Elaine ^a   Morris, Julian ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

Hybrid modelling; Model identification; Semi parametric models

Indexed keywords

BLACK BOXES; BLACK-BOX COMPONENTS; BLACK-BOX MODEL; CHEMICAL KINETICS; DATA-DRIVEN; HYBRID MODEL; HYBRID MODELLING; HYBRID PROCESS; MODEL IDENTIFICATION; PREDICTIVE PERFORMANCE; PROCESS VARIABLES; REGULARIZATION METHODS; SECOND LEVEL; SEMI-PARAMETRIC MODELS; SEMIPARAMETRIC; TOLUENE NITRATION PROCESS; TRAINING DATA; TWO-LEVEL APPROACH;

MODELS; PARAMETER ESTIMATION; POTENTIAL FLOW; TOLUENE;

MATHEMATICAL MODELS;

EID: 78649487934     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2010.05.002     Document Type: Article

References (32)

1. Bardow A., Marquardt W. Incremental and simultaneous identification of reaction kinetics: Methods and comparison. Chemical Engineering Science 2004, 59:2673-2684.
2. Craven P., Wahba G. Smoothing noisy data with spline functions. Numerische Mathematik 1979, 31:377-403.
3. Duarte B., Saraivay P.M., Pantelides C.C. Combined mechanistic and empirical modelling. International Journal of Chemical Reactor Engineering 2004, 2. Article A3.
4. Eubank R.L. Spline smoothing and non-parametric regression 1988, Marcel Dekker, Inc., New York.
5. Haerdle W. Applied Nonparametric Regression 1990, Cambridge University Press, Cambridge.
6. Haerdle W., Mueller M., Sperlich S., Werwatz A. Non-parametric and semiparametric models 2004, Springer, Berlin.
7. Hastie T.J., Tibshirani R.J. Generalized additive models 1990, Chapman and Hall, London.
8. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Computation 1992, 4:1-58.
9. Gong J., Wahba G., Johnson D.R., Tribbia J. Adaptive tuning of numerical weather prediction models: Simultaneous estimation of weighting, smoothing and mechanistic parameters. Monthly Weather Review 1998, 126:210.
10. Green P.J., Silverman B.W. Non-parametric regression and generalized linear models: A roughness penalty approach 1994, Chapman and Hall, London.
11. Kahrs O., Marquardt W. Incremental identification of hybrid process models. Computers & Chemical Engineering 2008, 32:694-705.
12. Lima P.V, Saraiva P.M. A semi-mechanistic model building framework based on selective and localized model extensions. Computers & Chemical Engineering 2007, 31:361-373. GEPSI-PSE Group.
13. Montague G., Morris J. Neural network contributions in biotechnology. Trends in Biotechnology 1994, 12:312-324.
14. Oliveira R. Combining first principles modelling and artificial neural networks: A general framework. Computers & Chemical Engineering 2004, 28:755-766.
15. Price, K. & Storn, R. (1996). MATLAB code of the differential evolution algorithm. On-line available at Accessed 05.12.05. http://www.icsi.berkeley.edu/~storn/code.html.
16. Process Systems Enterprise gPROMS advanced user guide 2004, Process Systems Enterprise Ltd., 23.02.2004.
17. Psichogios D.D., Ungar L.H. A hybrid neural network-First principles approach to process modelling. AIChE Journal 1992, 38(10):1499-1511.
18. Reinsch C. Smoothing by spline functions. Numerische Mathematik 1967, 10:177-183.
19. Roths T., Marth M., Weese J., Honerkamp J. A generalized regularization method for nonlinear ill-posed problems enhanced for nonlinear regularization terms. Computer Physics Communications 2001, 139:279-296.
20. Ruppert D., Wand M.P., Carroll R.J. Semiparametric regression 2003, Cambridge University Press, Cambridge.
21. Rusinowski H., Stanek W. Hybrid model of steam boiler. Energy 2010, 35(2):1107-1113.
22. Saraceno, A., Curcio, S., Calabro, V., & Iorio, G. (in press). A hybrid neural approach to model batch fermentation of 'ricotta cheese whey' to ethanol. Computers & Chemical Engineering.
23. Teixeira A.P., Carinhas N., Dias J.M.L., Cruz P., Alves P.M., Carrondo M.J.T., et al. Hybrid semi-parametric mathematical systems: Bridging the gap between systems biology. Journal of Biotechnology 2007, 132:418-425.
24. Thompson M.L., Kramer M.A. Modelling chemical processes using prior knowledge and neural networks. AIChE Journal 1994, 40(8):1328-1340.
25. Tian Y., Zhang J., Morris J. Modelling and optimal control of a batch polymerization reactor using a hybrid stacked recurrent neural network model. Industrial & Engineering Chemistry Research 2001, 40:4525-4535.
26. Vapnik V. The nature of statistical learning theory 1999, Springer-Verlag, New York.
27. Wan X., Pekny J.F., Reklaitis G.V. Simulation-based optimization with surrogate models-Application to supply chain management. Computers & Chemical Engineering 2005, 29:1317-1328.
28. Wang X., Chen J., Liu C., Pan F. Hybrid modeling of penicillin fermentation process based on least square support vector machine. Chemical Engineering Research and Design 2010, 88(4):415-420.
29. Weese J. A regularization method for nonlinear ill-posed problems. Computer Physics Communications 1993, 77:429-440.
30. Yan W., Shao H., Wang X. Soft sensing modeling based on support vector machine and Bayesian model selection. Computers & Chemical Engineering 2004, 28:1489-1498.
31. Yeow Y.L., Wickramasinghe S.R., Hanb B., Leong Y.-K. A new method of processing the time-concentration data of reaction kinetics. Chemical Engineering Science 2003, 58:3601-3610.
32. Zaldivar J.M., Alós M., Molga E., Hernández H., Westertep K.R. Aromatic nitration by mixed acid: Slow liquid-liquid reaction regime. Chemical Engineering and Processing 1995, 34:543-559.