Experimental design for the identification of hybrid reaction models from transient data

Chemical Engineering Journal

Volumn 141, Issue 1-3, 2008, Pages 264-277

  Brendel, Marc ^a   Marquardt, Wolfgang ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Chemical reactor; Data driven kinetics; Dynamic model; Experimental design; Hybrid modeling; Incremental identification; Reaction network

Indexed keywords

CHEMICAL REACTORS; DESIGN OF EXPERIMENTS; DYNAMIC MODELS; REACTION KINETICS;

DATA-DRIVEN KINETICS; HYBRID MODELING; INCREMENTAL IDENTIFICATION; REACTION NETWORK;

IDENTIFICATION (CONTROL SYSTEMS);

CHEMICAL REACTORS; DESIGN OF EXPERIMENTS; DYNAMIC MODELS; IDENTIFICATION (CONTROL SYSTEMS); REACTION KINETICS;

EID: 44749084304     PISSN: 13858947     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cej.2007.12.027     Document Type: Article

References (39)

1. Berger R.J., Stitt E.H., Marin G.B., Kapteijn F., and Moulijn J.A. EUROKIN. Chemical reaction kinetics in practice. CATTECH 5 1 (2001) 30-60
2. Helbig A., Abel O., and Marquardt W. Model predictive control for on-line optimization of semi-batch reactors. Proceedings of the 1998 American Control Conference, vol. 1. Philadelphia (1998) 1695-1699
3. Psichogios D.C., and Ungar L.H. A hybrid neural network - first principles approach to process modeling. AIChE J. 38 10 (1992) 1499-1511
4. Tholudur A., and Ramirez W.F. Neural-network modeling and optimization of induced foreign protein production. AIChE J. 45 8 (1999) 1660-1670
5. Van Lith P.F., Betlem B.H.L., and Roffel B. A structured modeling approach for dynamic hybrid fuzzy first-principles models. J. Process Control 12 (2002) 605-615
6. Brendel M., Mhamdi A., Bonvin D., and Marquardt W. An incremental approach for the identification of reaction kinetics. Proceedings of the 7th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2003. HongKong (2003) 177-182
7. O. Kahrs, W. Marquardt, The validity domain of hybrid models and its application in process optimization, Chem. Eng. Process. 46 (11) (2007) 1054-1066.
8. Walter E., and Pronzato L. Qualitative and quantitative experiment design for phenomenological models-a survey. Automatica 26 2 (1990) 195-213
9. Emery A.F., and Nenarokomov A.V. Optimal experiment design. Meas. Sci. Technol. 9 (1998) 864-876
10. Asprey S.P., and Macchietto S. Statistical tools for optimal dynamic model building. Comput. Chem. Eng. 24 (2000) 1261-1267
11. Marquardt W. Model-based experimental analysis of kinetic phenomena in multi-phase reactive systems. Trans. IChemE, Part A: Chem. Eng. Res. Des. 83 A6 (2005) 561-573
12. Brendel M. Incremental Identification of Complex Reaction Systems (2006), VDI-Verlag, Düsseldorf
13. Atkinson A.C., and Donev A.N. Optimum Experimental Designs (1996), Oxford Statistical Science Series, Oxford
14. McKay M.D., Conover W.J., and Beckman R.J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 (1979) 239-245
15. J.P.C. Kleijnen, S.M. Sanchez, T.W. Lucas, T.M. Cioppa, A user's guide to the brave new world of designing simulation experiments, Tech. rep., Tilburg University, Center for Economic Research, 2003
16. Kinzel W., and Ruján P. Improving a network generalization ability by selecting examples. Europhys. Lett. 13 (1990) 473-477
17. Seung H.S., Opper M., and Sompolinsky H. Query by committee. Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (1992), ACM Press, New York 287-294
18. Cohn D. Neural network exploration using optimal experiment design. In: Cowan J.D., Tesauro G., and Alspector J. (Eds). Advances in Neural Information Processing Systems, vol. 6 (1994), Morgan Kaufmann, San Francisco 679-686
19. Cohn D., Atlas L., and Ladner R. Improving generalization with active learning. Mach. Learn. 15 2 (1994) 201-221
20. Zhang B.T. Accelerated learning by active example selection. Int. J. Neural Syst. 5 1 (1994) 67-75
21. Hasenjäger M., and Ritter H. Active learning in neural networks. In: Jain L.C., and Kacprzyk J. (Eds). New Learning Paradigms in Soft Computing, vol. 84 of Studies in Fuzziness and Soft Computing (2002), Physica-Verlag GmbH, Heidelberg, Germany 137-169
22. Brendel M., Bonvin D., and Marquardt W. Incremental identification of kinetic models for homogeneous reaction systems. Chem. Eng. Sci. 61 (2006) 5404-5420
23. MacKay D.J.C. A practical Bayesian framework for backpropagation networks. Neural Comput. 4 3 (1992) 448-472
24. Agarwal M. Combining neural and conventional paradigms for modelling, prediction and control. Int. J. Syst. Sci. 28 1 (1997) 65-81
25. Oliveira R. Combining first principles modelling and artificial neural networks: a general framework. Comput. Chem. Eng. 28 (2004) 755-766
26. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer, Heidelberg
27. Marquardt W. Towards a process modeling methodology. In: Berber R. (Ed). Methods of Model-based Control, vol. 293 of NATO-ASI Ser. E, Applied Sciences (1995), Kluwer, Dordrecht 3-41
28. Bonvin D., and Rippin D.W.T. Target factor analysis for the identification of stoichiometric models. Chem. Eng. Sci. 45 12 (1990) 3417-3426
29. Bard Y. Nonlinear Parameter Estimation (1974), Academic Press, New York
30. Bardow A., and Marquardt W. Incremental and simultaneous identification of reaction kinetics: methods and comparison. Chem. Eng. Sci. 59 13 (2004) 2673-2684
31. Okabe A., Boots B., Sugihara K., and Chiu S.N. Spatial Tessellations-Concepts and Applications of Voronoi Diagrams (2000), John Wiley & Sons, New York
32. Clark P.A., and Westerberg A.W. Optimization for design problems having more than one objective. Comput. Chem. Eng. 7 (1983) 259-278
33. Bronshtein I.N., and Semendyayev K.A. Handbook of Mathematics. 3rd ed. (1985), Springer, London
34. D. Ruppen, A Contribution to the Implementation of Adaptive Optimal Operation for Discontinuous Chemical Reactors, Ph.D. Thesis, ETH Zürich, 1994.
35. Ruppen D., Bonvin D., and Rippin D.W.T. Implementation of adaptive optimal operation for a semi-batch reaction system. Comput. Chem. Eng. 22 1-2 (1998) 185-199
36. MacKay D.J.C. Bayesian interpolation. Neural Comput. 4 3 (1992) 415-447
37. Kramer M.A. Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37 2 (1991) 233-243
38. Jade A.M., Srikanth B., Jayaraman V.K., Kulkarni B.D., Jog J.P., and Priya L. Feature extraction and denoising using kernel PCA. Chem. Eng. Sci. 58 (2003) 4441-4448
39. Kahrs O., Brendel M., and Marquardt W. Incremental identification of NARX models by sparse grid approximation. Proceedings of the 16th IFAC World Congress. July 3-8, 2005, Prague, Czech Republic (2005)