Model-based experimental analysis of kinetic phenomena in multi-phase reactive systems

Chemical Engineering Research and Design

Volumn 83, Issue 6 A, 2005, Pages 561-573

  Marquardt, W ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Differential algebraic and partial differential equations; Diffusion; Falling film heat transfer; High resolution measurement techniques; Inverse problems; Model selection; Modelling; Parameter estimation; Reaction kinetics

Indexed keywords

DIFFUSION; HEAT TRANSFER; ITERATIVE METHODS; KINETIC ENERGY; LIQUIDS; MATHEMATICAL MODELS; MEASUREMENTS; THIN FILMS;

MECHANISTIC MODELING; MULTI-PHASE REACTION MODELING; RESOLUTION MEASUREMENTS;

CHEMICAL REACTIONS;

FALLING FILM; HEAT TRANSFER;

EID: 22744438969     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1205/cherd.05086     Document Type: Article

References (114)

1. Abou Khachfe, R. and Jarny, Y., 2001, Determination of heat sources and heat transfer coefficient for two-dimensional heat flow - numerical and experimental study, Int J Heat Mass Transfer, 44: 1309-1322.
2. Abramowich, F. and Silverman, B.W., 1998, Wavelet decomposition approaches to statistical inverse problems, Biometrika, 85: 115-129.
3. Agarwal, M., 1997, Combining neural and conventional paradigms for modelling, prediction and control, Int J Systems Science, 28: 65-81.
4. Alifanov, O., Artyukhin, E. and Rumyantsev, S., 1995, Extreme Methods for Solving III-Posed Inverse Problems with Applications to Inverse Heat Transfer Problems (Begell House, New York, USA).
5. Alsmeyer, F., Koß, H.-J. and Marquardt, W., 2004, Indirect spectral hard modeling for the analysis of reactive mixtures, Applied Spectroscopy, 58: 975-985.
6. Amrhein, M., Srinivasan, B., Bonvin, D. and Schumacher, M.M., 1999, Calibration of spectral reaction data, Chemometrics Intelligent Lab Systems, 46: 249-264.
7. Antonov, L. and Nedeltcheva, D., 2000, Resolution of overlapping UV-Vis absorption bands and quantitative analysis, Chem Soc Rev, 29: 217-227.
8. Ascher, U.M. and Haber, E., 2001, Grid refinement and scaling for distributed parameter estimation problems, Inverse Problems, 17:571-590.
9. Asprey, S.P., 2003, Statistical dynamic model building: applications of semi-infinite programming, in Asprey, S.P. and Macchietto, S. (eds). Dynamic Model Development, 85-104 (Elsevier, Amsterdam, The Netherlands).
10. Asprey, S.P. and Macchietto, S., 2000, Statistical tools in optimal model building, Comput Chem Engg, 24: 1261-1267.
11. Atkinson, A.C. and Donev, A.N., 1992, Optimum Experimental Designs (Oxford University Press, Oxford, UK).
12. Bärmann, F. and Biegler-König, F., 1992, On a class of efficient learning algorithms for neural networks, Neural Networks, 5: 139-144.
13. Bard, Y., 1974, Nonlinear Parameter Estimation (Academic Press, New York, USA).
14. Bardow, A., 2004, Model-based Experimental Analysis of Multicomponent Diffusion in Liquids (VDI-Fortschritt-Berichte, Reihe 3, Nr.821. VDI-Verlag, Düsseldorf, Germany).
15. Bardow, A. and Marquardt, W., 2003, On the optimal design of diffusion experiments, AIChE Annual Meeting, San Francisco, USA.
16. Bardow, A. and Marquardt, W., 2004a, Identification of diffusive transport by means of an incremental approach, Comput Chem Engg, 28: 585-595.
17. Bardow, A. and Marquardt, W., 2004b, Incremental and simultaneous identification of reaction kinetics: methods and comparison, Chem Eng Sci, 59: 2673-2684.
18. Bardow, A., Göke, V., Koß, H.J., Lucas, K. and Marquardt, W., 2005a, Concentration-dependent diffusion coefficients from a single experiment using model-based Raman spectroscopy, Fluid Phase Equilibria, 228-229: 357-366.
19. Bardow, A., Göke, V., Koß, H.J., Lucas, K. and Marquardt, W., 2005b, Ternary diffusion coefficients from model-based 1-D-Raman spectroscopy. World Congress of Chemical Engineering, Glasgow, July 2005.
20. Bardow, A., Marquardt, W., Göke, V., Koss, H.-J. and Lucas, K., 2003, Model-based measurement of diffusion using Raman spectroscopy, AIChE J, 49: 323-333.
21. Bates, D.M. and Watts, D.G., 1988, Nonlinear Regression and its Applications (Wiley, New York, USA).
22. Beck, J.V., Lackwell, B. and St. Clair, C.R., 1985, Inverse Heat Conduction: III-Posed Problems (Wiley, New York, USA).
23. Berger, R.J., Stitt, E.H., Marin, G.B., Kapteijn, F. and Moulijn, J.A., 2001, Eurokin - chemical reaction kinetics in practice, CATTECH, 5(1): 30-60.
24. Bird, R.B., 2004, Five decades of transport phenomena, AIChE J, 50: 273-287.
25. Binder, T., Blank, L., Dahmen, W. and Marquardt, W., 2002, On the regularization of dynamic data reconciliation problems, Journal of Process Control, 12: 557-567.
26. Boggs, P.T., Byrd, R.H., Rogers, J.E. and Schnabel, R.B., 1992, User's Reference Guide for ODRPACK Version 2.01. Software for Weighted Orthogonal Distances Regression. National Institute of Standards and Technology, NISTIR 92-4834.
27. Bonvin, D. and Rippin, D.W.T., 1990, Target factor analysis for the identifcation of stoichiometric models, Chem Eng Sci, 45: 3417-3426.
28. Brendel, M., 2005, Incremental identification of complex reaction systems, PhD thesis, RWTH Aachen, in preparation.
29. Brendel, M. and Marquardt, W., 2003, Stepwise refinement of sparse grids in data mining applications, Proc IFAC Symp System Identification, SYSID 2003, 1667-1672.
30. Brendel, M. and Marquradt, W., 2005, An algorithm for multivariate function estimation based on hierarchically refined spare grids, Computing and Visualization in Science, submitted.
31. Brendel M., Bonvin, D. and Marquardt, W., 2005a, Incremental identification of complex kinetics in homogeneous reaction systems, Chem Eng Sci, submitted.
32. Brendel, M., Bonvin, D. and Marquardt, W., 2005b, Experimental design for incremental identification of reaction models, in preparation.
33. Brendel, M., Mhamdi, A., Bonvin, D. and Marquardt, W., 2003, An incremental approach for the identification of reaction kinetics, Proc IFAC Symp Advanced Control of Chemical Processes, ADCHEM 2003, 177-182.
34. Britt, H.I. and Luecke, R.H., 1973, The estimation of parameters in nonlinear implicit models, Technometrics, 15: 233-247.
35. Chang, J.-S. and Hung, B.-C., 2002, Optimization of batch polymerization reactors using neural-network rate-function models, Ind Eng Chem Res, 11: 2716-2727.
36. Chen, C.-C. and Mathias, P.M., 2002, Applied thermodynamics for process modelling, AIChE J, 48: 194-200.
37. Chen, S., Cowan, C.F.N. and Grant, P.M., 1992, Orthogonal least squares algorithm for radial basis function networks, IEEE Trans Neural Networks, 2: 302-309.
38. Connors, K.A., 1990, Chemical Kinetics: The Study of Reaction Rates in Solution (VCH, New York, USA).
39. Daoutidis, P. and Kravaris, C., 1991, Inversion and zero dynamics in nonlinear multivariable control, AIChE J, 37: 527-538.
40. Engl, H.W., Hanke, M. and Neubauer, A., 1996, Regularization of Inverse Problems (Kluwer, Dordrecht, The Netherlands).
41. Fourestey, G. and Moubachir, M., 2005, Solving inverse problems involving the Navier-Stokes equations discretized by a Lagrange-Galerkin method, Computer Methods in Applied Mechanics and Engineering, 194: 877-906.
42. Froment, G.F. and Bischoff, K.B., 1990, Chemical Reactor Analysis and Design (John Wiley & Sons, New York, USA).
43. Fukushima, E., 1999, Nuclear magnetic resonance as a tool to study flow, Ann Rev Fluid Mechanics, 31: 95-123.
44. Garcke, J., Griebel, M. and Thess, M., 2001, Data mining with sparse grids, Computing, 67: 225-253.
45. Gladden, L.F., 2003a, Magnetic resonance: ongoing and future role in chemical engineering research, AIChE J, 49: 2.
46. Gladden, L.F., 2003b, Recent advances in MRI studies of chemical reactors: ultrafast imaging of multiphase flows, Topics in Catalysis, 24(October): 19-28.
47. Göke, V., 2005, Messung von Diffusionskoeffizienten mittels eindimensionaler Raman-Spektroskopie, Dissertation, RWTH Aachen.
48. Groß, S., Soemers, M., Mhamdi, A., Al Sibai, F., Marquardt, W., Reusken, A. and Renz, U., 2005, Optimization-based identification of boundary conditions in falling film experiments using high resolution temperature measurements, Int J Heat Mass Transfer, in press.
49. Groß-Hardt, E., Slusanschi, E., Bücker, H.M. and Bishof, C.H., 2004, Practical shape optimization of a levitation device for single droplets, AIChE J, submitted.
50. Haario, H. and Turunen, I., 2003, Methodological aspects in the modelling of novel unit operations, in Asprey, S.P. and Macchietto, S. (eds). Dynamic Model Development, 1-20 (Elsevier, Amsterdam, The Netherlands).
51. Hanke, M. and Scherzer, O., 2001, Inverse problems light: numerical differentiation, Amer Math Monthly, 108: 512-521.
52. Hansen, P.C., 1998, Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion. SIAM Monoqs on Mathematical Modeling and Computation, Society for Industrial and Applied Mathematics.
53. Hastie, T., Tibshirani, R. and Friedman, J., 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics. Corrected 3rd printing, 2003 (Springer, New York, USA).
54. Heine, C., Kupferschläger, K., Stapf, S. and Blümich, B., 2002, NMR velocimetry of falling liquid films, J Magn Reson, 154: 311-316.
55. Heinemann, M., 2003, Experimental Analysis, Modeling and Dynamic Simulation of Thermodynamic and Kinetic Phenomena in Gel-stabilized Enzyme Carriers (VDI-Verlag, Düsseldorf, Germany).
56. Heinemann, M., Limper, U. and Büchs, J., 2004, New insights in the spatially resolved dynamic pH measurement in macroscopic large absorbent particles by confocal laser scanning microscopy, J Chromatography A, 1024: 45-53.
57. Heinemann, M., Meinberg, H., Büchs, J., Koss, H.-J. and Ansorge-Schumacher, M.B., 2005, Method for quantitative determination of spatial polymer distribution in alginate beads using Raman spectroscopy, Applied Spectroscopy, 59: 280-285.
58. Hirschorn, R.M., 1979, Invertibility of nonlinear control systems, SIAM J Control and Optimization, 17: 289-297.
59. Hohl, R., Blum, J., Buchholz, M., Lüttich, T., Auracher, H. and Marquardt, W., 2001, Model-based experimental analysis of pool boiling heat transfer with controlled wall temperature transients, Int J Heat Mass Transfer, 44: 2225-2238.
60. Hosten, L.H., 1979, A comparative study of short cut procedures for parameter estimation in differential equations, Comput Chem Engg, 3: 117-126.
61. Huang, C.-H. and Wang, S.-P., 1999, A three-dimensional inverse heat conduction problem in estimating surface heat flux by conjugate gradient method, Int J Heat and Mass Transfer, 42: 3387-3403.
62. Kador, L., Schittkowski, T., Bauer, M. and Fan, Y., 2003, Three-dimensional material analysis by confocal Raman microspectroscopy, Applied Optics, 40: 4965-4970.
63. Kamenski, D.I. and Dimitrov, S.D., 1993, Parameter estimation in differential equations by applications of rational functions, Comput Chem Engg, 17: 643-651.
64. Kirsch, A., 1996, An Introduction to the Mathematical Theory of Inverse Problems (Springer, New York, USA).
65. Kittrell, J.R., 1970, Mathematical modelling of chemical reactions, Adv Chem Eng, 8: 97-183.
66. Körkel, S., Kostina, E., Bock, H.G. and Schlöder, J.P., 2004, Numerical methods for optimal control problems in design of robust optimal experiments for dynamic processes, Optimization Methods and Software, 19: 327-338.
67. Kristensen, N.R., Madsen, H. and Jorgensen, S.B., 2004, A method for the systematic improvement of stochastic grey box models, Comput Chem Engg, 28: 1431-1449.
68. Kurtz, M.J. and Henson, M.A., 1998, State and disturbance estimation for nonlinear systems affine in the unmeasured variables, Comput Chem Engg, 22: 1441-1459.
69. Küppers, M., Heine, C., Han, S., Stapf, S. and Blümich, B., 2002, In-situ observation of diffusion and reaction dynamics in gel microreactors by chemically resolved NMR microscopy, Appl Magn Reson, 22: 235-246.
70. Kyritsis, D.C., Felton, P.G., Huang, Y.P. and Bracco, F.V., 2000, Quantitative two-dimensional instantaneous Raman concentration measurements in a laminar methane jet, Applied Optics, 39(36): 6771-6780.
71. Lekhal, A., Chaudhari, R.V., Wilhelm, A.M. and Delmas, H., 1999, Mass transfer effects on hydroformylation catalyzed by a water soluble complex, Catalysis Today, 48: 265-272.
72. Ljung, L., 1987, System Identification. Theory for the User (Prentice Hall, Englewood Cliffs, Upper Saddle River, NJ, USA).
73. Lüttich, T., Mhamdi, A. and Marquardt, W., 2005, Design formulation and solution of multi-dimensional inverse heat conduction problems, Numerical Heat Transfer, Part B, Fundamentals, 37: 111-133.
74. Madar, J., Abonyi, J., Roubos, A. and Szeifert, F., 2003, Incorporating prior knowledge in a cubic spline approximation - application to the identification of reaction kinetic models, Ind Engg Chem Res, 42: 4043-4049.
75. Mahoney, A.W., Doyle, F.J. and Ramkrishna, D., 2002, Inverse problems in population balances: growth and nucleation from dynamic data, AIChE J, 48: 981-990.
76. Marquardt, W., 1995, Towards a process modeling methodology, in Berber, R. (ed.). Methods of Model-based Control, 3-41 (NATO-ASI Series, Kluwer, Dordrecht, The Netherlands).
77. Marquardt, W., 2004, Model-based experimental analysis: a systems approach to mechanistic modeling of kinetic phenomena, in Floudas, C.A. and Agarwal, R. (eds). Proc Int Conf on Foundations of Process Design, FOCAPD 2004, 165-183.
78. Martens, H. and Naes, T., 1989, Multivariate Calibration (Wiley, New York, USA).
79. McKay, B., Willis, M. and Barton, G., 1997, Steady-state modeling of chemical process systems using genetic programming, Comput Chem Engg, 21: 981-996.
80. Meditch, J.S. and Hostetter, G.H., 1974, Observers for systems with unknown inaccessible inputs, Int J Control, 19: 473-480.
81. Merzliak, T. and Pfennig, A., 2004, Development of a model for the description of intra-diffusion in homogeneous liquid Lennard-Jones mixtures, Molecular Simulation, 30: 459-468.
82. Mhamdi, A. and Marquardt, W., 1999, An inversion approach to the estimation of reactions rates in chemical reactors, European Control Conference, ECC'99, F1004-1.
83. Mhamdi, A. and Marquardt, W., 2001, A regularization approach to state estimation, Proc American Control Conference, 2001, Vol. 6, 4228-4233.
84. Mhamdi, A. and Marquardt, W., 2003, Estimation of reaction rates by nonlinear system inversion, Proc IFAC Symp Advanced Control of Chemical Processes, ADCHEM 2003, 931-936.
85. Modigell, M. and Schagen, A., 2005, Luminescence technique for the measurement of local concentration distribution in liquid films, Experiments in Fluids, 38: 174-184.
86. Norgaard, M., Ravn, O., Poulsen, N.K. and Hansen, L.K., 2000, Neural Networks for Modelling and Control of Dynamic Systems: A Practitioners Handbook (Springer, London, UK).
87. Oppenheim, A. and Schafer, R., 1999, Discrete-Time Signal Processing (Prentice-Hall, Englewood Cliffs, NJ, USA).
88. Oliveira, R., 2004, Combining first principles modelling and artificial neural networks: a general framework, Comput Chem Engg, 28: 755-766.
89. Popper, K., 1959, The Logic of Scientific Discovery (Hutchinson, London, UK).
90. Prasad, A.K., 2000, Particle image velocimetry, Current Science, 79: 51-60.
91. Psichogios, D.C. and Ungar, L.H., 1992, A hybrid neural network - first principles approach to process modeling, AIChE J, 38:1499-1511.
92. Psichogios, D.C. and Ungar, L.H., 1994, SVD-NET: an algorithm that automatically selects network structure, IEEE Trans Neural Networks, 5: 513-515.
93. Reeve, D.E. and Spivack, M., 1994, Determination of a source term in the linear diffusion equation, Inverse Problems, 10: 1335-1344.
94. Reinecke, N. and Mewes, D., 1997, Multielectrode capacitance sensors for the visualization of transient two-phase flows, Experimental Thermal and Fluid Science, 15: 253-266.
95. Reinsch, C.H., 1967, Smoothing by spline functions, Num Math, 10: 177-183.
96. Schaich D., Becker, R. and King, R., 2001, Qualitative modelling for automatic identification of mathematical models of chemical reaction systems, Control Engg Practice, 9: 1373-1381.
97. Silverman, L.M., 1969, Inversion of multivariable linear systems, IEEE Trans Autom Control, 14: 270-276.
98. Skrifvars, H., Leyffer, S. and Westerlund, T., 1998, Comparison of certain MINLP algorithms when applied to a model structure determination and parameter estimation problem, Comp Chem Engg, 22: 1829-1835.
99. Stewart, W.E., Shon, Y. and Box, G.E.P., 1998, Discrimination and goodness of fit of multiresponse mechanistic models, AIChE J, 44: 1404-1412.
100. Taavitsainen, V.M. and Haario, H., 2001, Rapid estimation of chemical kinetics by implicit calibration - I, J Chemometrics, 15:215-239.
101. Taavitsainen, V.M., Haario, H. and Laine, M., 2003, Rapid estimation of chemical kinetics by implicit calibration - II, J Chemometrics, 17: 140-150.
102. Taylor, R. and Krishna, R., 1993, Multicomponent Mass Transfer (Wiley-Interscience, New York, USA).
103. Tholudur, A. and Ramirez, W.F., 1996, Optimization of fed-batch bio-reactors using neural network parameter function models, Biotechnol Prog, 12: 302-309.
104. Tholudur, A. and Ramirez, W.F., 1999, Neural network modelling and optimization of induced foreign protein production, AIChE J, 45: 1660-1670.
105. Tikhonov, A.N. and Arsenin, V.Y., 1977, Solution of III-Posed Problems, (V. H. Winston and Sons, Washington DC, USA).
106. van Lith, P.F., Betlem, B.H.L. and Roffel, B., 2002, A structured modeling approach for dynamic hybrid fuzzy first-principles models, J Proc Cont, 12: 605-615.
107. van Nhu, N., Wanner, A., Tiltscher, H. and Lercher, J.A., 2001, On the interfacial mass transfer and the location of the chemical reaction in a fluid/fluid reacting system at elevated temperatures and pressures, Catalysis Today, 66: 335-344.
108. Verheijen, P.J.T., 2003, Model selection. An overview of practices in chemical engineering, in Asprey, S.P. and Macchietto, S. (eds). Dynamic Model Development, 85-104 (Elsevier, Amsterdam, The Netherlands).
109. Wahba, G., 1990, Spline Models for Observational Data (SIAM, Philadelphia, USA).
110. Walter, E. and Pronzato, L., 1990, Qualitative and quantitative experiment design for phenomenological models - a survey, Automatica, 26: 195-213.
111. Walter, E. and Pronzato, L., 1997, Identification of Parametric Models from Experimental Data (Springer, Berlin, Germany).
112. Wang, K., Hawley, M.C. and Furney, T.D., 2003, A selectivity study of 2,4-pentanediol hydrogenolysis combining experiments and computer simulation, Chem Eng Sci, 58: 4271-4285.
113. Yeow, Y.L., Pokethitiyook, P., Cheah, M.Y., Dang, H.D.T. and Law, C.K.P., 2004, An alternative way of analyzing the progress curves of enzymecatalyzed reactions, Biochemical Engineering Journal, 21: 1-10.
114. Yeow, Y.L., Wickramasinghe, S.R., Han, B. and Leong, Y.-K., 2003, A new method of processing the time-concentration data of reaction kinetics, Chem Eng Sci, 58: 3601-3610.