Mathematical Problems in Fitting Kinetic Models—Some New Perspectives

Fitting kinetic data with kinetic rate expressions has been a challenge for many years. In hopes of providing some guidelines for tackling this problem, kinetic parameter estimation is revisited, covering such topics as data transformations, nonlinear single and multi-response techniques, optimization techniques, and parameter confidence measures. Among the optimization techniques discussed are direct and indirect methods, use of higher-order derivatives, and global optimization. New perspectives on statistical tests for model and parameter confidence are discussed, paying particular attention to exact, nonlinear methods and pointing out the distinct lack of these tests throughout the kinetics literature. In each section, either a single-site or a dual-site Langmuir-Hinshelwood (L-H) example model is shown for illustration of the aforementioned concepts.

[1]  David W. Bacon,et al.  Statistical assessment of chemical kinetic models , 1975 .

[2]  Douglas M. Bates,et al.  Nonlinear Regression Analysis and Its Applications , 1988 .

[3]  William H. Press,et al.  Simulated Annealing Optimization over Continuous Spaces , 1991 .

[4]  M. J. Box A Comparison of Several Current Optimization Methods, and the use of Transformations in Constrained Problems , 1966, Comput. J..

[5]  M. F. Cardoso,et al.  The simplex-simulated annealing approach to continuous non-linear optimization , 1996 .

[6]  James V. Beck,et al.  Parameter Estimation in Engineering and Science , 1977 .

[7]  M. Guay,et al.  Optimization and sensitivity analysis for multiresponse parameter estimation in systems of ordinary , 1995 .

[8]  John B. Butt,et al.  On the estimation of catalytic rate equation parameters , 1984 .

[9]  George E. P. Box,et al.  The Bayesian estimation of common parameters from several responses , 1965 .

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  H. L. Lucas,et al.  DESIGN OF EXPERIMENTS IN NON-LINEAR SITUATIONS , 1959 .

[12]  Aimo A. Törn,et al.  Global Optimization , 1999, Science.

[13]  William L. Goffe,et al.  SIMANN: FORTRAN module to perform Global Optimization of Statistical Functions with Simulated Annealing , 1992 .

[14]  Donald G. Watts,et al.  Estimating parameters in nonlinear rate equations , 1994 .

[15]  J. M. Smith Chemical Engineering Kinetics , 1980 .

[16]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[17]  John E. Dennis,et al.  An Adaptive Nonlinear Least-Squares Algorithm , 1977, TOMS.

[18]  O. Levenspiel Chemical Reaction Engineering , 1972 .

[19]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[20]  W. R. Witkowski,et al.  Approximation of parameter uncertainty in nonlinear optimization-based parameter estimation schemes , 1993 .

[21]  Steven P. Asprey,et al.  Kinetic studies using temperature-scanning: the steam-reforming of methanol , 1999 .

[22]  Eldon Hansen,et al.  Global optimization using interval analysis , 1992, Pure and applied mathematics.

[23]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[24]  Jonas Mockus,et al.  Application of Bayesian approach to numerical methods of global and stochastic optimization , 1994, J. Glob. Optim..