Optimum reference temperature for reparameterization of the Arrhenius equation. Part 1 : Problems involving one kinetic constant

The Arrhenius equation is one of the most well-known equations in the chemical field and is widely used to describe the temperature dependence of kinetic constants. This equation contains two parameters, the frequency factor and the activation energy, which are usually estimated from experimental data. However, the correlation between the two parameter estimates is usually very high and in many cases is practically equal to one. This makes the precise identification of the parameter values very difficult. The high parameter correlation can be diminished through reparameterization of the Arrhenius equation and definition of a reference temperature. For problems involving a single kinetic constant, it is shown here both analytically and through numerical examples that the proper definition of the reference temperature allows for estimation of the parameters of the Arrhenius equation without correlation and with minimum relative error, leading to improvement of the parameter estimation procedure.

[1]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[2]  A. B. Bendtsen,et al.  Chemometric analysis of a detailed chemical reaction mechanism for methane oxidation , 1998 .

[3]  G. Froment,et al.  A hybrid genetic algorithm for the estimation of parameters in detailed kinetic models , 1998 .

[4]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  J. Pinto,et al.  Preparation of high loading silica-supported nickel catalyst: analysis of the reduction step , 2002 .

[6]  S. Logan,et al.  The Origin and Status of the Arrhenius Equation , 1982 .

[7]  E. Biscaia,et al.  Nonlinear parameter estimation through particle swarm optimization , 2008 .

[8]  S. Chuang,et al.  Temperature programmed decomposition of polypropylene: in situ FTIR coupled with mass spectroscopy study , 1998 .

[9]  N. Kalogerakis,et al.  Applied parameter estimation for chemical engineers , 2000 .

[10]  Eligius M. T. Hendrix,et al.  A comparison of algorithms for global characterization of confidence regions for nonlinear models , 1994 .

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

[12]  George E. P. Box,et al.  FITTING EMPIRICAL DATA * , 1960 .

[13]  A. Krause,et al.  Temperature-programmed desorption as a tool to extract quantitative kinetic or energetic information for porous catalysts , 2006 .

[14]  D. Himmelblau,et al.  Optimization of Chemical Processes , 1987 .

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

[16]  Robert B. Schnabel,et al.  Computational Experience With Confidence Regions and Confidence Intervals for Nonlinear Least Squares , 1986 .

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

[18]  N. Draper,et al.  Applied Regression Analysis , 1967 .

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

[20]  David Mautner Himmelblau,et al.  Process analysis by statistical methods , 1970 .

[21]  Sandro Macchietto,et al.  Nonlinear transformations for parameter estimation , 1988 .

[22]  Azael Fabregat,et al.  Nonlinear kinetic parameter estimation using simulated annealing , 2002 .

[23]  Determination of Arrhenius constants by linear and nonlinear fitting , 1992 .

[24]  Michael L. Brisk,et al.  "Sequential experimental design for precise parameter estimation. 1. Use of reparameterization". Reply to comments , 1986 .

[25]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[26]  T. Rimensberger,et al.  "Sequential experimental design for precise parameter estimation. 1. Use of reparameterization". Comments , 1986 .

[27]  J. R. Kittrell,et al.  Mathematical Modeling of Chemical Reactions , 1970 .

[28]  Lubomír Kubáček,et al.  Statistical properties of linearization of the Arrhenius equation via the logarithmic transformation , 1997 .

[29]  Rolf Sundberg,et al.  Statistical aspects on fitting the Arrhenius equation , 1998 .

[30]  Michael L. Brisk,et al.  Sequential experimental design for precise parameter estimation. 1. Use of reparameterization , 1985 .

[31]  Charles G Hill,et al.  Introduction to Chemical Engineering Kinetics & Reactor Design , 1977 .

[32]  T. Brubaker,et al.  Nonlinear Parameter Estimation , 1979 .

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

[34]  N. Brauner,et al.  Statistical analysis of linear and nonlinear correlation of the Arrhenius equation constants , 1997 .