The effects of different experimental designs on parameter estimation in the kinetics of a reversible chemical reaction

Abstract The common methods used by chemists to obtain the estimates of the kinetic rate constants are deterministic ones. The statistical methods, such as D -optimum design (DOD), can offer a better way to deal with this problem. But the kinetic model of a reversible reaction is nonlinear, the DOD is locally optimal at the value of the initial chosen parameters. The goal of this article is to try to put different experimental design techniques, i.e., uniform design (UD), orthogonal design (OD) and DOD into a common framework, and to attempt to gain some insight on when, where and which of these three experimental methods can be expected to work well. The extensive Monte Carlo experiments have been done in order to compare the performances of these methods. The results show that the DOD often gives the best performance, but it is easy to break down in estimation of parameters, when the initial parameters are far away from the true parameters. The OD also breaks down in some situations. The UD is the most stable, and it works well in all situations.

[1]  J. S. Hunter,et al.  Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. , 1979 .

[2]  W. Näther Optimum experimental designs , 1994 .

[3]  Anthony C. Atkinson,et al.  Optimum Experimental Designs , 1992 .

[4]  Toby J. Mitchell,et al.  An algorithm for the construction of “ D -optimal” experimental designs , 2000 .

[5]  K Ang,et al.  A NOTE ON UNIFORM DISTRIBUTION AND EXPERIMENTAL DESIGN , 1981 .

[6]  A. Atkinson The Usefulness of Optimum Experimental Designs , 1996 .

[7]  W. D. Ray,et al.  Statistics for Experiments. An Introduction to Design, Data Analysis and Model Building , 1979 .

[8]  Wing-Hong Chan,et al.  Screening the fabrication conditions of ultrafiltration membranes by using the uniform design and regression analysis methods , 1998 .

[9]  Yong Zhang,et al.  Uniform Design: Theory and Application , 2000, Technometrics.

[10]  S. M. Lewis,et al.  Orthogonal Fractional Factorial Designs , 1986 .

[11]  C. Nachtsheim Orthogonal Fractional Factorial Designs , 1985 .

[12]  L. Haines The application of the annealing algorithm to the construction of exact optimal designs for linear-regression models , 1987 .

[13]  W. J. Studden,et al.  Theory Of Optimal Experiments , 1972 .

[14]  Kai-Tai Fang,et al.  Sequential number‐theoretic optimization (SNTO) method applied to chemical quantitative analysis , 1997 .

[15]  Jian-hui Jiang,et al.  Uniform design applied to nonlinear multivariate calibration by ANN , 1998 .

[16]  J HickernellF,et al.  The Uniform Design and Its Applications , 1995 .

[17]  Peter Zinterhof,et al.  Monte Carlo and Quasi-Monte Carlo Methods 1996 , 1998 .

[18]  Dolores Pérez-Bendito,et al.  Kinetic methods in analytical chemistry , 1988 .

[19]  Z. Galil,et al.  Construction Methods for D-Optimum Weighing Designs when n ≡ 3(mod 4) , 1985 .

[20]  R. Caflisch Monte Carlo and quasi-Monte Carlo methods , 1998, Acta Numerica.

[21]  H. Chernoff Locally Optimal Designs for Estimating Parameters , 1953 .

[22]  Z. Galil,et al.  Construction Methods for $D$-Optimum Weighing Designs when $n \equiv 3 (\operatorname{mod} 4)$ , 1982 .

[23]  Donald E. Knuth,et al.  On Methods of Constructing Sets of Mutually Orthogonal Latin Squares Using a Computer. I , 1960 .

[24]  Toby J. Mitchell,et al.  An Algorithm for the Construction of “D-Optimal” Experimental Designs , 2000, Technometrics.

[25]  Kai-Tai Fang,et al.  An example of a sequential uniform design: Application in capillary electrophoresis , 1997 .

[26]  L. Brown,et al.  Jack Carl Kiefer collected papers , 1985 .

[27]  H. Niederreiter Quasi-Monte Carlo methods and pseudo-random numbers , 1978 .

[28]  M. E. Johnson,et al.  Generalized simulated annealing for function optimization , 1986 .

[29]  Fan Gong,et al.  An improved algorithm of sequential number-theoretic optimization (SNTO) based on clustering technique , 1999 .

[30]  Peter Winker,et al.  Uniformity and Orthogonality , 1998 .

[31]  Y. Zhu,et al.  A method for exact calculation of the discrepancy of low-dimensional finite point sets I , 1993 .

[32]  Arnold J. Stromberg,et al.  Number-theoretic Methods in Statistics , 1996 .

[33]  Barbara Bogacka,et al.  D- and T-optimum designs for the kinetics of a reversible chemical reaction , 1998 .