On the feasibility and reliability of nonlinear kinetic parameter estimation for a multi-component photocatalytic process

Industrial Liaison Research Institute College of Mechanical & Industrial System Engineering Korea
Korean Journal of Chemical Engineering (Impact Factor: 1.06). 18(5):652-661. DOI: 10.1007/BF02706382

ABSTRACT Nonlinear kinetic parameter estimation plays an essential role in kinetic study in reaction engineering. In the present study,
the feasibility and reliability of the simultaneous parameter estimation problem is investigated for a multi-component photocatalytic
process. The kinetic model is given by the L-H equation, and the estimation problem is solved by a hybrid genetic-simplex
optimization method. Here, the genetic algorithm is applied to find out, roughly, the location of the global optimal point,
and the simplex algorithm is subsequently adopted for accurate convergence. In applying this technique to a real system and
analyzing its reliability, it is shown that this approach results in a reliable estimation for a rather wide range of parameter
value, and that all parameters can be estimated simultaneously. Using this approach, one can estimate kinetic parameters for
all components from data measured in only one time experiment.

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    ABSTRACT: The development of predictive models is a time consuming, knowledge intensive, iterative process where an approximate model is proposed to explain experimental data, the model parameters that best fit the data are determined and the model is subsequently refined to improve its predictive capabilities. Ascertaining the validity of the proposed model is based upon how thoroughly the parameter search has been conducted in the allowable range. The determination of the optimal model parameters is complicated by the complexity/non-linearity of the model, potentially large number of equations and parameters, poor quality of the data, and lack of tight bounds for the parameter ranges. In this paper, we will critically evaluate a hybrid search procedure that employs a genetic algorithm for identifying promising regions of the solution space followed by the use of an optimizer to search locally in the identified regions. It has been found that this procedure is capable of identifying solutions that are essentially equivalent to the global optimum reported by a state-of-the-art global optimizer but much faster. A 13 parameter model that results in 60 differential-algebraic equations for propane aromatization on a zeolite catalyst is proposed as a more challenging test case to validate this algorithm. This hybrid technique has been able to locate multiple solutions that are nearly as good with respect to the “sum of squares” error criterion, but imply significantly different physical situations.
    Computers & Chemical Engineering. 01/2004;