An adaptive strategy for solving kinetic model concomitant estimation — Reduction problems

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Abstract
In order to simultaneously estimate the parameters and to reduce a complex kinetic model, an adaptive strategy which combines effective adaptive random search (ARS) and statistical ridge analysis steps is developed. As demonstrated, this strategy can save computational time because the estimation is not repeated with each reduced model. The use of ARS is preferred for highly nonlinear models and cases having multiple parameter constraints, guaranteeing reliability for interactively obtaining the global reduced model parameter solution.