Abstract
Mathematical models that describe chemical engineering processes are not exact. Therefore, it is important to develop parameter-estimation algorithms that account for possible model uncertainties. In this article, as a follow-up to earlier work by Poyton et al. (Comput. Chem. Eng. 2006, 30, 698) and Varziri et al. (Comput. Chem. Eng. 2007), we investigate the performance of an approximate maximum likelihood estimation (AMLE) algorithm for parameter estimation in nonlinear dynamic models with model uncertainties and stochastic disturbances. We examine the applicability of AMLE to cases in which some of the states are unmeasured, and we demonstrate that AMLE can be employed in models with nonstationary process disturbances. Theoretical confidence interval expressions are obtained and are compared to empirical box plots from Monte Carlo simulations. Use of the methodology is illustrated using a continuous stirred tank reactor model.
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