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We discuss the modeling, simulation, and, for the first time, optimization of the reactive flow in a channel of a catalytic monolith with detailed chemistry. We use boundary layer approximation to model the process and obtain a high dimensional PDE. We discuss numerical methods based on the efficient solution of high dimensional stiff DAEs arising from spatial semi-discretization and SQP method for the optimal control problem parameterized by the direct approach. We have investigated the application of conversion of ethane to ethylene which involves a complex reaction scheme for gas phase and surface chemistry. Our optimization results show that the maximum yield, an improvement of a factor of two, is achieved for temperatures around 1300 K. [ABSTRACT FROM AUTHOR]

Estimating model parameters from experimental data is crucial to reliably simulate dynamic processes. In practical applications, however, it often appears that the data contains outliers. Thus, a reliable parameter estimation procedure is necessary that delivers parameter estimates insensitive (robust) to errors in measurements. Another difficulty that occurs in practical applications is that the experiments performed to obtain measurements for parameter estimation are expensive, but nevertheless do not guarantee satisfactory parameter accuracy. The optimization of one or more dynamic experiments in order to maximize the accuracy of the results of a parameter estimation subject to cost and further technical inequality constraints leads to very complex non-standard optimal control problems. Newly developed successful methods and software for design of optimal experiments for nonlinear processes are based on the expansion of the problem at the nominal value of parameters which lie in a (possibly large) confidence region. Robust optimal experiments, that are insensitive against uncertainties in parameter values, should be obtained if we optimize the experiments in min-max fashion (worst-case design) over the whole range (confidence region) of an uncertainty set. The paper presents new effective algorithms for robust parameter estimation and design of robust optimal experiments in dynamic systems. Numerical results for a real-life application from biochemical engineering are presented. [ABSTRACT FROM AUTHOR]