PRACTICAL ISSUES IN DISTRIBUTED-PARAMETER ESTIMATION - GRADIENT COMPUTATION AND OPTIMAL EXPERIMENT DESIGN

Parameter estimation in nonlinear distributed-parameter systems is usu ally accomplished by minimizing an output least-square criterion, whic h is defined implicitly through the solution of the model equations. T his paper addresses itself to two important practical issues of the pa rameter-estimation procedure, i.e., the numerical procedure used to co mpute the gradient of the criterion with respect to the unknown parame ters, and the selection of experimental conditions, i.e., sensor locat ions and input signals. An experiment design procedure based on the se nsitivity matrix is presented. The methods for gradient computation an d experiment design have been successfully applied to several process models, and are illustrated in this paper with a simple heat-conductio n problem and a more complex model of a catalytic fixed-bed reactor.