Evolutionary Strategy in Iterative Experiment Design

Abstract

Process models play important role in computer aided process engineering, since most of advanced process monitoring, control, and optimization algorithms relay on a model of the process. In most of the cases, some parameters of the model should be estimated based on some experiments. One of the factors affecting the model prediction quality is the accuracy of these estimated parameters. Establishing optimal experiment design can maximise the confidence on the parameters, hereby increasing the confidence on the model prediction. The aim of this paper is to work out a modern experiment design tool to minimize the number of experiments while maximizing of their information content. This paper illustrates the applicability of ES for the design of feeding profile for a fed-batch biochemical reactor. The results illustrate that if the model structure is not accurate, the evolutionary strategy can result in more satisfactory parameter values than the classical sequential quadratic programming and nonlinear least squares algorithms.