Additive Sequential Evolutionary Design of Experiments

Process models play important role in computer aided pro- cess engineering. Although the structure of these models are a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal experiment design (OED) can maximize the confidence on the model parameters. The paper proposes a new additive sequential evolutionary experiment design approach to maximize the amount of information content of experiments. The main idea is to use the identified models to design new experiments to gradually improve the model accuracy while keeping the collected information from previous experiments. This scheme requires an effective optimization algorithm, hence the main contribution of the paper is the incorporation of Evolutionary Strategy (ES) into a new iterative scheme of optimal experiment design (AS-OED). This paper illustrates the applicability of AS-OED for the design of feeding profile for a fed-batch biochemical reactor.

[1]  Aleksey V. Nenarokomov,et al.  Uncertainties in parameter estimation: the optimal experiment design , 2000 .

[2]  K J Versyck,et al.  Introducing optimal experimental design in predictive modeling: a motivating example. , 1999, International journal of food microbiology.

[3]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[4]  Steven P. Asprey,et al.  On the Design of Optimally Informative Experiments for Dynamic Crystallization Process Modeling , 2004 .

[5]  K Bernaerts,et al.  Optimal experiment design for cardinal values estimation: guidelines for data collection. , 2005, International journal of food microbiology.

[6]  David A. Cohn,et al.  Neural Network Exploration Using Optimal Experiment Design , 1993, NIPS.

[7]  Jan F. M. Van Impe,et al.  Optimal control theory: A generic tool for identification and control of (bio-)chemical reactors , 2002, Annu. Rev. Control..

[8]  János Abonyi,et al.  Evolutionary Strategy in Iterative Experiment Design , 2005 .

[9]  A. Vande Wouwer,et al.  Practical issues in distributed parameter estimation: Gradient computation and optimal experiment design , 1996 .

[10]  Roos D. Servaes,et al.  Optimal temperature input design for estimation of the square root model parameters: parameter accuracy and model validity restrictions. , 2002, International journal of food microbiology.

[11]  Jan F. M. Van Impe,et al.  Optimal dynamic experiment design for estimation of microbial growth kinetics at sub-optimal temperatures: Modes of implementation , 2005, Simul. Model. Pract. Theory.