Sequential experimental design based on multiobjective optimization procedures

Model-based sequential experimental designs are frequently applied for discrimination of rival models and/or estimation of precise model parameters. Although the development and use of a single design criterion to perform the simultaneous model discrimination and precise parameter estimation seem ap...

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Bibliographic Details
Published in:Chemical engineering science 2010-10, Vol.65 (20), p.5482-5494
Main Authors: Alberton, André L., Schwaab, Marcio, Chalbaud Biscaia, Evaristo, Carlos Pinto, José
Format: Article
Language:English
Subjects:
Applied sciences
Chemical engineering
Construction
Criteria
Design engineering
Discrimination
Exact sciences and technology
Kinetics
Mathematical modeling
Mathematical models
Model discrimination
Optimization
Parameter identification
Pareto optimality
Sequential experimental design
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Summary:Model-based sequential experimental designs are frequently applied for discrimination of rival models and/or estimation of precise model parameters. Although the development and use of a single design criterion to perform the simultaneous model discrimination and precise parameter estimation seem appealing, published material indicates that previous attempts to develop such a single design criterion have not been successful. Despite that, this problem has rarely been analyzed with the help of multiobjective optimization procedures. In this work, a multiobjective optimization method based on the particle swarm optimization procedure is used to build the Pareto fronts in experimental design problems where distinct design criteria used for discrimination of rival models and/or estimation of precise model parameters are considered simultaneously. It is shown through the rigorous analysis of the Pareto sets that both design objectives are frequently conflicting, which means that optimum discrimination of rival models and estimation of precise model parameters cannot be performed simultaneously in many cases. However, it is also shown that the use of the posterior covariance matrix of estimated model parameters for model discrimination makes the design of experiments for the simultaneous optimum model discrimination and estimation of model parameters possible in many experimental design problems.
ISSN:0009-2509
1873-4405
DOI:10.1016/j.ces.2010.07.010