Model-Based Design of Parallel Experiments

Federico Galvanin · Sandro Macchietto · Fabrizio Bezzo

107 citations0/0 files storedIndustrial & Engineering Chemistry Research

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Optimal Experimental Design Methods Control Systems and Identification Simulation Techniques and Applications

Abstract

Advanced model-based experiment design techniques are essential for the rapid development, refinement, and statistical assessment of deterministic process models. One objective of experiment design is to devise experiments yielding the most informative data for use in the estimation of the model parameters. Current techniques assume that multiple experiments are designed in a sequential manner. However, multiple equipment can sometimes be available, and simultaneous (parallel) experiments could be advantageous in terms of time and resources utilization. The concept of model-based design of parallel experiments is presented in this paper. Furthermore, a novel criterion for optimal experiment design is proposed: the criterion aims at maximizing complementary information by considering different eigenvalues in the information matrix. The benefits of adopting such an approach are discussed through an illustrative case.

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1993Optimal design of experiments 1992Optimum Experimental Designs 1959Optimum Designs in Regression Problems 1959DESIGN OF EXPERIMENTS IN NON-LINEAR SITUATIONS 2004Design of Experiments in Chemical Engineering 1994Optimal Design of Experiments 2002Designing robust optimal dynamic experiments 2000Numerical methods for optimum experimental design in DAE systems

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