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HS Trap extraction has been optimized using an optimal design of experiments (O-DOE) approach, considering four instrumental and two sample preparation variables, as well as a blocking factor identified during preliminary assays.
In the optimal design of experiments setup, different optimality criteria can be considered depending on the objectives of the practitioner.
The evolutionary approach (EA), which is based on genetic algorithms (GA), is a well-proven stochastic strategy here used as a reference to evaluate our library optimization method (CLOM), which is based upon the optimal design of experiments (ODOE).
This paper presents a unique nested optimization framework for the co-design of a physical system (plant) and controller, which leverages optimal Design of Experiments (DoE) techniques for the plant optimization and extremum seeking for the control system optimization.
Optimal design of experiments (O-DOE) is a computational, statistically-oriented approach for designing experiences that are most informative according to a well-defined criterion.
However there are known difficulties in optimal design of experiments.
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Different formulations were designed, prepared and evaluated by employing response surface, optimal design of experiment technique.
Sampling designs of the sandwich tubes were constructed based on a D – optimal design of experiment (DOE) method.
Different formulations were designed, prepared and evaluated by employing response surface methodology and optimal design of experiment technique using Design Expert® ver 8.0.1 software.
This paper introduces a mathematical programming approach to systematically find optimal designs of experiments for linear algebraic models.
In this work we provide theoretical and numerical results regarding optimal designs of experiments with an emphasis on coupled problems like piezoelectrics and poroelastics.
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