Reducing the number of experiments in split-plot optimization designs
Two experiment reduction procedures for split-plot designs are investigated using a data set containing 160 experiments, consisting of 80 duplicate results for the optimization of a water-acetone-N,N-dimethylformamide mixture with HCl, o-dianisidine and H2O2 reagent system for the analytical determination of Cr(VI). Stabilities of the model coefficients and ANOVA mean squares are used as quality criteria to judge the effectiveness of the procedures. Only the procedure that avoids the possibility of eliminating entire replicates for any given set of process variable conditions seems to be feasible, since it does not result in loss of valuable modeling information. Its mean square ANOVA values remain stable for up to a 30% replicate reduction whereas its model coefficients are relatively constant for even 70 % replicate reduction. Since complete split-plot designs involving both process and mixture variables require large numbers of experiments, the economy gained by performing incomplete split-plot designs makes their use more attractive.
| Main Authors: | , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Sociedade Brasileira de Química
2004
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532004000200013 |
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