Split-plot designs and normal probability graphs for the optimization of chemical systems

An approximate procedure based on normal probability graphs for selecting significant parameters of models calculated from the results of split-plot designs is proposed. Its application can result in a substantial reduction in the number of experiments that need to be performed. The method is applied to three split-plot design results for real data reported in the literature: (1) three plasticizer mixture components with different extrusion rates and drying temperatures, (2) three fish pattie ingredients at different cooking and frying temperature and times and (3) Cr(VI) catalytic determinations employing three reagents of varying concentrations and three solvent components of varying proportions. Approximate models determined from the proposed procedure are compared with those determined using complete split-plot ANOVA analyses. The robustness of the procedure is tested for one of the split-plot design results using replication, main-plot error and sub-plot error variance estimates that change according to a 23 factorial design.