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Response surface methodology was used to optimize variables affecting the microwave extraction of zerumbone from Zingiber zerumbet using the Box Behnken design.
Combination of response surface methodology (RSM) and central composite design (CCD) has been employed to model and optimize variables using STATISTICA 10.0 software.
A full 33 factorial design coupled with statistical and graphical analysis of the results, by using analysis of variance (ANOVA) was applied to optimize variables in the process of rosehip seed oil extraction with SC-CO2.
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The general objectives of this research are to determine relationships among yield, protein and N rates, using spatial regression analysis of yield monitor data; and to optimize variable-rate application (VRA) of nitrogen fertilizer for wheat.
Quantitative and qualitative assessments were used to optimize variable preparation and selection.
The factor matrix was rotated using the varimax method to optimize variable loadings.
RSM based CCRD model was used to optimize variable factors that affect dye decolorization properties of cell-free supernatant containing crude enzyme mixture from C. militaris cultures.
In the construction of the logistic regression model, the forward stepwise technique was used to optimize variable selection, in order to select a set of variables that could contribute to the outcome and that this contribution revealed statistically significant.
Results show that the particle swarm optimization is superior to the NLP optimization techniques in finding the values of optimizing variables.
To estimate the value of the objective function in a space of optimizing variables the mathematical model of a plate heat exchanger is developed.
To estimate the value of objective function in a space of optimizing variables the mathematical model of plate heat exchanger is developed.
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