Multiple response optimization fits the optimum values of the factors and the responses as 0.4 mL/L of coagulant, 7.0 mL/L of flocculant and 82, 72 and 13% of COD, turbidity and TOCsoluble removal at alkaline pH, respectively.
Predict values of multiple response at optimal condition were that yield = 9.36%, α1/α2 chain ratio = 1.76, β chain percent = 32.81, gel strength = 459 g, viscosity = 3.2 mPa s and melting point = 20.4 °C.
Further, Central Composite Design (CCD) was used to optimize the most significant medium constituents and the optimum values estimated by multiple response optimization were 0.5%Tween-80Tween-80(NH4 2HPO4, and 0.1% MgSO4·7H2O, with a predicted value of 5038.86 IU ml−1 lipase-production.
As the larger value of MPI corresponds better multiple response characteristics, the optimum combination can be obtained by selecting the largest level average of each parameter.
Additionally, a multiple response based on the normalization of the concentrations by the highest values was built to establish a compromise condition between the two analytes.
In this method, the ANN is used to estimate the response values for unpracticed treatments in a way that the multiple responses are obtained simultaneously.
It is shown that these methods give design response values that are very close to the true values obtained from multiple time history analyses.
A multiple response optimization based on the validation of a relevant compromise between all responses at a time resulted in the following optimum values: 20% d.b. water content, 9.1 ∗ 105 Pa steam pressure and 17 s processing time.
Numerical multiple response optimization was applied to select an optimized formula (OF) with the goals of minimizing PS and maximizing ZP absolute value and EE.
Multiple response signal-to noise ratio.
Most studies measured multiple response variables.
