Sentence examples for multiple response optimization from inspiring English sources
Predictive models using ANOVA and multiple response optimization revealed that optimal Cr VI) removal efficiency occurred at 11 V and 18.6 min treatment time to give 50 % Cr VI) removal efficiency with a consumption of 15.46 KWh m− 3 energy.
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.
Response surface methodology and multiple response optimization were used to search for an optimized formula.
It was concluded that response surface methodology and multiple response optimization could be successfully used to design and optimize extended release formulations with desired preplanned release profile.
Multiple response optimization was applied to the experimental data to discover the optimal conditions for a set of response, simultaneously, by using a desirability function.
The response surface methodology (RSM) and multiple response optimization utilizing the polynomial equation were used to search for the optimal formulation with specific release rate at different time intervals and to quantify the effect of each formulation variables.
From multiple response optimizations, the optimal combination of parameters are current 1 amp, pulse on-time 0.98 μs, pulse off-time 0.03 μs, tool material 0.31 and the powder (suspended particles) 0.64.
Predictive model, using multiple response optimizations, revealed that CSM RO and NF250 membranes showed the optimal efficiency with 20.24% and 18.98% water recovery, 90.22% and 70.64% salt rejection and 17.87 and 9.35 kWh/m3 of SEC respectively.
The response surface methodology and multiple response optimizations utilizing a polynomial equation were used to search for the optimal formulation with a specific release rate at different time intervals.
The Design-Expert® software [16] applies the desirability function approach of Derringer and Suich [20] for multiple response optimizations.
Coetzer et al. [19] applied dual response surface optimization for optimising the variable conditions for a coal gasification process, and discussed a number of approaches for multiple response optimizations.
