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In the upper-level optimization, the response surface method (RS M, is selected.
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A numerical optimization was also performed for the simultaneous multiple optimization of the response variables resulting in the desirable equilibrium headspace concentration of soursop volatile flavor compounds.
To target both optimized and robust conditions a multiple optimization combining the response surface and the propagation of error methods were employed.
Then it becomes possible to realize the independent optimization between the response time and sensitivity of hydrogen sensors.
In this study, fabrication process of gelatin-chitosan MCs and its optimization using the Response Surface Methodology (RS M is reported.
The factors significant at 95% (p < 0.05) were considered to have a significant effect on AMVL production and were used for further optimization by the response surface methodology.
For the optimization process, the response surface method (RSM) using central composite design (CCD) was applied to generate a map of the response.
Then the simulation work is carried further to provide data for helmet design analysis and optimization using the response surface methodology (RS M.
A set of operating conditions to achieve syngas with a desired quality for different applications is also proposed by optimization of the response surface of each index.
Among all the multivariate models for optimization analytical, the response surface methodology is the best it is a powerful mathematical and statistical technique for developing, improving, and optimizing of the conditions in multivariate systems.
Our results indicated that whereas simulations using the a priori parameter estimates give consistently positive flow bias, unconstrained optimization to the response data results in parameter values that are very different from the a priori parameter set.
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