Sentence examples for multivariate optimization method from inspiring English sources

A multivariate optimization method using central composite design (CCD) was designed to obtain the best conditions of the adsorption.

In the present work, the important influential factors affecting the growth and activity of the new strain were initially unknown and so there were a potentially very large number of permutations to study if a multivariate optimization method such as RSM were used from the outset.

Several significant parameters controlling the performance of the MIP sensor were examined and optimized using multivariate optimization methods such as Plackett Burman design (PBD) and central composite design (CCD).

s−1), deposition time of AgNPs on to the MIP surface (s), stirring rate of loading solution (rpm), electrode loading time (min), pH of Britton Robinson Buffer (BRB) solution were examined and optimized using multivariate optimization methods such as Plackett Burman design (PBD) and central composite design (CCD).

A Hessian matrix and multivariate optimization methods are used to examine and obtain the optimal solution of the mathematical model.

The utility of multivariate optimization methods in the determination of aqueous photolysis rates of organic compounds is examined in this study.

To overcome this shortcoming, multivariate optimization methods are considered as promising techniques which use experimental design in conjunction with multivariate analysis techniques to optimize different processes [19, 20, 21, 22].

The influence of incubation and extraction temperatures and times, coating fibre material and salt addition on SPME efficiency was investigated by multivariate optimization methods using reduced factorial and Doehlert matrix designs.

As detailed above, RSM is a useful and practicable method among different multivariate optimization methods.

Among multivariate optimization methods that can be used more reliably to find global maxima, the response surface methodology (RSM) has previously been shown to be powerful and practicable for program optimization and is often used to identify the relative significance of different factors, interactions between factors and optimal level of test variables.

Comparative optimizations of the experimental conditions were also carried out by multivariate or simplex optimization method.