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Response surface methodology as a multiple responses optimization technique was utilized to optimize several important factors such as the biomass content of the medium (w/v), inoculum size and agitation rate.
Then the D-optimal experimental design software was utilized to optimize the preparation of the GO based catalysts.
Statistical experimental design was utilized to optimize this adsorption process.
A design of experiment approach was utilized to optimize the nanocrystalline suspensions/powders.
Taguchi experimental design was utilized to optimize amidoximation process and dye removal.
In addition, a modified alternating iterative algorithm was utilized to optimize the cost function.
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Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM.
And genetic algorithms (GAs), a general-purpose global optimization method, are utilized to optimize the membership functions of the fuzzy controller.
After designing output feedback SMC, teaching and learning based optimization (TLBO) technique is utilized to optimize feedback gain and switching vector of the controller.
It is expected that the proposed steady-state model, parameter estimation and optimization methods can be utilized to optimize and design modem-type FCC processes under full and partial combustion modes.
The derived models are utilized to optimize the amplifier structure through particle swarm optimization (PSO) to obtain a large resonance frequency subject to other performance constraints.
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