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A probabilistic statistical model originated using Box-Behnken response surface methodology and process variables were optimized by nonlinear optimization.
The design variables were optimized by using genetic algorithm and the optimal solutions were obtained.
In this study, the microwave-assisted extraction (MAE) variables were optimized using a face-centered central composite design of response surface methodology for optimal recoveries of total flavonoid content (TFC) and antioxidant capacities (DPPH and ABTS) of Vernonia amygdalina leaf extract.
Several important experimental variables were optimized.
The actual values of the three independent variables were optimized using a central composite design.
Electrochemical and flow variables were optimized to examine ECF reactor contaminants removal efficiency.
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After that, for the fixed-short-time two-impulse Lambert rendezvous problem without sufficient time to adjust the separation angle by distributing a parking time like the open-time Lambert rendezvous problem, three-impulse Lambert rendezvous involving multiple optimization variables is given and the variables are optimized by the optimization algorithm IA to obtain an optimal and small Δv.
The influence of relevant experimental variables was optimized.
To decrease the energy consumption of the process, decision variables are optimized by r-SQP method within given constraints.
The trajectories of the manipulated variables are optimized by minimizing the breakthrough of one of the main reactants in the reactor effluent.
In each cell, both the indicators and the power variables are optimized which makes the local problem more interesting than that of [36].
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