The search process is based on a four-objective optimization problem that minimizes the number of selected predictors, maximizes the predictive accuracy of a data-driven model and optimizes two information theoretic metrics of relevance and redundancy, which guarantee that the selected subsets are highly informative and with little intra-subset similarity.
A three-level Box Behnken factorial design combining with a response surface methodology (RSM) was employed for modeling and optimizing three operations parameters of the flotation on lead flotation.
Experiment design-response surface methodology (RS M is used in this work to model and optimize two responses in the hydrogenation of tetralin to decalin using bimetallic Ir Pt-SBA-15 catalyst.
Experiment design-response surface methodology (RSM) is used in this work to model and optimize two responses in the process of activation of methane (C1) using ethane (C2) as co-reactant into higher hydrocarbons, over Zn-containing zeolite catalysts.
This paper develops a multiobjective operational model for an industrial cracking furnace system that describes the operation of each furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene.
Response surface methodology (RS M with a central composite rotatable design (CCRD) based on five levels was employed to model and optimize four experimental operating conditions of extraction temperature (10 90 °C) and time (6 30 h), particle size (6 24 mm) and water to solid (W/S, 10 50) ratio, obtaining polysaccharides from Althaea officinalis roots with high yield and antioxidant activity.
At the first two stages, some inappropriate orders are rejected and in the next stages, a mathematical model optimizes each order's delivery time and costs.
For this purpose, a hybrid generalised regression neural network (GRNN particle swarm optimisation (PSO) model was designed to optimize three input parameters, including the charge temperature, engine load, and EGR rate.
Given the low p Ka (∼2.2) associated with Fe(III -bound water, we optIII -boundee additional models possessing either one hydroxide and one water (twe permutatioptimized hydroxide inithreey bound tradditionalher His86 or His88) or two hydroxides also bound to the Fe center.
The corresponding values of parameters to optimize SVR model using three optimization strategies are presented in Table 4.
