• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.876-888
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• 2001

For a large scaled optimization based on response surface methods, an efficient quadratic approximation method is presented in the context of the trust region model management strategy. If the number of design variables is η, the proposed method requires only 2η+1 design points for one approximation, which are a center point and tow additional axial points within a systematically adjusted trust region. These design points are used to uniquely determine the main effect terms such as the linear and quadratic regression coefficients. A quasi-Newton formula then uses these linear and quadratic coefficients to progressively update the two-factor interaction effect terms as the sequential approximate optimization progresses. In order to show the numerical performance of the proposed method, a typical unconstrained optimization problem and two dynamic response optimization problems with multiple objective are solved. Finally, their optimization results compared with those of the central composite designs (CCD) or the over-determined D-optimality criterion show that the proposed method gives more efficient results than others.

• Food Science of Animal Resources
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• v.30 no.1
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• pp.66-72
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• 2010

Pediococcus pentosaceus SA131 was isolated from jeotgal, is the bacteriocin producer against bovine mastitis pathogens, Streptococcus uberis E290, Enterococcus gallinarum E362, and Staphylococcus epidermidis ATCC 12228. The medium composition for pediocin SA131 production by P. pentosaceus SA131 was optimized using response surface methodology. Component of medium was studied as carbon source (glucose, fructose, lactose, glycerol, sucrose, maltose, and mannitol), nitrogen source (beef extract, yeast extract, peptone, malt extract, and tryptone), mineral and surfactant ($MgSO_4$, $KH_2PO_4$, $(NH_4)_2SO_4$, $MnSO_4$, NaCl, sodium acetate, and Tween 80). Through one factor-at-a-time experiment, glucose, fructose, yeast extract, malt extract, NaCl, $MgSO_4$, and Tween 80 were determined as the good ingredient. The effects of major factors for pediocin SA131 production were investigated by two-level fractional factorial designs (FFD). By a $2^4$ FFD, fructose, yeast extract, and $MnSO_4$ were found to be the important factors for the bacteriocin production. Subsequently, a $2^3$ central composite design (CCD) was adopted to derive a statistical model for optimizing the composition of the fermentation medium. The estimated optimum composition for the production of pediocin SA131 by P. pentosaceus SA131 was as follows; 0.13% fructose, 1% glucose, 1.8% yeast extract, 2.58% $MnSO_4$, 0.2% NaCl, and 0.2% Tween 80. The pediocin production under optimized medium was increased to 1,000 AU/mL, compared to the 400 AU/mL in MRS medium.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.215-223
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• 2012

Response surface methodology(RSM) is a very useful statistical techniques for improving and optimizing the product process. By this reason, RSM has been utilized extensively in the industrial world, particularly in the circumstances where several product variables potentially influence some quality characteristic of the product. In order to estimate the optimal condition of product variables, an experiment is being conducted defining appropriate experimental region. However, this experimental region can vary with the experimental circumstances and choice of a researcher. Response surface designs can be classified, according to the shape of the experimental region, into spherical and cuboidal. In the spherical case, the design is either rotatable or very near-rotatable. The central composite design(CCD)s widely used in RSM is an example of 5-level and spherical design. The cuboidal CCDs(CCDs with ${\alpha}=1$) is appropriate when an experimental region is cuboidal but this design dose not satisfy the rotatability as it is not spherical. Practically, a 3-level spherical design is often required in the industrial world where various level of experiments are not available. Box-Behnken design(BBD)s are a most popular 3-level spherical designs for fitting second-order response surfaces and satisfy the rotatability but the experimental region does not vary with the number of variables. The new experimental design with expanded experimental region can be considered if the predicting response at the extremes are interested. This paper proposes a new 3-level spherical RSM which are constructed to expand the experimental region together with number of product variables.