• Food Science and Biotechnology
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• v.18 no.4
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• pp.959-964
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• 2009

Production of the antioxidant substances by Bacillus polyfermenticus SCD was investigated using shake-flask fermentation. The one-factor-at-a-time method was first employed to determine the key ingredients for optimal medium composition, then further investigation of the medium composition was performed using response surface methodology (RSM). The antioxidant activity was measured using 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) assays. After screening various elements, fructose, tryptone, and $MgSO_4\;7H_2O$ were chosen as the main factors for study in the statistical experimental design. Central composite design (CCD) was then used to determine the optimal concentrations of these 3 components. Under the proposed optimized medium containing 2.8% fructose, 1.34% tryptone, 0.015% $MgSO_4\;7H_2O$), 0.5% NaCl, and 0.25% $K_2HPO_4$, the model predicted an antioxidant activity of 80.5% ($R^2=0.9421$. The actual experimental results were in agreement with the prediction.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.1
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• pp.26-32
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• 2005

In this paper, the effect of various regression models is investigated on structural optimization using the central composite method. Three bar truss and the upper platform of a satellite are optimized using various regression models that are polynomial, exponential and log functions. Response surface method is non-gradient, semi-global, discrete and fast converging in optimization problem. Sampling points are extracted by the design of experiments using the central composite method. Response surface is generated using the various regression functions. Structural analysis for calculating constraints is executed to find static and dynamic responses. From this study, it is verified that the response surface method has advantage in optimum value and computation time in comparison to other optimization methods.

• Journal of the Korean institute of surface engineering
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• v.51 no.5
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• pp.303-308
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• 2018

Currently hydrofluoric acid (HF) based glass etch method is widely used for anti-glare (AG) surface treatment since it can effectively alleviate the specular reflection problem with relatively low processing cost. However, due to the environmental regulation and safety problem, it is essential to develop alternative technology to replace this method. For this, in this paper, we propose sand blasting based AG surface treatment method for display glass. To characterize the sand blasting process, surface roughness, haze, surface durability, and flatness are considered as process outputs and central composite design (CCD) method and response surface model (RSM) method are applied to model each process output. Models for surface roughness and haze showed 96.44% and 97.24% of R-squared values, respectively and they can be applied to optimize AG surface treatment process for various haze level requirements of display industries.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.852-857
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• 2004

A new and efficient method for estimating the statistical moments of a system performance function has been developed. The method consists of two steps: (1) An approximate response surface is generated by a quadratic regression model, and (2) the statistical moments of the regression model are then calculated by experimental design techniques proposed by Seo and $Kwak^{(4)}$. In this approach, the size of experimental region affects the accuracy of the statistical moments. Therefore, the region size should be selected suitably. The D-optimal design and the central composite design are adopted over the selected experimental region for the regression model. Finally, the Pearson system is adopted to decide the distribution type of the system performance function and to analyze structural reliability.

• Computers and Concrete
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• v.15 no.2
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• pp.215-229
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• 2015

The emerging technology of self compacting concrete, fiber reinforcement together reduces vibration and substitute conventional reinforcement which help in improving the economic efficiency of the construction. The objective of this work is to find the regression model to determine the response surface of mix proportioning Steel Fiber Reinforced Self Compacting Concrete (SFSCC) using statistical investigation. A total of 30 mixtures were designed and analyzed based on Design of Experiment (DOE). The fresh properties of SCC and mechanical properties of concrete were studied using Response Surface Methodology (RSM). The results were analyzed by limited proportion of fly ash, fiber, volume combination ratio of two steel fibers with aspect ratio of 50/35: 60/30 and super plasticizer (SP) dosage. The center composite designs (CCD) have selected to produce the response in quadratic equation. The model responses included in the primary stage were flowing ability, filling ability, passing ability and segregation index whereas in harden stage of concrete, compressive strength, split tensile strength and flexural strength at 28 days were tested. In this paper, the regression model and the response surface plots have been discussed, and optimal results were found for all the responses.

• Food Science of Animal Resources
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• v.43 no.2
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• pp.374-381
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• 2023

In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.

• Journal of Environmental Science International
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• v.23 no.3
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• pp.359-368
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• 2014

The aim of our research was to apply experimental design methodology in the optimization of N, N-Dimethyl-4-nitrosoaniline (RNO, which is indictor of OH radical formation) degradation using gas mixing-circulation plasma process. The reaction was mathematically described as a function of four independent variables [voltage ($X_1$), gas flow rate ($X_2$), liquid flow rate ($X_3$) and time ($X_4$)] being modeled by the use of the central composite design (CCD). RNO removal efficiency was evaluated using a second-order polynomial multiple regression model. Analysis of variance (ANOVA) showed a high coefficient of determination ($R^2$) value of 0.9111, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the experimental data. The application of response surface methodology (RSM) yielded the following regression equation, which is an empirical relationship between the RNO removal efficiency and independent variables in a coded unit: RNO removal efficiency (%) = $77.71+10.04X_1+10.72X_2+1.78X_3+17.66X_4+5.91X_1X_2+3.64X_2X_3-8.72X_2X_4-7.80X{_1}^2-6.49X{_2}^2-5.67X{_4}^2$. Maximum RNO removal efficiency was predicted and experimentally validated. The optimum voltage, air flow rate, liquid flow rate and time were obtained for the highest desirability at 117.99 V, 4.88 L/min, 6.27 L/min and 24.65 min, respectively. Under optimal value of process parameters, high removal(> 97 %) was obtained for RNO.

• Membrane and Water Treatment
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• v.7 no.2
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• pp.127-141
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• 2016

Electrodialysis (ED) is known to be a useful membrane process for desalination, concentration, separation, and purification in many fields. In this process, it is desirable to work at high current density in order to achieve fast desalination with the lowest possible effective membrane area. In practice, however, operating currents are restricted by the occurrence of concentration polarization phenomena. Many studies showed the occurrence of a limiting current density (LCD). The limiting current density in the electrodialysis process is an important parameter which determines the electrical resistance and the current utilization. Therefore, its reliable determination is required for designing an efficient electrodialysis plant. The purpose of this study is the development of a predictive model of the limiting current density in an electrodialysis process using response surface methodology (RSM). A two-factor central composite design (CCD) of RSM was used to analyze the effect of operation conditions (the initial salt concentration (C) and the linear flow velocity of solution to be treated (u)) on the limiting current density and to establish a regression model. All experiments were carried out on synthetic brackish water solutions using a laboratory scale electrodialysis cell. The limiting current density for each experiment was determined using the Cowan-Brown method. A suitable regression model for predicting LCD within the ranges of variables used was developed based on experimental results. The proposed mathematical quadratic model was simple. Its quality was evaluated by regression analysis and by the Analysis Of Variance, popularly known as the ANOVA.

• Food Science of Animal Resources
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• v.39 no.2
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• pp.222-228
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• 2019

This research was motivated by our encounter with the situation where an optimization was done based on statistically non-significant models having poor fits. Such a situation took place in a research to optimize manufacturing conditions for improving storage stability of coffee-supplemented milk beverage by using response surface methodology, where two responses are $Y_1$=particle size and $Y_2$=zeta-potential, two factors are $F_1$=speed of primary homogenization (rpm) and $F_2$=concentration of emulsifier (%), and the optimization objective is to simultaneously minimize $Y_1$ and maximize $Y_2$. For response surface analysis, practically, the second-order polynomial model is almost solely used. But, there exists the cases in which the second-order model fails to provide a good fit, to which remedies are seldom known to researchers. Thus, as an alternative to a failed second-order model, we present the heterogeneous third-order model, which can be used when the experimental plan is a two-factor central composite design having -1, 0, and 1 as the coded levels of factors. And, for multi-response optimization, we suggest a modified desirability function technique. Using these two methods, we have obtained statistical models with improved fits and multi-response optimization results with the predictions better than those in the previous research. Our predicted optimum combination of conditions is ($F_1$, $F_2$)=(5,000, 0.295), which is different from the previous combination. This research is expected to help improve the quality of response surface analysis in experimental sciences including food science of animal resources.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.29-43
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• 2019

The calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. This method can be extended to other optimization tasks using GA in transportation studies.