• Applied Chemistry for Engineering
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• v.32 no.3
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• pp.320-325
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• 2021

In this study, an optimization for the production of water emulsion was designed by adding an extract of wheat sprout, which is known to contain a large amount of antioxidants. The central composite design of reaction surface analysis method (CCD-RSM) was used for the optimization process. The amount of emulsifier, emulsification time, and added amount of wheat sprout extract were selected as independent variables based on our preliminary experiments. The mean droplet size (MDS), viscosity, and emulsion stability index (ESI) were set as the responses to evaluate the stability of the emulsion. For each independent variable, the P-value and coefficient of determination were evaluated to verify the reliability of the experiments. From the result of CCD-RSM, optimum conditions for the emulsification were determined as 23.6 min, 7.7 wt.%, and 3.9 wt.% for the emulsification time, amount of emulsifier, and amount of sprout, respectively. From the optimized condition obtained, MDS, viscosity, and ESI after 7 days from reaction were estimated as 252.3 nm, 616.7 cP, and 88.7%, respectively. The overall satisfaction was 0.9137, which supported the validity of the experiments, and the error rate was measured at 0.5% or less by advancing the experiments. Therefore, an optimized process for producing an emulsion by adding the malt extract was designed by the CCD-RSM.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.3
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• pp.302-307
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• 2015

The automobile is an important means of transportation. For this reason, the automotive wheel is also an important component in the automotive industry because it acts as a load support and is closely related to safety. Thus, the wheel design is a very important safety aspect. In this paper, an optimal design for minimizing automotive wheel stress and increasing wheel safety is described. To study the optimal design, a central composite design (CCD) and D-optimal design theory are applied, and the approximate function using the response surface method (RSM) is generated. The optimal solutions using the non-dominant sorting genetic algorithm (NSGA-II) are then derived. Comparing CCD and D-optimal solution accuracy and verified the CCD can deduce more accuracy optimal solutions.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.

• Journal of Microbiology and Biotechnology
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• v.15 no.5
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• pp.930-937
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• 2005

The Plackett-Burman design and the response surface method (RSM) with a central composite design (CCD) were employed to develop a culture medium for ansamitocin P-3 production using Actinosynnema pretiosum ATCC 31565. Among the 11 nutrients tested using the Plackett-Burman design, two carbon sources, sucrose and dextrin, and two nitrogen sources, polypeptone and yeast extract, were selected. Optimization of the concentrations of the selected nutrients was then performed using RSM with CCD. After two rounds of RSM, the optimum concentrations ($\%w/v$) of sucrose, dextrin, polypeptone, and yeast extract were identified as 4.5, 4.5, 0.16, and 0.89, respectively. The maximum ansamitocin P-3 titer was 45.2 mg/l with the optimized medium, which was about 6 times higher than that (7.315 mg/l) obtained with an $R_{2}YE$ medium before optimization.

• The KSFM Journal of Fluid Machinery
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• v.16 no.4
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• pp.22-27
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• 2013

The purpose of this paper is to perform the structural design of the small scale vertical-axis wind turbine (VAWT) blade using a response surface method(RSM). First, the four design factors that have a strong influence on the structural response of blade were selected. Analysis conditions were calculated by using the central composite design(CCD), which is a typical design of experiment for the response surface method(RSM). Also, the significance of the central composite design(CCD) was verified using analysis of variance(ANOVA). The finite element analysis was performed for the selected analytical conditions for the application of response surface method(RSM). Finally, a optimization problem was solved with a objective function of blade weight and a constraint of allowable stress to achieve a optimal structural design of blade.

• Environmental Engineering Research
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• v.24 no.1
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• pp.24-30
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• 2019

The present study explores the applicability of response surface methodology (RSM) in conjunction with central composite design (CCD) matrix to statistically optimize ettringite crystallization process for the removal of sulphate from landfill leachate. A three factor-five coded level CCD with 20 runs, was performed to estimate the best fitted model. The RSM results indicated that the fitted quadratic regression model could be appropriate to predict sulfate removal efficiency. The pH was identified as the most dominant parameter affecting sulphate removal. 61.6% of maximum sulphate removal efficiency was obtained at pH of 11.06 for a 1.87 of $Ca/SO_4$ and 0.51 of $Al/SO_4$ molar ratios. The operating cost for ettringite crystallization at optimized conditions was calculated to be 0.52 $/$m^3$. The significance of independent variables and their interactions were tested by analysis of variance. Scanning electron microscope (SEM) and SEM coupled with energy dispersive X-Ray spectroscopy results confirmed the formation of ettringite crystal and were used to describe its morphology features.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.186-191
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• 2015

Presently, rapidly changing and unstable global economic environments demand engineers. Products should be designed to increase profits by lowering costs and provide distinguished performance compared with competitors. This study aims to optimize the design of the power-transmission drive shaft. The mass is reduced as an objective function, and the stress is constrained under a constant value. To reduce the number of experiments, CCD (central composite design) and D-Optimal are used for the experimental design. RSM (response surface methodology) is employed to construct a regression model for the objective functions and constraint function. In this problem, there is only one objective function for the mass. The other objective function gives 1; thus, NSGA-II is used.

• Journal of Korean Society on Water Environment
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• v.26 no.1
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• pp.35-43
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• 2010

This experimental design and response surface methodology (RSM) have been applied to the investigation of the electrocoagulation/flotation of dye wastewater. The electrocoagulation/flotation reactions were mathematically described as a function of parameters current (A), NaCl concentration (B), initial RhB concentration (C) and time (D) being modeled by use of the central composite design (CCD). The application of RSM using the CCD yielded the following regression equation, which is an empirical relationship between the RhB removal (%) and test variables in RhB removal (%) = $-300.42+129.21{\cdot}Current+46.99{\cdot}NaCl-0.11{\cdot}RhB-+43.71{\cdot}Time-5.67{\cdot}Current{\cdot}NaCl-3.18{\cdot}Current{\cdot}Time-2.41{\cdot}NaCl{\cdot}Time-19.79{\cdot}Current^2-2.27{\cdot}NaCl^2-1.59{\cdot}Time^2$. the model predictions agreed well with the experimentally observed result ($R^{2}=0.9728$). The estimated ridge of maximum response and optimal conditions for RhB removal (%) using canonical analysis was 99.4% (A: 1,77 A, NaCl concentration: 2.23 g/L, RhB concentration: 56.12 mg/L, Time: 9.98 min). To confirm this optimum condition, three additional experiments were performed and RhB removal (%) were within range of 86.87% (95% PI low)~111.93% (95% PI high) obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.557-564
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• 2019

The characteristics of the torque and torque ripple of Interior Permanent Magnet Synchronous Motor(IPMSM) are influenced by the size and position of the rotor magnet and the size of the stator slot. This paper deals with the optimal design method for improving torque and torque ripplerate for IPMSM using Response Surface Methodology(RSM). Two objective functions of torque output and torque ripple were derived from the sensitivity analysis by Plackett-Burmann(PB) for the characteristic variables affecting torque and torque ripple. Secondary characteristic variables were selected from the derived objective function and RSM secondary regression model function was estimated by the experiment schedule and analysis results according to the Central Composite Design (CCD). The reliability of the secondary regression model was verified using ANOVA table. The analysis according to the experimental schedule was verified by JMAG(Ver. 18.0) which is Finite Element Method(FEM) software. The torque output of IPMSM applied with final characteristic variables was increased torque output by 11.5 % and the torque ripplerate was reduced by 9.1 %.

• Journal of the Korean Society of Safety
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• v.30 no.1
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• pp.8-13
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• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.