• 대한영양사협회학술지
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• 제20권3호
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• pp.212-226
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• 2014

The purpose of this study was to determine the optimal composite recipe of rice muffin using three different amounts of Chinese artichoke (Stachys sieboldii MIQ) powder, brown sugar, and egg. Response surface methodology (RSM) was used to obtain 16 experimental points (including three replicates of Chinese artichoke powder, brown sugar, and egg), and the Chinese artichoke rice muffin formulation was optimized using rheology. The results of the sensory evaluation showed very significant values for color, texture, sweetness, and overall quality (P<0.05). The results of the color, texture, and chemical analyses showed significant values for crumb redness (P<0.01), crumb yellowness (P<0.05), crust redness (P<0.05), crust yellowness (P<0.001), crust lightness (P<0.05), adhesiveness (P<0.01), springiness (P<0.001), gumminess (P<0.01), cohesiveness (P<0.05), moisture content (P<0.05), and sweetness (P<0.05). As a result, optimum formulations obtained by numerical and graphical methods were found to be 8.28 g of Chinese artichoke powder, 66.20 g of brown sugar, 111.72 g of sticky rice powder, 30 g of rice powder, and 59.37 g of egg.

• 품질경영학회지
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• 제41권2호
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• pp.249-260
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• 2013

Purpose: The main objective of this paper is to propose an RD method by developing a neural network (NN)-based estimation approach in order to provide an alternative aspect of response surface methodology (RSM). Methods: A specific modeling procedure for integrating NN principles into response function estimations is identified in order to estimate functional relationships between input factors and output responses. Finally, a comparative study based on simulation is performed as verification purposes. Results: This simulation study demonstrates that the proposed NN-based RD method provides better optimal solutions than RSM. Conclusion: The proposed NN-based RD approach can be a potential alternative method to utilize many RD problems in competitive manufacturing nowadays.

• 한국환경보건학회지
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• 제48권3호
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• pp.167-175
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• 2022

Background: Disinfection is essential to provide drinking water from a water source. The disinfection process mainly consists of the use of chlorine and ozone, but when chlorine is used as a disinfectant, the problem of disinfection by-products arises. In order to resolve the issue of disinfection by-products, electro-chlorination technology that produces chlorine-based disinfectants from salt water through electrochemical principles should be applied. Objectives: This study surveys the possibility of optimally producing active chlorine from synthetic NaCl solutions using an electro-chlorination system through RSM. Methods: Response surface methodology (RSM) has been used for modeling and optimizing a variety of water and wastewater treatment processes. This study surveys the possibility of optimally producing active chlorine from synthetic saline solutions using electrolysis through RSM. Various operating parameters, such as distance of electrodes, sodium chloride concentration, electrical potential, and electrolysis time were evaluated. Results: Various operating parameters, such as distance of electrodes, sodium chloride concentration, electrical potential, and electrolysis time were evaluated. A central composite design (CCD) was applied to determine the optimal experimental factors for chlorine production. Conclusions: The concentration of the synthetic NaCl solution and the distance between electrodes had the greatest influence on the generation of hypochlorite disinfectant. The closer the distance between the electrodes and the higher the concentration of the synthetic NaCl solution, the more hypochlorous acid disinfectant was produced.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2007년도 추계 학술대회논문집
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• pp.92-99
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• 2007

In this paper, optimization of the vaned centrifugal compressor was carried out at a given mass flow rate condition. Firstly, impeller optimization was conducted using response surface method (RSM) which is one of optimization methods. After the optimization of the impeller was completed, diffuser optimization was performed with the optimized impeller. In these processes, Navier-Stokes solver was used to calculate the flow inside the centrifugal compressor. And the optimization is performed with Box-Behnken design method which is efficient for fitting second-order response surfaces to reduce the number of calculations required. As a result, compared with the reference model, the efficiency and the pressure ratio of the optimized impeller and diffuser are found to be increased. The performance at off-design conditions is presented.

• 한국유체기계학회 논문집
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• 제19권2호
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• pp.16-20
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• 2016

Design optimization of a transonic compressor rotor(NASA rotor 37) was carried out using response surface method(RSM) which is one of the optimization methods. A numerical simulation was conducted using ANSYS CFX by solving three-dimensional Reynolds-averaged Navier Stokes(RANS) equations. Response surfaces that were based on the results of the design of experiment(DOE) techniques were used to find an optimal shape of blade which has the maximum aerodynamic performance. Two objective functions, viz., the adiabatic efficiency and the loss coefficient were selected with three design configurations to optimize the blade shape. As a result, the efficiency of the optimized blade is found to be increased.

• International Journal of Automotive Technology
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• 제8권1호
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• pp.93-102
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• 2007

The frontal crash optimization of an engine room member using the response surface method was studied. The engine room member is composed of the front side member and the sub-frame. The thicknesses of the panels on the front side member and the sub-frame were selected as the design variables. The purpose of the optimization was to reduce the weight of the structure, under the constraint that the objective quantity of crash energy is absorbed. The response surface method was used to approximate the crash behavior in mathematical form for optimization procedure. To research the effect of the regression method, two different methodologies were used in constructing the response surface model, the least square method and the moving least square method. The optimum with the two methods was verified by the simulation result. The precision of the surrogate model affected the optimal design. The moving least square method showed better approximation than the least square method. In addition to the deterministic optimization, the reliability-based design optimization using the response surface method was executed to examine the effect of uncertainties in design variables. The requirement for reliability made the optimal structure be heavier than the result of the deterministic optimization. Compared with the deterministic optimum, the optimal design using the reliability-based design optimization showed higher crash energy absorption and little probability of failure in achieving the objective.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권3호
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• 한국식품조리과학회지
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• 제25권2호
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• pp.234-242
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• 2009

This study was performed to find the optimum ratio of ingredients for the manufacture of kiwifruit jam. The experiment was designed according to the D-optimal design of RSM (response surface methodology), which included 18 experimental points with 4 replicates for three independent variables (sugar $35{\sim}60%$, pectin $0.1{\sim}1.0.%$, kiwifruit paste $0.37{\sim}0.90%$). The compositional and functional properties of the prepared products were measured, and these values were applied to mathematical models. A canonical form and trace plot showed the influence of each variable on the quality attributes of the final product mixture. By use of the F-test, viscosity, color values (L, a, b), and sensory characteristics (color) were expressed by a linear model, while the L color value and select sensory characteristics (smell, taste, overall acceptance) were also expressed by a quadratic model. The optimum formulations by the numerical and graphical methods, were similar, and with the numerical method it presented as: sugar, pectin, and barley sproutling powder at 49.7%, 0.5%, and 0.6%, respectively. The above results demonstrate the feasibility of preparing kiwifruit jam added with barley sproutling powder, and therefore, the commercialization of a kiwifruit jam marketed as a functional food is deemed possible.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.404-409
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• 2004

A compressor loop pipe is the most important part in a refrigerator from the view of structural vibration and noise. Vibration energy generated from a compressor's inner body is transmitted to the shell and outside through the loop pipe. For this reason it is very important to design a compressor loop pipe. But, for geometrical complexity and dynamic nonlinearity of the loop pipe, analysis and design of the loop pipe is very difficult. So the statistical and experimental methods have to be used for design of this system. The response surface method (RSM) becomes a popular meta-modeling technique f3r the complex system as this loop pipe. As starting point of loop pile's optimization, FEA model and simple experimental model are used instead of the real loop pipe model. After RS model was constructed, using sensitivity-based optimizer performed optimization for the loop pipe. And the moving least square method (MLSM) was applied to reduce the approximation error.