The purpose of this study was to optimize dough properties using response surface methodology (RSM) and to demonstrate the performances of dough prepared under optimized conditions. Dough mixed with yeast, margarine, salt, sugar and wheat flour was prepared by fermentation process. Hardness, cohesiveness and springiness of dough were selected as critical quality attributes. The critical formulations (yeast and water) and process (fermentation time) variables were selected as critical input variables based on preliminary experiment. Box-Behnken design (BBD) was used as RSM. As a result, the quardratic, the squared and the linear model respectively provided the most appropriate fit (R2>90) and had no significant lack of fit (p>0.05) on critical quality attributes (hardness, cohesiveness and springiness). The accurate prediction of dough characteristics was possible from the selected models. It was confirmed by validation that a good correlation was obtained between the actual and predicted values. In conclusion, the methodologies using RSM in this study might be applicable to the optimization of fermented foods containing various wheat flour and yeast.

The purpose of this study was to optimize dough properties using response surface methodology (RSM) and to demonstrate the performances of dough prepared under optimized conditions. Dough mixed with yeast, margarine, salt, sugar and wheat flour was prepared by fermentation process. Hardness, cohesiveness and springiness of dough were selected as critical quality attributes. The critical formulations (yeast and water) and process (fermentation time) variables were selected as critical input variables based on preliminary experiment. Box-Behnken design (BBD) was used as RSM. As a result, the quardratic, the squared and the linear model respectively provided the most appropriate fit (R2>90) and had no significant lack of fit (p>0.05) on critical quality attributes (hardness, cohesiveness and springiness). The accurate prediction of dough characteristics was possible from the selected models. It was confirmed by validation that a good correlation was obtained between the actual and predicted values. In conclusion, the methodologies using RSM in this study might be applicable to the optimization of fermented foods containing various wheat flour and yeast.

Response surface methodology (RSM) is one of the statistical methods for optimizing dependent variable in response to independent variables, and has been used in various field of food engineering. The model coded by RSM has a canonical formulation of 2nd order polynomial with the normalized ranges of independent variables. To accurately accomplish the optimization using RSM an adequate experimental design, i.e., response surface design, is necessary. Response surface design is determined by type of design and number of independent variables. In this study, we are to develop a response surface design applicable for optimizing hulled barley (Hordeum vulgare) production under various conditions of temperature and humidity in forage growing system. As a result, 3 experimental designs were conceived for future RSM; central composite design (CCD), inscribed central composite design (ICCD) and equiradial design. Each design requires experimental trials of 13, 13, and 8, respectively. We will further select one of the designs for actual experiments for finding the optimal temperature and humidity necessary for maximizing fresh forage production in the system.

RSM (response surface method) is a statistical method that optimizes a response variable (dependent variable) according to multiple explanatory variables (independent variable) [1]. RSM visualizes responses of the target depending on experimental conditions, using a regression equation containing an intercept, and coefficients of first-order, second-order, and interactive terms (equation 1). Response surface experimental design is a method for designing RSM experiments [2] which aims to identify the optimal number of trials (number of data points) and number of conditions (range of experimental variables) according to the order of the regression model. Generally, the number of trials in an experiment is composed of central points, factorial points, and axial (or star) points, which varies depending on the number of variables. In this study, we used three widely used response surface experimental designs, i.e., simplex, central composite, and equiradial designs to propose experimental set-up applicable for a future study regarding the effects of storage conditions (e.g., temperature and humidity) on glucosinolate content.

The determining the appropriate dosage of coagulant is very important, because dosage of coagulant in the coagulation process for wastewater affects removing the amount of pollutants, cost, and producing sludge amount. Accordingly, in this study, in order to determine the optimal PAC dosage in the coagulation process, CCD (Central composite design) was used to proceed experimental design, and the quadratic regression models were constructed between independent variables (pH, influent turbidity, PAC dosage) and each response variable (Total coliform, E.coli, PSD (Particle size distribution) (‹10 μm), TP, PO4-P, and CODcr) by the RSM (Response surface methodology). Also, Considering the various response variables, the optimum PAC dosage and range were derived. As a result, in order to maximize the removal rate of total coliform and E.coli, the values of independent variables are the pH 6-7, the influent turbidity 100-200 NTU, and the PAC dosage 0.07-0.09 ml/L. For maximizing the removal rate of TP, PO4-P, CODcr, and PSD(‹10 μm), it is required for the pH 9, the influent turbidity 200-250 NTU, and the PAC dosage 0.05-0.065 ml/L. In the case of multiple independent variables, when the desirable removal rate for total coliform, E.coli, TP, and PO4-P is 90-100 % and that for CODcr and PSD(‹10 μm) is 50-100 %, the required PAC dosage is 0.05-0.07 ml/L in the pH 9 and influent turbidity 200-250 NTU. Thus, if the influent turbidity is high, adjusting pH is more effective way in terms of cost since a small amount of PAC dosage is required.

Response surface methodology (RSM) based on a Box-Behnken Design (BBD) was applied to optimize the thermal-alkaline pre-treatment operating conditions for anaerobic digestion of flotation scum in food waste leachate. Three independent variables such as thermal temperature, NaOH concentration and reaction time were evaluated. The maximum methane production of 369.2 mL CH4/g VS was estimated under the optimum conditions at 62.0°C, 10.1% NaOH and 35.4 min reaction time. A confirmation test of the predicted optimum conditions verified the validity of the BBD with RSM. The analysis of variance indicated that methane production was more sensitive to both NaOH concentration and thermal temperature than reaction time. Thermal-alkaline pretreatment enhanced the improvement of 40% in methane production compared to the control experiment due to the effective hydrolysis and/or solubilization of organic matters. The fractions with molecular weight cut-off of scum in food waste leachate were conducted before and after pre-treatment to estimate the behaviors of organic matters. The experiment results found that thermal-alkaline pre-treatment could reduce the organic matters more than 10kD with increase the organic matters less than 1kD.

PURPOSES : In this study blast furnace slag, an industrial byproduct, was used with an activating chemicals, Ca(OH)2 and Na2SiO3 for carbon capture and sequestration as well as strength development. METHODS: This paper presents the optimized mixing design of Carbon-Capturing and Sequestering Activated Blast-Furnace Slag Mortar. Design of experiments in order to the optimized mixing design was applied and commercial program (MINITAB) was used. Statistical analysis was used to Box-Behnken (B-B) method in response surface analysis. RESULTS : The influencing factors of experimental are water ratio, Chemical admixture ratio and Curing temperature. In the results of response surface analysis, to obtain goal performance, the optimized mixing design for Carbon-Capturing and Sequestering Activated Blast- Furnace Slag Mortar were water ratio 40%, Chemical admixture ratio 58.78% and Curing temperature of 60℃. CONCLUSIONS: Compared with previous studies of this experiment is to some extent the optimal combination is expected to be reliable.

본 연구는 신경망 알고리즘 및 반응표면법을 이용하여 부품의 최적화 설계 치수를 예측하고, 예측된 데이터의 신뢰성을 상호 검증하는 하는 데 있다. 부하가 변할 때, 부품의 치수를 변화시켜 가며 응력 및 변형량의 변화를 해석 데이터로 수집하여 반응표면법 및 신경망학습에 이용하였다. 이를 위해 임의의 조건에서 반응표면법으로 최적화 설계를 수행하고, 동일한 조건에서 신경망 알고리즘의 예측결과와 비교하였다. 그 결과 최대 3.0%의 치수 오차를 보이는 것으로 나타났다. 또한 검증을 위해 반대로 동일한 하중 및 치수 조건에서 유한요소해석을 통해 응력 및 처짐량을 구해 반응표면법 및 신경망학습의 결과를 비교하였으며, 이때 4.2%의 오차를 보였다. 이는 부품의 사양 변경 시 최적화 설계를 위해 반응표면법 및 신경망을 이용할 수 있으며 신뢰성이 있음을 알 수 있었다. 특히 신경망 학습을 통해 보다 효과적으로 최적화 설계가 가능함을 확인할 수 있었다.

소화시스템에 사용되는 고압의 소화가스 저장용기에는 저장용기의 파손을 막아주는 안전밸브가 있다. 이러한 안전밸브의 내부에는 원형 박판의 파열 판이 들어 있는데, 저장용기의 내압이 위험수준에 도달하면 파열 판이 파손하여 내압을 배출하는 역할을 한다. 안전밸브의 설계인자는 파열 판의 두께, 안전밸브의 유로 직경, 플라스틱 패킹 링의 내부 직경 그리고 파열 판을 고정하는 볼트 안쪽의 필렛 반경이 있다. 이중에서 파열 판의 두께는 0.2mm로 고정을 하였다. 요인배치법을 사용하여 주효과를 결정하였고 회귀방정식을 유도하였다. 이러한 회귀방정식은 추후 안전밸브의 설계에 있어서 기초 설계 자료로서 활용할 수 있도록 실험 결과와 비교하여 검증하였다. 검증실험 및 회귀방정식에 의한 결과의 오차는 약 정도인 것을 확인하였다. 그리고 반응표면법을 사용하여 기밀테스트 압력인 25MPa에서 파열할 수 있는 안전밸브의 최적 모델을 결정하였다.

This study investigated the application of experimental design methodology to optimization of conditions of air-plasma and oxygen-plasma oxidation of N, N-Dimethyl-4-nitrosoaniline (RNO). The reactions of RNO degradation were described as a function of the parameters of voltage (X1), gas flow rate (X2) and initial RNO concentration (X3) and modeled by the use of the central composite design. In pre-test, RNO degradation of the oxygen-plasma was higher than that of the air-plasma though low voltage and gas flow rate. The application of response surface methodology (RSM) yielded the following regression equation, which is an empirical relationship between the RNO removal efficiency and test variables in a coded unit: RNO removal efficiency (%) = 86.06 + 5.00X1 + 14.19X2 - 8.08X3 + 3.63X1X2 - 7.66X2 2 (air-plasma); RNO removal efficiency (%) = 88.06 + 4.18X1 + 2.25X2 - 4.91X3 + 2.35X1X3 + 2.66X1 2 + 1.72X3 2 (oxygen-plasma). In analysis of the main effect, air flow rate and initial RNO concentration were most important factor on RNO degradation in air-plasma and oxygen-plasma, respectively. Optimized conditions under specified range were obtained for the highest desirability at voltage 152.37 V, 135.49 V voltage and 5.79 L/min, 2.82 L/min gas flow rate and 25.65 mg/L, 34.94 mg/L initial RNO concentration for air-plasma and oxygen-plasma, respectively.

The aim of this research was to apply experimental design methodology in the optimization condition of electrochemical oxidation of Rhodamine B(RhB). The reactions of electrochemical oxidation were mathematically described as a function of parameters amounts of current, NaCl dosage, pH and time being modeled by the use of the central composite design, which was used for fitting quadratic response surface model. The application of response surface methodology using central composite design(CCD) technique yielded the following regression equation, which is an empirical relationship between the removal efficiency of RhB and test variable in actual variables: RhB removal (%) = 3.977 + 23.279․Current + 49.124․NaCl － 5.539․pH － 8.863 ․time － 22.710․Current․NaCl + 5.409․Current․time + 2.390․NaCl․time + 1.061․pH․time － 0.570․time2. The model predicted also agree with the experimentally observed result(R2 = 91.9%).