• Journal of the Korean Society of Safety
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• v.38 no.6
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• pp.9-15
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• 2023

Hydrogen is gaining attention as a sustainable and renewable energy source, potentially replacing fossil fuels. Its high diffusivity, wide flammable range, and low ignition energy make it prone to ignition even with minimal friction, potentially leading to fire and explosion risks. Workplaces manage ignition risks by classifying areas with explosive atmospheres. However, the effective installation of a blast wall can significantly limit the spread of hydrogen, thereby enhancing workplace safety. To optimize the wall installation of this barrier, we employed the response surface methodology (RSM), considering variables such as wall distance, height, and width. We performed 17 simulations using the Box-Behnken design, conducted using FLACS software. This process yielded two objective functions: explosion likelihood near the barrier and explosion overpressure affecting the blast wall. We successfully achieved the optimal solution using multi-objective optimization for these two functions. We validated the optimal solution through verification simulations to ensure reliability, maintaining a margin of error of 5%. We anticipated that this method would efficiently determine the most effective installation of a blast wall while enhancing workplace safety.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.12
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• pp.689-697
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• 2017

As a fundamental study to apply the new flocculation method using ballast in water treatment process, the optimal conditions for general and ballast coagulant dosage, and pH, which are known to have a significant influence, were derived by response surface methodology. Poly aluminum chloride (PAC) and magnetite ballast were used as a general coagulant and ballast, respectively. Coagulation experiments were performed by jar-tester using the kaolin based synthetic water. The effects of three independent variables (pH, PAC, and ballast) on response variables (turbidity removal rate and average settling velocity of flocs) and the optimum condition of independent variables to induce the optimum flocculation were obtained by 17 experimental conditions designed by Box-Behnken procedure. After performing experiments, the quadratic regression model was derived for each of response variables, and the response surface analysis was conducted to explore the correlation between independent variables and response variables. The $R^2$ values for the turbidity removal rate and the average settling velocity were 0.9909 and 0.8295, respectively. The optimal conditions of independent variables were 7.4 of pH, 38 mg/L of PAC and 1,000 mg/L of ballast. Under these conditions, the turbidity removal rate was more than 97% and the average settling velocity exceeded 35 m/h.

• Journal of the East Asian Society of Dietary Life
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• v.23 no.6
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• pp.799-809
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• 2013

This study was performed to determine the optimal composition of a makgeolli administered nuruk, water and Asparagus cochinchinensis. The experiment was designed base on BBD (box-behnken design), and an evaluation was carried out by means of RSM (response surface methodology), which included 15 experimental points with 3 replicates for the three independent variables nuruk, water and Asparagus cochinchinensis. The nuruk levels were tested in a range of 10~30 g, the water levels in a range 120~180% and Asparagus cochinchinensis was tested within a range of 2~6% by the weight of cooked-rice. Using the F-test, brix and appearance were expressed as a linear model, whereas the pH, acidity, DPPH radical scavenging, L-value, savory taste, taste, fresh aroma, after swallow and overall acceptability were expressed as a quadratic model. Increased amount of Asparagus cochinchinensis led to the reduction of the sensory scores for appearance, flavor, taste, texture and overall quality. The optimum formulation by numerical and graphical method were similar: nuruk 24.50 g, water 174.95% and Asparagus cochinchinensis 2.40%.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.11
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• pp.831-836
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• 2010

In this paper, Plasma Enhanced Chemical Vapor Deposition (PECVD) $SiO_2$ film properties are modeled using statistical analysis and neural networks. For systemic analysis, Box-Behnken's 3 factor design of experiments (DOE) with response surface method are used. For characterization, deposited film thickness and film stress are considered as film properties and three process input factors including plasma RF power, flow rate of $N_2O$ gas, and flow rate of 5% $SiH_4$ gas contained at $N_2$ gas are considered for modeling. For film thickness characterization, regression based model showed only 0.71% of root mean squared (RMS) error. Also, for film stress model case, both regression model and neural prediction model showed acceptable RMS error. For sensitivity analysis, compare to conventional fixed mid point based analysis, proposed sensitivity analysis for entire range of interest support more process information to optimize process recipes to satisfy specific film characteristic requirements.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.253-262
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• 2020

This study provides a comparative analysis of experiments-based enhancements and sensitivity evaluations for the structural design of an automatic ocean salt collector under various load conditions. The sizing variables of the structural members were considered as design factors. The strength and weight performances were selected as output responses. The design of experiments used in the comparative study consisted of the orthogonal array design, Box-Behnken design, and central composite design. The response surface model, one of the metamodels, was applied to the approximate model generation. The design enhancement performance metrics, including numerical costs and weight minimization, according to the design of experiments, were compared from the best design case results. The central composite design method showed the most enhanced design results for the structural design of the automatic ocean salt collector.

• Journal of Microbiology and Biotechnology
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• v.27 no.7
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• pp.1209-1215
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• 2017

Microwave sterilization was performed to inactivate the spores of biofilms of Bacillus cereus involved in foodborne illness. The sterilization conditions, such as the amount of water and the operating temperature and treatment time, were optimized using statistical analysis based on 15 runs of experimental results designed by the Box-Behnken method. Statistical analysis showed that the optimal conditions for the inactivation of B. cereus biofilms were 14 ml of water, $108^{\circ}C$ of temperature, and 15 min of treatment time. Interestingly, response surface plots showed that the amount of water is the most important factor for microwave sterilization under the present conditions. Complete inactivation by microwaves was achieved in 5 min, and the inactivation efficiency by microwave was obviously higher than that by conventional steam autoclave. Finally, confocal laser scanning microscopy images showed that the principal effect of microwave treatment was cell membrane disruption. Thus, this study can contribute to the development of a process to control food-associated pathogens.

• The Korean Journal of Food & Health Convergence
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• v.5 no.5
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• pp.11-25
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• 2019

Extraction of oil from Moringa oleifera seed using Response Surface Methodology (RSM) was investigated. Effects of three factors namely: sample mass, particle size and extraction time on the response, Moringa oleifera a volume extracted, were determined. The Box-Behnken design of RSM was employed which resulted in 15 experimental runs. Extraction was carried out in a 250 ml Soxhlet extractor with Hexane and Ethanol as solvent. The Moringa oleifera seed powder was packed inside a muslin cloth placed in a thimble of the Soxhlet extractor. The extraction was carried out at 60℃ using thermostatic heating mantle. The solvent in the extracted oil was evaporated and the resulting oil further dried to constant weight in the oven. This study demonstrates that Moringa oleifera oil can be extracted from its seed using ethanol and acetone as extraction solvent. The optimum process variables for both solvent (ethanol and acetone) was determined at sample weight of 40 g, particle size of 325 ㎛ and extraction time of 8 hours. It can be deduced that using acetone as solvent produces a higher yield of oil at the same optimum variable conditions compared to when ethanol was used.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.48-52
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• 2019

Virtual metrology, which is one of APC techniques, is a method to predict characteristics of manufactured films using machine learning with saving time and resources. As the photoresist is no longer a mask material for use in high aspect ratios as the CD is reduced, hard mask is introduced to solve such problems. Among many types of hard mask materials, amorphous carbon layer(ACL) is widely investigated due to its advantages of high etch selectivity than conventional photoresist, high optical transmittance, easy deposition process, and removability by oxygen plasma. In this study, VM using different machine learning algorithms is applied to predict the thickness of ACL and trained models are evaluated which model shows best prediction performance. ACL specimens are deposited by plasma enhanced chemical vapor deposition(PECVD) with four different process parameters(Pressure, RF power, $C_3H_6$ gas flow, $N_2$ gas flow). Gradient boosting regression(GBR) algorithm, random forest regression(RFR) algorithm, and neural network(NN) are selected for modeling. The model using gradient boosting algorithm shows most proper performance with higher R-squared value. A model for predicting the thickness of the ACL film within the abovementioned conditions has been successfully constructed.

• Steel and Composite Structures
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• v.46 no.4
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• pp.525-538
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• 2023

This paper focuses on the energy absorption of lattice core sandwich structures of different configurations. The diamond lattice unit cell, which has been extensively investigated for energy absorption applications, is the starting point for this research. The energy absorption behaviour of sandwich structures with an expanded metal sheet as the core is investigated at low-velocity impact loading. Numerical simulations were carried out using ABAQUS/EXPLICIT and the results were thoroughly compared with the experimental results, which indicated desirable accuracy. A parametric analysis, using a Box-Behnken design (BBD), as a method for the design of experiments (DOE), was performed. The samples fabricated in three levels of parameters include 0.081, 0.145, and 0.562 mm² Cell sizes, and 0, 45, and 90-degree cell orientation, which were investigated. It was observed from experimental data that the angle of cells orientation had the highest degree of influence on the specific energy absorption. The results showed that the angle of cells orientation has been the most influential parameter to increase the peak forces. The results from using the design expert software showed the optimal specific energy absorption and peak force to be 1786 J/kg and 26314.4 N, respectively. The obtained R2 values and normal probability plots indicated a good agreement between the experimental results and those predicted by the model.

• Geomechanics and Engineering
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• v.31 no.5
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• pp.529-543
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• 2022

The lattice-spring-based synthetic rock mass model (LS-SRM) technique has been extensively employed in large open-pit mining and underground projects in the last decade. Since the LS-SRM requires a complex and time-consuming calibration process, a robust approach was developed using the Response Surface Methodology (RSM) to optimize the calibration procedure. For this purpose, numerical models were designed using the Box-Behnken Design technique, and numerical simulations were performed under uniaxial and triaxial stress states. The model input parameters represented the models' micro-mechanical (lattice) properties and the macro-scale properties, including uniaxial compressive strength (UCS), elastic modulus, cohesion, and friction angle constitute the output parameters of the model. The results from RSM models indicate that the lattice UCS and lattice friction angle are the most influential parameters on the macro-scale UCS of the specimen. Moreover, lattice UCS and elastic modulus mainly control macro-scale cohesion. Lattice friction angle (flat joint fiction angle) and lattice elastic modulus affect the macro-scale friction angle. Model validation was performed using physical laboratory experiment results, ranging from weak to hard rock. The results indicated that the RSM model could be employed to calibrate LS-SRM numerical models without a trial-and-error process.