[논문]혼합물 실험 계획법을 활용한 세정용 계면활성제 혼합물 조성의 최적화

송마리아; 진병석

doi:10.14478/ace.2021.1072

혼합물 실험 계획법을 활용한 세정용 계면활성제 혼합물 조성의 최적화
Optimization of Surfactant Mixture Composition for Cleansing Using Mixture Experiment Design 원문보기

공업화학 = Applied chemistry for engineering, v.32 no.5, 2021년, pp.574 - 580

송마리아 (동덕여자대학교 보건향장학과) , 진병석 (동덕여자대학교 보건향장학과)

초록
AI-Helper

최고 품질의 클렌징 제품 개발을 위해서 계면활성제 혼합물 조성의 최적화를 시도하였다. 사전실험을 통해 세정력, 기포형성력, 오염률에서 각각 우수한 특성을 나타내는 계면활성제 3종 sodium cocoyl alaninate (SCoA), cocamidopropyl betaine (CPB), decyl glucoside (DG)을 선정하였다. 계면활성제 혼합물의 심플렉스 중심 설계 배열에 따른 실험을 수행하고, 실험에서 얻어진 데이터를 가지고 회귀분석을 실시하였다. 통계적으로 유의미한 반응 표면 모델식을 구하고, 세 개의 반응변수의 동시 최적화 과정을 통해 계면활성제 혼합물의 최적 조성은 SCoA (0.22), CPB (0.78), DG (0.00)으로 구해졌다.

Abstract ▼ AI-Helper

The main goal of this study was to find an optimal surfactant mixture composition for the development of the best performing cleansing products. Three different surfactants including sodium cocoyl alaninate (SCoA), cocamidopropyl betaine (CPB), and decyl glucoside (DG) were selected, which showed excellent properties in detergency, foaming height, and contamination rate through preliminary experiments. The experiments by simplex centroid design matrix for surfactant mixtures were performed, and the regression analysis was conducted with the experimental data. Surface response model equations, which is statistically significant (p < 0.05), were obtained. The optimal composition of the surfactant mixture was also determined as SCoA (0.22), CPB (0.78), and DG(0.00) from simultaneous optimization of three response variables.

주제어

표/그림 (12)

그림 Figure 1. Flowchart for experimental design.
그림 Figure 2. Comparison of detergency of surfactants.
그림 Figure 3. Comparison of foaming height of surfactants.
그림 Figure 4. Comparison of black ink contamination rate of surfactant.
그림 Figure 5. Effect of mixture composition on detergency (a) response surface plot (b) maximum value of detergency at optimized mixture composition.
표 Table 1. Design Matrix for the Mixture Experiment
표 Table 2. The Analysis Result of the Regression and Variance for Detergency
그림 Figure 6. Effect of mixture composition on foaming height (a) response surface plot (b) maximum value of foaming height at optimized mixture composition.
그림 Figure 7. Effect of mixture composition on contamination rate (a) response surface plot (b) maximum value of contamination rate at optimized mixture composition.
표 Table 3. The Analysis Result of the Regression and Variance for Foaming Height
표 Table 4. The Analysis Result of the Regression and Variance for Contamination Rate
그림 Figure 8. Results of simultaneous optimization.

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