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首页> 外文期刊>Separation and Purification Technology >Optimization of ultrasound-assisted ultrafiltration of Radix astragalus extracts with hollow fiber membrane using response surface methodology
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Optimization of ultrasound-assisted ultrafiltration of Radix astragalus extracts with hollow fiber membrane using response surface methodology

机译:响应面法优化中空纤维膜超声辅助黄芪提取物的超滤

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摘要

Response surface methodology (RSM) with a central composite rotatable design (CCRD) was employed to optimize the process of ultrasound-assisted ultrafiltration (UF) for Radix astragalus mixtures. The effects and mutual interaction of several parameters, namely ultrasonic power, ultrasonic irradiation mode, trans-membrane pressure (TMP) and temperature, on fouling degree (Y1) and process duration (Y2) were investigated simultaneously. The analysis of variance (ANOVA) demonstrates that the second order polynomial regression models were appropriate and significant, with R2 of 0.9820 and 0.9581 for Y1 and Y2, respectively. The study also shows that TMP is the most significant factor, followed by the temperature, ultrasonic power and irradiation mode. The desirability function approach was used to find the optimum conditions to minimize fouling degree and process duration simultaneously. The optimum conditions were found to be at ultrasonic power of 120 W, continuous ultrasonic irradiation mode, TMP of 0.60 bar and temperature of 20 °C. The predicted responses are 40.2% for fouling degree and 57 min for process duration, which are in good agreement with the results obtained from the confirmation experiments, valued about 38.5-43% and 53-58 min respectively. The results indicate that the regression models are adequate and RSM is an efficient optimization tool for multi-responses and multi-variables study.
机译:采用响应面方法(RSM)和中央复合可旋转设计(CCRD)来优化黄芪混合物的超声辅助超滤(UF)工艺。同时研究了超声功率,超声辐照方式,跨膜压力(TMP)和温度对结垢度(Y1)和工艺持续时间(Y2)的影响和相互影响。方差分析(ANOVA)表明,二阶多项式回归模型是适当且显着的,Y1和Y2的R2分别为0.9820和0.9581。研究还表明,TMP是最重要的因素,其次是温度,超声功率和照射模式。期望函数方法用于找到最佳条件,以同时最小化结垢程度和过程持续时间。发现最佳条件是在120 W超声功率,连续超声照射模式,0.60 bar的TMP和20°C的温度下。污垢度的预测响应为40.2%,过程持续时间的预测响应为57分钟,与确认实验的结果高度吻合,分别约为38.5-43%和53-58分钟。结果表明,回归模型是足够的,RSM是用于多响应和多变量研究的有效优化工具。

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