垂盆草提取物流化床制粒过程参数的响应曲面模型及偏最小二乘模型研究

An Investigation Into the Effect of Process Parameters of Sedi Herba<\i> Extract Granulation in Fluid Bed on Granule Size Distribution by Response Surface Model and Partial Least Squares Model

    摘要
  • 摘要: 目的 研究响应曲面回归模型及偏最小二乘回归模型对流化床制粒的颗粒粒径分布拟合结果。方法 采用流化床制粒制备垂盆草颗粒，利用Box-Behnken试验设计考察粘合剂加入速度(X1)，液固比(X2)，进风温度（X3）对颗粒粒径的影响，并分别用响应曲面回归模型及偏最小二乘回归模型研究过程参数对粒径分布的拟合情况。结果 回归分析结果表明响应曲面回归模型及偏最小二乘回归模型均能较好的模拟流化床制粒结果，且响应曲面回归模型具有较好的模型拟合精度和预测能力。结论 结合实验设计与不同的统计模型可深入研究流化床制粒过程，提升对流化床制粒过程的理解，为今后该产品产业化发展提供了参考和技术支持。

     

    Abstract: OBJECTIVE To explore the particle size distribution of granules prepared by fluid bed granulation via response surface regression model (RSM) and partial least square regression model (PLS). METHODS Sedi Herba extract was granulated by fluid bed granulation. Box-Behnken design in RSM was utilized to study the effects of binder addition rate (X1)， the liquid to solid ratio (X2) and air temperature (X3) on particle size distribution. Moreover， RSM and PLS were employed to explore the influence of process parameters on particle size distribution. RESULTS The results demonstrated that both of RSM and PLS could fit the fluid bed granulation well. Furthermore， RSM model exhibited better model fitting precision and prediction ability. CONCLUSION We could understand the process of fluid bed granulation profoundly based on experimental design and the different statistical models. A robust and high prediction model could be achieved， which could provide reliable basis and technical support for further production.

     

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