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Using experimental design to optimize the process parameters in fluidized bed granulation on a semi-full scale.

机译:使用实验设计以半满规模优化流化床制粒工艺参数。

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A face-centered central composite design was applied in order to optimize the granulation process on a semi-full scale (30-kg batch) for the geometric mean granule size. The granulation process variables investigated were: inlet air temperature, inlet airflow rate, spray rate and inlet air humidity. Based on the process variables, the theoretical powder bed moisture content after the spraying process and a measure for the droplet size were determined. Multiple regression modeling was used to develop two models for the granule size: an empirical model, based on the four process parameters, and a fundamental model, based on the balance between the granule growth affected by the theoretical powder bed moisture content and the droplet size and the breakage effect of the airflow rate. These regression models were used to optimize the granulation process to obtain a granule size between 300 and 500 &mgr;m. Additional experiments confirmed that these models were valid. Other granule properties, namely the geometric standard deviation, the Hausner index, the angle of repose and the moisture content, were evaluated at the optimal operation conditions.
机译:应用了以面心为中心的中央复合材料设计,以优化半平均规模(每批30千克)的制粒工艺,以实现几何平均粒度。研究的制粒过程变量为:进气温度,进气流量,喷雾速率和进气湿度。根据工艺变量,确定喷涂过程后的理论粉末床含水量和液滴尺寸的度量。多元回归模型用于开发两种粒度的模型:基于四个工艺参数的经验模型,以及基于受理论粉床含水量和液滴尺寸影响的颗粒生长之间平衡的基础模型以及气流速率的破坏效果。这些回归模型用于优化制粒过程,以获得300至500μm的颗粒大小。其他实验证实这些模型是有效的。在最佳操作条件下评估了其他颗粒特性,即几何标准偏差,Hausner指数,休止角和水分含量。

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