Prediction of the Particle Size Distribution Parameters in a High Shear Granulation Process Using a Key Parameter Definition Combined Artificial Neural Network Model

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• 作者：Huiman Yu ; Jinsheng Fu ; Leping Dang ; Yuensin Cheong ; Hongsing Tan ; Hongyuan Wei
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2015
• 出版时间：November 4, 2015
• 年：2015
• 卷：54
• 期：43
• 页码：10825-10834
• 全文大小：507K
• ISSN：1520-5045

文摘

Various methodologies have been proposed in the literatures to predict the particle size distribution (PSD) in agglomeration processes. However, there is no universal model that can completely satisfy industrial needs due to the complexity of the agglomeration process and the unclear mechanisms of particle growth. A systematic approach using an artificial neural network (ANN) model to predict PSD parameters of different agglomeration processes in high shear mixer was developed. In order to reduce input dimensions, several dimensional or dimensionless parameter groups are defined to represent the raw material properties, equipment geometries, and operation conditions as inputs. According to statistical analysis, an ANN model combined with the key parameter definitions can predict product mean particle size (MPS) within an average absolute relative error (AARE) of 7.18%, a standard deviation (蟽) of 7.72%, and a correlation coefficient (R) of 0.9657 (while for distribution span prediction AARE is 5.76%, 蟽 is 5.88%, and R is 0.9759). The results demonstrate that the ANN model developed in this work can predict the product PSD parameters with good accuracy over a wide range of operating conditions, material properties, and equipment scales.