丙烯酰胺反相微乳液渗滤体系聚合动力学模型

Kinetic modeling of acrylamide polymerization in percolating inverse microemul-sions via BP artificial neural networks

在丙烯酰胺反相微乳液渗滤体系聚合动力学研究的基础上,采用BP人工神经网络建立了渗滤体系微乳液聚合动力学数学模型。结果表明该模型对研究体系具有联想及预测能力,并可初步识别聚合体系是否属于渗滤体系,揭示了BP模型可作为有效手段应用于聚合反应建模。

Based on the kinetic study of acrylamide polymerization in percolating inverse microemulsions, artificial neural network (ANN) with back-propagation of error(BP)was adopted to model the polymerization. The capability of association, prediction and recognition of BP model in the microemulsion polymerization suggested that ANN could be employed as an efficient approach to modeling in the field of polymerization reaction.