含缓冲液的双水相体系相平衡的神经网络模型

The Neural Network Model of the Phase Equilibrium of Aqueous Two-Phase System Containing Buffering Components

摘要: 针对含有C₄mimBr和K₃C₆H₅O₇/H₃C₆H₅O₇的双水相体系在不同pH值的相平衡数据,建立神经网络模型对其进行了关联和预测,提出了隐含层节点数和误差控制值的二维寻优法,获得了较为满意的计算精度.与热力学方程对比显示,所建模型的精度与Wilson方程基本相当,优于NRTL方程.

Abstract: The neural network was employed to study the phase equilibrium data of the aqueous two-phase system (pH=5,6,7 and 8), which consisted ofC₄mimBr and K₃C₆H₅O₇/H₃C₆H₅O₇. The method of two dimensional optimization was proposed in order to obtain the appropriate node number of the hidden layer and the error control value. The accuracy comparison between the suggested model and the thermodynamic equations showed that the neural network was almost equal to Wilson equation, and superior to NRTL equation.