基于BP神经网络与遗传算法的甘草黄酮动态吸附工艺优化研究

Study on the Dynamic Adsorption Technology Optimization of Licorice Flavonoids Based on BP Neural Network and Genetic Algorithm

在Box-Behnken试验设计的基础上,利用AB-8大孔树脂对甘草黄酮动态吸附的工艺参数进行优化。通过BP神经网络进行AB-8大孔树脂对甘草黄酮动态吸附过程的仿真模拟,利用遗传算法进行全局寻优,获得了最优工艺条件:上柱液黄酮质量浓度1.93 mg/mL,流速374 r/min,pH 5.53。在此条件下吸附率可达93.38%。结果表明,BP神经网络与遗传算法结合可应用于甘草黄酮动态吸附工艺的多目标优化。

On the basis of Box-Behnken experiment design, AB-8 macroporous resin was used to optimize the dynamic adsorption technology parameters of licorice flavonoids. The mathematic model which could be used to simulate the dynamic adsorption process of licorice flavonoids by AB-8 macroporous resin was established by BP neural network. Genetic algorithm was applied in global optimization on the basis of the BP neural network; the optimal technology conditions were obtained： the column liquid concentration on 1.93 mg/mL, velocity 374 r/min and pH 5.53. The adsorption rate could reach 93.38% under these conditions. The results showed that BP nettral network combined with genetic algorithm could be applied to the multi-objective optimization of dynamic adsorption technology of licorice flavonoids.