天然产物研究与开发 ›› 2016, Vol. 28 ›› Issue (4): 586-590.doi: 10.16333/j.1001-6880.2016.4.021

• 开发研究 • 上一篇    下一篇

基于BP神经网络的纤维性根茎药材酶解提取-超滤纯化的临界通量与压力预测

王继龙,刘晓霞,魏舒畅*,柳春,范凌云,金辉   

  1. 甘肃中医药大学,兰州 730000
  • 出版日期:2016-04-30 发布日期:2016-05-13

Prediction of Critical Flux and Pressure of Enzymolsis Extraction Ultrafiltration Purification for Fibrous Rhizome Herbs Based on BP Neural Network

WANG Ji-long,LIU Xiao-xia,WEI Shu-chang*,LIU Chun,FAN Ling-yun,JIN Hui   

  1. Gansu University of Chinese Medicine,Lanzhou 730000,China
  • Online:2016-04-30 Published:2016-05-13

摘要: 为了有效解决纤维性根茎药材在采用酶解提取-超滤纯化集成技术过程中的膜污染问题,保证该集成技术的顺畅使用,以红芪酶解液的超滤数据为基础,采用BP神经网络构建了临界通量和临界压力预测模型,对模型的模拟能力和对黄芪的适用性进行了检验,并对利用连接权法计算得到的输入变量的相对贡献进行了敏感性分析。结果表明,所建模型具有很好的模拟能力和适用性,模拟的临界通量和临界压力的绝对误差和误差率的平均值分别为1.5228 L/(m2·h)、0.0032 MPa和3.46%、2.50%,R2分别为0.96和0.95;对黄芪模拟的绝对误差和误差率的平均值分别为1.4360 L/ (m2·h)、0.0034 MPa和3.93%、2.80%;输入变量对临界通量和压力的相对贡献大小顺序均为黏度>浓度>pH>温度。

关键词: 红芪, 黄芪, 酶解提取, 超滤, 临界通量, 临界压力, BP神经网络

Abstract: The aim of this study was to effectively solve the problem of membrane fouling when integrated techniques of the enzymolsis extraction-ultrafiltration purification were used in fibrous rhizome herbs and keep the integrated techniques using smoothly.The prediction model of critical flux and pressure was established based on the ultrafiltration data of enzymatic hydrolysate of Hedysari Radix by BP neural network.The performance and applicability of the model were evaluated.Then sensitivity analysis of input variables were performed using connection weights method to assess the relative contribution of input variables.Results indicated that the model had better performance and applicability.Mean absolute error,mean error rate and R2 of critical flux and pressure were 1.5228 L/(m2·h) and 0.0032 MPa,3.46% and 2.50%,0.96 and 0.95,respectively.Mean absolute error and mean error rate of critical flux and pressure for Astragali Radix were 1.4360 L/(m2·h) and 0.0034 MPa,3.93% and 2.80%,respectively.The relative contribution of input variables to critical flux and pressure presented the same order of viscosity>concentration>pH>temperature.

Key words: Hedysari Radix, Astragali Radix, enzymolsis extraction, ultrafiltration, critical flux, critical pressure, BP neural network

中图分类号: 

R284.2