基于计算机化学方法的中药膜过程研究
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摘要
中药膜分离过程是一个非线性系统,其工艺数据具有多变量、非线性、强噪声、自变量相关、非正态分布、非均匀分布等全部或部分特征.为了探索中药复杂体系膜过程优化原理与方法,提出使用神经网络、支持向量机等计算机化学方法建立预测模型,并开发实现集成化的综合分析和预测系统.结果表明,该集成化的综合分析系统较单一分析建模预测精度更高.同时介绍了定量结构-活性/性质相关性(QSAR/QSPR)研究的主要步骤及采用该方法开展的"超滤膜对生物碱类等物质的透过/截留及其定量结构关系的研究".
The membrane separation process of Chinese medicine′s water extraction is a non-linear system and its process data have the features of multi-variable,non-linear,strong noise,independent varieties related to each other,abnormal and nonuniform distribution etc.In order to research the principles and methods of membrane separation process in the complex system of Chinese medicine,computer chemistry methods of artificial neutral network,support vector machine are proposed to build up a forecasting model in this paper,and integrated analysis and forecasting system was developed.The result shown that this integrated analysis system is more accurate than single analysis modeling in forecasting.At the same time,the main steps of quantitative structure-activity/property relationships(QSAR/QSPR) and the study on transmission rate and QSPR of alkaloids with UF membranes were introduced.
The membrane separation process of Chinese medicine′s water extraction is a non-linear system and its process data have the features of multi-variable,non-linear,strong noise,independent varieties related to each other,abnormal and nonuniform distribution etc.In order to research the principles and methods of membrane separation process in the complex system of Chinese medicine,computer chemistry methods of artificial neutral network,support vector machine are proposed to build up a forecasting model in this paper,and integrated analysis and forecasting system was developed.The result shown that this integrated analysis system is more accurate than single analysis modeling in forecasting.At the same time,the main steps of quantitative structure-activity/property relationships(QSAR/QSPR) and the study on transmission rate and QSPR of alkaloids with UF membranes were introduced.
引文
[1]郭立玮,付廷明,李玲娟.面向中药复杂体系的陶瓷膜污染机理研究思路与方法[J].膜科学与技术,2009,29(1):1-7.
[2]Teodosiu C,Pastravanu O,Macoveanu M.Neural net-work models for ultrafiltration and backwashing[J].Wat Res,2000,34(18):4371-4380.
[3]张国俊,刘忠洲.膜过程中超滤膜污染机制的研究及其防治技术进展[J].膜科学与技术,2001,21(4):39-45.
[4]Niemi H,Bulsari A,Palosaari S.Si mulation of mem-brane separation by neural networks[J].J Membr Sci,1995,102(15):185-191.
[5]Razavi M A,Mortazavi A,Mousavi M.Application ofneural networks for crossflow milk ultrafiltration si mu-lation[J].Intern Dairy J,2004,14(1):69-80.
[6]Razavi M A,Mortazavi A,Mousavi M.Dynamic mod-eling of milk ultrafiltration by artificial neural network[J].J Membr Sci,2003,220(1-2):47-58.
[7]李玲娟,翟双灿,郭立玮,等.用支持向量机预测中药水提液膜分离过程[J].计算机与应用化学,2010,27(2):149-154
[8]李玲娟,郭立玮.基于特征提取的中药水提液膜分离预测系统[J].计算机工程与设计,2010,31(9):2023-2026.
[9]陈一超,曾三友,张好春,等.基于遗传神经网络的地震预测研究[J].计算机应用与软件,2008,4:135-137.
[10]邓乃杨,田硬杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2005:1-342.
[11]李盼池,许少华.支持向量机在模式识别中的核函数特性分析[J].计算机工程与设计,2005,2:302-304.
[12]范英飚,潘妍.基于支持向量机自适应核的改进算法[J].计算机工程与设计,2008,12:6073-6111.
[13]朱树先,张仁杰.支持向量核函数选择的研究[J].科学技术与工程,2008,8(16):4513-4517.
[14]杨靖,陈杰,余嵘.纳滤/反渗透分离中有机物的特征参数对截留率的影响研究[J].膜科学与技术,2006,26(2):36-40.
[15]郭立玮编著.中药分离原理与技术[M].北京:人民卫生出版社,2010:117-122.
[16]乔园园,张明涛,主编.简明计算机化学教程[M].天津:南开大学出版社,2005:72-86.
[17]董洁.基于模拟体系定量构效(QSAR)与传质模型和动力学分析的黄连解毒汤超滤机理研究[D].博士学位论文.南京:南京中医药大学,2009.
[2]Teodosiu C,Pastravanu O,Macoveanu M.Neural net-work models for ultrafiltration and backwashing[J].Wat Res,2000,34(18):4371-4380.
[3]张国俊,刘忠洲.膜过程中超滤膜污染机制的研究及其防治技术进展[J].膜科学与技术,2001,21(4):39-45.
[4]Niemi H,Bulsari A,Palosaari S.Si mulation of mem-brane separation by neural networks[J].J Membr Sci,1995,102(15):185-191.
[5]Razavi M A,Mortazavi A,Mousavi M.Application ofneural networks for crossflow milk ultrafiltration si mu-lation[J].Intern Dairy J,2004,14(1):69-80.
[6]Razavi M A,Mortazavi A,Mousavi M.Dynamic mod-eling of milk ultrafiltration by artificial neural network[J].J Membr Sci,2003,220(1-2):47-58.
[7]李玲娟,翟双灿,郭立玮,等.用支持向量机预测中药水提液膜分离过程[J].计算机与应用化学,2010,27(2):149-154
[8]李玲娟,郭立玮.基于特征提取的中药水提液膜分离预测系统[J].计算机工程与设计,2010,31(9):2023-2026.
[9]陈一超,曾三友,张好春,等.基于遗传神经网络的地震预测研究[J].计算机应用与软件,2008,4:135-137.
[10]邓乃杨,田硬杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2005:1-342.
[11]李盼池,许少华.支持向量机在模式识别中的核函数特性分析[J].计算机工程与设计,2005,2:302-304.
[12]范英飚,潘妍.基于支持向量机自适应核的改进算法[J].计算机工程与设计,2008,12:6073-6111.
[13]朱树先,张仁杰.支持向量核函数选择的研究[J].科学技术与工程,2008,8(16):4513-4517.
[14]杨靖,陈杰,余嵘.纳滤/反渗透分离中有机物的特征参数对截留率的影响研究[J].膜科学与技术,2006,26(2):36-40.
[15]郭立玮编著.中药分离原理与技术[M].北京:人民卫生出版社,2010:117-122.
[16]乔园园,张明涛,主编.简明计算机化学教程[M].天津:南开大学出版社,2005:72-86.
[17]董洁.基于模拟体系定量构效(QSAR)与传质模型和动力学分析的黄连解毒汤超滤机理研究[D].博士学位论文.南京:南京中医药大学,2009.
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