中药巴布剂粘度的在线检测研究
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摘要
膏药作为中药的四大剂型之一,有着悠久的历史,在治疗一些疾病有很好的效果。本文是以科技部项目“中药新型经皮给药系统关键技术与评价”为背景研制一套“基于传感器网络技术的贴膏剂智能成型设备”。从而形成具有自主知识产权的专利技术,对中药外用贴剂的生产工艺做出重大革新,有利于对我国传统中药贴剂生产技术的稳步推进和技术标准的建立。进一步提升中药经皮给药系统的科技含量和产品标准,促进中药经皮给药的标准化、工程化和现代化发展。
本文研究的中药贴膏粘度检测主要在适合项目要求的前提下解决实时在线检测的问题。本文在成型设备中的中药贴膏粘度在线检测方面进行了研究,从中药膏体粘度的特性、传统测粘度的方法、中药成型设备要求等方面分析,设计一套适合中药粘度在线检测反馈控制的设备。在硬件上,电机、上位机、PLC、传感器等形成一套完整的检测控制方案。在软件上,实现了PLC实现数据采集、PLC与上位机的通讯、计算并控制粘度等流程。最后,用旋转电流法系统进行了数据采集,并对数据建立最小二乘模型和BP神经网络模型,设计了合理的拟合计算过程,并对模型的预测结果进行比较分析。通过比较,BP神经网络模型更符合中药贴膏的精度要求,也方便今后进一步提高精度。并用VC++、MATLAB和数据库实现了BP神经网络的预测算法程序。
课题设计的系统可以准确的对中药生产过程中的粘度参数进行实时在线检测,及时反馈到PLC和计算机中进行处理和实时控制,确保膏药的质量,实现生产的自动化。其次,中药粘度的在线检测为粘度的在线检测提供了一种新的方法,可应用在其他的工业领域实时在线检测和控制,从而提高生产效率,保证产品的质量。
Plaster as one of the four traditional Chinese medicine has a long history and positive impact on the treatment of some diseases. This article develops a "sensor network technology based on intelligent shaping equipment of plaster.", which is based on Science and Technology Project," Chinese new plaster systems and evaluation of key technologies". This will improve the patented technology with independent intellectual property rights and make significant innovation of traditional Chinese medicine in China. It will improve the progress of production technology and the establishment of technical standards. It will further enhance technology and product standards of plaster system, and promote the standardization and modernization of Chinese medicine.
This study mainly solves the problem of real-time online plaster viscosity's detection according to the requirements of project .We have done lots of research about Chinese plaster viscosity of online detection including viscosity characteristics of plaster, the traditional methods of measuring liquid's viscosity, the requirements of project, etc. Then we design a set of equipment which can measure the plaster's viscosity and control with feedback. In terms of hardware, the equipment consists of motor, PC, PLC and sensors and so on .In terms of software, PLC acquires data from sensor, PC computes and send data to PLC by communication, and then PLC control the viscosity according to the default parameter. Finally, we get data by the device, build model by least squares method and BP neural network model, and design the fitting and calculation process. And then the model predictions were compared. By comparison, BP neural network is more consistent with precision of plaster's viscosity. Besides, it is convenient to improve the plaster's viscosity accuracy in the future. And BP neural network prediction algorithm program is completed by VC++,MATLAB and database.
The system can accurately get the on line detection viscosity and timely feedback to the PLC and PC for processing and real-time controlling to ensure the quality of the plaster and improve the automation of production. Secondly, the online detection of Chinese plaster's viscosity provides a new method which can be applied in other industries to real-time online monitor and control viscosity to enhance efficiency of production and ensure product's quality.
本文研究的中药贴膏粘度检测主要在适合项目要求的前提下解决实时在线检测的问题。本文在成型设备中的中药贴膏粘度在线检测方面进行了研究,从中药膏体粘度的特性、传统测粘度的方法、中药成型设备要求等方面分析,设计一套适合中药粘度在线检测反馈控制的设备。在硬件上,电机、上位机、PLC、传感器等形成一套完整的检测控制方案。在软件上,实现了PLC实现数据采集、PLC与上位机的通讯、计算并控制粘度等流程。最后,用旋转电流法系统进行了数据采集,并对数据建立最小二乘模型和BP神经网络模型,设计了合理的拟合计算过程,并对模型的预测结果进行比较分析。通过比较,BP神经网络模型更符合中药贴膏的精度要求,也方便今后进一步提高精度。并用VC++、MATLAB和数据库实现了BP神经网络的预测算法程序。
课题设计的系统可以准确的对中药生产过程中的粘度参数进行实时在线检测,及时反馈到PLC和计算机中进行处理和实时控制,确保膏药的质量,实现生产的自动化。其次,中药粘度的在线检测为粘度的在线检测提供了一种新的方法,可应用在其他的工业领域实时在线检测和控制,从而提高生产效率,保证产品的质量。
Plaster as one of the four traditional Chinese medicine has a long history and positive impact on the treatment of some diseases. This article develops a "sensor network technology based on intelligent shaping equipment of plaster.", which is based on Science and Technology Project," Chinese new plaster systems and evaluation of key technologies". This will improve the patented technology with independent intellectual property rights and make significant innovation of traditional Chinese medicine in China. It will improve the progress of production technology and the establishment of technical standards. It will further enhance technology and product standards of plaster system, and promote the standardization and modernization of Chinese medicine.
This study mainly solves the problem of real-time online plaster viscosity's detection according to the requirements of project .We have done lots of research about Chinese plaster viscosity of online detection including viscosity characteristics of plaster, the traditional methods of measuring liquid's viscosity, the requirements of project, etc. Then we design a set of equipment which can measure the plaster's viscosity and control with feedback. In terms of hardware, the equipment consists of motor, PC, PLC and sensors and so on .In terms of software, PLC acquires data from sensor, PC computes and send data to PLC by communication, and then PLC control the viscosity according to the default parameter. Finally, we get data by the device, build model by least squares method and BP neural network model, and design the fitting and calculation process. And then the model predictions were compared. By comparison, BP neural network is more consistent with precision of plaster's viscosity. Besides, it is convenient to improve the plaster's viscosity accuracy in the future. And BP neural network prediction algorithm program is completed by VC++,MATLAB and database.
The system can accurately get the on line detection viscosity and timely feedback to the PLC and PC for processing and real-time controlling to ensure the quality of the plaster and improve the automation of production. Secondly, the online detection of Chinese plaster's viscosity provides a new method which can be applied in other industries to real-time online monitor and control viscosity to enhance efficiency of production and ensure product's quality.
引文
[1]陈惠钊.粘度测量[M].北京:中国计量出版社,2003,10-25.
[2]余建林.自动毛细管粘度计的原理及应用[J].分析仪器,2003,1:27-28.
[3]李必超,马连山.粘度测量的几种方法[J].石油仪器,2004,18(3):57-58.
[4]李鹏,徐立军,董峰.非牛顿液体粘度的在线测量方法[J].天津大学学报,2003,36(2):170-173.
[5]ASSAELMJ,DALAOUTIN K. Prediction of the Viscosity of Liquid Mixtures: An Improved Approach [J]. Rheologica Acta( S0035-4511),2000,21(2):357- 358.
[6]Zhang,Q.Z.New Development of Viscosity Measurement[M].USA:Prentice Hall.1997.
[7]Paul Kalotay. Density and Viscosity Monitoring Systems Using Coriolis Flow Meters [J]. ISA Transactions,1999,38:303-308.
[8]王红理.液体粘度测量之讨论[J].大学物理实验,1994,7(2):32-35.
[9]胡鉴安,秦毅.旋转式粘度计工作原理及其主要部件设计[J].同济大学学报,1994,22(3):391-394.
[10]Wolfgang Drahm , Hege Bjonnes , et al. A Coriolis Meter with Direct Viscosity Measurement[J].IEE Computing and Control,2003,14:42-43.
[11]Jon Watson and Solartron Mobrey. Measuring viscosity [J ].Hydrocarbon Engineering,2001.
[12]王子延,许新,邱平.具有线性输出的靶式流量传感器[J].仪器仪表学,1995,12(2):191-193.
[13]王巧梭.在线粘度测量及其应用[J],玻璃工业,2001,4:26-29.
[14]G J Q,Warring HE,Zasadzinsk Jetal.Magnetic Needle Viscometer for langmuirmongdayers,Langmuir(S0734—7463),2002,18(7):28-30.
[15]锁斌,孙振东.对石英晶体传感器的粘度测量特性的研究[J],电子测量技术,2007,30(9):45-48.
[16]赵春阳.提高粘度测试准确性的方法[J].玻璃钢,1998,4(4):25-26.
[17]俞惠娟.ND-J粘度自动测量仪的研制[J].计量技术,1980.
[18]DVP—PLC应用技术手册.台达电子工业股份有限公司.2007.6-9.
[19]董艺.单片机与PLC的区别及其对控制系统设计方案选择的影响[J].巢湖学院学报,2007,9(3):65-69.
[20]禹恒洲.基于PLC的烟泊复合生产张力控制[J].计算机测量与控制,2007,53(12)2:25-26.
[21]马兵,陈志军.PLC控制系统在无料钟高炉中的应用[J].冶金动力,2002.
[22]胡上序,陈德钊.观测数据的分析与处理[M].浙江大学出版,1996:87-88.
[23]易芳.采用MATLAB的线性回归分析[J].兵工自动化,2004, 23 (2):68-69.
[24]韩力群.人工神经网络理论、设计及应用[M].北京:清华大学出版社,1998 27(1):9-13.
[25]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科学技术出版社,2003,23-30.
[26]L. Zhou, et al., Modeling and Control for Nonlinear Time-Delay System via Pattern Recognition[J].IEEE Trans.Neural Networks,1992,3(6):993-997
[27] Chen S. and S.A.Billing, Neural networks for nonlinear dynamic system modeling and identification[J]. International Journal of Control, 1990, 56(2):1687-1689.
[28]许东,吴铮.基于MATLAB6.0的系统分析与设计--神经网络[M].西安:西安电子科技大学出版社,2002,34-40.
[29]Zeidenberg,Matthew.Neural network models in artificial intelligence [J]. Ellis Horwood,1990.
[30] Demuth,Howard B.Neural network design[M].Beijing:China Machine Press,2002,20-30.
[31]高隽编著.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2003,49-70.
[32]廖庆华,徐小增,梁杰中.一种改进BP神经网络在感应电机速度控制中的应用[J].计算机仿真,2003,20(6):28-30.
[33]Specht D E. Probabilistic Neural Networks[J]. Neural Networks,1990,3(1):109-118.
[34]飞思科技产品研发中心井中经网络理论与Matlab7实现[M].北京:电子工业出版社,2005.
[35]陈善广,鲍勇.BP神经网络学习算法的研究[J].应用基础与工程科学学报, 1995,3:447-442.
[36]蒋仕章,蒲家宁.成品油顺序输送时的混油粘度计算与误差分析[J].油气储运,2003,22(2).
[2]余建林.自动毛细管粘度计的原理及应用[J].分析仪器,2003,1:27-28.
[3]李必超,马连山.粘度测量的几种方法[J].石油仪器,2004,18(3):57-58.
[4]李鹏,徐立军,董峰.非牛顿液体粘度的在线测量方法[J].天津大学学报,2003,36(2):170-173.
[5]ASSAELMJ,DALAOUTIN K. Prediction of the Viscosity of Liquid Mixtures: An Improved Approach [J]. Rheologica Acta( S0035-4511),2000,21(2):357- 358.
[6]Zhang,Q.Z.New Development of Viscosity Measurement[M].USA:Prentice Hall.1997.
[7]Paul Kalotay. Density and Viscosity Monitoring Systems Using Coriolis Flow Meters [J]. ISA Transactions,1999,38:303-308.
[8]王红理.液体粘度测量之讨论[J].大学物理实验,1994,7(2):32-35.
[9]胡鉴安,秦毅.旋转式粘度计工作原理及其主要部件设计[J].同济大学学报,1994,22(3):391-394.
[10]Wolfgang Drahm , Hege Bjonnes , et al. A Coriolis Meter with Direct Viscosity Measurement[J].IEE Computing and Control,2003,14:42-43.
[11]Jon Watson and Solartron Mobrey. Measuring viscosity [J ].Hydrocarbon Engineering,2001.
[12]王子延,许新,邱平.具有线性输出的靶式流量传感器[J].仪器仪表学,1995,12(2):191-193.
[13]王巧梭.在线粘度测量及其应用[J],玻璃工业,2001,4:26-29.
[14]G J Q,Warring HE,Zasadzinsk Jetal.Magnetic Needle Viscometer for langmuirmongdayers,Langmuir(S0734—7463),2002,18(7):28-30.
[15]锁斌,孙振东.对石英晶体传感器的粘度测量特性的研究[J],电子测量技术,2007,30(9):45-48.
[16]赵春阳.提高粘度测试准确性的方法[J].玻璃钢,1998,4(4):25-26.
[17]俞惠娟.ND-J粘度自动测量仪的研制[J].计量技术,1980.
[18]DVP—PLC应用技术手册.台达电子工业股份有限公司.2007.6-9.
[19]董艺.单片机与PLC的区别及其对控制系统设计方案选择的影响[J].巢湖学院学报,2007,9(3):65-69.
[20]禹恒洲.基于PLC的烟泊复合生产张力控制[J].计算机测量与控制,2007,53(12)2:25-26.
[21]马兵,陈志军.PLC控制系统在无料钟高炉中的应用[J].冶金动力,2002.
[22]胡上序,陈德钊.观测数据的分析与处理[M].浙江大学出版,1996:87-88.
[23]易芳.采用MATLAB的线性回归分析[J].兵工自动化,2004, 23 (2):68-69.
[24]韩力群.人工神经网络理论、设计及应用[M].北京:清华大学出版社,1998 27(1):9-13.
[25]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科学技术出版社,2003,23-30.
[26]L. Zhou, et al., Modeling and Control for Nonlinear Time-Delay System via Pattern Recognition[J].IEEE Trans.Neural Networks,1992,3(6):993-997
[27] Chen S. and S.A.Billing, Neural networks for nonlinear dynamic system modeling and identification[J]. International Journal of Control, 1990, 56(2):1687-1689.
[28]许东,吴铮.基于MATLAB6.0的系统分析与设计--神经网络[M].西安:西安电子科技大学出版社,2002,34-40.
[29]Zeidenberg,Matthew.Neural network models in artificial intelligence [J]. Ellis Horwood,1990.
[30] Demuth,Howard B.Neural network design[M].Beijing:China Machine Press,2002,20-30.
[31]高隽编著.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2003,49-70.
[32]廖庆华,徐小增,梁杰中.一种改进BP神经网络在感应电机速度控制中的应用[J].计算机仿真,2003,20(6):28-30.
[33]Specht D E. Probabilistic Neural Networks[J]. Neural Networks,1990,3(1):109-118.
[34]飞思科技产品研发中心井中经网络理论与Matlab7实现[M].北京:电子工业出版社,2005.
[35]陈善广,鲍勇.BP神经网络学习算法的研究[J].应用基础与工程科学学报, 1995,3:447-442.
[36]蒋仕章,蒲家宁.成品油顺序输送时的混油粘度计算与误差分析[J].油气储运,2003,22(2).