基于ARM9的生物发酵过程数字控制系统研究
|
摘要
随着科学技术的发展,生物发酵拥有广阔的发展前景。近年来,生物发酵技术和发酵装置作为生物技术产业化的基础,已得到了人们的广泛关注。发酵过程本身复杂,控制难度很大。同时随着生产技术的进步,对过程控制的精度要求也越来越高。因此,开发适用于工业级的生物发酵控制系统,减少生产成本,增加产量,提高产品的质量,具有重要的现实意义。
本文根据生物发酵的流程特点和当今国内市场的切实需要,在总结国内外相关研究的基础上,针对非线性、时变、大滞后的发酵过程,将先进的控制技术应用到生物发酵控制系统中。生物发酵过程中,发酵液的温度、溶解氧浓度、补料量是非常重要的参数,既影响细胞的生长也影响产物的形成。对于不同发酵对象及不同容量的发酵罐,温度的变化比较大,为了实现系统的通用性,应用模糊PID算法对温度进行控制。对于生物发酵过程,精确的溶解氧浓度和补料控制的数学模型很难建立,传统的控制方法很难在溶氧控制系统和补料控制系统中达到好的效果,引入模糊神经网络控制,将模糊控制理论的逻辑推理技术和神经网络的自学习能力有机结合起来。并将改进的模糊神经网络应用于青霉素发酵过程中,实现溶氧和补料系统的优化控制。
系统在硬件设计上采用ARM9微处理S3C2410A作为主控制器,提高了系统的运行速度和数据处理能力,并根据系统所要实现的功能,设计了硬件模块进行数据采集和远程监控。为满足生物发酵过程监控的实时性和处理复杂多任务的需要,构建嵌入式Linux软件平台,并在这个平台下实现生物发酵参数的实时采集和控制。在系统中嵌入Internet,通过Internet在Windows环境下实现远程监控。在客户机的Windows环境下搭建Java开发环境,利用Java Applet技术访问SQL数据库,实现控制系统的实时监控。
本文的工作重点包括:主要参数测量与控制、补料控制、系统的总体设计、嵌入式系统设计、应用程序设计以及通信设计。本发酵过程数字控制系统对发酵过程进行实时监控、优化操作,不仅能避免人工操作的不确定因素,提高自动化水平,而且能够对发酵过程中主要参数进行有效控制,实现智能补料和远程控制,具有重要的现实意义。
With the development of technology, foreground of biology fermentation will be much wider. In recent years, people are paying more and more attention to biology fermentation technology and fermentation equipments which are the foundation of biology technical industry. However the fermentation process is very complex and difficult to control. And along with the demand for growing produce, the demand for the presision of process control is becoming even higher. Thus it is very important to design a fermentation process control system, so as to increase produce efficiency and reduce industry cost.
According to the profile of bio-fermentation process the current market requirement and the relative research at home and abroad. In allusion to time-varying, nonlinearity, time-lag and random of the biology Fermentation control process, the intelligent control technology is incorporated into the control system. Fermentation temperature, dissolved oxygen concentration and feeding quantity are very important parameter in the fermentation process, which not only affect cell growth, but also affect the formation product. For different objects and different fermentation capacity fermenters, there are relatively large changes in temperature. In order to achieve the system interoperability, fuzzy-PID algorithm is applyed to control the temperature. For bio-fermentation process, it is difficult to establish the mathematical model of dissolved oxygen concentration and feeding. Traditional control method is difficult to achieve good control results in the dissolved oxygen control system and feeding system .So fuzzy neural network control algorithm is employed. Fuzzy neural network solution control algorithm combines logic control techniques of fuzzy theory and self-learning ability of neural networks. And the improved fuzzy neural network algorithm is used in penicillin fermentation to achieve optimal control of dissolved oxygen and feeding system.
ARM9 MPU-S3C2410A is used as the main control unit, which increased the operating speed and data handling capacity of the system. According to the function of the system,modules of parameter measure and network communication which collects data and monitors remotely are designed. In order to satisfy the demand of real-time ability and multitask management, build embedded Linux software platform. In the Linux platform,achieved biological fermentation paremeters real-time acquisition and controlling. Achieve remote monitoring through embedded internet in the Windows environment. Java development environment is built in the Windows environment,and Java Applet technology is used to access SQL database to achieve real-time monitoring of the digital control system.
The focus of the work includes the measurement and control of main parements, the feeding control, the system design, embedded system design, application programme design and communication design. The fermentation digital control system can monitor and optimize the fermentation process on the real time. It not only avoid the uncertain factors in manual operation, raise the automation level,but also control the main parameter efficiently , intelligent feeding and remote control. It has important practical significance.
本文根据生物发酵的流程特点和当今国内市场的切实需要,在总结国内外相关研究的基础上,针对非线性、时变、大滞后的发酵过程,将先进的控制技术应用到生物发酵控制系统中。生物发酵过程中,发酵液的温度、溶解氧浓度、补料量是非常重要的参数,既影响细胞的生长也影响产物的形成。对于不同发酵对象及不同容量的发酵罐,温度的变化比较大,为了实现系统的通用性,应用模糊PID算法对温度进行控制。对于生物发酵过程,精确的溶解氧浓度和补料控制的数学模型很难建立,传统的控制方法很难在溶氧控制系统和补料控制系统中达到好的效果,引入模糊神经网络控制,将模糊控制理论的逻辑推理技术和神经网络的自学习能力有机结合起来。并将改进的模糊神经网络应用于青霉素发酵过程中,实现溶氧和补料系统的优化控制。
系统在硬件设计上采用ARM9微处理S3C2410A作为主控制器,提高了系统的运行速度和数据处理能力,并根据系统所要实现的功能,设计了硬件模块进行数据采集和远程监控。为满足生物发酵过程监控的实时性和处理复杂多任务的需要,构建嵌入式Linux软件平台,并在这个平台下实现生物发酵参数的实时采集和控制。在系统中嵌入Internet,通过Internet在Windows环境下实现远程监控。在客户机的Windows环境下搭建Java开发环境,利用Java Applet技术访问SQL数据库,实现控制系统的实时监控。
本文的工作重点包括:主要参数测量与控制、补料控制、系统的总体设计、嵌入式系统设计、应用程序设计以及通信设计。本发酵过程数字控制系统对发酵过程进行实时监控、优化操作,不仅能避免人工操作的不确定因素,提高自动化水平,而且能够对发酵过程中主要参数进行有效控制,实现智能补料和远程控制,具有重要的现实意义。
With the development of technology, foreground of biology fermentation will be much wider. In recent years, people are paying more and more attention to biology fermentation technology and fermentation equipments which are the foundation of biology technical industry. However the fermentation process is very complex and difficult to control. And along with the demand for growing produce, the demand for the presision of process control is becoming even higher. Thus it is very important to design a fermentation process control system, so as to increase produce efficiency and reduce industry cost.
According to the profile of bio-fermentation process the current market requirement and the relative research at home and abroad. In allusion to time-varying, nonlinearity, time-lag and random of the biology Fermentation control process, the intelligent control technology is incorporated into the control system. Fermentation temperature, dissolved oxygen concentration and feeding quantity are very important parameter in the fermentation process, which not only affect cell growth, but also affect the formation product. For different objects and different fermentation capacity fermenters, there are relatively large changes in temperature. In order to achieve the system interoperability, fuzzy-PID algorithm is applyed to control the temperature. For bio-fermentation process, it is difficult to establish the mathematical model of dissolved oxygen concentration and feeding. Traditional control method is difficult to achieve good control results in the dissolved oxygen control system and feeding system .So fuzzy neural network control algorithm is employed. Fuzzy neural network solution control algorithm combines logic control techniques of fuzzy theory and self-learning ability of neural networks. And the improved fuzzy neural network algorithm is used in penicillin fermentation to achieve optimal control of dissolved oxygen and feeding system.
ARM9 MPU-S3C2410A is used as the main control unit, which increased the operating speed and data handling capacity of the system. According to the function of the system,modules of parameter measure and network communication which collects data and monitors remotely are designed. In order to satisfy the demand of real-time ability and multitask management, build embedded Linux software platform. In the Linux platform,achieved biological fermentation paremeters real-time acquisition and controlling. Achieve remote monitoring through embedded internet in the Windows environment. Java development environment is built in the Windows environment,and Java Applet technology is used to access SQL database to achieve real-time monitoring of the digital control system.
The focus of the work includes the measurement and control of main parements, the feeding control, the system design, embedded system design, application programme design and communication design. The fermentation digital control system can monitor and optimize the fermentation process on the real time. It not only avoid the uncertain factors in manual operation, raise the automation level,but also control the main parameter efficiently , intelligent feeding and remote control. It has important practical significance.
引文
[1]史密斯.生物技术概论[M].北京:北京科技出版社,2006.
[2]郁梅.基于以太网的生物发酵过程嵌入式两级计算机控制系统的通信技术[D].上海交通大学,2004.
[3]刘尧猛.基于神经网络的发酵过程建模及控制开发环境研究[D].天津大学,2003.
[4]欧阳平凯.发酵工程关键技术及其应用[M].北京:化学工业出版社.2005.
[5]黄儒强.生物发酵核技术与设备操作[M].北京:化学工业出版社.2006.
[6]余龙江.发酵工程原理与技术应用[M].北京:北京化学工业出版社,2006.
[7]史仲平,潘丰.发酵过程解析、控制与检测技术[M].北京:化学工业出版社.2005.
[8]D.S.B.ernstein.On bridging the theory/practice gap[J].IEEE Control Systems,1996:64-70.
[9]王庆林.经典控制理论的发展[J].自动化博览,1996(5):22-25.
[10]邱立友.发酵工程与设备[M].中国农业出版社,2007.08.
[11]陈坚,刘立明,堵国成.发酵过程优化原理与技术[M].北京:化学工业出版社,2009.
[12]范茂兴,潘丰,盛炳乾等.氨基酸发酵微机控制系统[J].无锡轻工业学院学报,1994,13(1):57-66.
[13]Yen-Chun Liu,Feng-Sheng Wang,Wen-Chien Lee,On-line monitoring and controlling system for fermentation process[J].Biochemical Engineering Journal,2001,7:17-25.
[14]张亚刚.发酵过程计算机控制系统的设计与研究[D].天津轻工业学院,2001,12.
[15]肖应旺,徐宝国等.发酵过程实时监测与控制系统的研究[J].测控技术,2003,22(7):18-20.
[16]王秀清,李淑清等.发酵过程PLC控制系统的研究[J].天津科技大学学报,2004,19(1).
[17]熊伟丽,徐保国等.基于PLC的Fuzzy-PI发酵温度控制系统[J].计算机工程,2005,31(9):7-9
[18]俞金寿,何衍庆.集散控制系统原理及应用[M].化学工业出版社,1985.
[19]管国强,黄达明,吴春笃.一种小型低成本发酵过程集散控制系统[J].江苏大学学报(自然科学版),2002,23(1):58-61.
[20]张红奎,温瑞.DCS在金霉素发酵中的应用[J].仪器标准化与计量,2005(4):44-46.
[21]张玉珊,王静,毋茂盛.基于DCS的发酵系统设计与研究[J].河南师范大学学报(自然科学版),2004,32(4):33-36.
[22]Frank J.Romeu.Development of biotechnology control system[J].ISA Transactions,1995,34:3-19.
[23]潘丰.FPC2000集散控制系统的设计[J].自动化与仪表,2002(4):24-28.
[24]吴新忠,乔宏颖,任子辉.现场总线技术综述[J].工矿自动化,2004(1):23-25.
[25]姜映红.基于PROFIBUS现场总线技术的啤酒发酵过程PLC自动控制系统[D].兰州理工大学,2006.4.
[26]王海霞,徐保国.基于PROFIBUS-DP的β-干路聚糖酶发酵控制系统的研究[J].计算机测量与控制,2005,13(10):1044-1046.
[27]何用辉,黄耀志.基于CAN总线的啤酒发酵监控系统[J].计算机测量与控制.2006,14(2):202-204.
[28]王献伟,赵霞,吴胜昔.基于基金会现场总线的发酵罐控制系统设计与实施[J].测控技术,2005,24(2),29-31.
[29]Zlateva.P.Binary control of fermentation processes[J].Cybernetics and Systems,2000(31),Sept:669-88.
[30]Zhang Zhi-huan.Research on multiple model control of nonlinear system and simulation[J].(Fac.of Inf.Sci.&Technol.,Ningbo Univ.,China),v 15,n 7,July 2003:919-21
[31]Wang Haixia,Xiao Yingwang,Xu Baoguo.Optimization control of material makeup ferment process in batches based on SVM-IGA[J],Computer Measurement&Control,v 13,n 7,2005:674-6.
[32]Levisauskas,D.Tekorius,T Model-based optimization of fed-batch fermentation processes using predetermined type feed-rate time profiles:a comparative study[J].Information Technology and Control,v 34,n 3,2005:231-6.
[33]Odetunji,O.A.Kehinde,O.O.Computer simulation of fuzzy control system for gari fermentation plant[J].Journal of Food Engineering,v 68,n 2,May 2005:197-207.
[34]Riid,A.Rustern,E.Automatic linguistic inversion of a fuzzy model for fed-batch fermentation control[J].2006 International Conference on Intelligent Engineering Systems,2006:66-69.
[35]Nagy,Zoltan Kalman.Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks[J].Chemical Engineering Journal,2007:127,Mar 1:95-109.
[36]Kawohl,M.Heine,T.,King,R.Model based estimation and optimal control of fed-batch fernuwUation processes for the production of antibiotics[J].Chemical Engineering&Processing:Process Intensification,v 46,n 11,Nov.2007,23-41.
[37]Jiang Weijin,Xu Yusheng.A novel multi-agent automated negotiation model based on associated intent,Agent Computing and multi-Agent System[J].Springer Berlin/Heidelberg,2006,7:608-613.
[38]Fang Lei,AntsaklisPanos J.,Tzimas Anastasis,Asynchronous consensus protocols:PmVn;nary results,simulations and open questions[J].Proceedings of the 44th IEEE Conference on Decision and Control,and the European Control Conference,CDC-ECC 05,2005:2194-4-2199.
[39]Zhaoshu Liu,Gangyan Li,Lili Zhu.The study of key technologies in manufacturing management-control integration[J].Computational Intelligence for Modelling,Control and Automation,2006.
[40]Zoltan Kalman Nagy.Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks[J].Chemical Engineering Journal 127,2007:95-105.
[41]王海霞,肖应旺.基于SVM-IGA的补料分批发酵过程优化控制[J].计算机测量与控制,2005,13(7):674-676.
[42]肖杰,周泽魁.蚁群算法在啤酒发酵控制优化中的应用[J],信息与控制.2004,33(4):508-512.
[43]王斌,孙安.生物发酵过程的温度控制模型研究[J].西安交通大学学报.2004(7):737-739.
[44]潘丰,李海波,顾蕊.基于递归补偿模糊神经网络的发酵过程建模[J].南京理工大学学报(自然科学版),2005(29):108-111.
[45]王树青,元英进.生化过程自动化技术[M].北京:化学工业出版社,1999.
[46]刘北平,高孔荣等.食品与发酵工业中的计算机应用技术[M].广州:华南理工大学出版社,1999.
[47]刘振宁.发酵工程技术与实践[M].上海:华东理工大学出版社,2007.
[48]张粤.青霉素发酵温度模糊控制[J].计算机测量与控制,2002:115-117.
[49]杨延方,黄美滨.溶解氧在发酵过程中的作用及自控方法[J].黑龙江医药,1999(12):201-202.
[50]刘玉平,林建强.浅谈如何提高发酵生产的溶解氧浓度[J].医药工程设计杂志.2005(26):23-25.
[51]汪志锋.生物发酵过程状态预报与性能监控研究[D].上海交通大学.2003.
[52]肖应旺,徐保国.抗生素发酵补料智能控制系统[J].化工自动化及仪表,2004(2):17-20.
[53]何艳玲,邬建国,路福平等.间歇补料分批发酵提高纳他霉素产量[J].药物生物技术,2002,9(4):224.
[54]姜长远.模糊神经网络模型及其应用研究[D].南京师范大学,2005.
[55]刘俊强.基于模糊神经网络的仿真系统建模方法及其应用研究[D].哈尔滨工业大学,2000.12.
[56]于乃功,阮晓钢.青霉素发酵过程优化问题研究[J].第二十二届中国控制会议论文集,武汉理工大学出版社,2003:141-145.
[57]殷铭,张兴华,戴先中.基于模糊神经网络的发酵过程溶解氧预估控制[J].控制与决策,2000,15(5):523-526.
[58]Kitsuta Y,Kishimoto M,Fuzzy supervisory control of glutamic acid production[J].Biotech and Bioeng,1995,45:135
[59]王博,孙玉坤,嵇小辅.基于数据场聚类的模糊神经网络在发酵过程中的应用[J].仪器仪表学报,2009,30(5):944-948.
[60]孙海蓉.模糊神经网络的研究及其应用[D].华北电力大学,2006.
[61]李士勇.模糊控制、神经控制和智能控制论[M].哈尔滨工业大学出版社,2004:2-9.
[62]戚以政,江叔雄.生化反应动力学与反应器[M].北京:化学工业出版社,1999.
[63]Lim,H.C.,Tayeb,Y.J.,Modak,J.M.& Bonte,P.Computational Algorithms for Optimal Feed Rates for A Class of Fed-Batch Fermentation:Numerical Results for Pencillin and CellMass Production[M].Biotech.& Bioeng.,1986,28:1408-1420.
[64]Cuthrell J.E.,Biegler LT.Simultaneous optimization and solution methods for batch reactor control profiles[J].Comput Chem Eng,1989,13:49-55.
[65]Kleman GL.,ChalmersJJ,Luli GW,Strohl WR.A predictive and feedback control algorithm maintains a constant glucose concentration in fed-batch fermentation[J].Appl Environ Microbiol.1991,57:910.
[66]王立新.模糊系统与模糊控制教程[M].北京:清华大学出版社,2003.06.
[67]于明,范书瑞,曾祥烨.ARM9嵌入式系统设计与开发教程[M].北京:电子工业出版社,2006.
[68]USER'S MANUAL(S3C241 OX 32-Bit RISC Microprocessor Revision 1.2).
[69]徐英慧.ARM9嵌入式系统设计:基于S3C2410与Linux.[M].北京:北京航空航天大学出版社.2007
[70]何立民,单片机应用技术选编(5)[M],北京航空航天大学出版社,1997.10.
[71]张毅刚,彭喜源等.MCS-51单片机应用设计[M].哈尔滨工业大学出版社,1997.
[72]何立民.单片机应用技术选编(6)[M].北京航空航天大学出版社,1998.
[73]王幸之,王雷等.单片机应用系统抗干扰技术[M].北京航空航天大学出版社,2000.
[74]许先斌,熊慧君,李洲.基于ARM9的嵌入式Linux开发流程的研究[J].微计算机信息,2006,04Z:87-89.
[75]Karim Yagbmour.构建嵌入式Linux系统[M].北京:中国电力出版社,2004.
[76]孙大泽,袁文菊,张海峰.嵌入式设计及Linux驱动程序开发-基于ARM9处理器[M].北京:电子工业出版社.2005.
[77]肖蒙,陈永刚,张友鹏等.基于B/S结构的远程监控技术研究与应用[J].兰州铁道学院学报(自然科学版),2003,22(6):35-37.
[78]汤碧玉,曾楠,郑灵翔等.嵌入式系统中基于Web的远程监控设计与实现[J].厦门大学学报(自然科学版),2004,43(5):632-635.
[79]胡葭,方勇.基于Java的嵌入式远程监控系统[J].遥测遥控,2005,26(5):62-65.
[80]Schulzrinne H,Casner S.,Frederich R.etc.RTP:A Transport Protocol for Real-Time Applicatinons[S].RFC 1889,1996.
[81]Mo Guam Guangjie Han,Hai Zhao.The Embedded Internet Technology Based on a Real-Time Kernel for non-PC devices[J].Proceedings of the 2004 IEEE International Conference on Networking,sensing &Control.Taipei,Taiwan,March 21-23,2004.
[2]郁梅.基于以太网的生物发酵过程嵌入式两级计算机控制系统的通信技术[D].上海交通大学,2004.
[3]刘尧猛.基于神经网络的发酵过程建模及控制开发环境研究[D].天津大学,2003.
[4]欧阳平凯.发酵工程关键技术及其应用[M].北京:化学工业出版社.2005.
[5]黄儒强.生物发酵核技术与设备操作[M].北京:化学工业出版社.2006.
[6]余龙江.发酵工程原理与技术应用[M].北京:北京化学工业出版社,2006.
[7]史仲平,潘丰.发酵过程解析、控制与检测技术[M].北京:化学工业出版社.2005.
[8]D.S.B.ernstein.On bridging the theory/practice gap[J].IEEE Control Systems,1996:64-70.
[9]王庆林.经典控制理论的发展[J].自动化博览,1996(5):22-25.
[10]邱立友.发酵工程与设备[M].中国农业出版社,2007.08.
[11]陈坚,刘立明,堵国成.发酵过程优化原理与技术[M].北京:化学工业出版社,2009.
[12]范茂兴,潘丰,盛炳乾等.氨基酸发酵微机控制系统[J].无锡轻工业学院学报,1994,13(1):57-66.
[13]Yen-Chun Liu,Feng-Sheng Wang,Wen-Chien Lee,On-line monitoring and controlling system for fermentation process[J].Biochemical Engineering Journal,2001,7:17-25.
[14]张亚刚.发酵过程计算机控制系统的设计与研究[D].天津轻工业学院,2001,12.
[15]肖应旺,徐宝国等.发酵过程实时监测与控制系统的研究[J].测控技术,2003,22(7):18-20.
[16]王秀清,李淑清等.发酵过程PLC控制系统的研究[J].天津科技大学学报,2004,19(1).
[17]熊伟丽,徐保国等.基于PLC的Fuzzy-PI发酵温度控制系统[J].计算机工程,2005,31(9):7-9
[18]俞金寿,何衍庆.集散控制系统原理及应用[M].化学工业出版社,1985.
[19]管国强,黄达明,吴春笃.一种小型低成本发酵过程集散控制系统[J].江苏大学学报(自然科学版),2002,23(1):58-61.
[20]张红奎,温瑞.DCS在金霉素发酵中的应用[J].仪器标准化与计量,2005(4):44-46.
[21]张玉珊,王静,毋茂盛.基于DCS的发酵系统设计与研究[J].河南师范大学学报(自然科学版),2004,32(4):33-36.
[22]Frank J.Romeu.Development of biotechnology control system[J].ISA Transactions,1995,34:3-19.
[23]潘丰.FPC2000集散控制系统的设计[J].自动化与仪表,2002(4):24-28.
[24]吴新忠,乔宏颖,任子辉.现场总线技术综述[J].工矿自动化,2004(1):23-25.
[25]姜映红.基于PROFIBUS现场总线技术的啤酒发酵过程PLC自动控制系统[D].兰州理工大学,2006.4.
[26]王海霞,徐保国.基于PROFIBUS-DP的β-干路聚糖酶发酵控制系统的研究[J].计算机测量与控制,2005,13(10):1044-1046.
[27]何用辉,黄耀志.基于CAN总线的啤酒发酵监控系统[J].计算机测量与控制.2006,14(2):202-204.
[28]王献伟,赵霞,吴胜昔.基于基金会现场总线的发酵罐控制系统设计与实施[J].测控技术,2005,24(2),29-31.
[29]Zlateva.P.Binary control of fermentation processes[J].Cybernetics and Systems,2000(31),Sept:669-88.
[30]Zhang Zhi-huan.Research on multiple model control of nonlinear system and simulation[J].(Fac.of Inf.Sci.&Technol.,Ningbo Univ.,China),v 15,n 7,July 2003:919-21
[31]Wang Haixia,Xiao Yingwang,Xu Baoguo.Optimization control of material makeup ferment process in batches based on SVM-IGA[J],Computer Measurement&Control,v 13,n 7,2005:674-6.
[32]Levisauskas,D.Tekorius,T Model-based optimization of fed-batch fermentation processes using predetermined type feed-rate time profiles:a comparative study[J].Information Technology and Control,v 34,n 3,2005:231-6.
[33]Odetunji,O.A.Kehinde,O.O.Computer simulation of fuzzy control system for gari fermentation plant[J].Journal of Food Engineering,v 68,n 2,May 2005:197-207.
[34]Riid,A.Rustern,E.Automatic linguistic inversion of a fuzzy model for fed-batch fermentation control[J].2006 International Conference on Intelligent Engineering Systems,2006:66-69.
[35]Nagy,Zoltan Kalman.Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks[J].Chemical Engineering Journal,2007:127,Mar 1:95-109.
[36]Kawohl,M.Heine,T.,King,R.Model based estimation and optimal control of fed-batch fernuwUation processes for the production of antibiotics[J].Chemical Engineering&Processing:Process Intensification,v 46,n 11,Nov.2007,23-41.
[37]Jiang Weijin,Xu Yusheng.A novel multi-agent automated negotiation model based on associated intent,Agent Computing and multi-Agent System[J].Springer Berlin/Heidelberg,2006,7:608-613.
[38]Fang Lei,AntsaklisPanos J.,Tzimas Anastasis,Asynchronous consensus protocols:PmVn;nary results,simulations and open questions[J].Proceedings of the 44th IEEE Conference on Decision and Control,and the European Control Conference,CDC-ECC 05,2005:2194-4-2199.
[39]Zhaoshu Liu,Gangyan Li,Lili Zhu.The study of key technologies in manufacturing management-control integration[J].Computational Intelligence for Modelling,Control and Automation,2006.
[40]Zoltan Kalman Nagy.Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks[J].Chemical Engineering Journal 127,2007:95-105.
[41]王海霞,肖应旺.基于SVM-IGA的补料分批发酵过程优化控制[J].计算机测量与控制,2005,13(7):674-676.
[42]肖杰,周泽魁.蚁群算法在啤酒发酵控制优化中的应用[J],信息与控制.2004,33(4):508-512.
[43]王斌,孙安.生物发酵过程的温度控制模型研究[J].西安交通大学学报.2004(7):737-739.
[44]潘丰,李海波,顾蕊.基于递归补偿模糊神经网络的发酵过程建模[J].南京理工大学学报(自然科学版),2005(29):108-111.
[45]王树青,元英进.生化过程自动化技术[M].北京:化学工业出版社,1999.
[46]刘北平,高孔荣等.食品与发酵工业中的计算机应用技术[M].广州:华南理工大学出版社,1999.
[47]刘振宁.发酵工程技术与实践[M].上海:华东理工大学出版社,2007.
[48]张粤.青霉素发酵温度模糊控制[J].计算机测量与控制,2002:115-117.
[49]杨延方,黄美滨.溶解氧在发酵过程中的作用及自控方法[J].黑龙江医药,1999(12):201-202.
[50]刘玉平,林建强.浅谈如何提高发酵生产的溶解氧浓度[J].医药工程设计杂志.2005(26):23-25.
[51]汪志锋.生物发酵过程状态预报与性能监控研究[D].上海交通大学.2003.
[52]肖应旺,徐保国.抗生素发酵补料智能控制系统[J].化工自动化及仪表,2004(2):17-20.
[53]何艳玲,邬建国,路福平等.间歇补料分批发酵提高纳他霉素产量[J].药物生物技术,2002,9(4):224.
[54]姜长远.模糊神经网络模型及其应用研究[D].南京师范大学,2005.
[55]刘俊强.基于模糊神经网络的仿真系统建模方法及其应用研究[D].哈尔滨工业大学,2000.12.
[56]于乃功,阮晓钢.青霉素发酵过程优化问题研究[J].第二十二届中国控制会议论文集,武汉理工大学出版社,2003:141-145.
[57]殷铭,张兴华,戴先中.基于模糊神经网络的发酵过程溶解氧预估控制[J].控制与决策,2000,15(5):523-526.
[58]Kitsuta Y,Kishimoto M,Fuzzy supervisory control of glutamic acid production[J].Biotech and Bioeng,1995,45:135
[59]王博,孙玉坤,嵇小辅.基于数据场聚类的模糊神经网络在发酵过程中的应用[J].仪器仪表学报,2009,30(5):944-948.
[60]孙海蓉.模糊神经网络的研究及其应用[D].华北电力大学,2006.
[61]李士勇.模糊控制、神经控制和智能控制论[M].哈尔滨工业大学出版社,2004:2-9.
[62]戚以政,江叔雄.生化反应动力学与反应器[M].北京:化学工业出版社,1999.
[63]Lim,H.C.,Tayeb,Y.J.,Modak,J.M.& Bonte,P.Computational Algorithms for Optimal Feed Rates for A Class of Fed-Batch Fermentation:Numerical Results for Pencillin and CellMass Production[M].Biotech.& Bioeng.,1986,28:1408-1420.
[64]Cuthrell J.E.,Biegler LT.Simultaneous optimization and solution methods for batch reactor control profiles[J].Comput Chem Eng,1989,13:49-55.
[65]Kleman GL.,ChalmersJJ,Luli GW,Strohl WR.A predictive and feedback control algorithm maintains a constant glucose concentration in fed-batch fermentation[J].Appl Environ Microbiol.1991,57:910.
[66]王立新.模糊系统与模糊控制教程[M].北京:清华大学出版社,2003.06.
[67]于明,范书瑞,曾祥烨.ARM9嵌入式系统设计与开发教程[M].北京:电子工业出版社,2006.
[68]USER'S MANUAL(S3C241 OX 32-Bit RISC Microprocessor Revision 1.2).
[69]徐英慧.ARM9嵌入式系统设计:基于S3C2410与Linux.[M].北京:北京航空航天大学出版社.2007
[70]何立民,单片机应用技术选编(5)[M],北京航空航天大学出版社,1997.10.
[71]张毅刚,彭喜源等.MCS-51单片机应用设计[M].哈尔滨工业大学出版社,1997.
[72]何立民.单片机应用技术选编(6)[M].北京航空航天大学出版社,1998.
[73]王幸之,王雷等.单片机应用系统抗干扰技术[M].北京航空航天大学出版社,2000.
[74]许先斌,熊慧君,李洲.基于ARM9的嵌入式Linux开发流程的研究[J].微计算机信息,2006,04Z:87-89.
[75]Karim Yagbmour.构建嵌入式Linux系统[M].北京:中国电力出版社,2004.
[76]孙大泽,袁文菊,张海峰.嵌入式设计及Linux驱动程序开发-基于ARM9处理器[M].北京:电子工业出版社.2005.
[77]肖蒙,陈永刚,张友鹏等.基于B/S结构的远程监控技术研究与应用[J].兰州铁道学院学报(自然科学版),2003,22(6):35-37.
[78]汤碧玉,曾楠,郑灵翔等.嵌入式系统中基于Web的远程监控设计与实现[J].厦门大学学报(自然科学版),2004,43(5):632-635.
[79]胡葭,方勇.基于Java的嵌入式远程监控系统[J].遥测遥控,2005,26(5):62-65.
[80]Schulzrinne H,Casner S.,Frederich R.etc.RTP:A Transport Protocol for Real-Time Applicatinons[S].RFC 1889,1996.
[81]Mo Guam Guangjie Han,Hai Zhao.The Embedded Internet Technology Based on a Real-Time Kernel for non-PC devices[J].Proceedings of the 2004 IEEE International Conference on Networking,sensing &Control.Taipei,Taiwan,March 21-23,2004.