原油蒸馏装置SDG故障诊断应用研究
摘要
石油化工是我国的重要支柱产业之一,关系到国民经济能源、材料等许多方面,也是危险性极高的行业之一。科技和经济的发展使得石油化工过程日益大型化、复杂化。原油蒸馏过程是石油炼制的重要环节,它内在机理复杂,是一个复杂的传热,传质过程。生产中某些微小故障若不能及时排除,就有可能造成巨大的灾难。因此生产系统的安全性和可靠性就显得极其重要。提高系统安全性和可靠性的方法有多种,其中一个重要的方法就是采用故障诊断技术。
故障诊断是指对运行中的机械设备或装置的异常状态的检测、异常状态原因的识别以及预测。近年来,石化生产过程故障诊断逐渐成为各相关学科研究的热点问题,并且取得了一定的成果,涌现出了一些有效的方法和技术。基于深层知识模型的SDG方法由于其完备性等特点在众多方法中日趋显著。
本文首先对故障诊断技术进行了总结,概要介绍了SDG技术的原理及其用于石化过程故障诊断的优势;详细阐述了SDG模型特点、推理机制以及建模方法,并以常压塔为实例,对基于SDG故障诊断模型的建立过程进行了详细的论述。最后,进行了常压蒸馏实例推理研究,针对此前已经建立好的SDG故障诊断模型,采用反向推理正向验证的推理算法对其进行了分析研究。
The petrochemical industry is one of the important pillar industries of our country,concern a lot of respects,such as national economy energy,material, etc.,it is one of the dangerous and extremely high trades too.Science and technology and economic development make the course of petrochemical industry maximize,complicate day by day.Atmospheric and vacuum distillation process is indispensable for refining plant,this is a complicated procedure of transferring heat and mass with complex mechanism.If some small troubles can't be got rid of in time,may cause the enormous disaster in production.So the security and dependability of the production system seem extremely important.There are some kinds of methods in improving systematic security and dependability,one of important method among them is the fault diagnosis technology.
Fault diagnosis means to measure the unusual state of running mechanical equipment,to discern and predict the reason of the unusual state. In recent years,the fault diagnosis of petrochemical industry production process gradually becomes the hot problem of every relevant study,in which certain achievement has been made and some effective methods and technology have been summarized.SDG method based on the deep knowledge model is becoming more remarkable in numerous methods because of its characteristics,such as simple and easy,popularization and completeness,etc.
In this paper,the present fault diagnosis technology is summarized at first then the author introduces the principle of SDG and the advantage of using it in the fault diagnosis of petrochemical process.The characters of SDG model, reasoning mechanism and modeling method are deeply analyzed.An example of atmospheric tower is introduced,and modeling method based on SDG is well studied.At last,the backward reasoning and forward verification algorithm is applied to fault diagnosis of atmospheric distillation.
故障诊断是指对运行中的机械设备或装置的异常状态的检测、异常状态原因的识别以及预测。近年来,石化生产过程故障诊断逐渐成为各相关学科研究的热点问题,并且取得了一定的成果,涌现出了一些有效的方法和技术。基于深层知识模型的SDG方法由于其完备性等特点在众多方法中日趋显著。
本文首先对故障诊断技术进行了总结,概要介绍了SDG技术的原理及其用于石化过程故障诊断的优势;详细阐述了SDG模型特点、推理机制以及建模方法,并以常压塔为实例,对基于SDG故障诊断模型的建立过程进行了详细的论述。最后,进行了常压蒸馏实例推理研究,针对此前已经建立好的SDG故障诊断模型,采用反向推理正向验证的推理算法对其进行了分析研究。
The petrochemical industry is one of the important pillar industries of our country,concern a lot of respects,such as national economy energy,material, etc.,it is one of the dangerous and extremely high trades too.Science and technology and economic development make the course of petrochemical industry maximize,complicate day by day.Atmospheric and vacuum distillation process is indispensable for refining plant,this is a complicated procedure of transferring heat and mass with complex mechanism.If some small troubles can't be got rid of in time,may cause the enormous disaster in production.So the security and dependability of the production system seem extremely important.There are some kinds of methods in improving systematic security and dependability,one of important method among them is the fault diagnosis technology.
Fault diagnosis means to measure the unusual state of running mechanical equipment,to discern and predict the reason of the unusual state. In recent years,the fault diagnosis of petrochemical industry production process gradually becomes the hot problem of every relevant study,in which certain achievement has been made and some effective methods and technology have been summarized.SDG method based on the deep knowledge model is becoming more remarkable in numerous methods because of its characteristics,such as simple and easy,popularization and completeness,etc.
In this paper,the present fault diagnosis technology is summarized at first then the author introduces the principle of SDG and the advantage of using it in the fault diagnosis of petrochemical process.The characters of SDG model, reasoning mechanism and modeling method are deeply analyzed.An example of atmospheric tower is introduced,and modeling method based on SDG is well studied.At last,the backward reasoning and forward verification algorithm is applied to fault diagnosis of atmospheric distillation.
引文
[1]杨叔子,丁洪等.基于知识的诊断推理[M].清华大学出版社,1993,2.
[2]曹文亮.基于符号有向图的热力系统故障诊断方法研究[D].保定:华北电力大学,2006
[3]蒋浩天,E.L.拉赛尔.工业系统的故障检测与诊断[M].机械工业出版社,2003,9.
[4]孙玉良,闵祥禄.常减压蒸馏装置安全运行与管理[M].中国石化出版社,2006,6.
[5]侯祥麟.中国炼油技术(第二版)[M].中国石化出版社,1991:2-7.
[6]林世雄.石油炼制工程(第三版)[M].石油工业出版社,2000:1-4.
[7]杜殿林,张光红,吴重光.基于知识故障诊断系统所用的深层知识及SDG方法[J].化工自动化及仪表.2005,32(4):8-10.
[8]赵翔.故障诊断技术的研究现状与发展趋势[J].机床与液压,2002,4:3-6.
[9]胡峰,孙国基.过程监控技术及其应用[M].国防工业出版社,2001,1.
[10]陈玉东,施颂椒,翁正新.动态系统的故障诊断方法综述[J].化工自动化及仪表,2001,28(3):1-14.
[11]Frank P M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results[J].Automatica.1990,26(3):459-474.
[12]周东华,王桂增.故障诊断技术综述[J].化工自动化及仪表.1998,25(1):58-62.
[13]Isermann R.Fault diagnosis of machinesma parameter estimation and knowledge processing-tutorial paper[J].Automatica,1993,29(4):815-835.
[14]白方周,方瑾,张文明.国外定性仿真应用评述[J].系统仿真学报.1998,10(4):1-8.
[15]邵晨曦,白方周.定性仿真技术及应用[J].系统仿真学报.2004,16(2):202-208.
[16]王文辉,周东华.基于定性和半定性方法的故障检测与诊断技术[J].控制理论与应用.2002,19(5):653-666
[17]Patton R J.Robustness in model-based fault diagnosis[J].Annual reviews in contral.1997,21:103-123.
[18]Ye H,Wand G Z,Fang C Z.Application of wavelet transform to leak detection and location transport pipelines[J].Engineering Simulation,1996,13:1025-1032.
[19]叶昊,王桂增,方崇智.小波变化在故障诊断中的应用[J].自动化学报,1998,23(6):736-741.
[20]王由华.工业控制系统神经网络故障诊断方法研究[D].北京:北京化工大学.2002.
[21]Kuiper B.J.Qualitative Reasoning-Modeling & Simulation with Incomplete Knowledge[M].Cambridge:MIT Press,1994.
[22]Venkat Venkatasubramanian,Raghunathan Rengaswamy,Kewen Yin,Surya N.Kavuri.A review of process fault detection and diagnosis Part Ⅰ:Qualitative model based methods[J].Computers and Chemical Engineering.27(2003):293-311.
[23]Venkat Venkatasubramanian,Raghunathan Rengaswamy,Surya N.Kavuri.A review of process fault detection and diagnosis Part Ⅱ:Qualitative models and search strategies[J].Computers and Chemical Engineering.27(2003):313-326.
[24]Venkat Venkatasubramanian,Raghunathan Rengaswamy,Surya N.Kavuri,Kewen Yin.A review of process fault detection and diagnosis Part Ⅲ:Process history based methods[J].Computers and Chemical Engineering.27(2003):327-346.
[25]刘敏华.基于SDG模型的故障诊断及应用研究[D].北京:清华大学.2005.
[26]石宇,邱彤,陈丙珍.用于化工过程的SDG故障分析方法[J].化工进展,2006,12(25):1484-1488.
[27]高岩.化工过程故障诊断方法应用研究[D].无锡:江南大学.2005.
[28]杨帆,萧德云.SDG建模及其应用的进展[J].控制理论与应用,2005,22(05):767-774.
[29]ZHANG Beike,WU Chongguang,XIA Tao.Method and Modeling Study On Computer Automatic HAZOP Based On Signed Directed Graph(SDG)[A].Progress in Safety Science and Technology(Vol.Ⅳ)Part A-Proceedings of the 2004 International Symposium on Safety Science and Technology[C].Shanghai,China:Journal of Safety and Environment,2004,52-59.
[30]LI Anfeng,WU Chongguang,XIA Tao,etc.Computer-aided HAZOP Analysis Based on SDG[A].Progress in Safety Science and Technology(Vol.Ⅳ)Part B-Proceedings of the 2004 International Symposium on Safety Science and Technology[C].Shanghai,China:Journal of Safety and Environment,2004,2095-2102.
[31]刘宇慧,夏涛,张贝克,等.基于SDG的HAZOP单元建模方法[J].计算机仿真,2004,21(12):192-195.
[32]李安峰,夏涛,张贝克,等.化工过程SDG建模方法[J].系统仿真学报,2003,15(10):1364-1368.
[33]杨帆,萧德云.基于SDG的复杂系统故障传播规律分析[J].高技术通讯,2005,10(15):33-36.
[34]吴重光,夏涛,张贝克.基于符号定向图(SDG)深层知识模型的定性仿真[J].系统仿真学报,2003,15(10):1351-1355.
[35]Mylaraswamy D,Venkatasubramanian V.A hybrid framework for large scale process fault diagnosis[J].Computers and Chemical Engineering.1997,21:935-940.
[36]Dash S,Venkatasubramanian V.Challenges in the industrial applications of fault diagnostic systems[J].Computers and Chemical Engineering.2000,24(2):785-791.
[37]罗静.基于规则的SDG扩展推理引擎的研究与开发[D].北京:北京化工大学.2007.
[38]Jun Chen,John Howell.A self-validating control system based approach to plant fault detection and diagnosis[J].Computers and Chemical Engineering.25(2001):337-358.
[39]Mano Ram Maurya,Raghunathan Rengaswamy,Venkat Venkatasubramanian.Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets[J].Engineering Applications of Artificial Intelligence.17(2004):501-518.
[40]Mauna M R,Rengaswamy R,Venkatasubramanian V.A systematic framework for the development and analysis of signed digraphs for chemical processes.Ⅰ.Algorithms and analysis[J].Industrial and Engineering Chemistry Research.2003.42(20):4789-4810.
[41]Maurya M R,Rengaswamy R,Venkatasubramanian V.A systematic framework for the development and analysis of signed digraphs for chemical processes.Ⅱ.Control loops and flowshcct analysis[J].Industrial and Engineering Chemistry Research.2003.42(2):4811-4827.
[42]第一套常减压蒸馏装置操作规程[S].中国石油哈尔滨石化分公司.2005,10.
[2]曹文亮.基于符号有向图的热力系统故障诊断方法研究[D].保定:华北电力大学,2006
[3]蒋浩天,E.L.拉赛尔.工业系统的故障检测与诊断[M].机械工业出版社,2003,9.
[4]孙玉良,闵祥禄.常减压蒸馏装置安全运行与管理[M].中国石化出版社,2006,6.
[5]侯祥麟.中国炼油技术(第二版)[M].中国石化出版社,1991:2-7.
[6]林世雄.石油炼制工程(第三版)[M].石油工业出版社,2000:1-4.
[7]杜殿林,张光红,吴重光.基于知识故障诊断系统所用的深层知识及SDG方法[J].化工自动化及仪表.2005,32(4):8-10.
[8]赵翔.故障诊断技术的研究现状与发展趋势[J].机床与液压,2002,4:3-6.
[9]胡峰,孙国基.过程监控技术及其应用[M].国防工业出版社,2001,1.
[10]陈玉东,施颂椒,翁正新.动态系统的故障诊断方法综述[J].化工自动化及仪表,2001,28(3):1-14.
[11]Frank P M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results[J].Automatica.1990,26(3):459-474.
[12]周东华,王桂增.故障诊断技术综述[J].化工自动化及仪表.1998,25(1):58-62.
[13]Isermann R.Fault diagnosis of machinesma parameter estimation and knowledge processing-tutorial paper[J].Automatica,1993,29(4):815-835.
[14]白方周,方瑾,张文明.国外定性仿真应用评述[J].系统仿真学报.1998,10(4):1-8.
[15]邵晨曦,白方周.定性仿真技术及应用[J].系统仿真学报.2004,16(2):202-208.
[16]王文辉,周东华.基于定性和半定性方法的故障检测与诊断技术[J].控制理论与应用.2002,19(5):653-666
[17]Patton R J.Robustness in model-based fault diagnosis[J].Annual reviews in contral.1997,21:103-123.
[18]Ye H,Wand G Z,Fang C Z.Application of wavelet transform to leak detection and location transport pipelines[J].Engineering Simulation,1996,13:1025-1032.
[19]叶昊,王桂增,方崇智.小波变化在故障诊断中的应用[J].自动化学报,1998,23(6):736-741.
[20]王由华.工业控制系统神经网络故障诊断方法研究[D].北京:北京化工大学.2002.
[21]Kuiper B.J.Qualitative Reasoning-Modeling & Simulation with Incomplete Knowledge[M].Cambridge:MIT Press,1994.
[22]Venkat Venkatasubramanian,Raghunathan Rengaswamy,Kewen Yin,Surya N.Kavuri.A review of process fault detection and diagnosis Part Ⅰ:Qualitative model based methods[J].Computers and Chemical Engineering.27(2003):293-311.
[23]Venkat Venkatasubramanian,Raghunathan Rengaswamy,Surya N.Kavuri.A review of process fault detection and diagnosis Part Ⅱ:Qualitative models and search strategies[J].Computers and Chemical Engineering.27(2003):313-326.
[24]Venkat Venkatasubramanian,Raghunathan Rengaswamy,Surya N.Kavuri,Kewen Yin.A review of process fault detection and diagnosis Part Ⅲ:Process history based methods[J].Computers and Chemical Engineering.27(2003):327-346.
[25]刘敏华.基于SDG模型的故障诊断及应用研究[D].北京:清华大学.2005.
[26]石宇,邱彤,陈丙珍.用于化工过程的SDG故障分析方法[J].化工进展,2006,12(25):1484-1488.
[27]高岩.化工过程故障诊断方法应用研究[D].无锡:江南大学.2005.
[28]杨帆,萧德云.SDG建模及其应用的进展[J].控制理论与应用,2005,22(05):767-774.
[29]ZHANG Beike,WU Chongguang,XIA Tao.Method and Modeling Study On Computer Automatic HAZOP Based On Signed Directed Graph(SDG)[A].Progress in Safety Science and Technology(Vol.Ⅳ)Part A-Proceedings of the 2004 International Symposium on Safety Science and Technology[C].Shanghai,China:Journal of Safety and Environment,2004,52-59.
[30]LI Anfeng,WU Chongguang,XIA Tao,etc.Computer-aided HAZOP Analysis Based on SDG[A].Progress in Safety Science and Technology(Vol.Ⅳ)Part B-Proceedings of the 2004 International Symposium on Safety Science and Technology[C].Shanghai,China:Journal of Safety and Environment,2004,2095-2102.
[31]刘宇慧,夏涛,张贝克,等.基于SDG的HAZOP单元建模方法[J].计算机仿真,2004,21(12):192-195.
[32]李安峰,夏涛,张贝克,等.化工过程SDG建模方法[J].系统仿真学报,2003,15(10):1364-1368.
[33]杨帆,萧德云.基于SDG的复杂系统故障传播规律分析[J].高技术通讯,2005,10(15):33-36.
[34]吴重光,夏涛,张贝克.基于符号定向图(SDG)深层知识模型的定性仿真[J].系统仿真学报,2003,15(10):1351-1355.
[35]Mylaraswamy D,Venkatasubramanian V.A hybrid framework for large scale process fault diagnosis[J].Computers and Chemical Engineering.1997,21:935-940.
[36]Dash S,Venkatasubramanian V.Challenges in the industrial applications of fault diagnostic systems[J].Computers and Chemical Engineering.2000,24(2):785-791.
[37]罗静.基于规则的SDG扩展推理引擎的研究与开发[D].北京:北京化工大学.2007.
[38]Jun Chen,John Howell.A self-validating control system based approach to plant fault detection and diagnosis[J].Computers and Chemical Engineering.25(2001):337-358.
[39]Mano Ram Maurya,Raghunathan Rengaswamy,Venkat Venkatasubramanian.Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets[J].Engineering Applications of Artificial Intelligence.17(2004):501-518.
[40]Mauna M R,Rengaswamy R,Venkatasubramanian V.A systematic framework for the development and analysis of signed digraphs for chemical processes.Ⅰ.Algorithms and analysis[J].Industrial and Engineering Chemistry Research.2003.42(20):4789-4810.
[41]Maurya M R,Rengaswamy R,Venkatasubramanian V.A systematic framework for the development and analysis of signed digraphs for chemical processes.Ⅱ.Control loops and flowshcct analysis[J].Industrial and Engineering Chemistry Research.2003.42(2):4811-4827.
[42]第一套常减压蒸馏装置操作规程[S].中国石油哈尔滨石化分公司.2005,10.