智能工厂可配置的多层次建模方法
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摘要
工厂仿真模型通常被分为宏观和微观两类,宏观的仿真模型如ERP层模型,微观的仿真模型如PCS层模型,MES层模型介于宏观和微观之间。前人研究中建立某一层次的仿真模型通常都有重用率低和扩展性差的问题,为此提出一个用于工厂建模和仿真的四层框架:模型实体结构/模型库层、模型抽象层、工厂模型层、智能工厂仿真系统层。运用这种对工厂拓扑网络进行抽象的建模与仿真方法可以构建可扩展和易配置的仿真模型。本文使用此方法建立了智能工厂多层次仿真系统,最后通过3个案例说明方法的有效性和可靠性,为工厂建模和仿真提供了指导。
Traditionally,plant simulation model is classified as macroscopic or microscopic.An ERP-level model and a PCS-level model belong to macro and micro simulation model respectively while an MES-level model lies in-between.Simulation models of certain level established by former researchers usually have low reusability and poor scalability.To this end,a novel four-layered approach,which is composed of model entity structure/model base,model abstraction,plant modeling and IPS simulation systems layer, has been proposed for plant modeling and simulation.Configurable and reusable simulation model can be easily obtained by adopting the abstraction modeling and simulation method with respect to plant network. In this article,a multi-level intelligent plant simulation system is constructed with the aforementioned method and the effectiveness and reliability are verified through case studies.Therefore,it is expected to be widely used in plant modeling and simulation in practice.
Traditionally,plant simulation model is classified as macroscopic or microscopic.An ERP-level model and a PCS-level model belong to macro and micro simulation model respectively while an MES-level model lies in-between.Simulation models of certain level established by former researchers usually have low reusability and poor scalability.To this end,a novel four-layered approach,which is composed of model entity structure/model base,model abstraction,plant modeling and IPS simulation systems layer, has been proposed for plant modeling and simulation.Configurable and reusable simulation model can be easily obtained by adopting the abstraction modeling and simulation method with respect to plant network. In this article,a multi-level intelligent plant simulation system is constructed with the aforementioned method and the effectiveness and reliability are verified through case studies.Therefore,it is expected to be widely used in plant modeling and simulation in practice.
引文
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[17]Zheng Liyu(郑丽钰),Rong Gang(荣冈),Wu Jie(吴婕).Scheduling-event driven modeling and simulation of material flow process for petrochemical industries[J].Control and Instruments in Chemical Industry(化工自动化及仪表),2008,35(4):11-15
[18]Lee J K,Lim Y H,Chi S D.Hierarchical modeling and simulation environment for intelligent transportation systems[J].Simulation,2004,80(2):61-76
[19]Li Jianlie(李笕列).Research on model and data management in process industry[D].Hangzhou:Zhejiang University,2009
[20]Hu Y,Feng Y,Rong G.Model management for refinery multi-level simulation and optimization//World Congress[C].2011
[21]Zeigler B P,Praehofer H,Kim T G.Theory of Modeling and Simulation:Integrating Discrete Event and Continuous Complex Dynamic Systems[M].Academic Press,2000
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[2]Wang J,Rong G.Robust optimization model for crude oil scheduling under uncertainty[J].Industrial&Engineering Chemistry Research,2009,49(4):1737-1748
[3]Pitty S S,et al.Decision support for integrated refinery supply chains(1):Dynamic simulation[J].Computers&Chemical Engineering,2008,32(11):2767-2786
[4]Koo L Y,et al.Decision support for integrated refinery supply chains(2):Design and operation[J].Computers&Chemical Engineering,2008,32(11):2787-2800
[5]Proctor F.Smart Manufacturing and Construction Control Systems[EB/OL].[2011].http://www.nist.gov/el/isd/cs/upload/SMCSprogram2013.pdf
[6]Grossmann I.Enterprise-wide optimization:a new frontier in process systems engineering[J].AIChE Journal,2005,51(7):1846-1857
[7]Coalition S M L.Implementing 21st century smart manufacturing:Workshop Report,SMLC and US DOE[R].2011
[8]Scholten B.The Road to Integration:A Guide to Applying the ISA-95Standard in Manufacturing[M].Isa,2007
[9]Bonczek R H,Holsapple C W,Whinston A B.The evolving roles of models in decision support systems[J].Decision Sciences,1980,11(2):337-356
[10]Michaloski J,et al.Manufacturing Modeling and Simulation[EB/OL].[2011].http://www.nist.gov/el/isd/cs/manmodsim.cfm
[11]Goodrich R,Yeh J,Chukman G.Increase product value through process modeling[J].Hydrocarbon Processing,2006,85(6):83-88
[12]Yang Jiali(杨佳丽),Rong Gang(荣冈).Refinery planning optimization integrated with quality tracking and constraints[J].CIESC Journal(化工学报),2010,61(1):124-131
[13]Herrmann C,Thiede S,Kara S,et al.Energy oriented simulation of manufacturing systems—concept and application[J].CIRP Annals-Manufacturing Technology,2011,60(1):45-48
[14]Zhou Z W,Feng Y P,Rong G,et al.Virtual reality based process integrated simulation platform in refinery:virtual refinery and its application[J].China Petroleum Processing and Petrochemical Technology,2011,13(3):74-84
[15]Schwarz P.Simulation of systems with dynamically varying model structure[J].Mathematics and Computers in Simulation,2008,79(4):850-863
[16]Pei Ruiling(裴瑞凌),Rong Gang(荣冈).Flowsheet simulation platform of intelligent plant in oil refinery[J].Control and Instruments in Chemical Industry(化工自动化及仪表),2005,32(2):43-46
[17]Zheng Liyu(郑丽钰),Rong Gang(荣冈),Wu Jie(吴婕).Scheduling-event driven modeling and simulation of material flow process for petrochemical industries[J].Control and Instruments in Chemical Industry(化工自动化及仪表),2008,35(4):11-15
[18]Lee J K,Lim Y H,Chi S D.Hierarchical modeling and simulation environment for intelligent transportation systems[J].Simulation,2004,80(2):61-76
[19]Li Jianlie(李笕列).Research on model and data management in process industry[D].Hangzhou:Zhejiang University,2009
[20]Hu Y,Feng Y,Rong G.Model management for refinery multi-level simulation and optimization//World Congress[C].2011
[21]Zeigler B P,Praehofer H,Kim T G.Theory of Modeling and Simulation:Integrating Discrete Event and Continuous Complex Dynamic Systems[M].Academic Press,2000
[22]Neiro S M S,Pinto J M.A general modeling framework for the operational planning of petroleum supply chains[J].Computers&Chemical Engineering,2004,28(6/7):871-896