WO2023004802A1 - Procédé et dispositif de génération automatique d'un modèle de système de traitement industriel - Google Patents

Procédé et dispositif de génération automatique d'un modèle de système de traitement industriel Download PDF

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Publication number
WO2023004802A1
WO2023004802A1 PCT/CN2021/109857 CN2021109857W WO2023004802A1 WO 2023004802 A1 WO2023004802 A1 WO 2023004802A1 CN 2021109857 W CN2021109857 W CN 2021109857W WO 2023004802 A1 WO2023004802 A1 WO 2023004802A1
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WIPO (PCT)
Prior art keywords
model
information
model information
simulation
industrial process
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PCT/CN2021/109857
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English (en)
Chinese (zh)
Inventor
李朝春
白新
王冬
傅玲
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西门子（中国）有限公司
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Priority to PCT/CN2021/109857 priority Critical patent/WO2023004802A1/fr
Publication of WO2023004802A1 publication Critical patent/WO2023004802A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]

Definitions

  • the present disclosure relates to the field of simulation, and more particularly, to methods, apparatus, computing devices, computer readable storage media, and computer program products for automatically generating models of industrial process systems.
  • a first embodiment of the present disclosure proposes a method for automatically generating a model of an industrial process system, the method comprising:
  • the model information is communicated to the modeling and simulation engine for automatic generation of a model of the industrial process system by the modeling and simulation engine from the model information.
  • the second embodiment of the present disclosure proposes an apparatus for automatically generating a model of an industrial process system, the apparatus comprising:
  • a building unit configured to build an ontology related to the simulation of the industrial process system according to the hierarchical structure of the industrial process system
  • an acquisition unit configured to acquire design information and operation information of the industrial process system
  • an instantiation unit configured to instantiate the ontology according to the design information, the operation information, and a template library
  • a generating unit configured to generate model information based on the instantiated ontology
  • a model unit configured to transmit the model information to a modeling and simulation engine for automatic generation of a model of the industrial process system by the modeling and simulation engine based on the model information.
  • a third embodiment of the present disclosure proposes a computing device, which includes: a processor; and a memory for storing computer-executable instructions that, when executed, cause the processor to perform the first embodiment. Methods.
  • a fourth embodiment of the present disclosure proposes a computer-readable storage medium having computer-executable instructions stored thereon, and the computer-executable instructions are used to execute the method of the first embodiment.
  • a fifth embodiment of the present disclosure proposes a computer program product that is tangibly stored on a computer-readable storage medium and includes computer-executable instructions that, when executed, cause at least one processing The device executes the method of the first embodiment.
  • FIG. 1 illustrates a hierarchical structure of an exemplary industrial process system in which embodiments of the present disclosure may be applied;
  • FIG. 2 shows a data relationship diagram for an ontology according to an embodiment of the present disclosure
  • FIG. 3 shows a flowchart of an exemplary method for automatically generating a model of an industrial process system according to an embodiment of the present disclosure
  • FIG. 4 shows an exemplary apparatus for automatically generating a model of an industrial process system according to an embodiment of the present disclosure
  • Figure 5 illustrates an exemplary computing device for automatically generating a model of an industrial process system according to an embodiment of the disclosure.
  • the present invention proposes a solution to automatically generate a model of an industrial process system based on an ontology.
  • FIG. 1 illustrates a hierarchical structure of an exemplary industrial process system 100 in which embodiments of the present disclosure may be applied.
  • Industrial process system 100 may implement, for example, a continuous process.
  • the industrial process system 100 may include a plurality of hierarchies 101 , 102 , 103 , 104 organized from the bottom up.
  • the level 101 represents the equipment level.
  • the industrial process system 100 includes a plurality of equipment E1-E8 at the equipment level 101, and the equipment E1-E8 may be, for example, heat exchangers, turbines, pumps, condensers and the like.
  • the level 102 represents the unit level.
  • the industrial process system 100 includes a plurality of units U1-U8 at the unit level 102.
  • the units U1-U8 may be composed of several devices.
  • the unit U4 includes devices E3 and E5.
  • Hierarchy 103 represents the plant area level.
  • the industrial process system 100 includes multiple plant areas S1-S6 at the plant area level 103.
  • Plant areas S1-S6 may be composed of several units.
  • plant area S5 includes units U1 and U4.
  • the level 104 represents the park level.
  • the industrial process system 100 includes multiple parks P1-P5 at the park level 104.
  • the parks P1-P5 may consist of several plant areas.
  • the park P4 includes plant areas S2, S4 and S5.
  • Fig. 1 is only an illustration of the hierarchical structure of the industrial process system and not limiting.
  • the equipment level is the basic element that constitutes an industrial process system, and various levels above the equipment level (eg, unit level, plant level, park level) can be regarded as the system level.
  • FIG. 2 illustrates a data relational graph 200 for an ontology according to an embodiment of the present disclosure.
  • an ontology related to the simulation of the industrial process system 100 can be constructed according to the hierarchical structure of the industrial process system 100 in FIG. 1 and based on the data relationships in FIG. 2 .
  • Ontologies are shared conceptual, explicit, and formal descriptions.
  • Application systems can use ontology to explicitly declare the knowledge they contain, which is very useful for semantic modeling.
  • an ontology may include abstract definitions of entities and relationships between entities (eg, subordination, connection, etc.).
  • each entity can be referenced through associated keywords (As indicated by the arrow in Figure 2).
  • FIG. 3 shows a flowchart of an example method 300 for automatically generating a model of an industrial process system according to disclosed embodiments.
  • the example method 300 may be applied to the example industrial process system 100 shown in FIG. 1 .
  • step 301 an ontology related to the simulation of the industrial process system is constructed according to the hierarchical structure of the industrial process system. For example, according to the hierarchical structure of the industrial process system 100, a unified ontology for the industrial process system is generated based on the data relationship diagram 200 in FIG.
  • step 302 design information and operational information of the industrial process system are obtained.
  • the design information may represent information on physical and logical connection relationships related to structural elements (for example, equipment, etc.) of the industrial process system 100, information representing the setting content of each structural element, information representing control specifications, and information representing functional specifications. wait.
  • design information can be obtained from a Process Flow Diagram (PFD) or a Piping and Instrument Diagram (P&ID), or it can be added manually.
  • the operational information may represent various process information to be generated in the industrial process system 100, for example, pressure information, temperature information, and the like.
  • operational information may be obtained from a configuration repository of industrial process system 100, or may be added manually.
  • the method 300 proceeds to step 303 .
  • the ontology is instantiated according to design information, operation information and a template library.
  • the topology of the industrial process system can be obtained according to the design information, and the corresponding templates of each entity can be called from the template library to generate entity instances and connections between entities, and the data attributes of the entities can be instantiated according to the operation information.
  • the heat exchanger template can be called from the template library, and the design information and operation information can be used to fill the heat exchanger template, and based on the topological connection between the heat exchanger and other devices, the The data properties of the connection are populated.
  • step 304 model information is generated based on the instantiated ontology.
  • an instantiated ontology can be transformed into model information in a specific format for interacting with other data platforms.
  • step 305 the model information is transmitted to the modeling and simulation engine so that the modeling and simulation engine automatically generates a model of the industrial process system according to the model information.
  • the generated model information can be transmitted or pushed to the modeling and simulation engine, and the modeling and simulation engine can be triggered to perform modeling, so that the modeling and simulation engine can extract the device model and System model, and automatically drag and drop corresponding equipment modules on the modeling and simulation platform to form system modules, thus completing the modeling of industrial process systems.
  • step 304 may further include: based on the instantiated ontology, generating model information in a format readable by a modeling and simulation engine.
  • a format readable by a modeling and simulation engine eg, XML, OWL, CSV, TXT, etc.
  • a data converter e.g., XML, OWL, CSV, TXT, etc.
  • model information in XML format can be generated so that the model information can be easily shared by the modeling and simulation engine or other platforms.
  • method 300 may also optionally include step 306 before step 305 .
  • step 306 the model information is checked for sanity before being transmitted to the modeling and simulation engine. In this step, by performing a sanity check, it is possible to avoid providing incomplete model information to the modeling and simulation engine.
  • additional information may be requested to fill in missing information (for example, data, model parameters) in the model information until the model information passes the completeness check.
  • the generated model information may include device model information and system model information, and the system information is constructed based on the device model information.
  • the equipment model information corresponds to the equipment level
  • the system model information corresponds to the system level.
  • the system level is a layer above the equipment level, so the system model is composed of various equipment models.
  • step 306 may further include: performing a completeness check on the device model information; and performing a completeness check on the system model information after the device model information passes the completeness check. Since the system model is formed based on the equipment model, it is first necessary to perform a completeness check on the equipment model information, and then perform a completeness check on the system model information. Similarly, if the device model information fails the completeness check, additional information can be requested to fill in the missing information (for example, data, model parameters) in the device model information until the device model information passes the completeness check; if the system model If the information fails the completeness check, additional information may be requested to fill in missing information (for example, model parameters) in the system model information until the system model information passes the completeness check.
  • missing information for example, data, model parameters
  • step 306 may further include: checking the completeness of the data and model parameters of the device model information, and checking the completeness of the model parameters of the system model information.
  • checking the data in the equipment model information may refer to checking whether the data attributes that the equipment should possess as an entity are missing
  • checking the model parameters in the equipment model information may refer to checking whether the model parameters of the equipment used in the simulation process are missing (for example , whether a specific parameter is missing when the simulation is solved). Since the system model is composed of the equipment model, when the equipment model passes the completeness check, the data information in the system model information is also complete, but it is still necessary to check whether the model parameters of the system used in the simulation process are missing (for example, when solving the simulation is missing a specific parameter).
  • step 306 may further include: checking whether the device model information includes a template that does not exist in the template library; if not, adding the template to the template library. In this step, the template library of the device can be automatically discovered and expanded.
  • method 300 may also optionally include step 307 .
  • step 307 obtain simulation data generated by simulating the generated model by the modeling and simulation engine; obtain process data generated during operation of the industrial process system; and calibrate the model based on the simulation data and process data. Since the initially established simulation model may be obtained based on offline data, with the continuous operation of the industrial process system, some parameters or attributes have changed, the simulation model may no longer be suitable for the industrial process system, and the simulation model needs to be calibrated.
  • step 307 may further include: comparing the simulation data with the process data to determine whether the model is acceptable; when it is determined that the model is unacceptable, using the process data to calibrate the model. For example, it is possible to compare whether there is a large error between the simulation data and the actually obtained process data, if the error is large, the simulation model is unacceptable, and use the process data to discover which data or model parameters in the model information need is corrected to calibrate the simulation model.
  • step 307 may further include: calculating the key performance indicators of the model based on the simulation data and process data; determining whether the model is matched based on the key performance indicators; when determining that the model is not matching, using the process data Calibrate the model.
  • the key performance indicators of the model can be calculated, if it is determined that the key performance indicators have not met the original design requirements, then it is determined that the simulation model is not suitable, and the process data is used to discover which data or model parameters in the model information need to be modified. Correction to calibrate the simulation model.
  • method 300 may further include: modifying model information based on the calibrated model. For example, based on the calibrated data or model parameters in the simulation model, the corresponding data or model parameters in the model information can be modified accordingly, and the calibrated model and the modified model information can be stored or updated, so that the next modeling Calibrated model and modified model information can be recalled directly.
  • modeling and simulation are based on a unified process ontology, which can adapt to different types of industrial processes; engineers only need to have a certain understanding of field knowledge and design process data, and can generate information about industrial process systems. simulation models; for different industrial process systems, modeling and simulation cases can be easily managed in a hierarchical manner (e.g., through stored model information and models) for case benchmarking without opening different models on the simulation platform Compare.
  • FIG. 4 illustrates an exemplary apparatus 400 for automatically generating a model of an industrial process system according to an embodiment of the disclosure.
  • the apparatus 400 includes a construction unit 401 , an acquisition unit 402 , an instantiation unit 403 , a generation unit 404 , and a model unit 405 .
  • the construction unit 401 is configured to perform the process described above in relation to step 301 in the method 300
  • the acquisition unit 402 is configured to perform the process described in the above relation to step 302 in the method 300
  • the instantiation unit 403 is configured to perform the process described above in relation to step 302 in the method 300.
  • the generating unit 404 is configured to perform the process described above in relation to step 304 in the method 300
  • the model unit 405 is configured to perform the process described in relation to step 305 in the method 300 above .
  • the device 400 may also optionally include a checking unit 406 and a calibration unit 407 .
  • the checking unit 404 is configured to perform the process as described above with respect to step 306 in the method 300
  • the calibration unit 407 is configured to perform the process as described above with respect to step 307 in the method 300 .
  • FIG. 5 shows a block diagram of an exemplary computing device 500 for automatically generating a model of an industrial process system according to an embodiment of the disclosure.
  • the computing device 500 includes a processor 501 and a memory 502 coupled with the processor 501 .
  • the memory 502 is used to store computer-executable instructions, and when the computer-executable instructions are executed, the processor 501 executes the methods in the above embodiments (for example, any one or more steps of the aforementioned method 300).
  • a computer-readable storage medium carries computer-readable program instructions for implementing various embodiments of the present disclosure.
  • a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
  • a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, such as a printer with instructions stored thereon A hole card or a raised structure in a groove, and any suitable combination of the above.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disc read only memory
  • DVD digital versatile disc
  • memory stick floppy disk
  • mechanically encoded device such as a printer with instructions stored thereon
  • a hole card or a raised structure in a groove and any suitable combination of the above.
  • computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.
  • the present disclosure provides a computer-readable storage medium having computer-executable instructions stored thereon for performing various implementations of the present disclosure. method in the example.
  • the present disclosure provides a computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions that, when executed, cause At least one processor executes the methods in various embodiments of the present disclosure.
  • the various example embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, microprocessor or other computing device.
  • aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it is to be understood that the blocks, devices, systems, techniques, or methods described herein may serve as non-limiting Examples are implemented in hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof.
  • the computer-readable program instructions or computer program products used to execute various embodiments of the present disclosure can also be stored in the cloud, and when called, the user can access the program stored on the cloud for execution through the mobile Internet, fixed network or other networks.
  • the computer-readable program instructions of an embodiment of the present disclosure implement the technical solutions disclosed in accordance with various embodiments of the present disclosure.

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Abstract

L'invention concerne un procédé et un dispositif permettant de générer automatiquement un modèle d'un système de traitement industriel, et un support de stockage informatique. Le procédé consiste : à construire une ontologie liée à la simulation d'un système de traitement industriel selon une structure hiérarchique du système de procédé industriel ; à acquérir des informations de conception et des informations de fonctionnement du système de traitement industriel ; à instancier l'ontologie en fonction des informations de conception et des informations de fonctionnement ainsi qu'une bibliothèque de modèles ; à générer des informations de modèle sur la base de l'ontologie instanciée ; et à transmettre les informations de modèle à un moteur de modélisation et de simulation de sorte que le moteur de modélisation et de simulation génère automatiquement un modèle du système de traitement industriel en fonction des informations de modèle. Selon le procédé de génération automatique d'un modèle d'un système de traitement industriel, au moyen d'une ontologie de production de processus unifiée pour décrire divers systèmes de processus industriels, des ingénieurs ont besoin seulement d'avoir certaines connaissances pratiques et des données de processus de conception pour générer automatiquement le modèle, et peuvent facilement gérer le modèle de simulation.
PCT/CN2021/109857 2021-07-30 2021-07-30 Procédé et dispositif de génération automatique d'un modèle de système de traitement industriel WO2023004802A1 (fr)

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CN102365597A (zh) * 2009-03-30 2012-02-29 西门子公司 用于创建过程模型的装置和方法
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