Petrochemical device digital twin system construction method, petrochemical device digital twin system application method, medium and petrochemical device digital twin system equipment
By constructing a multi-model fusion digital twin system for petrochemical plants, the problems of opaque production processes and low training efficiency in petrochemical plants have been solved. This has enabled process optimization and safety training, reduced the cost of redundant construction, and improved production efficiency and economic benefits.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-18
- Publication Date
- 2026-03-20
AI Technical Summary
The production process of petrochemical plants is not transparent, and relying on experience to adjust makes it difficult to predict process changes. Traditional training methods are inefficient, and the fragmented data models lead to high costs of redundant construction, making it difficult to achieve process optimization and safety training.
Based on engineering design and production operation data, a steady-state mechanism model, a dynamic simulation model, and a lightweight 3D simulation model of the petrochemical plant are constructed. A digital twin system integrating multiple models is established to realize process prediction optimization, immersive training, and dynamic adjustment of production plans.
Transparently displaying the production process improves training efficiency, reduces construction costs, enables flexible adjustments, and enhances economic benefits and safety.
Smart Images
Figure CN121706310A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of petrochemical engineering technology, specifically to a method for constructing, applying, media, and equipment for a digital twin system of a petrochemical plant. Background Technology
[0002] The petrochemical industry, or petrochemical industry for short, generally refers to the chemical industry that uses petroleum and natural gas as raw materials. The petrochemical industry has a wide scope and many products, and is an important component of the chemical industry. It is characterized by numerous production units, long process flows, high degree of variable coupling, reliance on experience for program adjustments, a lack of efficient training methods, and a need to improve the immersive learning experience.
[0003] The petrochemical smart factory is a new intelligent manufacturing model for the petrochemical industry, covering the entire petrochemical production chain. It deeply integrates next-generation information and communication technologies with resources, processes, equipment, environment, and human manufacturing activities in petrochemical production. In the production process of petrochemical plants, there are frequent changes in production conditions and product plans. Improper operation, untimely adjustments, or inadequate training can lead to substandard product quality, directly impacting economic benefits and even causing safety accidents. Therefore, it is urgent to construct a digital twin system for production equipment processes through digital and intelligent means. This allows for flexible adjustments to production plans under different operating conditions in a virtual space, market-demand-oriented product optimization, and immersive simulation training that is both realistic and intuitive. This improves equipment efficiency, reduces operating costs, lowers the probability of accidents, and ultimately enhances the level of intelligence in equipment production operations. Summary of the Invention
[0004] To overcome the problems existing in related technologies, this disclosure provides a method for constructing, applying, media, and equipment for a digital twin system of a petrochemical plant.
[0005] According to a first aspect of the present disclosure, a method for constructing a digital twin system for a petrochemical plant is provided, comprising: Based on engineering design data and production operation data, a steady-state mechanism model and a dynamic simulation model of a petrochemical plant were established. A lightweight 3D simulation model of a petrochemical plant was established based on a 3D model delivered digitally. Based on the steady-state mechanism model of the petrochemical plant, the dynamic simulation model, and the lightweight three-dimensional simulation model, a digital twin system for the petrochemical plant with multi-model fusion is established.
[0006] In one embodiment, the engineering design data includes at least: process design data, equipment structure data, operation screen, control logic, control algorithm, and safety interlock logic; the production operation data includes at least: real-time operation data and laboratory analysis data; the digitally delivered 3D model covers the physical object model of the physical factory, and the physical object model of the physical factory includes at least: the shape, interface, accessories, and cable trays of equipment, pipelines, buildings, structures, electrical systems, and instruments.
[0007] In one embodiment, the steady-state mechanism model of the petrochemical plant includes core equipment units, which are connected according to the actual production process. A steady-state mathematical model is constructed based on the strict three-transfer-one-reaction process mechanism. The core equipment units include at least reactors, towers, containers, pumps, and heat exchangers. The dynamic simulation model includes a dynamic process simulation model and a control simulation model; The dynamic process simulation model takes all the core equipment units monitored and operated in the control room as simulation objects and establishes a dynamic mathematical model in the time domain based on the process mechanism. The control simulation model is an operation control model constructed using graphical configuration functions and methods, which includes an operation screen, control logic, control algorithm, and safety interlock logic. The lightweight 3D simulation model of the device is based on the digitally delivered 3D model. According to the simulation requirements, the geometry and load-bearing information of the 3D model are lightweighted, and the lightweight 3D model is rendered based on the actual appearance of the equipment and the production environment.
[0008] In one embodiment, establishing a steady-state mechanism model and a dynamic simulation model for a petrochemical plant based on engineering design data and production operation data includes: The steady-state mechanism model is obtained by inputting the engineering design data, and the steady-state mechanism model is calibrated by inputting the real-time operation data and the laboratory analysis data. The dynamic simulation model is obtained by inputting the engineering design data and the output data of the steady-state mechanism model; the dynamic process simulation model is corrected by inputting the production operation data. The dynamic simulation model includes a dynamic process simulation model and a control simulation model. The output data of the steady-state mechanism model includes the simulation calculation results output by the steady-state mechanism model.
[0009] In one embodiment, the creation of a lightweight 3D simulation model of the petrochemical plant based on a digitally delivered 3D model includes: The lightweight 3D simulation model is obtained by modeling the interactive operation logic of the field devices, which include manual valves, pumps, and compressors.
[0010] According to a second aspect of the present disclosure, a method for applying a digital twin system to a petrochemical plant is provided. This method is applied to a multi-model fusion digital twin system for a petrochemical plant. The multi-model fusion digital twin system is established based on engineering design data and production operation data, creating a steady-state mechanism model and a dynamic simulation model of the petrochemical plant. A lightweight 3D simulation model of the petrochemical plant is established based on a digitally delivered 3D model. The multi-model fusion digital twin system is then established based on the steady-state mechanism model, the dynamic simulation model, and the lightweight 3D simulation model. The method includes: Based on the digital twin system that integrates multiple models of the petrochemical plant, the system performs functions such as process prediction and optimization, immersive simulation training, operation method optimization, and dynamic adjustment of production plans.
[0011] In one embodiment, the dynamic simulation model includes a dynamic process simulation model and a control simulation model, and the method further includes: The dynamic process simulation model inputs control commands to operate the control valve, and inputs three-dimensional interactive operation commands to the dynamic process simulation model to operate the field equipment. The control commands are commands related to the operation of the control valve, and the three-dimensional interactive operation commands are operation commands of the field equipment output by the lightweight three-dimensional simulation model. The dynamic process simulation model outputs the status data of the control valves and instruments to the control simulation model, and the dynamic process simulation model outputs the status data of the field equipment to the lightweight three-dimensional simulation model. The control simulation model inputs the status data of the control valves and instruments, and outputs the control commands to the dynamic process simulation model. The lightweight 3D simulation model inputs the status data of the field equipment fed back by the dynamic process simulation model and displays it. The lightweight 3D simulation model outputs the operation instructions of the field equipment to the dynamic process simulation model.
[0012] In one embodiment, the process prediction and optimization function is used to monitor, predict, and optimize product indicators and energy consumption indicators, providing guidance for optimal process operation and precise control. The immersive simulation training function is used for driving training, parking training, production adjustment training, and accident handling training. The operation method optimization function is used for the verification and refinement of driving and parking operation procedures, as well as the verification and refinement of operation methods for handling accidents and anomalies. The dynamic adjustment function of the production plan is used to optimize and adjust the operation plan in response to changes in raw materials and product demand.
[0013] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the program is executed by a processor, the program implements the steps of the method for constructing a digital twin system for a petrochemical plant provided in the first aspect of the present disclosure or the method for applying a digital twin system for a petrochemical plant provided in the second aspect of the present disclosure.
[0014] According to a fourth aspect of the present disclosure, an electronic device is provided, including a processor and a memory; the memory stores a computer program thereon; the processor is configured to execute the computer program in the memory to implement the steps of the petrochemical plant digital twin system construction method provided in the first aspect of the present disclosure or the petrochemical plant digital twin system application method provided in the second aspect.
[0015] The above technical solutions achieve several key benefits. First, by integrating data from engineering design, digital delivery, and production operation, a method for constructing steady-state mechanism models, dynamic simulation models, and lightweight 3D simulation models is proposed. This enables seamless data and model transfer throughout the entire plant construction process, avoiding redundant construction by refining and chemical enterprises and significantly reducing construction costs. Second, data integration and interactive fusion between the steady-state mechanism models, dynamic simulation models, and lightweight 3D simulation models of petrochemical plants are achieved, providing a platform for intelligent applications such as plant optimization simulation. Finally, the various production processes of petrochemical plants are transparently displayed, enabling technicians to perform process prediction and optimization, immersive simulation training, operation method optimization, and dynamic adjustment of production plans. This provides technical support for improving the economic efficiency of petrochemical plants, reducing production costs, increasing training efficiency, and flexibly adjusting production plans.
[0016] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description
[0017] The accompanying drawings are provided to further understand the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof.
[0018] Figure 1 This is a flowchart illustrating a method for constructing a digital twin system for a petrochemical plant according to an exemplary embodiment.
[0019] Figure 2 This is a schematic diagram of a digital twin system for multi-model fusion of a petrochemical plant, according to an exemplary embodiment.
[0020] Figure 3 This is a flowchart illustrating a method for applying a digital twin system in a petrochemical plant, according to an exemplary embodiment.
[0021] Figure 4This is a block diagram illustrating a construction apparatus for a digital twin system of a petrochemical plant, according to an exemplary embodiment.
[0022] Figure 5 This is a block diagram of a digital twin system application device for a petrochemical plant, according to an exemplary embodiment.
[0023] Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation
[0024] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.
[0025] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.
[0026] In the description of this disclosure, unless otherwise stated, "multiple" means two or more, and other quantifiers are similar; "at least one," "one or more," or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one 'a' can represent any number of 'a's; as another example, one or more of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple; "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. The character " / " indicates that the preceding and following related objects are in an "or" relationship.
[0027] Although operations or steps are described in a specific order in the accompanying drawings in the embodiments of this disclosure, it should not be construed as requiring these operations or steps to be performed in the specific order or serial order shown, or requiring all of the shown operations or steps to be performed to obtain the desired result. In the embodiments of this disclosure, these operations or steps may be performed serially; they may be performed in parallel; or a portion of these operations or steps may be performed.
[0028] First, the application scenarios of this disclosure will be explained.
[0029] The petrochemical industry, or petrochemical industry for short, generally refers to the chemical industry that uses petroleum and natural gas as raw materials. The petrochemical industry has a wide scope and produces many products, making it an important component of the chemical industry. Petrochemical plants are a general term for petroleum and chemical plants. A petrochemical plant refers to all the equipment used in the series of processes from oil drilling and extraction to transportation and processing into finished chemical products. It mainly includes static equipment such as tower equipment, heat exchangers, reactors, storage tanks, and various shell-and-tube equipment, as well as dynamic equipment for transporting media. Examples include atmospheric and vacuum distillation units, catalytic cracking units, hydrocracking units, continuous reforming units, aromatics units, ethylene units, and oil tank farms.
[0030] However, with the rapid development of oil refining and petrochemical technologies, traditional petrochemical plants have the following problems: 1. As petrochemical plants become increasingly integrated and complex, and due to the high coupling between upstream and downstream process parameters, changes in production conditions can cause dynamic changes in a series of indicators with a single operational adjustment. Relying solely on operator experience makes it difficult to predict the changing trends of key product indicators, potentially leading to problems such as decreased target product yield and substandard products. Furthermore, adjusting operating parameters to achieve new product optimization schemes when market demand changes is also a significant challenge in production.
[0031] 2. In the petrochemical production process, every production or operational step may pose safety hazards, and the consequences of an accident could be disastrous. Furthermore, not every production step can be observed firsthand. Traditional training methods typically involve inviting experts and scholars to conduct scheduled, centralized lectures. This approach suffers from limitations such as dry content, lack of realism, low relevance, and lack of repetition. Especially for advanced training involving hazardous operations or where experimental conditions are unavailable, traditional methods are ill-suited to meeting the required training quality and efficiency.
[0032] 3. Data, models, and applications at different stages of petrochemical plants are scattered across different systems, leading to issues of redundant construction, long cycles, and high costs. For example, digitally delivered engineering data, documents, and 3D models are static engineering information and cannot yet support simulations of dynamic plant operations. Simulation training during the production phase still requires the development of 3D simulation models through digital reconstruction. Even if a steady-state mechanism model is established during the design phase, it does not yet support process optimization during the production phase. The production phase requires the reconstruction of a process mechanism model, which is difficult and costly. Moreover, dynamic process simulation models are only built based on one set of engineering design data, requiring the generation of multiple sets of data from the steady-state mechanism model as input to achieve more accurate simulations.
[0033] As described above, the internal production process of traditional petrochemical plants is not transparent, and technicians cannot make timely adjustments, which restricts the optimization of the production process and fails to meet the needs of improving quality and efficiency. There is an urgent need for a digital and intelligent means to simulate the real production process and realize intelligent applications such as process index prediction, optimization and adjustment, and simulation training under multiple operating conditions.
[0034] To overcome the problems existing in related technologies, this disclosure provides a method for constructing, applying, media, and equipment for a digital twin system of a petrochemical plant. Based on engineering design data and production operation data, a steady-state mechanism model and a dynamic simulation model of the petrochemical plant are established. A lightweight 3D simulation model of the petrochemical plant is established based on the digitally delivered 3D model. Based on the steady-state mechanism model, the dynamic simulation model, and the lightweight 3D simulation model, a multi-model fusion digital twin system of the petrochemical plant is established. Through the above technical solution, a digital twin system is constructed that can transparently display each production process of the petrochemical plant, enabling technicians to achieve process prediction and optimization, immersive simulation training, operation method optimization, and dynamic adjustment of production plans. This provides technical support for improving the economic benefits of petrochemical plants, reducing production costs, increasing training efficiency, and flexibly adjusting production plans.
[0035] The specific embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.
[0036] Figure 1 This is a flowchart illustrating a method for constructing a digital twin system for a petrochemical plant according to an exemplary embodiment, such as... Figure 1 As shown, the method for constructing a digital twin system for a petrochemical plant is applied to a petrochemical plant and includes the following steps.
[0037] In step S101, a steady-state mechanism model and a dynamic simulation model of the petrochemical plant are established based on engineering design data and production operation data.
[0038] In some embodiments, engineering design data includes at least: process design data, equipment structure data, operation screen, control logic, control algorithm, and safety interlock logic; for example, engineering design data includes: Piping & Instrument Diagram (P&ID), Process and Flowing Diagram (PFD), process design specification, process data table, equipment structure diagram, equipment data table, DCS operation screen, etc.
[0039] In some embodiments, production operation data includes at least: real-time operation data and laboratory analysis data.
[0040] In some embodiments, the mechanism model constructed in step S101 can be a steady-state mechanism model. For example, in step S101, steady state is the state in which operating conditions reach a stable state under the premise of satisfying material balance and heat balance.
[0041] For example, the petrochemical unit can be a hydrocracking unit. In step S101, the mechanism model of the hydrocracking unit can be constructed based on physical entity data, including core equipment units such as refining and cracking reactors, fractionation towers, separators, heat exchangers, coolers, heaters, compressors, and pumps. The equipment is connected and constructed according to the PFD diagram to obtain a steady-state mechanism model.
[0042] In this embodiment, the dynamic simulation model includes a dynamic process simulation model and a control simulation model. The dynamic process simulation model takes the core equipment unit monitored and operated in the control room of the device as the simulation object, and establishes a dynamic mathematical model in the time domain based on the process mechanism to simulate the dynamic changes and fault responses during start-up and shutdown. The control simulation model is constructed using graphical configuration functions and methods, and includes the operation screens, control logic, control algorithms, and safety interlock logic of each unit of the device.
[0043] In step S102, a lightweight three-dimensional simulation model of the petrochemical plant is established based on the digitally delivered three-dimensional model.
[0044] In some embodiments, the 3D model for digital delivery encompasses physical object models of the physical plant, such as the shape, interfaces, accessories, and cable trays of equipment, pipes, buildings, structures, electrical systems, instruments, etc.
[0045] In this embodiment, the digital delivery refers to the engineering information generated during the engineering construction process, namely, the digital static information. This information is mainly held by the design institute and the construction unit during the engineering construction phase. Through digital delivery, enterprises can digitally grasp and manage this engineering data, establish an engineering data center, and add the digital dynamic information after the enterprise is in operation, including management data (enterprise resource planning, production, sales, finance, etc.) and operational data (DCS system data, such as pressure / flow meter data, etc.). The combination of "static and dynamic" forms the foundation for the construction of a smart factory.
[0046] Understandably, the 3D model delivered digitally is a virtual visualization of the physical factory. It encompasses equipment models, piping models, building models, structural models, electrical models, and instrumentation models. Specifically, it includes physical object models of the factory, such as equipment outlines, interfaces, pipe fittings and auxiliary instruments, building outlines, roads, ladders, platforms, and cable trays. Since design institutes use dozens of different 3D design software programs to build these diverse models, and each specialty designs only a portion of the factory model (e.g., piping design software, cable design software, steel structure design software), these software programs need to be converted into a unified format required by the delivery platform and assembled into a complete factory model.
[0047] In step S103, a digital twin system for the petrochemical plant is established based on the steady-state mechanism model of the petrochemical plant, the dynamic simulation model, and the lightweight three-dimensional simulation model, which integrates multiple models.
[0048] Using the methods described above, a digital twin system is constructed that can transparently display the various production processes of a petrochemical plant. This enables technicians to perform process prediction and optimization, immersive simulation training, operation method optimization, and dynamic adjustment of production plans. This provides technical support for improving the economic benefits of petrochemical plants, reducing production costs, increasing training efficiency, and flexibly adjusting production plans.
[0049] In some embodiments, step S101 includes: The steady-state mechanism model is obtained by inputting engineering design data, and the steady-state mechanism model is calibrated by inputting real-time operation data and laboratory analysis data. The dynamic process simulation model is obtained by inputting the engineering design data and the output data of the steady-state mechanism model; the dynamic process simulation model is corrected by inputting the production operation data. The dynamic simulation model includes a dynamic process simulation model and a control simulation model. The output data of the steady-state mechanism model includes the simulation calculation results output by the steady-state mechanism model.
[0050] In some embodiments, the steady-state mechanism model of a petrochemical plant includes core equipment units, which are connected according to the actual production process. A steady-state mathematical model is constructed based on the strict three-transfer-one-reaction process mechanism. The core equipment units include at least reactors, towers, vessels, pumps, and heat exchangers.
[0051] In some embodiments, the dynamic simulation model includes a dynamic process simulation model and a control simulation model; wherein, the dynamic process simulation model takes all core equipment units monitored and operated in the control room as simulation objects and establishes a dynamic mathematical model in the time domain based on the process mechanism; the control simulation model is an operation control model that includes operation screens, control logic, control algorithms and safety interlock logic, constructed using graphical configuration functions and methods.
[0052] In some embodiments, the lightweight 3D simulation model of the device is based on the digitally delivered 3D model. The geometry and load-bearing information of the 3D model are lightweighted according to the simulation requirements, and the lightweight 3D model is rendered based on the actual appearance of the device and the production environment.
[0053] For example, see Figure 2 The steady-state mechanism model outputs simulation calculation results to the dynamic process simulation model. The dynamic process simulation model outputs the status of control valves and instruments to the control simulation model. It outputs the status data of field equipment such as manual valves, pumps, and compressors to the lightweight 3D simulation model. The control simulation model outputs control commands to the dynamic process simulation model. The lightweight 3D simulation model outputs the operation commands of field equipment to the dynamic process simulation model.
[0054] The above technical solutions integrate data from engineering design, digital delivery, and production operation, and propose methods for constructing steady-state mechanism models, dynamic simulation models, and lightweight 3D simulation models. This enables seamless data and model transfer throughout the entire plant construction process, avoids redundant construction by refining and chemical enterprises, and significantly reduces construction costs.
[0055] In some embodiments, step S102 includes: inputting the interactive operation logic of the field device and modeling it to obtain a lightweight three-dimensional simulation model, wherein the field device includes a manual valve, a pump, and a compressor.
[0056] The above technical solutions enable data integration and interactive fusion among the steady-state mechanism model, dynamic simulation model, and lightweight 3D simulation model of petrochemical plants, providing a platform for intelligent applications such as plant optimization simulation.
[0057] Figure 3 This is a flowchart illustrating an application method of a digital twin system for a petrochemical plant according to an exemplary embodiment, such as... Figure 3 As shown, the application method of the digital twin system of this petrochemical plant is applied to... Figure 1 The digital twin system of the petrochemical plant shown includes the following steps.
[0058] In step S301, the digital twin system based on the multi-model fusion of the petrochemical unit performs process prediction optimization, immersive simulation training, operation method optimization, and dynamic adjustment of production plan.
[0059] In some embodiments, the process prediction and optimization function is used to monitor, predict, and optimize product indicators and energy consumption indicators under different operating conditions, providing guidance for optimal process operation and precise control; the immersive simulation training function is used for start-up training, shutdown training, production adjustment training, and accident handling training; the operation method optimization function is used for the verification and refinement of start-up and shutdown operation procedures, and the verification and refinement of operation methods for handling accidents and anomalies; and the dynamic adjustment function of production plan is used for the optimization and adjustment of operation plans due to changes in raw materials and product demand.
[0060] Through the above technical solutions, the proposed digital twin system is applied to process prediction and optimization, immersive simulation training, operation method optimization, and dynamic adjustment of production plans in petrochemical plants, providing technical support for improving the economic benefits of petrochemical plants, reducing production costs, improving training efficiency, and flexibly adjusting production plans.
[0061] In some embodiments, the dynamic simulation model includes a dynamic process simulation model and a control simulation model. The above-mentioned application method of the digital twin system for petrochemical plants further includes: The dynamic process simulation model inputs control commands to operate the control valves, and inputs three-dimensional interactive operation commands to the dynamic process simulation model to operate the field equipment. Among them, the control commands are the commands related to the operation of the control valves, and the three-dimensional interactive operation commands are the operation commands of the field equipment output by the lightweight three-dimensional simulation model. The dynamic process simulation model outputs the status data of control valves and instruments to the control simulation model, and the dynamic process simulation model outputs the status data of field equipment to the lightweight 3D simulation model. The control simulation model inputs the status data of control valves and instruments, and outputs control commands to the dynamic process simulation model. The lightweight 3D simulation model inputs the status data of the field equipment fed back from the dynamic process simulation model for display, and outputs the operation commands of the field equipment to the dynamic process simulation model.
[0062] Through the above technical solutions and the multiple functions of the petrochemical plant digital twin system, it is possible to monitor and diagnose various complex operating conditions of the petrochemical plant, providing auxiliary guidance for the timely and rapid safe operation of the plant's process personnel and operators, which is conducive to achieving "safe, stable, long-cycle, and full-load" operation of the petrochemical plant.
[0063] Figure 4This is a block diagram illustrating a digital twin system construction apparatus for a petrochemical plant according to an exemplary embodiment. (Refer to...) Figure 4 The petrochemical plant digital twin system construction device 400 includes a first modeling module 401, a second modeling module 402, and a third modeling module 403.
[0064] The first modeling module 401 is used to establish a steady-state mechanism model and a dynamic simulation model of the petrochemical plant based on engineering design data and production operation data. The second modeling module 402 is used to build a lightweight three-dimensional simulation model of the petrochemical plant based on the digitally delivered three-dimensional model. The third modeling module 403 is used to establish a digital twin system for petrochemical plants based on the steady-state mechanism model, dynamic simulation model and lightweight 3D simulation model of the petrochemical plant.
[0065] In one embodiment, the engineering design data includes at least: process design data, equipment structure data, operation screen, control logic, control algorithm, and safety interlock logic; the production operation data includes at least: real-time operation data and laboratory analysis data; the digitally delivered 3D model covers the physical object model of the physical factory, and the physical object model of the physical factory includes at least: the shape, interface, accessories, and cable trays of equipment, pipelines, buildings, structures, electrical systems, instruments.
[0066] In one embodiment, the steady-state mechanism model of a petrochemical plant includes core equipment units, which are connected according to the actual production process. It is a steady-state mathematical model under stable operating conditions constructed based on the strict three-transfer-one-reaction process mechanism. The core equipment units include at least reactors, towers, vessels, pumps, and heat exchangers. Dynamic simulation models include dynamic process simulation models and control simulation models; Among them, the dynamic process simulation model takes all the core equipment units monitored and operated in the control room as the simulation objects and establishes a dynamic mathematical model in the time domain based on the process mechanism. The control simulation model is an operation control model constructed using graphical configuration functions and methods, which includes an operation screen, control logic, control algorithm, and safety interlock logic. The lightweight 3D simulation model of the device is based on the digitally delivered 3D model. According to the simulation requirements, the geometry and load-bearing information of the 3D model are lightweighted, and the lightweight 3D model is rendered based on the actual appearance of the equipment and the production environment.
[0067] In one embodiment, the first modeling module 401 is further configured to: The steady-state mechanism model is obtained by inputting engineering design data, and the steady-state mechanism model is calibrated by inputting real-time operation data and laboratory analysis data. The dynamic process simulation model is obtained by inputting the engineering design data and the output data of the steady-state mechanism model; the dynamic process simulation model is corrected by inputting the production operation data. The dynamic simulation model includes a dynamic process simulation model and a control simulation model. The output data of the steady-state mechanism model includes the simulation calculation results output by the steady-state mechanism model.
[0068] In one embodiment, the second modeling module 402 is further configured to: The interactive operation logic of the field devices is input to model a lightweight 3D simulation model. The field devices include manual valves, pumps, and compressors.
[0069] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0070] Figure 5 This is a block diagram illustrating a digital twin system application device for a petrochemical plant according to an exemplary embodiment. (Refer to...) Figure 5 The petrochemical plant digital twin system application device 500 includes an application module 501.
[0071] Application module 501 is used in a digital twin system based on multi-model fusion of petrochemical plants to perform process prediction and optimization functions, immersive simulation training functions, operation method optimization functions, and dynamic adjustment functions of production plans.
[0072] In one embodiment, application module 501 is further configured to: The dynamic process simulation model inputs control commands to operate the control valves, and inputs three-dimensional interactive operation commands to the dynamic process simulation model to operate the field equipment. Among them, the control commands are the commands related to the operation of the control valves, and the three-dimensional interactive operation commands are the operation commands of the field equipment output by the lightweight three-dimensional simulation model. The dynamic process simulation model outputs the status data of control valves and instruments to the control simulation model, and the dynamic process simulation model outputs the status data of field equipment to the lightweight 3D simulation model. The control simulation model inputs the status data of control valves and instruments, and outputs control commands to the dynamic process simulation model. The lightweight 3D simulation model inputs the status data of the field equipment fed back from the dynamic process simulation model for display, and outputs the operation commands of the field equipment to the dynamic process simulation model.
[0073] In one embodiment, the process prediction and optimization function is used to monitor, predict, and optimize product indicators and energy consumption indicators, providing guidance for optimal process operation and precise control; the immersive simulation training function is used for start-up training, shutdown training, production adjustment training, and accident handling training; the operation method optimization function is used for the verification and refinement of start-up and shutdown operation procedures, and the verification and refinement of operation methods for handling accidents and anomalies; and the dynamic adjustment function of production plan is used for the optimization and adjustment of operation plans due to changes in raw materials and product demand.
[0074] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0075] Figure 6 This is a block diagram illustrating an electronic device 600 according to an exemplary embodiment. For example... Figure 6 As shown, the electronic device 600 may include a processor 601 and a memory 602. The electronic device 600 may also include one or more of a multimedia component 603, an input / output (I / O) interface 604, and a communication component 605.
[0076] The processor 601 controls the overall operation of the electronic device 600 to complete all or part of the steps in the model construction method described above. The memory 602 stores various types of data to support the operation of the electronic device 600. This data may include, for example, instructions for any application or method operating on the electronic device 600, and application-related data such as sent and received messages, images, audio, and video. The memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 603 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 602 or transmitted via communication component 605. The audio component also includes at least one speaker for outputting audio signals. I / O interface 604 provides an interface between processor 601 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 605 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.
[0077] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method for constructing a digital twin system for a petrochemical plant.
[0078] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for constructing a digital twin system for a petrochemical plant. For example, the computer-readable storage medium may be the memory 602 including program instructions, which may be executed by the processor 601 of the electronic device 600 to complete the above-described method for constructing a digital twin system for a petrochemical plant or the method for applying a digital twin system for a petrochemical plant.
[0079] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for constructing a digital twin system for a petrochemical plant or the method for applying a digital twin system for a petrochemical plant.
[0080] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described method for constructing a digital twin system for a petrochemical plant or a method for applying a digital twin system for a petrochemical plant when executed by the programmable device.
[0081] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.
[0082] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction.
[0083] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.
Claims
1. A method for constructing a digital twin system for a petrochemical plant, characterized in that, include: Based on engineering design data and production operation data, a steady-state mechanism model and a dynamic simulation model of a petrochemical plant were established. A lightweight 3D simulation model of a petrochemical plant was established based on a 3D model delivered digitally. Based on the steady-state mechanism model of the petrochemical plant, the dynamic simulation model, and the lightweight three-dimensional simulation model, a digital twin system for the petrochemical plant with multi-model fusion is established.
2. The method for constructing a digital twin system for a petrochemical plant according to claim 1, characterized in that, The engineering design data includes at least: process design data, equipment structure data, operation screen, control logic, control algorithm, and safety interlock logic; the production operation data includes at least: real-time operation data and laboratory analysis data; the digitally delivered 3D model covers the physical object model of the physical factory, and the physical object model of the physical factory includes at least: the shape, interface, accessories, and cable trays of equipment, pipelines, buildings, structures, electrical systems, instruments.
3. The method for constructing a digital twin system for a petrochemical plant according to claim 2, characterized in that, The steady-state mechanism model of the petrochemical plant includes core equipment units, which are connected according to the actual production process. The steady-state mathematical model is constructed based on the strict three-transfer-one-reaction process mechanism. The core equipment units include at least reactors, towers, containers, pumps, and heat exchangers. The dynamic simulation model includes a dynamic process simulation model and a control simulation model; The dynamic process simulation model takes all the core equipment units monitored and operated in the control room as simulation objects and establishes a dynamic mathematical model in the time domain based on the process mechanism. The control simulation model is an operation control model constructed using graphical configuration functions and methods, which includes an operation screen, control logic, control algorithm, and safety interlock logic. The lightweight 3D simulation model of the device is based on the digitally delivered 3D model. According to the simulation requirements, the geometry and load-bearing information of the 3D model are lightweighted, and the lightweight 3D model is rendered based on the actual appearance of the equipment and the production environment.
4. The method for constructing a digital twin system for a petrochemical plant according to claim 3, characterized in that, The establishment of steady-state mechanism models and dynamic simulation models for petrochemical plants based on engineering design data and production operation data includes: The steady-state mechanism model is obtained by inputting the engineering design data, and the steady-state mechanism model is calibrated by inputting the real-time operation data and the laboratory analysis data. The dynamic simulation model is obtained by inputting the engineering design data and the output data of the steady-state mechanism model; the dynamic process simulation model is corrected by inputting the production operation data. The dynamic simulation model includes a dynamic process simulation model and a control simulation model. The output data of the steady-state mechanism model includes the simulation calculation results output by the steady-state mechanism model.
5. The method for constructing a digital twin system for a petrochemical plant according to claim 4, characterized in that, The lightweight 3D simulation model of the petrochemical plant based on the digitally delivered 3D model includes: The lightweight 3D simulation model is obtained by modeling the interactive operation logic of the field devices, which include manual valves, pumps, and compressors.
6. A method for applying a digital twin system in a petrochemical plant, characterized in that, A digital twin system for multi-model fusion of petrochemical plants is provided. This system is based on engineering design data and production operation data, establishing a steady-state mechanism model and a dynamic simulation model of the petrochemical plant. A lightweight 3D simulation model of the petrochemical plant is established based on a digitally delivered 3D model. The digital twin system is then built based on the steady-state mechanism model, the dynamic simulation model, and the lightweight 3D simulation model. The method includes: Based on the digital twin system that integrates multiple models of the petrochemical plant, the system performs functions such as process prediction and optimization, immersive simulation training, operation method optimization, and dynamic adjustment of production plans.
7. The application method of the digital twin system for petrochemical plants according to claim 6, characterized in that, The dynamic simulation model includes a dynamic process simulation model and a control simulation model, and the method further includes: The dynamic process simulation model inputs control commands to operate the control valve, and inputs three-dimensional interactive operation commands to the dynamic process simulation model to operate the field equipment. The control commands are commands related to the operation of the control valve, and the three-dimensional interactive operation commands are operation commands of the field equipment output by the lightweight three-dimensional simulation model. The dynamic process simulation model outputs the status data of the control valves and instruments to the control simulation model, and the dynamic process simulation model outputs the status data of the field equipment to the lightweight three-dimensional simulation model. The control simulation model inputs the status data of the control valves and instruments, and outputs the control commands to the dynamic process simulation model. The lightweight 3D simulation model inputs the status data of the field equipment fed back by the dynamic process simulation model and displays it. The lightweight 3D simulation model outputs the operation instructions of the field equipment to the dynamic process simulation model.
8. The application method of the digital twin system for petrochemical plants according to claim 6, characterized in that, The process prediction and optimization function is used to monitor, predict, and optimize product indicators and energy consumption indicators, providing guidance for optimal process operation and precise control. The immersive simulation training function is used for driving training, parking training, production adjustment training, and accident handling training. The operation method optimization function is used for the verification and refinement of driving and parking operation procedures, as well as the verification and refinement of operation methods for handling accidents and anomalies. The dynamic adjustment function of the production plan is used to optimize and adjust the operation plan in response to changes in raw materials and product demand.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 5 or any one of claims 6 to 8.
10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1 to 5 or any one of claims 6 to 8.