Systems and methods for operation and design of industrial systems

Abstract

The present disclosure relates to systems and methods for the operation and design of industrial systems. Configuring distributed control in an industrial system includes establishing an asset model representing a process control installation of the industrial system and creating an asset library of distributed control assets according to distributed control programming standards. The asset model includes modeling assets that define and represent the physical devices of the industrial system according to levels of physical model standards. Each of the distributed control assets has one or more predefined built-in facets. One of the distributed control assets in the asset library is mapped to each modeling asset to configure the process control installation of the industrial system and generate an asset-based control application for providing distributed control of the industrial system. Other aspects relate to automatically creating control applications based on an information model by using machine learning or an asset configurator tool.

Description

Technical Field

[0001] Aspects of the present disclosure generally relate to industrial process automation and control systems. More particularly, aspects of the present disclosure relate to systems and methods for performing industrial plant commissioning, design, visualization, simulation, diagnosis, and operation. Background Art

[0002] The need for distributed control topologies has led to the development of a programming language standard, such as IEC 61499, specifically for distributed (event-based) industrial applications. Generally, IEC 61499 defines a general architecture that enables application-centric design, where one or more applications defined by a network of interconnected function blocks are created for the entire system and then distributed to available devices. All devices within the system are described within the device model, and the system model reflects the topology of the system. The distribution of the application is described within the mapping model. Thus, the applications of the system are distributable but remain together. In this way, the applications contained in a project can be mapped and executed on multiple automation controllers.

[0003] In the IEC 61499 architecture model, distributable applications are built by interconnecting instances of reusable function block types with appropriate events and data connections in the same way as designing a circuit board with integrated circuits. Then, using IEC 61499-compliant software tools, these function blocks can be distributed over a network to IEC 61499-compliant physical devices (controllers), thereby configuring distributed control and automation systems from a library of reusable IEC 61499-compliant components. Summary of the Invention

[0004] Briefly, aspects of the present disclosure allow for the definition and modeling of assets with built-in facets based on the IEC 61499 standard. Such facets can be easily used by a user to map to physical devices or control languages or narratives (e.g., HMI, control, alarms & events, scanning, and / or event-driven mode, simulation, etc.). Additionally, flexible application design and automated creation solutions based on information models allow for improved distributed intelligence and aim for zero engineering effort in the design and automated creation for industrial applications. Aspects of the present disclosure also allow for the automated creation of control applications by using machine learning or asset configurator tools.

[0005] In one aspect, a method of configuring distributed control in an industrial system includes: establishing an asset model representing a process control installation of the industrial system and creating an asset library of distributed control assets according to distributed control programming standards. According to the method, the asset model includes a plurality of modeling assets defined according to levels of physical model standards and modeling assets representing physical devices of the industrial system. Each distributed control asset has one or more predefined built-in facets. The method further includes mapping one of the distributed control assets in the asset library to each modeling asset to configure the process control installation of the industrial system and generating at least one asset-based control application that, when executed by one or more controllers of the process control installation, provides distributed control of the industrial system.

[0006] In another aspect, a system includes a processor and a storage memory coupled to the processor. The storage memory stores processor-executable instructions that, when executed by the processor, configure the processor to establish an asset model representing a process control installation of the industrial system and create an asset library of distributed control assets according to distributed control programming standards. The asset model includes a plurality of modeling assets defined according to levels of physical model standards and representing physical devices of the industrial system. Each distributed control asset has one or more predefined built-in facets. The processor-executable instructions further configure the processor to map one of the distributed control assets in the asset library to each modeling asset to configure the process control installation of the industrial system and generate at least one asset-based control application that, when executed by one or more controllers of the process control installation, provides distributed control of the industrial system.

[0007] In yet another aspect, a method of establishing an asset control model for configuring a distributed control system includes creating an asset control model library configured to store a plurality of distributed control assets and defining the distributed control assets according to distributed control programming standards. The distributed control assets represent a process control installation of the industrial system and are mapped from one or more physical assets and one or more control assets defined according to levels of physical model standards. The method further includes providing one or more predefined built-in facets to each distributed control asset, populating the asset control model library with the distributed control assets having one or more predefined built-in facets, and generating at least one asset-based control application that, when executed by one or more controllers of the process control installation, provides distributed control of the industrial system.

[0008] Other features will be made apparent in part and pointed out in part hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a block diagram illustrating a process control system according to an embodiment.

[0010] Figure 2 is a flowchart of an example process for establishing assets of a process control system according to an embodiment.

[0011] Figure 3 is a block diagram illustrating an asset-based control model hierarchy for identifying physical assets of a process control system according to an embodiment.

[0012] Figure 4 is a block diagram illustrating an asset-based control model hierarchy for identifying control assets of a process control system according to an embodiment.

[0013] Figure 5 is a block diagram illustrating an asset having facets with definitions associated therewith according to an embodiment.

[0014] Figure 6 is an example of an asset model designed for a variable frequency drive according to an embodiment.

[0015] Figure 7 is an example of an equipment asset model consisting of three pressure transmitters according to an embodiment.

[0016] Figure 8 is an example screenshot illustrating the relationship between physical assets and control assets according to an embodiment.

[0017] Figure 9 is an example screenshot illustrating an asset library classification folder structure according to an embodiment.

[0018] Figure 10 is a block diagram of an example of an asset library established for a furnace draft control application according to an embodiment.

[0019] Figure 11 is an illustration Figure 2 of other aspects of an example process.

[0020] Figure 12 is an example screenshot illustrating an asset automation type object according to an embodiment.

[0021] Figure 13 is an example screenshot illustrating the user interface of an asset configurator tool according to an embodiment.

[0022] Figure 14 is a flowchart of an example process for generating a control application according to an embodiment.

[0023] Figure 15 and Figure 16 are example screenshots respectively illustrating the user interface of an automatically created asset and an instance of an asset according to an embodiment.

[0024] Figure 17 is an example screenshot of an automatically created human - machine interface associated with an asset according to an embodiment.

[0025] Figure 18 is an illustration Figure 14 of other aspects of an example process.

[0026] In all the figures, corresponding reference numerals indicate corresponding parts. Detailed Description

[0027] Referring now to the drawings, aspects of the present disclosure allow for the definition and modeling of assets and the generation of a library of such asset models for industrial systems. The asset library contains programming elements (e.g., basic blocks and composite blocks) required to build assets. In an embodiment, these programming elements are defined according to a distributed control programming standard such as IEC 61499. These models indicate the relationships between physical assets and control assets (e.g., different physical model levels), which allows the asset library to be mapped to physical devices as well as control languages or narratives. Additionally, aspects of the present disclosure utilize information models to build and design control applications to automatically execute or automatically create distributed control in industrial systems. For example, a configurator tool or machine learning can be used to automatically create an Asset Automation Type (AAT) function block network based on the information model and the asset library.

[0028] Furthermore, aspects of the present disclosure provide the ability to use the asset model library to design and simulate the operation of an industrial system, which allows for the evaluation of the simulated operation to identify potential improvements to the proposed system design. Aspects of the present disclosure also allow for the modification and refinement of the models based on user feedback and machine learning.

[0029] Figure 1 shows the basic structure of an exemplary process control system 100. In an embodiment, process 102 is communicatively coupled to controller 104 and sensor 106. The process has inputs 108 and 110, which include the inputs necessary for the process to create output 112. In an embodiment, input 108 includes the energy to power process 102, and input 110 includes physical or chemical raw materials to be used in process 102. Output 112 includes physical or chemical products from the process or energy generated in the form of electricity, etc.

[0030] The controller 104 sends data to the process 102 to direct the operation of the process 102 in accordance with the objectives of the controller 104. The data sent includes commands to operate various types of control elements or assets (such as valves, actuators, etc.) in the process. An asset can be any mechanical, chemical, electrical, biological, or combined mechanism or collection of mechanisms used to convert energy and materials into value-added products or production. The sensor 106 monitors the process at various points and collects data from those points. The sensor 106 sends the collected data to the controller 104. Based on the collected data, the controller 104 can then send additional commands to the process 102. In this way, the system forms a control feedback loop in which the controller 104 reacts to changes in the process 102 as observed by the sensor 106. Different actions performed by the process 102 in accordance with the commands of the controller 104 may cause changes in the data collected by the sensor 106, thereby causing further adjustments by the controller 104 in response to those changes. By implementing such a control feedback loop, the process 102 can be controlled by the controller 104 in an efficient manner.

[0031] Aspects of the present disclosure allow for the configuration of distributed control in industrial systems. Figure 2 is a flowchart of an example process for identifying assets of an industrial system, defining and establishing an asset model representing a process control installation of the industrial system, and creating an asset library of distributed control assets according to distributed control programming standards.

[0032] Starting at 201, embodiments of the present invention establish an asset model that includes a plurality of modeling assets defined according to levels of a physical model standard, and wherein each distributed control asset has one or more predefined built-in facets. In the illustrated embodiment, the method further includes mapping one of the distributed control assets in the asset library to each modeling asset to configure the process control installation of the industrial system and generating at least one asset-based control application that, when executed by one or more controllers 104 of the process control installation, provides distributed control of the industrial system.

[0033] In the illustrated embodiment, the process 102 includes furnace draft control of a power plant at 203. Continuing at 205, the assets of the process control installation are identified. Figure 3 illustrates an example asset-based control model that uses a top-down design approach to identify physical assets in a power plant. According to aspects of the present invention, the identified physical assets are consistent with a physical model standard such as the ISA S88 physical model. Similarly, reference Figure 4, The example asset - based control model also uses a top - down design approach to identify control assets (or templates) in a power plant. At 207, the identified assets are mapped to a physical model, e.g., the ISA S88 physical model, which includes the following levels: plant area, unit, equipment, and device.

[0034] Figure 3 Illustrates an asset - based control model hierarchy that identifies the physical assets of an industrial system. In the example shown, the industrial system is a power plant. The model hierarchy uses the ISA 88 physical model levels for initial design: plant; area; unit; equipment; and device. As shown, higher - level assets are combinations of lower - level assets and their own assets. Figure 4 Illustrates an asset - based control model hierarchy that identifies control assets, which are control languages for performing operations on the outputs of physical assets. A workflow for controlling the operation of a plant can be constructed from physical assets and control assets. For example, the furnace draft control (FDC) uses the following assets: variable frequency drive, motor, fan, power circuit breaker, pressure transmitter, etc. Each asset includes a representation of the physical device and an associated code for the aspect. Different assets require different aspects. For example, the pressure transmitter asset is mapped to the physical pressure transmitter device and its control logic. In another example, the asset is mapped to a control language or narrative.

[0035] Aspects define an asset hierarchy based on a control model. In an embodiment, an asset is a software object representing physical devices and / or control logic in an industrial system. The assets are based on the ISA - 88 physical model.

[0036] Table I below provides examples of physical model level names:

[0037] Table I

[0038]

[0039]

[0040] Further reference Figure 2 , At 209, the process output shown defines an asset model with the supported aspects, which can be customized according to customer requirements. Defining the asset model includes asset modeling and the identification of various aspects, the identification of the control logic aspect, and the identification of the human - machine interface (HMI) aspect. Figure 5Shows an asset definition with associated facets / capabilities. The model defines an asset as having built-in facets. A facet represents a function / feature. The built-in facets include one or more of the following: Document, Operator HMI, Alarm & Event, Simulation, and Control (Scan & Event Driven). Additionally, the built-in facets can include one or more of the following: Historian, Identification / Location, Advisor (System and Condition), IT Required Information / Attributes, Diagnosis / Health, and Test Automation.

[0041] At Figure 2 211, the subroutine establishes an asset using a distributed control programming standard such as the IEC 61449 standard. Figure 6 , Figure 7 and Figure 8 Illustrates an example of an asset on the HMI canvas consisting of function blocks. Figure 6 Is an example of an asset model designed for a variable frequency drive (e.g., the Altivar 71 variable speed drive available from Schneider Electric), which includes implemented facets. Figure 7 Is an example of an instrumented asset model consisting of three pressure transmitters employed by a triple measurement scheme / circuitry, including the supported facets. Figure 8 Is an example screenshot that illustrates the relationship between a physical asset and a control asset (equipment + instrumentation), i.e., the unit-level asset: a combination of equipment and instrumentation assets as part of a furnace ventilation control unit. At Figure 2 213, the process executes a predefined process to generate an asset library. Further reference Figure 2 , the process shown outputs a defined software asset at 215 and prepares an asset library package at 217. Figure 9 Is an example screenshot that illustrates the asset library classification folder structure. In this embodiment, the folder structure is organized according to physical models and predefined blocks, combination - logic blocks, basic blocks, etc.

[0042] The control logic of a machine, instrumentation, process, or building can be programmed, loaded onto a controller, or even distributed to several controllers. When commissioning a plant, first determine the assets required to establish an asset library. Further reference Figure 2 , the process shown outputs a library at 219 and ends at 221. Figure 10 Shows an example of an asset library established for a furnace ventilation control application mapped to the ISA 88 physical model.

[0043] Figure 11 Illustrates aspects of an example flowchart of the process continuing Figure 2 In an embodiment, the result (asset library) of Figure 2 serves as Figure 11The input, i.e., "automatically created" designed through the application of machine learning and asset configuration tool concepts. The method includes mapping one of the distributed control assets in the asset library to each modeled asset to configure the process control installation of an industrial system and generate at least one asset-based control application that, when executed by one or more controllers 104 of the process control installation, provides distributed control of the industrial system. As Figure 11 shown, the operation continues from the output of the asset library at 219 to automatically create a control application by using machine learning or an asset configurator tool.

[0044] If machine learning is employed at 1101, then the machine learning subroutine at 1103 receives the input of the information model and executes the rule set engine intelligence to map the information model to the assets in the asset library to automatically create a control application at 1105. The input represents the process control installation of the industrial system, and an asset model is established in response to the received input. In an embodiment, the input information model takes the form of a Scientific Apparatus Makers Association (SAMA) diagram familiar to those skilled in the art.

[0045] On the other hand, if an asset configurator tool is employed, then the asset configurator subroutine at 1107 generates an industrial control application at 1109 from the automatically populated assets and templates loaded from the asset library. Figure 12 is an example screenshot that illustrates the AAT object created using the configurator tool, and Figure 13 is an example screenshot that illustrates the user interface of the asset configurator tool through which a user can select the desired objects from the automatically populated asset library hierarchy and automatically create a complete application design for deployment.

[0046] In an embodiment, the user loads an existing application design file at 1111 to evolve or improve the existing industrial application design based on the information model and the user-driven rule set for automatically creating an application at 1105. Similarly, the input on which the application design is based represents the process control installation of the industrial system. In an embodiment, the existing application design file can be generated through machine learning intelligence or the previous use of the configurator tool. Figure 11 The example process terminates at 1113.

[0047] Figure 14 illustrates other aspects of an example workflow that illustrates the machine learning and configurator tool methods for generating a control application based on an information model input according to the present disclosure. Starting at 1401, the process receives information model input in the form of a SAMA diagram, a piping and instrumentation diagram (P&ID), etc. Based on this input, Figure 14The process executes one or more machine learning algorithms at 1403. In an embodiment, in response to the execution of an asset-based control application, the machine learning algorithms are trained based on the performance of the process control installation. The machine learning system extracts information from the received input at 1405 and retrieves, via an asset ontology service based on the extracted information, modeling assets associated with the process control installation from an asset database 1409 at 1407.

[0048] Further referring to Figure 14 , the process receives an information model input in the form of, for example, a SAMA diagram at 1411. Based on this input, Figure 14 the process executes a configuration tool at 1413 to define modeling assets associated with the process control installation based on the received input. In an alternative, Figure 14 the process directly loads a configuration file into the configuration tool at 1413 to define modeling assets associated with the process control installation based on configurable software objects. In this embodiment, the configurable software objects include one or more configurable facets based on the received input. In an embodiment, in response to the execution of an asset-based control application, the configuration tool is modified based on the performance of the process control installation.

[0049] Proceeding to 1417, Figure 14 the process applies ruleset intelligence via a database 1419 and retrieves distributed control assets from an asset repository at 1421 and 1423 based on the intelligence. As shown, the ruleset intelligence maps the information model to assets in the asset repository in two ways. The process generates an industrial application at 1425 via a user configuration tool or machine learning intelligence using the automatically populated assets and templates loaded from the asset repository.

[0050] Referring again to Figure 11 1111, the user loads an existing application design file based on an information model and a user-driven ruleset for automatically creating an application. As Figure 15 and 16 shown, the user creates a desired industrial application design by using a configurator tool to select asset automation type (AAT) objects (available via the asset repository). The process automatically creates assets or includes templates consisting of assets as part of the generation of a complete application based on the assets selected by the user.

[0051] In an embodiment, the user can create assets with configurable facets from AAT objects. In one example, the user can create an asset including facets such as an HMI, documentation, control logic, simulation, etc. to represent an industrial physical asset. In another example, the user can create an asset including facets such as an HMI, documentation, control logic, alarms and events, historian, diagnostics, etc. to represent an industrial physical asset. Figure 17is an example screenshot of an automatically created human-machine interface associated with an asset according to an embodiment. In this embodiment, as part of the complete application generation, the process automatically creates, via a configurator tool, a mapping to the HMI of the asset / asset template created in Figure 15 from the created asset / asset template.

[0052] Figure 18 Illustrates another example workflow for automatically creating an application design according to a machine learning or configurator tool approach.

[0053] In operation, a storage memory stores processor-executable instructions that, when executed by a processor, configure the processor to establish an asset model representing a process control installation of an industrial system and create an asset library of distributed control assets according to a distributed control programming standard. The asset model includes a plurality of modeling assets defined at levels according to a physical model standard, and each of the distributed control assets has one or more predefined built-in facets. The processor-executable instructions further configure the processor to map one of the distributed control assets in the asset library to each modeling asset to configure the process control installation of the industrial system and generate at least one asset-based control application that, when executed by one or more controllers of the process control installation, provides distributed control of the industrial system.

[0054] In an alternative embodiment, establishing an asset control model for configuring a distributed control system includes creating an asset control model library that is configured to store a plurality of distributed control assets and define the distributed control assets according to a distributed control programming standard. The distributed control assets represent a process control installation of an industrial system and are mapped from one or more control assets and one or more physical assets defined at levels according to a physical model standard. The asset control model library is populated with distributed control assets having one or more predefined built-in facets, and at least one asset-based control application is generated that, when executed by one or more controllers of the process control installation, provides distributed control of the industrial system.

[0055] The Abstract and the Summary of the Invention are provided to assist the reader in quickly determining the nature of the technical disclosure. They are submitted with the understanding that they will not be used to interpret or limit the scope or meaning of the claims. The Summary of the Invention is provided to introduce, in a simplified form, a selection of concepts further described in the Detailed Description. The Summary of the Invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to assist in determining the claimed subject matter.

[0056] For purposes of illustration, programs and other executable program components, such as an operating system, are shown herein as discrete blocks. It should be appreciated, however, that such programs and components reside at various times in different storage components of a computing device and are executed by one or more data processors of the device.

[0057] Although described in connection with exemplary computing system environments, embodiments of aspects of the present invention operate in conjunction with many other general purpose or special purpose computing system environments or configurations. The computing system environment is not intended to limit in any way the scope of use or functionality of any aspect of the present invention. The computing system environment must have real-time access to sensor-based data associated with an asset or collection of assets. Also, the computing system environment should not be construed as having any dependency or requirement related to any one or combination of components shown in the exemplary operating environment. Examples of well-known computing systems, environments, and / or configurations that may be suitable for the present invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, mobile phones, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0058] Embodiments of aspects of the present invention may be described in the general context of data and / or processor-executable instructions, such as program modules, where the instructions are stored in one or more tangible, non-transitory storage media and executed by one or more processors or other devices. In general, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. Aspects of the present invention may also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote storage media including memory storage devices.

[0059] In operation, a processor, computer, and / or server may execute processor-executable instructions (e.g., software, firmware, and / or hardware), such as those shown herein for implementing aspects of the present invention.

[0060] Embodiments of aspects of the present invention may be implemented with processor-executable instructions. The processor-executable instructions may be organized as one or more processor-executable components or modules on a tangible processor-readable storage medium. Aspects of the present invention may be implemented with any number and organization of such components or modules. For example, aspects of the present invention are not limited to the specific processor-executable instructions or the specific components or modules shown in the figures and described herein. Other embodiments of aspects of the present invention may include different processor-executable instructions with more or less functionality than shown and described herein.

[0061] Unless otherwise stated, the order of execution of the operations in the embodiments of the aspects of the present invention shown and described herein is not necessary. That is, unless otherwise stated, the operations may be executed in any order, and the embodiments of the aspects of the present invention may include more or fewer operations than those disclosed herein. For example, it is contemplated that a particular operation may be performed before, simultaneously with, or after another operation within the scope of the aspects of the present invention.

[0062] When introducing elements of aspects of the present invention or embodiments thereof, the articles "a", "the", and "said" mean that there is one or more elements. The terms "comprising", "including", and "having" are intended to be inclusive and mean that additional elements may exist in addition to the listed elements.

[0063] In view of the foregoing, it will be seen that several advantages of aspects of the present invention have been achieved and other beneficial results have been obtained.

[0064] Not all of the drawn components shown or described are required. In addition, some implementations and embodiments may include additional components. Variations in the arrangement and type of components may be made without departing from the spirit or scope of the claims as set forth herein. In addition, different or fewer components may be provided, and components may be combined. Alternatively or in addition, a component may be implemented by several components.

[0065] The foregoing description illustrates aspects of the present invention by way of example and not by way of limitation. Such description enables those skilled in the art to make and use aspects of the present invention and describes several embodiments, adaptations, variations, alternatives, and uses of aspects of the present invention, including what is presently considered to be the best mode of carrying out aspects of the present invention. In addition, it should be understood that aspects of the present invention are not limited in their application to the details of the construction and arrangement of components set forth in the following description or shown in the drawings. Aspects of the present invention are capable of having other embodiments and of being practiced or carried out in various ways. Moreover, it should be understood that the language and terminology used herein are for the purpose of description and should not be regarded as limiting.

[0066] Aspects of the present invention have been described in detail. It is obvious that modifications and variations are possible without departing from the scope of the aspects of the present invention as defined by the appended claims. It is expected that various changes can be made to the above-described structures, products, and processes without departing from the scope of the aspects of the present invention. In the foregoing specification, various preferred embodiments have been described with reference to the accompanying drawings. However, it is clear that various modifications and changes can be made to them, and additional embodiments can be implemented without departing from the broader scope of the aspects of the present invention as set forth in the following claims. Thus, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A method for configuring distributed control in an industrial system, the method comprises: Receiving an input representing a process control installation of an industrial system; In response to the received input, establishing an asset model representing the process control installation of the industrial system, the asset model including a plurality of modeling assets defined according to levels of a physical model standard, the modeling assets representing physical devices of the industrial system, wherein establishing the asset model includes executing one or more machine learning algorithms to extract information from the received input and retrieve, based on the extracted information, modeling assets associated with the process control installation from an asset database, and wherein establishing the asset model includes executing a configuration tool to define modeling assets associated with the process control installation; Creating an asset library of distributed control assets according to a distributed control programming standard, each of the distributed control assets in the asset library having one or more predefined built-in facets; Applying a mapping rule set to the modeling assets to map one of the distributed control assets from the asset library to each of the modeling assets, wherein the distributed control assets configure the process control installation of the industrial system; and Automatically generating at least one asset-based control application populated with the distributed control assets mapped to the modeling assets, wherein the at least one asset-based control application is configured to be executed by one or more controllers of the process control installation to provide control within the distributed control system of the industrial system.

2. The method according to claim 1, wherein The configuration tool defines modeling assets associated with the process control installation based on the received input and / or based on a configuration software object, and wherein the configuration software object includes one or more configurable facets based on the received input.

3. The method according to claim 2, further comprising at least one of the following: Training a machine learning algorithm based on the performance of the process control installation in response to the execution of the asset-based control application; and Modifying the configuration tool based on the performance of the process control installation in response to the execution of the asset-based control application.

4. The method according to claim 1, wherein applying the mapping rule set to the modeling assets includes applying rule set intelligent mapping and retrieving a distributed control asset from the asset library according to the intelligent mapping.

5. The method according to claim 1, wherein the modeling assets include one or more physical assets and one or more control assets, and wherein the asset model indicates the relationship between the physical assets and the control assets.

6. The method according to claim 1, further comprises: Receiving, from a user interface, a user selection of a distributed control asset in the asset library, the user selection indicating a specific quantity, arrangement, and configuration of the selected distributed control asset in a proposed industrial system design; Simulating the operation of the proposed industrial system design based on a predetermined set of conditions and the one or more asset-based control applications; and Evaluating the simulated operation to identify potential improvements to the proposed industrial system design.

7. The method according to claim 6, further comprising automatically populating an application library with the one or more asset-based control applications, wherein the application library includes dependencies between selected distributed control assets.

8. A system for configuring distributed control in an industrial system comprising: a processor; a memory coupled to the processor, the memory storing processor-executable instructions, wherein the processor-executable instructions are configured to be executed by the processor for: receiving an input representing a process control installation of an industrial system; establishing an asset model representing the process control installation of the industrial system in response to the received input, the asset model including a plurality of modeling assets defined according to levels of a physical model standard, the modeling assets representing physical devices of the industrial system, wherein establishing the asset model includes executing one or more machine learning algorithms to extract information from the received input and retrieve modeling assets associated with the process control installation from an asset database based on the extracted information, and wherein establishing the asset model includes executing a configuration tool to define the modeling assets associated with the process control installation; creating an asset library of distributed control assets according to a distributed control programming standard, each of the distributed control assets having one or more predefined built-in facets; applying a set of mapping rules to the modeling assets to map one of the distributed control assets from the asset library to each of the modeling assets, wherein the distributed control assets configure the process control installation of the industrial system; and automatically generating at least one asset-based control application populated with the distributed control assets mapped to the modeling assets, wherein the at least one asset-based control application is configured to be executed by one or more controllers of the process control installation to provide control within the distributed control system of the industrial system.

9. The system according to claim 8, wherein the configuration tool defines the modeling assets associated with the process control installation based on the input and / or based on configuration software objects to establish the asset model, and wherein the configuration software objects include one or more configurable facets based on the input to establish the asset model.

10. The system according to claim 9, wherein the processor-executable instructions are configured to be executed by the processor for at least one of the following: training a machine learning algorithm based on the performance of the process control installation in response to the execution of the asset-based control application; modifying the configuration tool based on the performance of the process control installation in response to the execution of the asset-based control application; and applying a rule set for intelligent mapping and retrieving distributed control assets from the asset library according to the intelligence to map the modeling assets.

11. The system according to claim 10, wherein the modeling assets include one or more physical assets and one or more control assets, and wherein the asset model indicates the relationship between the physical assets and the control assets.

12. The system according to claim 8, wherein the processor-executable instructions are configured to be executed by the processor for: Receive a user selection of a distributed control asset in an asset library from a user interface, the user selection indicating a particular quantity, arrangement, and configuration of the selected distributed control asset in a proposed industrial system design; Simulate the operation of the proposed industrial system design based on a predetermined set of conditions and the one or more asset-based control applications; And Evaluate the simulated operation to identify potential improvements to the proposed industrial system design.

13. The system of claim 8, wherein the processor-executable instructions are configured to be executed by a processor to automatically populate an application library with the one or more asset-based control applications, wherein the application library includes dependencies between the selected distributed control assets.

Images