Systems and methods for layered intelligent asset control application development and optimization

Abstract

Systems and methods are disclosed for layered intelligent asset control application development and integrated intelligent asset control system optimization. In various embodiments, a system can develop layered asset control applications and corresponding control hardware requirements. This can be used to create comprehensive intelligent asset control systems in order to perform various processes for a set of equipment elements. Intelligent assets associated with the system can utilize intelligent agents to balance operational constraints and operational objectives in order to determine operational parameters for real-time optimization of the processes, and implement appropriate controls to facilitate achieving improved operational objectives.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to the following provisional patent applications: (1) U.S. Provisional Application Serial No. 62 / 354,667, filed June 24, 2016, entitled “Cyber-Physical Systems”; (2) U.S. Provisional Application Serial No. 62 / 240,742, filed October 13, 2015, entitled “Architecture for Connecting Objects in the Industrial Internet of Things”; (3) U.S. Provisional Application Serial No. 62 / 279,224, filed January 15, 2016, entitled “Systems and Methods for Device-to-Enterprise Intrinsic Control”; and (4) U.S. Provisional Patent Application No. 62 / 406,926, filed October 11, 2016, entitled “Hierarchical Asset Control Application Development and Optimization for Systems and Methods”. The entire contents of the above patent applications are incorporated herein by reference. Background Technology

[0003] Existing process control systems evolved from earlier pneumatic and electronic analog control systems. These early control systems were designed to provide process control functions for control loops or related control loops in a production process in an automatic or semi-automatic but coordinated manner, typically through the appropriate settings of multiple controllers. Therefore, the architecture of early control systems was consistent with the flow of the production process, with each controller usually limited to controlling only a very small number of components in the overall production process operation. Thus, each controller could be rationally applied to its corresponding control operation component.

[0004] With the introduction of digital computer technology as a vehicle for process control, the potential scope of automated coordinated control has increased dramatically. However, to make these emerging digital control systems acceptable to the market, they were programmed to directly replicate the functionality and architecture of previous analog systems. One solution to achieve this was to provide a software configuration environment for “software block”-based process control to execute the exact functions of the analog control system components. Thus, the control system architecture that developed after the initial introduction of digital technology is aligned with the flow of production operations and is therefore designed for process-centric control. This setup and architecture have worked very well for the past 60 years and remain the foundation for process control system software design.

[0005] Initially, configuring the process control functions in these process-centric automation systems was efficiently done by engineers in industrial plants that had configured traditional analog control systems. This was because the control software was designed to replicate the functions and components of analog control systems. The knowledge gained from transferring from analog to digital control proved highly effective.

[0006] Many early digital control systems typically had control ranges roughly the same as those achieved in analog systems. This could be because the specifications of the control system had already been established, allowing the use of analog control systems, or it could be due to the skill level of the engineers using analog systems. In any case, with the introduction of digital systems, the control range did not expand to the potential range offered by the digital control system. At this stage, the limitations of process-centric design were not apparent.

[0007] As process control engineers have become more proficient and adept at using digital process control systems (DPS), a natural trend has emerged to control increasingly larger operational areas holistically through a single automation system. The 1980s saw a push for coordinated control of entire process units. Over time, the expectation has grown to control entire plant areas, trains, or even the entire factory using a single coordinated control strategy. Indeed, control strategies can be more unified and coordinated over larger operational areas, resulting in higher operational efficiency. However, the process-centric design of automation systems presents significant limitations that render such a strategy impractical. While partial control loops or even entire process units may seem manageable at the process-centric level, complexity increases exponentially with the scope. Figure 1 As illustrated in the exemplary piping and instrumentation diagram 100, process-centric control strategies present complex and challenging problems for coordinated control in large operating areas. In fact, process-centric control strategies are so complex that only industrial operations with significant central engineering capabilities can continue to evolve towards large-area coordinated control. Most industrial operations continue to control their plants in the same way as during the analog control system era. Therefore, the potential benefits of the transition from analog to digital control have not been realized.

[0008] To achieve coordinated control over large areas by combining traditional digital control system technology with open industrial software, a new type of control system called Enterprise Control System (ECS) has been designed. Although this represents a step forward in combined architecture, process-centric control strategy design methods still limit the effective scope of coordinated control strategies.

[0009] Process-centric design in traditional control software remains the preferred approach for industrial and industrial automation suppliers to develop control strategies. When batch control strategies become available, they help simplify some of the complexity of process-centric automated operation and control systems. Batch control strategies require low-level process and logic control of basic equipment and loops in a plant. However, this basic control is a higher-level batch control directed towards process units, trains, areas, and the products produced in those locations. While batch control strategies are significantly simpler than attempting a strictly process-centric approach, the complexity associated with engineering, operating, and maintaining automated and control systems remains relatively high. Furthermore, batch control strategies do not address various challenges related to system reliability and integrity, such as in the event of failures. Attached Figure Description

[0010] Figure 1 It is a schematic diagram of various equipment components associated with industrial systems.

[0011] Figure 2A This is a schematic diagram of the lower-level device elements and corresponding processes / process groups according to the ANSI / ISA-88 standard in various embodiments.

[0012] Figure 2B It is an industrial topology diagram of equipment components.

[0013] Figure 2C This is a schematic diagram of an extended example ANSI / ISA-88 physical device layer structure / packaging line.

[0014] Figure 3A This is a schematic diagram of a cyber-physical system (CPS) component according to various embodiments of the present disclosure.

[0015] Figure 3B This is a schematic diagram of an example component or controller module for a smart agent in a CPS associated with a smart asset or a group of smart assets, according to various embodiments of this disclosure.

[0016] Figure 3C This is a schematic diagram of elements of a smart asset according to various embodiments of the present disclosure.

[0017] Figure 4A This is a schematic diagram of an extended control hierarchy implemented in a hierarchical asset control application for real-time operational constraints / target controls according to various embodiments of this disclosure.

[0018] Figure 4B This is a schematic diagram of a component controlling real-time profit margin in a hierarchical asset control application according to various embodiments of the present disclosure.

[0019] Figure 4C It includesFigure 4B A simplified diagram illustrating the multi-objective optimization problem of the components in the diagram.

[0020] Figure 4D This is a schematic diagram of a real-time control loop for improving the operational profitability of a hierarchical asset control application according to various embodiments of the present disclosure.

[0021] Figure 4E This is a schematic diagram illustrating exemplary mechanisms for controlling layered asset control applications according to various embodiments of this disclosure.

[0022] Figure 5A This is a schematic diagram illustrating various embodiments of the Integrated Intelligent Asset Control System (ISACS) according to the present disclosure.

[0023] Figure 5B This is a schematic diagram illustrating an implementation of a comprehensive intelligent asset control system according to various embodiments of the present disclosure.

[0024] Figure 6 Examples of industrial equipment components that can be controlled and integrated with hierarchical asset control applications and corresponding control hardware to form an integrated intelligent asset control system according to various embodiments of this disclosure.

[0025] Figure 7 This is a flowchart of a method for developing and integrating hierarchical asset control applications and corresponding control hardware according to various embodiments of the present disclosure to create a comprehensive intelligent asset control system.

[0026] Figure 8A This is a flowchart illustrating the determination of a list of system devices for developing hierarchical asset control applications according to various embodiments of this disclosure.

[0027] Figure 8B This is a flowchart illustrating the determination of a list of smart assets for developing hierarchical asset control applications according to various embodiments of this disclosure.

[0028] Figure 8C This is a schematic diagram of a smart asset template for determining a smart asset list for developing hierarchical asset control applications, according to various embodiments of the present disclosure.

[0029] Figure 8D This is a flowchart of the verification and simulation of the hierarchical asset control application and corresponding control hardware according to various embodiments of this disclosure.

[0030] Figure 8E This is a flowchart illustrating the hardware instantiation of a hierarchical asset control application according to various embodiments of this disclosure.

[0031] Figure 9A This is an exemplary schematic diagram of an industrial process unit according to various embodiments of the present disclosure.

[0032] Figure 9B This is a schematic diagram of equipment elements controlled by a hierarchical asset control system for a single industrial process unit according to various embodiments of the present disclosure.

[0033] Figure 10 This is a schematic diagram of the components of a list of processing system devices according to various embodiments of the present disclosure for developing hierarchical asset control applications.

[0034] Figure 11 This is a schematic diagram of the components of an example of a hierarchical structure for developing a smart agent for hierarchical asset control applications according to various embodiments of this disclosure.

[0035] Figure 12 This is a schematic diagram illustrating the components of an example of simulation and verification of a hierarchical asset control application and corresponding control hardware according to various embodiments of this disclosure.

[0036] Figure 13 This is an example diagram of a control system model for optimizing the objectives and dynamic constraints of a hierarchical asset control application according to various embodiments of this disclosure.

[0037] Figure 14A This is a block diagram illustrating the determination of security risk constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0038] Figure 14B This is a logical flowchart illustrating the determination of security risk constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0039] Figure 15A This is a block diagram illustrating the determination of environmental risk constraints related to assets in a hierarchical asset control application, according to various embodiments of this disclosure.

[0040] Figure 15B This is a logical flowchart of determining environmental risk constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0041] Figure 16A This is a block diagram illustrating the determination of reliability risk constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0042] Figure 16B This is a logical flowchart illustrating the determination of reliability risk constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0043] Figure 17AThis is a block diagram illustrating the determination of security risk constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0044] Figure 17B This is a logical flowchart illustrating the determination of security risk constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0045] Figure 18A This is a normalized constraint diagram related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0046] Figure 18B This is a logic flowchart that normalizes constraints related to assets in a hierarchical asset control application according to various embodiments of this disclosure.

[0047] Figure 19A This is a radar visualization technique diagram for simultaneously controlling multiple targets and outcomes in a hierarchical asset control application according to various embodiments of this disclosure.

[0048] Figure 19B This is a radar visualization technique diagram for simultaneously controlling multiple constraints and operating profit results of a hierarchical asset control application according to various embodiments of the present disclosure.

[0049] Figure 19C This is a schematic diagram of the intersection of the operational boundaries and dynamic constraints of optimization points in the formation of a hierarchical asset control application according to various embodiments of this disclosure.

[0050] Figure 20A This is a risk constraint communication structure block diagram of a unit from an asset to a collection and configured as a hierarchical asset control application according to various embodiments of this disclosure.

[0051] Figure 20B This is a logical flowchart of a risk constraint communication structure for a unit of an asset-to-collection hierarchical asset control application, according to various embodiments of this disclosure.

[0052] Figure 21A This is a block diagram of the asset control communication structure from unit to set and from set to hierarchical asset control application assets according to various embodiments of this disclosure.

[0053] Figure 21B This is a logical flowchart of the asset control communication structure from unit to set and from set to hierarchical asset control application assets according to various embodiments of this disclosure.

[0054] Figure 22 This is an analytical view of a system for hierarchical asset control applications according to some embodiments of this disclosure.

[0055] Figure 23This is a schematic diagram of the components of an example of a hierarchical structure for developing a smart agent for hierarchical asset control applications according to various embodiments of this disclosure.

[0056] Figure 24 This is a schematic diagram illustrating the components of an example of a hierarchy of intelligent agents for hierarchical asset control applications, developed according to various embodiments of this disclosure.

[0057] Figure 25 This is a schematic diagram of the components of a hierarchical structure for developing a smart agent for hierarchical asset control applications according to various embodiments of this disclosure.

[0058] Figure 26 This is a schematic diagram illustrating the components of an example of a hierarchy of intelligent agents for hierarchical asset control applications, developed according to various embodiments of this disclosure.

[0059] Figure 27 The diagram illustrates a machine using a computer system as an example, which includes a set of executable instructions for causing the machine to perform any one or more methods discussed in this disclosure. Summary of the Invention

[0060] An embodiment of the hierarchical asset control application development method may include: accessing a list of devices; identifying industrial equipment elements; selecting a smart asset template from a smart asset template library to instantiate a smart agent for the device element; populating the selected template with operational constraints and operational target data; and iteratively connecting the instantiated smart agents for developing the hierarchical asset control application.

[0061] In some embodiments of the method, the selected smart asset template to be filled includes smart agent instantiation information, verification performed on the hierarchical asset control application, simulation performed on the hierarchical asset control application, control hardware requirements corresponding to the developed hierarchical asset control application, the developed hierarchical asset control application and corresponding control hardware requirements and device elements are integrated to create a comprehensive smart asset control system, and / or the comprehensive smart asset control system includes multiple smart asset control levels.

[0062] In other embodiments, the method may further include aggregating one or more smart asset templates to instantiate a smart agent for merging with smart assets, instantiating a smart asset group for the smart asset templates such that the smart asset templates are instantiated for use in a smart asset collection and / or configuring the smart asset templates to include application-specific data.

[0063] In other embodiments, the method may further include determining the asset operation library type and default requirements in industry-specific hierarchical control applications, where the smart asset application is developed as a smart agent for a specific equipment component control model, and the smart asset template includes data parameters relating to suggested assets interconnected with assets, operational constraints, operational objectives, high availability / critical parameters or parameters for industry-specific industrial applications, smart asset templates including supplier equipment-specific model information, and / or smart asset templates including operational parameters from a general equipment type model.

[0064] In other embodiments, the method may further include determining operational constraint parameters for reliability, environment, or security; determining operational target parameters for energy cost, material cost, product value, or profitability; determining operational efficiency parameters; and iteratively connecting parameters including: grouping smart assets to obtain smart asset groups to define parent / child control relationships between smart assets; and / or simulating hierarchical asset control applications involving the generation of virtualized device element data and the execution of process control elements.

[0065] An embodiment of a hierarchical asset control application development system may include: accessing a device list using a processor; identifying industrial device components using a processor; selecting a smart asset template from a smart asset template library using a processor to instantiate a smart agent for the device element; populating the selected template with operational constraints and operational target data using a processor; and iteratively connecting the instantiated smart agents through a processor to develop a hierarchical asset control application.

[0066] In some embodiments of the system, the selected smart asset template to be filled includes smart agent instantiation information, verification performed on the hierarchical asset control application, simulation performed on the hierarchical asset control application, control hardware requirements based on the developed hierarchical asset control application, the developed hierarchical asset control application and corresponding control hardware requirements and device elements to create a comprehensive smart asset control system, and / or the comprehensive smart asset control system includes multiple smart asset control levels.

[0067] In other embodiments, the system may further include aggregating one or more smart asset templates to instantiate a smart agent for merging with smart assets, instantiating a smart asset group for the smart asset templates such that the smart asset templates are instantiated for use in a smart asset collection, and / or configuring the smart asset templates to include application-specific data.

[0068] In other embodiments, the system may further include determining the asset operation library type and the default requirements of industry-specific hierarchical control applications. The smart asset application is developed as a smart agent for a specific equipment component control model. The smart asset template includes some data parameters, involving suggested assets interconnected with assets, operational constraints, operational objectives, high availability / critical parameters or parameters for industry-specific industrial applications, smart asset templates including supplier equipment-specific model information, and / or smart asset templates from general equipment type models.

[0069] In other embodiments, the system may further include determining operational constraint parameters for reliability, environment, or security; determining operational target parameters for energy cost, material cost, product value, or profitability; determining operational efficiency parameters; and iteratively connecting operations including: grouping smart assets to obtain smart asset groups to define parent / child control relationships between smart assets; and / or simulating hierarchical asset control applications involving the generation of virtualized device element data and the execution of process control elements.

[0070] Other embodiments of the hierarchical asset control application development method may include: accessing a list of devices; identifying industrial device elements; selecting a smart asset template from a smart asset template library to instantiate an asset application model of the device element; populating the selected template with operational constraints and operational target data; iteratively connecting the instantiated asset application models to develop a hierarchical asset control application; and wherein the selected smart asset template being populated includes smart agent instantiation information.

[0071] An embodiment of the optimization method for the integrated intelligent asset control system may include: accessing asset automation operation process parameters associated with assets within the integrated intelligent asset control system; developing a multifaceted dynamic process constraint to address operational limitations associated with the automated operation process; evaluating the multifaceted dynamic process constraint model to balance operational process constraints and process objectives; determining optimized process operation points for the multifaceted dynamic process constraints; using the optimized process operation points to determine hierarchical intelligent asset control actions; and executing the hierarchical intelligent asset control actions to transform the current operation value into an operation value that achieves a balance in operational constraints.

[0072] In other embodiments, the method may include, wherein operational constraints are derived from smart assets associated with a smart asset group within an integrated intelligent asset control system, the hierarchical asset control operation realizing control changes of smart assets associated with the integrated intelligent asset control system, determining the operation point of the optimization process based on reliability, security, and environmental process operational constraints, determining the operation point of the optimization process based on the aggregation of asset constraints of each smart asset in the smart asset group, the reliability constraints of the smart asset set being determined using a reliability risk model, the real-time reliability constraint being defined as: RT reliability risk = MAX(operational reliability risk, conditional reliability risk, reliability security risk), the environmental constraints of the smart asset set being determined using an environmental risk model, the real-time reliability constraint being defined as: RT environmental risk = MAX(operational environmental risk, conditional environmental risk, reliability environmental risk), the security constraints being determined using a security risk model, the real-time reliability constraint being defined as: RT security risk = MAX(operational security risk, conditional security risk, reliability security risk), dynamically controlling access to smart assets based on aggregated real-time security data, and / or aggregating real-time security data, including aggregated security data specific to the intelligent asset control system and external security data.

[0073] In other embodiments, the method may include: using linear analysis to determine the operation point of the optimized process, using nonlinear analysis to derive the development of multi-faceted process constraints, hierarchical intelligent asset control actions indicating the parameter operation status of intelligent assets or intelligent asset groups, hierarchical intelligent asset control actions indicating the parameter operation setpoint of intelligent assets or intelligent asset groups, hierarchical intelligent asset control actions indicating the parameter operation constraint threshold of intelligent assets or intelligent asset groups, accessing the parameters of the intelligent asset automated operation process associated with the hierarchical intelligent asset system / group retrieved from a data storage, and / or the operation process parameters including historical data or real-time data.

[0074] In other embodiments, the method may include: aggregating smart asset operation data; and using the smart asset operation data to improve efficiency or refine anticipated process command selection.

[0075] An embodiment of the integrated intelligent asset control system optimization system may include: using a processor to access asset automation operation process parameters associated with assets within the integrated intelligent asset control system; using a processor to develop multifaceted dynamic process constraints to address operational constraints related to the automated operation process; using a processor to evaluate multifaceted dynamic process constraint models to balance operational process constraints and process objectives; using a processor to determine optimized process operation points for the multifaceted dynamic process constraints; using a processor to determine hierarchical intelligent asset control actions using the optimized process operation points; and using a processor to execute hierarchical intelligent asset control actions to transform from the current operation value to an operation value that balances the operational constraints.

[0076] In other embodiments, the system may include, wherein operational constraints are derived from intelligent assets associated with a group of intelligent assets within the integrated intelligent asset control system, the hierarchical asset control operation realizing control changes of intelligent assets associated with the integrated intelligent asset control system, determining the operation point of the optimization process based on reliability, security, and environmental process operational constraints, determining the operation point of the optimization process based on the aggregation of asset constraints of each intelligent asset in the intelligent asset group, the reliability constraints of the intelligent asset set being determined using a reliability risk model, the real-time reliability constraint being defined as: RT reliability risk = MAX(operational reliability risk, conditional reliability risk, reliability security risk), the environmental constraints of the intelligent asset set being determined using an environmental risk model, the real-time reliability constraint being defined as: RT environmental risk = MAX(operational environmental risk, conditional environmental risk, reliability environmental risk), the security constraints being determined using a security risk model, the real-time reliability constraint being defined as: RT security risk = MAX(operational security risk, conditional security risk, reliability security risk), and dynamically controlling access to intelligent assets based on aggregated real-time security data, and / or aggregating real-time security data, including aggregated security data specific to the intelligent asset control system and external security data.

[0077] In other embodiments, the system may include using linear analysis to determine the optimized process operation point, using nonlinear analysis to derive the development of multi-faceted process constraints, hierarchical intelligent asset control actions indicating the parameter operation status of intelligent assets or intelligent asset groups, hierarchical intelligent asset control actions indicating the parameter operation setpoint of intelligent assets or intelligent asset groups, hierarchical intelligent asset control actions indicating the parameter operation constraint threshold of intelligent assets or intelligent asset groups, accessing the parameters of the intelligent asset automated operation process associated with the hierarchical intelligent asset system / group retrieved from a data storage, and / or the operation process parameters include historical data or real-time data.

[0078] In other embodiments, the system may include: aggregating smart asset operation data; and using the smart asset operation data to improve efficiency or refine anticipated process command selection.

[0079] Other embodiments of the integrated intelligent asset control system optimization method may include: accessing intelligent asset automated operation process parameters; developing multifaceted dynamic constraints at the intelligent asset level to consider operational constraints related to the automated operation process; evaluating a multifaceted dynamic process constraint model of the intelligent asset to balance operational process constraints and process objectives; determining an optimized process operation point for the intelligent asset for the multifaceted dynamic process constraints; requesting intelligent asset control actions for the child intelligent asset from parent asset transmission data elements used by the parent asset portion; receiving intelligent asset control actions generated from the optimized process operation point; and executing hierarchical intelligent asset control actions to transform from the current operation value to an operation value that balances operational constraints.

[0080] 1. Development of Layered Asset Control Applications

[0081] Industrial equipment operation / processes are essentially a set of equipment elements organized in a specific way to perform a process. The ANSI / ISA-88 standard is one method for organizing plant equipment. In contrast to the process-centric model discussed above, according to this disclosure, a hierarchical view of operation / processes can be developed as a hierarchical collection of assets, rather than a single control "process." By modeling individual assets and asset groups throughout the hierarchy, significant improvements in efficiency and effectiveness can be achieved through more granular control solutions. This asset-centric control perspective facilitates the development and implementation of unified control strategies for industrial equipment operation / processes without the drawbacks of traditional process-centric control development methods.

[0082] The following detailed description of the accompanying figures illustrates how to use the basic equipment elements of an industrial system to develop hierarchical asset control applications and corresponding control hardware. Once the hierarchical asset control applications and corresponding control hardware are validated, they can be integrated with the basic equipment elements to create a comprehensive intelligent asset control system. This system enables the effective and efficient execution of industrial processes through hierarchical control of intelligent assets and their groups. In some execution processes, the comprehensive intelligent asset control system can be controlled and managed to optimize various operational constraints and / or objectives.

[0083] Figure 2AThe ANSI / ISA-88 standard for describing devices and processes, process control steps, and device 200 is presented. The general relationships between process model 210, process control model 220, and physical model 230 are described. Process 212 is a series of activities that take action on matter or energy. Process stage 214 is part of a process that typically operates independently of other process stages. Process operation 216 is a process activity that typically causes a change in the material being processed. Combined minor processing activities belong to process actions 218. Process 222 is the highest level in the process control model 220 hierarchy and defines the strategies used to perform process operations. Unit process 224 consists of a set of ordered operations. Operation 226 is a set of ordered stages that define a processing sequence. The smallest element of program control is stage 228. Process unit 232 is capable of orchestrating all processing activities. Unit 234 is used to coordinate the functions of lower-level entities. Device module 236 is used to coordinate the functions of other device modules. Control module 238 is the lowest-level group of devices that can perform control.

[0084] Figure 2B This is a schematic diagram of a typical industrial topology 250 for exemplary industrial equipment operation / process according to this disclosure. Typically, topology 250 is unique to industrial operations because it describes the basic equipment assets involved in the operation and how these assets interoperate to achieve their functions and obtain business outputs. As depicted, topology 250 is hierarchical and includes primary assets at the lowest level. These primary asset groups form asset sets, which are then combined into more complex asset sets, and so on. For example, primary assets can be combined into a higher-level work unit asset set (or process unit). The work unit asset set, in turn, can be combined into a higher-level area / train asset set. The area / train asset set is further combined into a higher-level set called a plant asset set. The plant asset set becomes a unit asset set, then a company asset set, and finally a value chain asset set.

[0085] Figure 2C An exemplary mapping of the ANSI / ISA-88 physical device component layer structure 275 and the package line layer structure 280 is shown.

[0086] According to this disclosure, compared to a typical industrial equipment topology 200, a hierarchical asset control application and corresponding control hardware have been developed to facilitate optimized granular control of intelligent assets (as described below). In other words, the automation system architecture in each industrial operation is developed in a way that ensures the architecture and topology of the automation system closely correspond to or match the architecture and topology of the industrial operation. This alignment between the two architectures simplifies the task of relevant users (e.g., engineers, maintenance personnel, and operators) in designing and maintaining the automation system, because these users do not need to understand and coordinate the differences between the underlying equipment architecture 200 and the hierarchical asset control application and the layered asset control application and corresponding control hardware, which are superimposed on and integrated with the typical industrial equipment topology 200. Examples of an integrated intelligent asset control system for intelligent assets developed according to embodiments of this disclosure are as follows: Figure 5A and Figure 5B As shown, the smart asset building module used in its development is as follows: Figure 3A As shown in 3B and 3C, which are described in more detail below.

[0087] The industrial operations described in this disclosure can be viewed as a collection of hierarchical intelligent assets, rather than a single "process". This hierarchical intelligent asset-centric design facilitates the development and implementation of unified control strategies in industrial operations and avoids the drawbacks of traditional process-centric approaches. Figure 3A 3B and 3C illustrate various aspects of smart assets, including building blocks associated with development-level asset control applications and control hardware applied to facilitate granular control and optimization, as shown in... Figure 4A The relevant examples are shown in 4B and 4C.

[0088] Based on currently available information, one aspect of developing a hierarchical asset control application and corresponding control hardware is to utilize Cyber-Physical Systems (CPS) as building block elements. A CPS is an automatic or semi-automatic control system that includes sensors and / or actuators, along with associated hardware (e.g., processors or computers) capable of executing software code / modules in the form of intelligent agents (IAs) or "virtual avatars" to perform measurement and control functions. Figure 3AExample components of a CPS according to some embodiments of this disclosure are illustrated. As depicted, a CPS 300 may include one or more sensors 305, one or more actuators 310, a controller unit 320, and a communication module 315, among other components. In some embodiments, the sensors 305 and actuators 310 may be embodied in a single device or unit. The actuators 310 may be embedded or networked to a smart asset. One or more smart agents 325 in the controller unit 320 may implement control strategies or algorithms to convert measurement data from one or more sensors 305 and inputs from any setpoint into output signals to improve the operational efficiency and / or other characteristics of the smart asset. The communication module 315 may facilitate receiving data from one or more sensors 305 and sending output signals to one or more actuators 310. In some embodiments, the communication module 315 may include a network interface that can support data flow between the CPS 300 and other CPSs and / or higher-level asset sets within an integrated smart asset control system. The network interface may include one or more network adapter cards, a wireless network interface card (e.g., an SMS interface, a Wi-Fi interface, an interface for various generations of mobile communication standards, including but not limited to 1G, 2G, 3G, 3.5G, 4G, LTE, etc.), Bluetooth, a router, an access point, a wireless router, a switch, a multilayer switch, a protocol converter, a gateway, a bridge, a bridging router, a hub, a digital media receiver, and / or a repeater. In some embodiments, the communication module 315 may utilize Internet of Things (IoT) data to combine with other CPSs to form part of a comprehensive intelligent asset control system that communicates with underlying industrial equipment, hierarchical asset control applications, and corresponding control hardware components.

[0089] The extended control aspects of the integrated intelligent asset control system can be divided into two general categories of real-time control: (1) operational objectives and (2) operational constraints. For example, providing real-time control for basic business objectives, such as profitability, operational profitability, and operational efficiency, can be one type of business objective. The second type of operational constraints can provide real-time control of dynamic constraints on objectives, such as security risks, environmental risks, and safety risks. Figure 3BExamples of virtual avatars or intelligent agents 325 in a CPS associated with intelligent assets or groups of intelligent assets are illustrated, providing real-time control over the dynamic constraints of fundamental business objectives and goals (i.e., real-time control of profitability, reliability, process efficiency, personal safety risks, environmental risks, and security risks). In some embodiments, intelligent agent 325 may include a real-time profitability controller 330, a real-time process efficiency controller 335, a real-time reliability risk controller 355, a real-time environmental risk controller 345, and a real-time personal safety risk controller 350. In other embodiments, intelligent agent 325 may also include a real-time security risk controller 360. It should be noted that in some embodiments, intelligent agent 325 may include more or fewer controllers (or control modules) to provide control over more or fewer domains based on a particular execution process. In some embodiments, a coordinator module 340 may be included in intelligent agent 325 for coordinating the execution of controllers according to a control hierarchy associated with an integrated intelligent asset control system, such as referenced in... Figure 5B The description is as follows. In other embodiments, the intelligent agent 325 may include modules other than the real-time controller. Examples of such modules may include, but are not limited to, a history recording module 365 and a small (or large) data analysis engine 370. Real-time control via the intelligent agent 325 enables the intelligent asset management system to control intelligent assets or combinations of intelligent assets within a comprehensive intelligent asset control system, facilitating operational optimization and system management of the comprehensive intelligent asset control system.

[0090] One aspect of developing a comprehensive intelligent asset control system is the rapid implementation of virtual avatars / intelligent agents associated with factory equipment components. Equipment consists of numerous components that need to be monitored and controlled. To fully monitor / manage data associated with various aspects of factory equipment components (e.g., safety, personal safety, environment, reliability, performance, profitability, data, and similar aspects), virtual avatars / intelligent agents can be used to consider and manage multiple attributes associated with each intelligent asset. In some embodiments, virtual avatars / intelligent agents are developed using generic templates based on vendor-provided operating specifications. This allows operating characteristics to be created as a library and provides equipment asset types to allow users to quickly connect equipment components to networked virtual avatars / instantiated intelligent agents, thus developing hierarchical asset control applications. In some implementations, higher-level intelligent asset templates can link lower-level intelligent assets together to form higher-level intelligent asset groups, further advancing the development of hierarchical asset control applications.

[0091] Figure 3C This is a diagram illustrating exemplary components of a smart asset 375 according to some embodiments of the present disclosure. The smart asset 375 may include reference... Figure 3AThe described CPS 300 and other electrical components 385 (e.g., switches), power system 380, and underlying equipment elements 390 (e.g., mechanical hardware such as pumps, compressors, etc.) are described. Through sensors, actuators, controllers including the intelligent agent, and communication modules, the intelligent asset 375 can automatically or semi-automatically monitor and control its performance and operation. In some embodiments, the intelligent asset 375 may utilize a communication module including a network interface to connect to a communication network to communicate with other intelligent assets and sets of intelligent assets, and / or report asset data, process data, asset health status, alarms and events, and / or other data applicable to a specific application to a remote computer or server system.

[0092] CPSs / intelligent agents enable the design and development of hierarchical asset control applications and corresponding control hardware, which can be integrated with equipment components in typical industrial topologies to create integrated intelligent asset control systems. With decreasing price and power requirements and corresponding increases in memory, computing power, and networking capabilities, CPSs / intelligent agents can be integrated into every equipment component of a typical industrial topology, created at the level of intelligent assets and / or intelligent asset groups, enabling efficient and effective management, control, and in some cases, optimization of the integrated intelligent asset control system. By combining CPSs / intelligent agents with intelligent assets and intelligent asset groups, the overall control problem of industrial operations can be decomposed into multiple smaller control problems of multiple automatic or semi-automatic control system components that can be flexibly combined. Decomposing the control problem into smaller, manageable components significantly reduces the complexity of the overall control problem and provides various other advantages discussed in this disclosure. This integrated control system uses a baseline of equipment components from a typical industrial topology, integrating a hierarchical structure of intelligent assets and intelligent asset sets, each layer including an automatic or semi-automatic system containing an intelligent agent, providing real-time control for intelligent assets, intelligent asset sets, or other intelligent asset groups, and is defined herein as an Integrated Intelligent Asset Control System (ISACS). Figure 5A and Figure 5B An example of hierarchical intelligent asset organization in a comprehensive intelligent asset control system is shown, and described in more detail below.

[0093] Extended control hierarchy

[0094] like Figure 4A The extended control hierarchy shown can be used for real-time control of industrial operations. Once intelligent assets are created and implemented as hierarchical asset control applications according to the implementation methods disclosed herein, intelligent management and optimization will be significantly improved, and granular operational target and constraint control and optimization can be achieved. Figure 4AThe spectrum of operational constraints / operational target variables 400 that can be controlled / optimized for smart assets / smart asset groups 375 is shown. It should be understood that, depending on the operational characteristics associated with a particular implementation, other priority spectra are also possible. Alternatively, the operational constraints and target variables 400 can be evenly distributed across each smart asset, with each smart asset capable of managing all operational constraints and target variables 400 as appropriate. Figure 4A The illustrated embodiment can manage prioritized operational constraints / goals across each level in the hierarchy, or distribute and partition prioritized operational constraints / goals across different levels of the hierarchy. For example, Figure 4A The distributed constraint / goal management illustrated involves security risk, a prerequisite, and is therefore controlled, managed, and optimized at the lowest level of the hierarchy. Security and environmental risks, due to their criticality, reside at the lowest level of the second layer. Above security and environmental risks is asset reliability risk, as reduced reliability can lead to decreased asset performance or even asset failure, severely limiting asset value. Above security can be traditional efficiency controls. Finally, where appropriate, is real-time profitability control. Incorporating security risk, environmental risk, reliability risk, efficiency, and profitability controls into every component of industrial operations allows for maximizing the value from each smart asset and forms a method for optimizing hierarchical asset control applications and corresponding control hardware. In some embodiments, hierarchical asset control applications and corresponding control hardware apply extended real-time control to each equipment asset, unit / work cell, area / train, plant, fleet, enterprise, and / or value chain. Thus, each smart asset and group of smart assets or other smart asset groups can operate in a way that is optimal in terms of safety, economy, and environment. In this way, the entire enterprise and value chain can be controlled in real time, facilitating the optimization of real-time control and management to meet multiple operational constraints / operational goals.

[0095] 2. Extended real-time control

[0096] The Hierarchical Asset Control Application (HACA) described above not only addresses the challenges related to operational efficiency in controlling industrial operations but also facilitates the implementation of unified control strategies. Beyond operational efficiency, other emerging areas also require effective real-time control. In some embodiments, the HACA application can be extended to effectively control these emerging areas, such as, but not limited to, reliability risks, safety risks, environmental risks, and profitability.

[0097] It should be understood that various types and quantities of constraints (e.g., security risks) and various types and quantities of objectives (e.g., real-time operating profits) are described herein, but there is no limitation on the type and quantity of constraints or objectives described herein.

[0098] Traditionally, most variables related to measuring and managing the profitability of industrial operations have remained relatively stable. For example, in the past, industrial plant management could typically contract with their electricity suppliers, effectively setting the price per unit of electricity for a year. With the price remaining constant throughout the year, there was no need to control it. Today, with the deregulation of electricity grids worldwide, electricity prices can and often more frequently change. For example, in the open grids of the United States, prices can change every 15 minutes. In the United Kingdom, prices can change every 20 minutes. Therefore, business variables that were once nearly constant (i.e., electricity prices or energy costs) are now undergoing real-time changes. The same is true for natural gas, raw materials, and production value (the value of products in industrial operations) in many parts of the world. The traditional approach of trying to manage these business variables using monthly data generated by Enterprise Resource Planning (ERP) reports is no longer sufficient. Hierarchical asset control applications can facilitate the industry's shift from transactional variable management to real-time control by enabling extended real-time control over smart assets and sets of smart assets or other groups of smart assets.

[0099] In some embodiments, business variables for industrial operations that change in real time include, but are not limited to: energy costs (energy costs per unit consumed), material costs (raw material costs per unit consumed), and production value (the value of producing each unit of product). Since these three combined variables tend to compete with each other, they can be controlled together to maximize operating profits, as... Figure 4B The waterfall chart is shown in the image. All four components in this waterfall chart have a degree of real-time variability, making them candidates for effective real-time control provided by the hierarchical asset control application.

[0100] Figure 4B The components of the waterfall diagram shown are represented as follows: Figure 4C The simplified diagram illustrates a multi-objective optimization problem with constraints. While this model is a rough simplification of a real-world optimization challenge, it can demonstrate some characteristics of extending the control domain. For example, the three components of real-time profitability are typically influenced by a combination of security risks, environmental risks, and equipment constraints, manifested in terms of maximum operational throughput and reliability. Since maximum operational throughput is usually a fixed value and may not be controllable during operation, the constraints that are typically controllable are security risks, operational risks, and equipment reliability (referred to as reliability risks). The combination of these models shows how real-time process control can extend beyond traditional control approaches used to improve operational efficiency to include real-time control of security risks, environmental risks, reliability, and profitability within hierarchical asset control applications.

[0101] exist Figure 4DThe application of real-time control to objectives, such as improved operational profitability, is illustrated. A prerequisite for controlling such objectives is the ability to measure the variables to be controlled. In this case, it involves measuring operational profitability through real-time accounting (RTA). RTA is described in detail in U.S. Patent No. 7,685,029, entitled "Accounting Approach Based on Real-Time Activities," the entire contents of which are incorporated herein by reference. After measurement, information and RTAs can be collected and displayed in a real-time decision support control panel for consideration by personnel responsible for the operational subject area. In some embodiments, personnel can use this information to monitor automated control of profitability. In other embodiments, it is also possible to directly and manually control profitability in a manner similar to manual process control. In other embodiments, a combination of automated control and manual operation is also possible. In the case of automated control methods, an automated profit controller may be employed. Automated control methods may be superior to manual control methods when speed, analytical complexity, and repeatability are important considerations. In this way, the real-time profitability of operations can be controlled.

[0102] As mentioned above, real-time profitability is constrained by the reliability, safety, and environmental risks of the operational components under consideration. Effective control of these risks can improve operational profitability. To control these risks, an automated or semi-automated control system can measure these constraining variables in real time, enabling appropriate personnel to measure the information. In some embodiments, the automated or semi-automated control system can use real-time measurement information and any other inputs to control each variable. Applying real-time control to these constraining variables can lead to the deconstraint, allowing more operating profits to be realized in a safe and environmentally sustainable manner, which is crucial for most production and manufacturing operations.

[0103] Real-time control model

[0104] In this paper, real-time control combines measurement-based decision-making to influence expected outcomes within a time frame related to the time constant of an operation or business process. Real-time control can include automatic and manual controls performed on feedback or predictive measurements, such as... Figure 4E As shown.

[0105] Through hierarchical asset control applications, extended control includes applying real-time control by leveraging real-time measurements available within the hierarchical asset control application to improve safety rules, safety risks, environmental risks, reliability risks, efficiency (traditional process control), and profitability in real time. Similar to traditional process control used to improve operational efficiency, the actual variables under direct control can be variables at a lower level than the target variable. For example, to improve operational efficiency, actual direct control can be performed on variables or operational parameters such as asset flow, level, temperature, and pressure. In some embodiments, the control of these variables is refined to values ​​that improve operational efficiency and meet objectives. The same applies to personal safety risks, safety risks, environmental risks, reliability risks, and profitability objectives.

[0106] Appropriate mechanisms for hierarchical asset control application control can be implemented according to... Figure 4E The diagram categorizes controls based on two dimensions. Controls can be manual or automatic, and can be based on feedback, prediction, and / or any suitable technology. All four categories in the diagram represent viable options for applying control to a hierarchical asset control application. An implementation process can proceed from manual feedback control to automatic feedback control, to manual predictive control, and then to automatic predictive control. This order can vary across different implementations. Feedback control is generally much less expensive than predictive control; therefore, most industrial operations employ a feedback approach unless a significant added value can be achieved by applying predictive control.

[0107] Manual control is often the most suitable approach when engineers attempt to learn the dynamics of a system in order to better characterize or model the system for developing and applying automatic controls. Because many target variables to be controlled through extended control characteristics of an asset control system have not previously been measured or characterized in real time, manual control methods can be used until the characteristics of the target variables are understood to a degree that allows for the effective development of automatic controls.

[0108] In industrial operations, automatic control is implemented in two basic ways: first, logic control developed for machine control applications, which has been used in many other applications; and second, algorithmic process control developed for more continuous process applications. In some embodiments of this disclosure, a combination of these two conventional control approaches can provide automated control from intelligent assets and sets of intelligent assets or other groups of intelligent assets all the way to the enterprise level, addressing profitability objectives, reliability risks, security risks, environmental risks, and / or safety rules across the entire ISCAS (Intelligent Assets, Services, and Criteria).

[0109] Figure 5AA hierarchical asset control application and corresponding control hardware are illustrated according to a first embodiment of this disclosure as an integrated intelligent asset control system 500 for example industrial equipment operation / process. Industrial equipment operation / process may include primary (or basic) intelligent assets 501 with minimum-level integration functionality. For example, in the case of an oil refinery, primary intelligent assets 501 may be devices such as pumps to force crude oil to move in a specific direction, or heat exchangers to heat incoming crude oil. Each of these primary intelligent assets 501 may be controlled automatically or semi-automatically by a cyber-physical system (CPS) with intelligent agents to ensure optimal or efficient operation as described above, or linked as a group of intelligent assets, also controlled as elements within the hierarchical asset control application and corresponding control hardware.

[0110] As described, in some embodiments, each CPS in the integrated intelligent asset control system is associated with an intelligent asset and has its own automatic or semi-automatic control system, including a virtual avatar or intelligent agent (IA), which defines and executes intelligent asset-specific control policies, and in some cases, these intelligent asset-specific control policies are part of a hierarchical asset control application and corresponding control hardware. The hierarchical asset control application, along with the corresponding control hardware and underlying device components, work together to form... Figure 5A The integrated intelligent asset control system 500 shown can have each primary intelligent asset (e.g., a basic asset) equipped with a CPS including an intelligent agent responsible for the automatic or semi-automatic control of the intelligent asset. Similarly, (e.g.) Figure 5B As shown), higher-level intelligent assets (e.g., collections of intelligent assets) can have their own intelligent agents that effectively merge lower-level intelligent assets and / or groups of intelligent assets and / or coordinate the activities of lower-level intelligent agents. For example, an intelligent agent controlling an entire unit / work unit can be integrated into an intelligent agent controlling a train / area. The intelligent agent controlling the train / area can be integrated into a factory intelligent agent, the intelligent agent controlling the factory can be integrated into a fleet intelligent agent, these intelligent agents can be integrated into an enterprise intelligent agent, and finally into a value chain intelligent agent. In other words, in hierarchical asset control applications and corresponding control hardware and integrated intelligent asset control systems, higher-level control strategies encompass lower-level control strategies facilitated by intelligent agent characteristics (e.g., polymorphism, inheritance, late binding, encapsulation, etc.).

[0111] In some embodiments, the control system associated with each CPS controls the functions within the associated asset and the transmission of any information that needs to be obtained from or sent to other CPSs. Because the control system within each CPS provides appropriate control over sensor-based data associated with the smart assets, most of the underlying data generated within each CPS is used within the CPS's control strategy and does not require access anywhere else in the plant system. Furthermore, prior to the incorporation of smart agents into the hierarchical smart asset set control strategy, the effective automatic or semi-automatic control of primary assets by smart agents means that smart asset set level or other smart asset grouping controls become easier to design and build.

[0112] Intelligent asset control systems thus integrate the entire enterprise and value chain into real-time control to improve the unified and coordinated operation of industrial enterprises, thereby driving significant cost improvements. Furthermore, this architecture allows control to be extended to the enterprise control system level and even to the value chain control system level. This asset-centric perspective on industrial operations and the enterprise not only makes the associated control strategies easier to design, implement, and execute, but also effectively represents how industrial companies view their fundamental operations. For example, engineers in industrial operations typically understand and describe operations from an asset perspective. This natural alignment with how industrial personnel understand their operations and work enhances their understanding of automated systems, as the systems are consistent with their viewpoint.

[0113] It should be noted that, in addition to Figure 5A Beyond the lowest (primary intelligent asset) level of the architecture coupled to CPS as described in 5B, the topology of the integrated intelligent asset control system can be software-defined and developed as hierarchical asset control applications and corresponding control hardware. Therefore, the integrated intelligent asset control system according to the first embodiment of this disclosure has a software-defined architecture as a hierarchical asset control application.

[0114] Figure 5B An example of an integrated intelligent asset control system according to a second embodiment of this disclosure is shown. In this embodiment, a higher-level asset set may also have an associated CPS with an intelligent agent 550. For example... Figure 5B As shown, each basic smart asset 551, unit / work unit 552, area / train 553, factory 554, fleet, enterprise, and value chain smart asset group can have associated CPS including smart agents. Thus, as... Figure 5BThe topology of the integrated intelligent asset control system shown can be defined by a combination of hardware and software as hierarchical asset control applications and corresponding control hardware. In some embodiments, the CPS associated with a set of intelligent assets may have different components than the CPS associated with the intelligent assets. For example, the types of sensors and actuators in the CPS associated with a primary intelligent asset may differ from the types of sensors and actuators in the CPS associated with a group of intelligent assets. Components such as specific types of sensors and actuators can be selected based on the environment in which the intelligent asset or group of intelligent assets operates. Figures 7-8E This section discusses in more detail how to develop hierarchical asset control applications and corresponding control hardware.

[0115] Figure 6 Another example of the operation / process of basic industrial equipment according to some embodiments of this disclosure is shown, in which hierarchical asset control applications and corresponding control hardware can be applied to the basis of a physical equipment hierarchy.

[0116] Figure 6 A distillation column 600 (e.g., in an oil refinery) is described. Once configured as a smart asset / smart asset group including CPS and smart agent components, the distillation column can be considered a unit asset set in a hierarchical asset control application. In this example, the equipment can be divided into three basic sections: feed, bottom product, and distillate. The bottom section consists of a set of equipment such as pumps, valves, measuring devices, etc. A smart asset control system is developed and provides a cyber-physical system (“CPS”) to each of the three equipment modules, which includes a virtual avatar / smart agent responsible for providing monitoring and control functions for the underlying equipment. The hierarchical asset control application and system control hardware also provide a smart agent to the distillation column smart asset group, which can coordinate and control the lower-level smart agents associated with and control the three equipment modules. In some embodiments, an integrated smart asset control system or smart asset control system can provide CPS to the distillation column smart asset group and the three equipment modules.

[0117] exist Figures 7-8E The discussion will delve deeper into the development of layered asset control applications and corresponding control hardware, involving access to a library of smart asset templates that are instantiated as instances of smart asset virtual avatars / smart agents. For example, Figure 6 The distillation column described in [the document] can correspond to a smart asset template (in [the document]). Figure 8C (described in more detail below). In some implementations, Figure 6 The equipment components can also be used to define and identify corresponding smart asset templates to develop hierarchical asset control applications for distillation column components and the distillation column itself. Smart asset templates can include information and control actions related to personal safety, security, environment, reliability, performance, profitability, etc. (Example templates are available in...)Figure 8C (As shown in the diagram and described in more detail below). Operators can interact with the hierarchical asset control system at lower and / or higher levels. Virtual avatar templates / libraries allow for adjustments by senior operators to be automatically sent as commands to lower-level plant assets, facilitating lower-level plant assets through asset control / efficiency elements of the templates and the developed hierarchical asset control applications.

[0118] Layered asset control application development

[0119] Figure 7 A flowchart of a hierarchical asset control method is shown, illustrating the application and corresponding control hardware development and integration according to various embodiments of this disclosure to create a comprehensive intelligent asset control system. It should be understood that, depending on the system implementation and application, various aspects and functional descriptions of this function can be implemented in any number of computing devices, some of which include components such as... Figure 27 As shown. For example, hierarchical asset control application development, control hardware identification, various aspects of integrated intelligent control asset system integration, runtime optimization, and / or system management and / or control can be implemented as desktop applications, mobile applications, cloud-based applications, and / or any other number of computer components or implementations. Figures 8A-8E The elements of the overall process for developing hierarchical asset control applications are described in more detail below. Figure 9-12 illustrates various aspects of the development and integration of hierarchical asset control applications and corresponding control hardware to create an integrated intelligent asset control system involving exothermic reactor equipment components.

[0120] like Figure 7As shown, the hierarchical asset control application development process 700 includes first determining a list of industrial equipment components to be controlled, and in some cases, the environment in which the equipment components will be controlled (step 710), facilitating the operation of the industrial application. The system selects a first equipment component from the list of equipment components and queries the smart asset template library (step 725) to select one or more smart asset templates (step 720). The system instantiates a smart agent template for the smart asset (or a set of smart assets) (step 730). Once instantiated, smart agent data, including operational characteristics, objectives, and constraint data, is commissioned and developed by an appropriate data source or database (step 740). One element of the smart agent data includes smart asset parent / child control information and smart asset interconnectivity information. This information is used to develop smart asset parent-child control relationships when developing the hierarchical asset control application (step 750). The hierarchical asset control application is verified (step 760) and, if approved, used to develop the corresponding execution control hardware requirements. A comprehensive smart asset control system can be developed as a hierarchical asset control application; any corresponding control hardware element is integrated with the underlying industrial equipment element (step 770). Finally, the corresponding control hardware and equipment components are integrated to form a comprehensive intelligent asset control system, in order to develop a fully-encompassing hierarchical industrial process control system (step 780) (e.g. Figure 5B (As shown).

[0121] Figure 8A A flowchart illustrating a more detailed process for identifying and verifying device elements according to various embodiments of this disclosure is provided. This logical flow represents various processes corresponding to the listing of device elements and characteristic parameters in step 710. A list of devices is obtained (step 800), which may correspond to the entirety or a portion of the industrial system to be converted into a virtual industrial system. This list of devices may be obtained manually or automatically and does not necessarily have to be a list of devices for the entire system. Once obtained, device elements on the list are selected (step 802). Depending on the specific implementation, there may be several methods for selecting the first device element. Selection may be based on criticality, scale, known associated sub-assets, or any other characteristic or combination thereof. Any device can be selected as long as it is part of the system to be analyzed. Since device elements are analyzed based on their own characteristics, any “child” devices identified as that device will also be captured in the characteristic description (step 804). It should be understood that a “parent” device may have multiple “child” devices, and the disclosed relationships and principles support their converse propositions. The system will query each identified device element (step 806), and if it is not already identified, it will identify it (step 808) and analyze each device element in the entire list. After the last device element in the device list has been processed, verification of each device element that has been processed will be performed (step 810).

[0122] Devices can be determined through automated or user-guided methods, accessed from a device library, or some combination of both. Upon input, the system can query each logical location of the specified device and receive information describing various characteristics of the device. Automated methods may include a "crawl" function, which, given a series of logical addresses, allows the system to query each logical location of the specified device element and receive information describing various characteristics of that device element.

[0123] Once the complete list of devices is determined, device mapping is performed, where each physical device is mapped to one or more smart asset templates 720. Figure 8C An example of a smart asset template and corresponding data element 840 is shown. To aid in the mapping of each physical device, the smart asset template library 725 is accessed for each device element. If one or more characteristics of the physical device are determined to match the characteristics of the virtual smart asset template, a mapping is formed.

[0124] It should be understood that the mapping from physical devices to smart asset templates does not necessarily have to be a one-to-one relationship. Many-to-one relationships may exist, determined in part by the relative complexity of the characteristics associated with the physical device. It should be understood that smart agents can be created for each smart asset, as well as for a set of smart assets organized as a smart asset set, a set of smart assets organized as a smart asset unit, or a smart asset composed of one or more device elements. Furthermore, the principles of this disclosure suggest that more than one smart asset template can be used to describe a single device element, or more than one smart agent can be used to describe a single smart asset or a single set of smart assets or other groups of smart assets.

[0125] When the mapping of each physical device is complete, a smart asset data structure is created for each device element (step 730). Several possible relationships exist between device elements and smart agent templates. These include one-to-one relationships between device elements and smart asset templates, as well as many-to-one relationships. It should be understood that each device is converted into a virtual smart asset, representing each instance of the physical device.

[0126] To develop accurate virtual models of equipment components for integration into hierarchical asset control applications, several features are developed for each equipment component and populated into an intelligent agent data structure (step 740). The amount and type of data included in the intelligent agent data structure may include asset identification data, asset industrial application data, physical modeling data, including but not limited to availability, platform and security constraints, and system characteristics that describe the hardware and equipment components in detail. For all equipment components in the industrial application, the process continues until a hierarchical arrangement of parent-child asset control relationships is established (step 750). This mapping further develops a virtual intelligent agent data structure for each equipment component to form a complete virtual hierarchical structure representing assets of physical equipment components associated with the industrial system.

[0127] The complete virtual hierarchical structure representing the assets can be verified (step 760) and / or simulated to confirm that the virtual representation of the hierarchical asset control application operates as expected. This complete hierarchical asset control application can then be provided to the system for operating the corresponding physical industrial system.

[0128] Figure 8B A flowchart illustrating the determination of smart asset templates and the identification of smart data templates according to various embodiments of the present disclosure is shown. This logical flow represents a corresponding… Figure 7 The process includes selecting one or more smart asset templates (step 720), creating smart agent instances (step 730), and developing and populating smart agents (step 740). For each verified device element that has been processed, a corresponding smart agent instance is defined based on the device list and available entries in the smart asset library 822 (step 820). Once a smart agent instance is defined for a given smart asset (step 820), a smart asset instance is defined for any known child smart assets of the parent asset just defined (step 820) (step 824).

[0129] It should be understood that there may be partially incomplete or completely unrepresented smart asset templates within the smart asset library 822. This disclosure takes these possibilities into account and allows subsequent smart asset representations to update the smart asset library 822. In this way, the smart asset library 822 will improve over time with each subsequent system use. This disclosure also considers the use of “generic” smart asset templates available from organizations such as ASME or IEEE. Such smart asset templates may lack some information that is beneficial to the processes described herein; however, they may be useful in describing some basic characteristics of equipment components (e.g., cooling pumps).

[0130] For each smart asset instance, a mapping is formed if one or more characteristics of the physical device match one or more characteristics of the smart asset template. It should be understood that this mapping from smart assets to smart asset templates does not have to be a one-to-one relationship; a many-to-one relationship can exist, partly determined by the relative complexity of the characteristics associated with the device element. When each smart asset mapping is complete, a smart agent data structure is created for each smart asset (step 826). A one-to-one relationship exists between device elements and smart assets. Each smart asset can serve as a virtual representation of a device element within the developed hierarchical asset control application construct.

[0131] To develop accurate virtual models of equipment components, several features can be developed for each physical asset and populated into the intelligent agent data structure. To help determine the operational capabilities of a particular intelligent asset, for a specific industrial application, step 828 defines characteristics such as constraints for each intelligent asset. These constraints may include, but are not limited to, environmental, reliability, and safety constraints defined on a per-asset basis. It should be understood that while there may be general equipment components, how that equipment component is used for a specific industrial application, as an operational characteristic of the intelligent asset, may substantially affect the nature of one or more operational constraints, just as operational characteristics affect intelligent assets. As one example of various types, due to the batch nature of the operation, a pump that operates continuously to move drinking water from one location to another may have a significantly shorter lifespan than the same pump that runs only twice a day to move cotton syrup from one container to another. In this case, the reliability, safety, and environmental factors of the same equipment component (e.g., the pump) may vary significantly across any number of industrial applications. For intelligent assets, step 828 defines each of these operational constraints and, in step 830, incorporates them into an intelligent agent based on each intelligent asset (or, in some cases, based on a grouping of intelligent assets). Each virtual asset representative can have various representational data used to partially determine how a particular industrial application interacts with supporting equipment. It should be recognized that a range of constraint data can be incorporated into each asset. In various embodiments of smart assets, sets, and subsets, constraints may in some cases be null values ​​or values ​​determined after the integrated smart asset control system has been running. Although operational constraints such as security, reliability, environmental, and cybersecurity constraints have been discussed herein, it should be understood that a wide variety of constraint types and underlying data can be applied.

[0132] Step 831 creates parent / child control relationships to develop a hierarchical structure for intelligent assets and various possible corresponding controls for the integrated intelligent asset control system. Once all asset characteristics are defined, including any defined constraint data 828, step 832 populates the intelligent asset data structure into the asset and stores it in the intelligent asset database (step 834) for reuse as needed. Examples of such reuse could be for assets in similar industrial applications or for subsequent debugging of a series of assets. Furthermore, it should be recognized that the precision of reusing the intelligent asset data structure to represent assets in various industrial applications may increase, thereby further advancing one of the stated goals of industrial design to achieve good system characterization and repeatability. This process is repeated for all intelligent assets within the industrial system (step 836). Once each intelligent asset is defined as an intelligent agent after instantiation / debugging, the entire system's intelligent asset list 838 is available for further processing.

[0133] Figure 8C This is an illustration of examples of intelligent asset templates and data structures 840 in various embodiments of this disclosure. To develop accurate virtual models of physical assets, several features are developed and populated into the intelligent agent data structure of each device unit 740. The amount and type of data contained in the intelligent agent data structure are diverse, but may include asset identification data, asset industrial application data, physical modeling data, including but not limited to availability, platform and security constraints, and hardware and system characteristics used to describe the physical asset in detail. It should be recognized that the type and quantity of data vary greatly depending on the specific industrial application and corresponding supporting equipment.

[0134] Figure 8D This is a flowchart illustrating the verification and simulation of the hierarchical asset control application in various embodiments of this disclosure. For specific implementations of the system, any of these steps may be omitted or optional. A complete list of system smart assets is obtained through step 842, and processing from device elements to smart asset data structures can be performed, with optional verification and / or optional simulation performed before the proposed solution is deployed with the physical industrial system. Mapping is performed for each smart asset using parent / child control relationships, and step 844 verifies that each communication and control path exists and is available. If step 846 determines that there are problems related to these parent / child communication paths, remediation step 848 is performed, continuing until problem resolution step 858. Remedial measures may include, but are not limited to, changing the device list, smart asset list, and / or smart agent data structures. This verification can be beneficial before actual hardware control in industrial applications to prevent very serious consequences. Once the asset parent / child relationships are verified, the hierarchical asset control application can be generated.

[0135] However, before applying or instantiating a hardware-based system, it may be beneficial to simulate the complete, validated hierarchical asset control application 850 and optional portion 862 to determine if unexpected or undesirable results exist or may occur in the hardware-based industrial system. Similar to validation step 844, step 850 simulates the entire hierarchical asset control application. If any problems are identified in step 854 related to the simulated operation of the hierarchical asset control application, remediation step 856 can be performed, and execution can continue until the problem is resolved in step 858.

[0136] Upon completion, a valid and simulated hierarchical asset control application is generated, and the corresponding control hardware requirements can be developed using step 860. The control hardware / software corresponding to the hierarchical asset control application can then be used to integrate the control hardware / software with the underlying equipment components to ultimately establish a comprehensive intelligent asset control system (e.g., such as...). Figure 5B (As shown). In some embodiments, the control hardware corresponding to the integrated intelligent asset control system or hierarchical asset control application can be reviewed using simulation tools before the actual hardware integration and / or runtime execution of the running industrial application. Figure 8E This is a flowchart illustrating the hardware instantiation of the hierarchical asset control application in various embodiments of this disclosure. As long as the verified and simulated hierarchical asset control application exists and is retrieved by system 870, in step 872 the system itself can automatically request, or the user can input, the spare capacity of the restricted host resources. Once completed, step 874 can generate a list of required hardware platforms and the hardware platform requirements of all smart assets stored in the smart agent database. Step 875 selects a hardware platform type and proceeds to step 876, assigning the required hardware platform to the target machines. Each smart agent may include: creating a list of each required hardware platform type using a target machine instance, selecting a hardware platform type, selecting a smart agent for that hardware platform type, collecting availability and resource requirements for the smart agent industrial application, determining the target number required for the application, adding other target hardware platforms as needed, attempting to install the application on the required number of target machines, checking the application's characteristics, and preventing the use of unused target hardware platform capacity.

[0137] Embodiments of this disclosure consider the balance of available resources, which may involve considering whether more than one hardware type is being used and whether the application meets spare capacity requirements to determine appropriate alternative resources. If resources exceed capacity, the system can remove resources from the target; otherwise, a new target machine is created and resources are subtracted from the previously specified target machine. This process can continue until all smart agents (step 880) and all agents (step 882) have been processed within a particular platform. Once all smart assets 882 have been processed and the various characteristics of the hardware instances of the validated hierarchical industrial asset system / application have been determined, a summary report 886 is generated, comparing the options with the system designer and defining the preferred configuration. At this point, in step 886, the system or the system's user can select and adjust various parameter options to modify the existing configuration of the restricted host resources defined in step 874. These adjustments can affect the operation of the industrial system, and any adjustment can operate the system in an alternative manner.

[0138] One example of this is tuning the system to a desired optimal state to maximize specific characteristics such as profit, runtime, or security. Many such tunings are possible and are conceivable in various embodiments of this disclosure. Once all the tunings have been made, the final hardware design is submitted to a repository such as a system definition database 888 for later use, if necessary.

[0139] We should realize Figure 8E The system described herein can be instantiated using a variety of computing resource configurations. Examples may include the resources depicted in Figure 28. The principles of this disclosure envision allocating any computing resources to achieve the benefits of distributed points of failure, reduced infrastructure costs, modularity, portability, or other benefits specific to a particular industrial application.

[0140] 3. Example processing

[0141] Figure 9A This is a diagram illustrating an example organization of industrial process unit equipment according to various embodiments of the present disclosure. An example unit comprising three reactor units 900 is shown. Depending on the implementation of the system, hierarchical asset control applications can be developed for individual units and appropriately replicated, operated independently and / or in coordination for specific units. Reactor units A904, B905, and C906 are connected to a cooling water reservoir 901, which is shared by all reactor units supplied by pumps 902 within the processing unit 900. Temperature indicators 903TI and flow indicators 903FI measure the temperature and flow rate of wastewater leaving the plant. Valve positioner 903FV controls the flow rate of cooling water directly into the effluent to reduce the effluent temperature.

[0142] It should be understood that, Figure 9AIt is a representation of many devices controlled by an integrated intelligent asset control system, which can be at the unit / work unit asset level. Figure 9B Can be in such a state Figure 5B The level of smart assets described. Figure 9B The complete reactor unit 905 in the middle can correspond to Figure 9A Reactor unit A 904 is included. Depending on the specific industrial application, one or both may be part of a comprehensive intelligent asset control system architecture. Hierarchical asset control applications have been developed for various levels throughout the architecture.

[0143] Figure 9B This is a diagram illustrating industrial process examples of various embodiments of this disclosure.

[0144] The illustrated complete reactor unit 905 includes a reactor vessel 910 and multiple sensors and valve positioners. Pressure indicator 910PI, temperature indicator 910TI, and level indicator 910LI provide measurements within the reactor vessel. A reactor cooling jacket 911 with an associated flow valve 911FV controls the flow rate of cooling water entering the reactor cooling jacket 911. Valve positioner 915FV controls the flow rate of warm water from the reactor cooling jacket 911 to the effluent and monitors the temperature and flow rate of the effluent exiting the reactor unit 910 via flow indicator sensor 915FI and temperature indicator 915TI. A reactor agitator 912 stirs the reagents to ensure complete reaction. "Product C" has an associated pump 913 and flow valve 913FV. An emergency reaction quench tank 914 and associated flow valve 914FV are used to stop the reaction and solidify the reactants, rendering reactor vessel 910 unusable. Heat exchanger 920 controls the temperature of the cooling water pumped to reactor jacket 911, and is associated with a series of sensors and valve positioners. Temperature indicator 920TI, flow indicator 920FI, and valve positioner 921FV measure and maintain the cooling water to heat exchanger 920. Freshwater pump 940 pumps freshwater to the heat exchanger. This freshwater is pumped into reactor jacket 911 to remove heat from reactor vessel 910. The valve positioner associated with this freshwater pump 940FV controls the flow rate of cooling water entering heat exchanger 920. Pump 950 for "Reagent A" pumps the reagent material into reactor vessel 910. Associated with the pump are flow sensor 950FI and valve positioner 950FV. A similar structure exists for "Reagent B," where pump 960 pumps the reagent into reactor vessel 910. Associated with the pump are flow sensor 960FI and valve positioner 960FV.

[0145] When the pressure inside reactor vessel 910 exceeds the limit, pressure relief valve 970 opens to release gas into a chimney that is open to the external environment.

[0146] In this example, the supplied cooler and reservoir 980 are used to cool the common water to the desired temperature, and then pump it into the reactor cooling jacket 911 using pump 985. Furthermore, under abnormally high temperature conditions, it can be pumped directly into the reactor cooling jacket 911 to rapidly slow down or stop the reaction while protecting the reactor vessel. This cooled water can also be added directly to wastewater to reduce BTU release into the environment. Figure 10 This diagram illustrates various aspects of the initial processing of basic equipment elements for developing hierarchical asset control applications, as shown in various embodiments of this disclosure. In the illustrated exothermic reactor industrial system 1000, step 1040 determines and obtains a list of equipment describing the equipment elements comprising the reactor industrial system 1000 and their corresponding parameters. This list will consist of the various equipment elements shown and described in Figure 9. Step 1050 selects the first equipment element to begin processing from the equipment list. In this example, exothermic reactor 910 is selected. As previously stated, any equipment element can be selected to begin the process. In this case, exothermic reactor 910 is selected because it is the basic equipment in the system and is known to have various parent / child relationships with asset groups, such as flow valve 913FV and any associated equipment for “Product C” 1010, heat exchanger asset group 1020, and flow valve 960FV associated with “Reagent B” asset group 1030. Each sub-element of exothermic reactor 910 is queued for processing and marked as a potential parent / child control element for subsequent processing. For example, add a list of potential sub-control device elements connected to the parent asset, exothermic reactor 910. Step 1060 processes each device element in the list until all device elements have been processed. In this example, heat exchanger 920 can be processed and all sub-elements reach heat exchanger 920, such as freshwater pump 940 and flow valves 940FV and 921FV. As with the initial parent asset, only all devices need to be processed; there is no specific order in which each device asset is processed. Figure 11 This is a diagram illustrating various aspects of an example of a hierarchical structure for developing intelligent agents for hierarchical asset control applications according to various embodiments of this disclosure. Utilizing all devices defined in the reactor industrial system 1100, the system now defines asset instances based on the device list in step 820 and creates an intelligent agent for each intelligent asset. Similar to processing the device list, the system can select any point for processing. In this example, exothermic reactor 910 is selected, and the asset instance in step 820 is defined based on the characteristics defined in the device list. To aid in defining intelligent asset templates in the intelligent asset library 822, various intelligent asset templates can be used to begin characterizing intelligent assets. Example intelligent asset templates are shown in... Figure 8CAs shown in the diagram. It should be recognized that there is a high degree of flexibility regarding the quantity and type of data that can be populated into such smart asset templates. Smart asset / smart agent data for a group can come from usage data / operational characteristics of operational history, can be provided by component vendors, and can be used as a generic component model (e.g., a typical heat exchanger with a reliability curve...).

[0147] Once the device components are mapped to and defined by the smart asset template, step 824 selects the smart asset template components and creates a smart agent data structure for each smart asset 1110 in preparation for populating other feature data. In this example, a smart asset template corresponding to the exothermic reactor is created and populated with available information, which may include, but is not limited to, the exothermic reactor name, category, model, serial number, and application type. This data is available during the initial processing of the device list and is populated into the smart asset data structure when a smart asset data structure specifically for the exothermic reactor 910 is selected and created.

[0148] Once the smart asset and industrial application are identified, define the constrained characteristics of asset 828 (exothermic reactor 910 in this example). While various limitations may exist, safety, environmental, and reliability constraints can be considered in specific examples. An example of a safety constraint could be that the exothermic reactor can only be operated up to an absolute maximum of 500 MPa, as indicated by pressure gauge 910PI. Violation of this constraint could lead to catastrophic failure of the exothermic reactor 910 and pose a substantial safety hazard. An example of an environmental constraint could involve the temperature of the water in the exothermic reactor jacket 911 due to the exothermic reaction within the reactor. For example, as indicated by thermometer 910TI, warm water from the jacket can only be discharged into the local water supply at a temperature of 100 degrees Fahrenheit, as indicated by thermometer 910TI. Failure to comply with such operational risk constraints could result in regulatory penalties and harm to the local environment. Finally, an example of a reliability constraint could be given, such as the exothermic reactor 910 having a maximum service life of 100,000 hours to support continuous material handling. Exceeding this limit may result in reduced efficiency of the exothermic reactor 910 and / or a risk of reactor component malfunction, both of which would affect the control, management, and optimization of the integrated intelligent asset control system and the processes associated with it. Each of these, and any other constraints, populates the intelligent asset data structure 832 for a given device component (exothermic reactor 910 in this example). This now-completed exothermic reactor intelligent agent data structure can be stored in the intelligent asset database 834. This process of creating intelligent agent data structures is repeated for all devices, such as the “Product C” pump 913 and valve 913FV device group, the heat exchanger device group 1020, and the “Reagent B” device group 1030.

[0149] Once all constraints have been calculated and a smart asset base is in place, any optimizations for the system or any user-defined variables (such as real-time operating profit) can be derived, and the optimizations created for each asset 1120 are executed and stored in the smart agent of each smart asset. This process continues until a smart asset data structure has been created for each element of the physical equipment, and a complete list 838 of system / application assets exists for the reactor industrial system 1100, with each smart asset containing a corresponding smart asset solution 1130 populated with relative constraints, objectives, and optimizations.

[0150] Figure 12 These are diagrams illustrating various aspects of examples of simulation and verification of hierarchical asset control applications and corresponding control hardware according to various embodiments of this disclosure.

[0151] This verification can include verification of the smart assets themselves as well as verification of the hierarchy formed by the smart asset components. When a complete system / application smart asset list for the reactor industrial system 1200 exists in step 842, step 844 verifies the communication and control paths of each parent / child asset. Various methods exist to verify such paths, including but not limited to intra-asset communication, such as packet "ping" or other more formal handshake protocols, such as, but not limited to, TCP / IP addressing. Verification of an example industrial system may include starting verification using the asset list and determining which parent element can communicate to each child element. In this example, heat exchanger 920, which controls the temperature of the cooling water pumped to reactor jacket 911, will verify its child assets. It includes a temperature indicator 920TI for providing cooling water temperature measurement, a flow indicator 920FI for measuring cooling water flow, a valve positioner 920FV for controlling cooling water flow, and a valve positioner 921FV for controlling the flow of cooling water to the heat exchanger. This process continues until the entire smart asset list has been evaluated and verified, and communication within the control hierarchy formed by the individual smart assets and the hierarchical asset control application's asset set has been verified. Step 846: The system queries to determine if any problems or anomalies exist in the parent / child verification. If any problems are found, they are addressed in remediation step 848, and once remediation step 852 is complete, or if no problems exist, the process continues with the steps of simulating the complete verification of the hierarchical industrial system / application. Given an example of a complete, verified hierarchical industrial asset structure for reactor industrial system 1200, a simulation of the hierarchical asset control application 1220 can be performed before the control system is commissioned to further confirm that the proposed industrial solution will function as expected when instantiated in hardware. If a simulation of the hierarchical application control application is performed in step 1230 and a problem is identified, that problem can be raised for remediation. For example, if pump 985, responsible for supplying cooling water from the cooler and reservoir 980 forming cooler asset 1210, is not of an appropriate type in terms of characteristics such as flow rate, physical size, or any other parameter, the system can remediate this problem in step 856, as determined by the system or user before setting up the hardware system. Such repairs may include, but are not limited to, adjusting the asset template for a specific asset, adjusting the smart asset data structure to accommodate other considerations, or replacing a specific device (in this case, pump 985) with equipment suitable for the industrial application. Once such a replacement has been completed, the entire process should be repeated: identifying the equipment list 710, selecting one or more asset templates 720, creating the smart agent data structure 730, developing the smart agent 740, and validating and simulating the entire system to thoroughly validate the entire integrated smart asset control system. Once the fully validated hierarchical asset control application is available, step 1240 can create and deploy hardware instances supporting the control system, and combine with... Figure 8EAny control hardware (such as) required to convert the hierarchical application control application described in the document into a comprehensive intelligent asset control system. Figure 5B (As shown in the image) It is integrated with the underlying device components.

[0152] The extended control aspects of tiered asset control applications are actually divided into two general categories of real-time control. The first is providing real-time control for the fundamental objectives of the business, which may include, but is not limited to, profitability, operational profitability, operational efficiency, and asset reliability. The second is providing real-time control for the dynamic constraints on the objectives, such as, but not limited to, security risks, environmental risks, and security rules. (Appendix) Figure 13 This diagram illustrates an example of basic system model optimization functionality for hierarchical asset control applications, focusing on objectives and dynamic constraints. Each box in the diagram represents an aspect of the overall control system. "Process control for improving operational efficiency" pertains to process control related to the target asset. Each additional function will be referenced in the appendix below. Figure 13 and Figure 3B Described separately.

[0153] Control system components

[0154] The extended control aspects of layered asset control applications can be divided into two general categories: real-time control and other related controls. For example, providing real-time control over fundamental business objectives, such as profitability, operational profitability, operational efficiency, and asset reliability, could be one category. The second category could involve providing real-time control over dynamic constraints on objectives, such as security risks, environmental risks, and security rules. (Appendix) Figure 13 An example of a control system model for the system's objectives and dynamic constraints is shown. Each box in the figure represents an aspect or component of the overall control system. (Appendix) Figure 3B Example components of a smart agent 325 in a CPS associated with a smart asset, a collection of smart assets, or other groupings of smart assets are shown for providing real-time control over the dynamic constraints of fundamental business objectives and goals (i.e., real-time control of profitability, reliability, process efficiency, security risks, environmental risks, and security rules). In some embodiments, the smart agent 325 may include a real-time (RT) revenue controller 330, an RT process efficiency controller 335, an RT reliability controller 355, an RT environmental risk controller 345, and an RT security risk controller 350. It should be noted that in some embodiments, the smart agent 325 may include more or fewer controllers, thereby providing control over more or fewer domains. In some embodiments, a coordinator module 340 may be included in the smart agent for coordinating the execution of controllers according to a control hierarchy. In other embodiments, the smart agent 325 may include modules other than RT controllers. Examples of such modules may include, but are not limited to, a history module 365 and a small data analytics engine 370.

[0155] Reference Appendix Figure 3B In some embodiments, the intelligent agent 325 may include other modules, such as a history module 365 and a small data analytics engine 370. The history module 365 can generate historical data associated with an asset or asset group. Such historical data may include sensor measurements, setpoints, deviations from setpoints, corrective actions taken, etc., and may provide a snapshot of the status or health of the asset or asset group at a given point in time. Historical data may be reported to a higher-level asset set (e.g., for calculations) and / or exported to other systems, such as those described in the appendix. Figure 22 The big data analytics engine 2205 is described. The small data analytics engine 370 can collect and analyze data associated with assets or asset groups on-site, and report the results directly or through a higher-level intelligent agent to the attached... Figure 22 The big data analytics engine 2205 in [the system / platform]. (See attached reference.) Figure 13 Box 1315 represents the process control functions provided by an automated system for process control associated with the target asset. These process control functions can be provided by an attached... Figure 3B The RT process efficiency controller 335 shown is implemented. Each of the other functions will now be described separately in the following description.

[0156] I. Real-time profitability control to improve operational profitability

[0157] In some embodiments, the real-time profitability control function can be divided into two components, corresponding to box 1305 and box 1300, such as... Figure 13 The model shown illustrates this. These control functions at the level of intelligent assets or intelligent asset sets can be represented by the industrial asset base 1325 or other structures defined herein (including hierarchical asset control applications or integrated intelligent asset control systems), and can be represented by the RT profit margin controller 330, in particular. Figure 3B The business revenue controller 330a and operating revenue controller 330b shown are implemented. As used herein, operating profitability is defined as the profitability created by decisions made in industrial operations for the business, while business profitability is defined as the profitability created by decisions made in industrial operations for the business. The role of operating profitability is guided by the role of business profitability.

[0158] Business profitability control function 1300 represents control decisions that encompass business decisions at the operational level and above. These decisions may include things such as production according to contractual commitments. For example, a contractual commitment might involve producing a specified quantity of products on a specified delivery date. Fulfilling the commitment during normal production operations, without significant risk, is acceptable at this point. If something occurs during operations that increases the risk of the contract not being fulfilled, a decision at this level may be necessary to control the outcome. For example, if the system determines that the reliability of assets required to meet the production schedule is declining at an unexpected rate and that intelligent assets may require maintenance, a decision may need to be made at this point to determine whether the best business decision is to risk continuing operations until the commitment is fulfilled or to shut down for maintenance. Such control decisions are typically made manually by experienced production managers (manual control), but can be made automatically once sufficient knowledge is gained through new performance measurement experience in operations (automatic control). A suitable automatic control system could be an expert system that simulates the decision-making process of an optimal production manager. The output of business profitability control function 1300 can be cascaded to the real-time profitability control function 1305 of operations, and these controls can work together to guide real-time profitability control strategies (setpoints, automatic / manual, etc.).

[0159] In some embodiments, it is useful to view real-time profitability control as the main loop of a cascaded control strategy, where the secondary loops are process control loops traditionally associated with improved operational efficiency. From this perspective, the output of the real-time profitability controller will be the setpoint of the controller designed to improve operational efficiency. Each setpoint in a process control system needs to be set within a specific value or a specific range of values ​​suitable for operation to maximize operating profit margin. The real-time profitability controller can set these setpoints accordingly.

[0160] From a manual control perspective, the way to achieve real-time profitability control is for the operator responsible for setting the setpoints of the operations controller to review the real-time profitability metrics of the operations and modify the setpoints within each permissible range. The operator then reviews the real-time profitability measurements to determine if they are increasing, decreasing, or remaining unchanged. If they are increasing, the operator will continue to change the setpoint in the direction leading to the increase. If they are decreasing, the operator will revert to the setpoint to reverse the decrease; if they do not change, the operator can try other setpoints. Over time, the operator will learn which setpoints provide the best benefit at each stage of operation and prioritize setpoint actions accordingly. Because the real-time profitability of the process is dynamic, the operator should continuously modify the setpoints to identify opportunities for ongoing improvement.

[0161] By utilizing expert system technology, operators can develop automated real-time revenue controllers using an automated approach. Over time, and with experience in manual revenue capability control, appropriate setpoint priorities, values, and directions of change can be determined for different phases of operation. Expert system controllers can be developed to directly and automatically execute the most advantageous continuous setpoint modifications, and this can be achieved using the components shown in Figure 28.

[0162] Alternatively, for measuring real-time profitability associated with each part of the operation, an integral controller can be used. Based on prior experience with real-time profitability, the setpoint of the real-time profitability controller can be set to a high range. The output of the integral controller can be configured to drive incremental signals based on the incremental signals deviating from the setpoint. These signals can be appended to each process controller setpoint in a directional-compensated manner, increasing or decreasing the incremental value of each process control setpoint to promote profitability in the appropriate direction. In this case, integral control is a suitable choice because the control loop does not depend on the inherent physical cycle when operating correctly. Some real-time profitability control loops may not be limited to a fixed period because they may be determined manually.

[0163] To understand the purpose of the following example as shown in the attached figure, the features and functions are implemented and executed through an operable integrated intelligent asset control system developed by a hierarchical asset control application and the corresponding control hardware developed for the underlying equipment components.

[0164] Real-time profitability measurement is designed to determine the real-time component profit impact of a smart asset, a collection of smart assets, or other groupings of smart assets in actual operation over a finite time period. In some embodiments, this measurement may be defined by the following example equation:

[0165] RT Profitability = Production Value - (Energy Cost * Energy Consumption + Material Cost * Material Consumption) Δt

[0166] In this context, production value is the quantity of goods produced within the considered time frame multiplied by the current value (Δt) of those goods. The value of produced goods can be established in various ways, depending on the customer. For example, if the customer uses transfer pricing, the value of the produced goods could be the transfer price. Another example is the current market value of the goods at the time of production, even though they may not actually be sold at that price. How the customer should implement this variable is a human-based, rather than science-based, decision. Energy consumption cost equals the energy cost at the time of consumption multiplied by the amount of energy consumed. Material cost is equivalent to the material cost at the time of consumption multiplied by the quantity of materials consumed.

[0167] II. Determination of Real-Time Asset Security Risk Constraints (Appendix) Figure 14A It's a flowchart, attached.Figure 14B This is a logical flowchart illustrating the determination of security risk constraints associated with assets in a hierarchical asset control application according to various embodiments of this disclosure. Security risk refers to the probability that an asset may be involved in an inherent security event multiplied by the expected severity of the security event, which is based on past experience, speculation, or a combination of both. Defined security risk measurements may be very similar to other constraint measurements, such as reliability measurements. The key distinguishing aspect of security risk is the severity of the associated consequences. Reliability failures may result in equipment shutdown and damage, but the expected severity of the damage is below a safety threshold. Security risk implies that the extent of equipment damage may be much greater and could pose a hazard to personnel and plant facilities. Compared to simple reliability risk, each industrial operation can assess the level of consequences necessary to treat the risk as a security risk, rather than just a reliability risk.

[0168] The overall reliability (RT) security risk of a tiered asset control application can be defined by the maximum values ​​of the derived reliability security risk, operational security risk, and conditional security risk. The maximum values ​​for reliability security risk, operational security risk, and conditional security risk are chosen because they provide a method for identifying the three most critical risks associated with the operational process at a given time.

[0169] The reliability and security risk input module 1402 determines the reliability and security risk, and this value is determined by the following relationship:

[0170] Security risk = MAX(p1S(t)*E1, p2S(t)*E2, ..., p n S(t)*E n ).

[0171] Where: pS i (t) is the probability of security event I occurring at time t, and E i This is the expected outcome when event I occurs.

[0172] There are two basic aspects to the types of security incidents that may occur in hierarchical asset control applications. The first is asset failure leading to a security incident. The second is errors in the processes corresponding to the assets or asset groups that may cause security failures.

[0173] In the first case, from the perspective of reliability measurement, p i The term S(t) is essentially equivalent to the probability of equipment failure. E i The item represents the level of expected consequences resulting from this error. Since safety risks are typically a critical issue in industrial operations, E... i Setting it to the maximum expected consequence associated with the defined safety fault is likely appropriate and prudent. It should be recognized that other values ​​can be set and are considered part of this disclosure. (The last sentence, "Let E...", appears to be an error and is left untranslated.) iThe decision to set the expected consequence level to the highest consequence level is the responsibility of plant management or other parties. In either case, the safety risk measurement methods and calculations work in the same way. Once the asset reliability measurement assessment of the smart assets is completed, the corresponding development smart asset safety risk assessment will proceed directly. The only information that needs to be added is the expected consequence value for each potential safety risk event.

[0174] The second type of safety failure is caused by process safety failures, which may involve multiple simultaneously changing variables that collectively lead to a safety event. The likelihood of a process safety event occurring within a smart asset, a collection of smart assets, or another group of smart assets can be determined by analyzing past events, identifying a combination of key indicators of past safety incidents using historical data within current or similar assets. Following the analysis, any potential indicators are used to determine whether the current operating conditions within the asset have an inherent probability of recurring process safety events of a similar type.

[0175] This determination can be accomplished by utilizing a real-time workflow designed to identify guiding indicators. This should be done for all past process safety incidents associated with the asset. (Process safety risk pS) i (t) can be determined by calculating the average time between past advance warning values ​​and the occurrence of a safety event, and extending that time to t in the probability analysis. Setting the probability to a high value immediately upon the occurrence of a key indicator may be appropriate when handling safety risk analysis. This is typically the responsibility of plant management or other responsible parties. It should be noted that in hierarchical asset control applications, there may not be enough safety events to form a basis for a comprehensive process safety risk analysis. In these cases, analyzing safety incidents occurring in other similar or comparable operations may help provide the necessary historical information base for reliable analysis. Libraries of control application safety data for such similar or comparable hierarchical asset control applications can be obtained from various sources.

[0176] The effectiveness of safety risk assessment is linked to the expected value of the safety incident's consequences, Ei. The expected value of a safety incident's consequences, Ei, is a numerical indicator of the potential damage, injury, or death associated with a predicted type of incident. Because severity is a combined qualitative and quantitative indicator, Ei can be expressed as a standardized value to facilitate a stronger relative assessment of safety risks between potential incidents. Ei may represent the maximum potential severity of the worst past incidents of the same type, rather than the expected severity. A better representation should be a warning applicable to safety risks in industrial operations. Similarly, it is the responsibility of plant management or other responsible parties to adopt the maximum potential severity as the expected severity.

[0177] The Operational Safety Risk Input Module 1404 identifies operational safety risks, and these risks are determined by characteristics such as the thoroughness and timeliness of safety inspections. These inspections can be defined by the industrial application operator according to local standards or the relevant regulatory body (such as OSHA). Operational safety risks are also identified by the field safety team or other responsible parties.

[0178] The Conditional Security Risk Input Module 1406 determines the conditional security risk, and its value is determined based on the analysis results of past security incidents to identify the key indicators of potential security events. If key indicators of potential security events are identified, the conditional security risk value is set according to past experience, based on the expected time and severity of the event. The on-site security team or other responsible party determines the conditional security risk.

[0179] To assess overall security risks, the entire process will receive reliability security risk information 1410, operational security risk information 1411, and conditional security risk information 1412. This information can be input from the user into the system or from the system itself in real time. For example, it can be transferred from a storage device to the system 1403, or from a repository outside the system, such as with the storage device.

[0180] Using these inputs and the inputs from the Conditional Security Risk Workflow Monitor 1405, the system will determine the reliability security risk 1413, operational security risk 1414, and conditional security risk 1415 as defined above. The overall RT security risk 1416 of the hierarchical asset control application is then assessed by the maximum values ​​of the derived reliability security risk, operational security risk, and conditional security risk.

[0181] The overall RT security risk assessment result 1416 will analyze the output of the overall security risk 1408 and apply any action rules to be assessed 1417 based on the overall RT security risk. The notification module 1407 will provide notifications 1419 to the system users or the system itself, and the workflow triggering and corrective action module 1409 will trigger workflows 1418 that are prohibited by the system to maintain or remedy the observed and determined conditions.

[0182] The overall RT safety risk 1416 can be used by the real-time safety control function 1320 and provides constraints to the real-time efficiency control 1315 and real-time profitability control 1305 functions. The control function for real-time safety risk involves two approaches. First, it involves safety control of the production process through effective process control strategies, aiming to keep the process within safe limits. Second, it involves assessing the current real-time safety risk and the changes in safety risk over a defined time period associated with each asset and asset set to determine whether the safety risk characteristics have reached an unacceptable level. Customers (e.g., operations management and professional teams) can define unacceptable levels of safety risk. If the safety risk controller determines that the safety risk is approaching an unacceptable level, the controller can determine appropriate actions and send messages to the appropriate controllers in the process control system and / or the real-time profitability control system to take action. For example, if the safety risk is approaching an unacceptable level, the initial safety risk control action could be sending a message to the real-time profitability control function, which can then take action to slow production to reduce the real-time safety risk. If the real-time safety risk is more significant, then the safety risk control measures might be sending a control message to the process control system to directly reduce production by changing the setpoint to reduce the safety risk, or shutting down some or all of the processes to avoid the safety incident, or a combination of both.

[0183] In some embodiments, real-time security risk control can be achieved by Figure 3B The RT security risk controller 350 depicted herein is implemented. In some embodiments, the controller for real-time security risk control may be based on an expert system or automated workflow that simulates security expert responses and sends appropriate outputs to a real-time profitability control function or a process control function, or more preferably both. In other embodiments, real-time security risk control may be implemented manually, for example, by a security professional.

[0184] III. Determination of Real-Time Asset and Environmental Risk Constraints

[0185] Figure 15A It's a block diagram. Figure 15B It is a logic flowchart that represents the environmental risk constraints related to assets in a hierarchical asset control application as determined according to various embodiments of this disclosure.

[0186] Environmental risk is a special case of safety risk, where anticipated damage may occur outside the plant boundary. Environmental risk is measured by extrapolating (or inferring) the probability of an asset being involved in an inherent environmental event, multiplied by the expected severity of that event, based on past experience, or a combination of both.

[0187] The overall reliability (RT) environmental risk of a tiered asset control application can be defined by the maximum value of the derived reliability environmental risk, operational environmental risk, and conditional environmental risk. The maximum value of these three risks is chosen because it provides a method for identifying the three most critical risks associated with the operational process at a given time.

[0188] The reliability environmental risk input module 1521 determines the reliability environmental risk and its value is determined by the following relationship:

[0189] Environmental risk = MAX(p1E(t)*E1, p2E(t)*E2, ..., p n E(t)*E n )

[0190] Where: pE i (t) is the probability of an environmental event occurring at time t, and E i It is the expected consequence when event I occurs.

[0191] There are two main types of environmental events that can occur in industrial operations. The first is the failure of an asset that leads to an environmental event. The second is the failure of a process that could cause environmental failure in the entire asset or a group of assets.

[0192] In the first case, from a reliability measurement perspective, the piS(t) term is essentially equivalent to the probability of equipment failure. The Ei term is the level of expected consequences resulting from such failure. Since environmental risk is often a critical issue in industrial operations, setting Ei to the maximum expected consequence associated with the defined environmental failure may be appropriate and desirable. It should be recognized that other values ​​can also be set and are also part of this disclosure. Setting Ei to the maximum value of the expected consequence level is the responsibility of plant management or other parties. In either case, the environmental risk measurement method and calculation are the same. Once the asset reliability measurement assessment has been completed, the asset environmental risk assessment can be performed directly. The only information that needs to be added is the expected consequence value for each potential environmental risk event.

[0193] The second type of environmental failure stems from process environmental failures, which may involve multiple variables changing simultaneously, collectively leading to an environmental event. The likelihood of a process environmental event occurring within an asset or group of assets can be determined by analyzing past events identified through historical data from current or similar assets, using combinations of key indicators of past environmental events. Following this analysis, any potential indications are used to determine whether the current operating conditions within the asset represent an inherent probability of a similar type of recurring process environmental event.

[0194] The above determination can be accomplished by identifying the real-time workflow of key indicators. This should be done for all past process environmental events related to the asset. The pSi(t) of the process environmental risk can be established by determining the average time between past key indicators and environmental occurrence rates and extrapolating the time to t in a probability analysis. When performing environmental risk analysis, the probability of a key indicator can be set to a high value immediately upon its occurrence. This is typically the responsibility of plant management or other responsible parties. It should be noted that in hierarchical asset control applications, there may not be enough environmental events to form a basis for a comprehensive process environmental risk analysis. In these cases, analyzing environmental events that have occurred in other similar or analogous operations may help provide the necessary historical information basis for reliability analysis. Such environmental databases for hierarchical asset control applications can be obtained from various sources.

[0195] The effectiveness of environmental risk measurement will be correlated with the expected outcome value of the environmental event, Ei. The expected outcome value Ei is a numerical indicator of the degree of damage, injury, or death that may be anticipated for a predicted type of event. Because severity is a combined qualitative and quantitative indicator, Ei can be expressed as a standardized value to establish a stronger relative assessment of the environmental risk in potential events. Ei may be the maximum potential severity of the worst past events of the same type, rather than the expected severity. This may better indicate the need for caution in dealing with environmental risks in industrial operations. Similarly, it is the responsibility of plant management or other responsible parties to adopt the expected severity of the maximum potential severity.

[0196] The Operational Environment Risk Input Module 1523 identifies operational environment risks and is determined by characteristics such as the completeness and timeliness of environmental inspections. These inspections can be defined by the industrial application operator, such as local standards or equivalent regulatory bodies (such as OSHA or EPA). The on-site environmental team or other responsible parties determine the operational environment risks.

[0197] The conditional environmental risk input module 1525 determines the conditional environmental risk by identifying key indicators of potential environmental events, which are determined through analysis of past environmental events. If a potential indicator of a potential environmental event is confirmed, the conditional environmental risk value is set based on the expected timing and severity of the event based on past experience. The on-site environmental team or other responsible parties make decisions regarding the environmental risk.

[0198] To assess overall environmental risk, the process will receive reliability environmental risk information 1530, operational environmental risk information 1531, and conditional environmental risk information 1532. This information can be input into the system by a user or input in real time from the system itself. For example, it can be transferred to the system from a storage device 1522, or from a repository outside the system, such as with the storage device.

[0199] Using these inputs and the inputs from the Conditional Environment Risk Workflow Monitor 1524, the system will determine the reliability environmental risk 1533, operational environmental risk 1534, and conditional environmental risk 1535 as defined above. Based on the derived reliability environmental risk, operational environmental risk, and conditional environmental risk, the maximum value of these risks is used to assess 1536 the overall RT environmental risk of the hierarchical asset control application for tiered asset control.

[0200] As a result of the overall RT assessment, Environmental Risk 1536 will analyze the output of Overall Environmental Risk 1527 and apply any action rules 1537 to be assessed based on the overall RT environmental risk. Notification module 1526 will provide notifications 1539 to system users or the system itself, and workflow triggering and corrective action module 1528 will trigger prohibited workflows 1538 to maintain or remedy observed and identified conditions. 1540 represents the Environmental Risk Analysis Measurement Module.

[0201] Real-time environmental risk can be considered a special case of real-time safety risk. Real-time environmental risk control function 1320 provides constraints for real-time efficiency control 1315 and real-time profitability control function 1305. The control function for real-time environmental risk may involve two approaches. First, it involves continuously controlling environmental emissions (gases, liquids, and solids) during the process by applying effective process control strategies to keep emissions within safe limits. This involves direct measurement of emissions in gaseous and liquid streams, and a lower degree of measurement for solid waste. Second, it involves assessing the current real-time environmental risk and changes in environmental risk over a defined time period associated with each asset and asset group to determine if the environmental risk characteristics are approaching an unacceptable level. An unacceptable level of environmental risk can be defined by the enterprise (e.g., operations management and a professional team) and / or an external agency. In some embodiments, real-time environmental risk control function 1320 can be implemented via an RT environmental risk controller 345 depicted in Figure (3B). If the environmental risk controller determines that an unacceptable level has been encountered, it determines appropriate actions and sends messages to the appropriate controllers of the process control system and / or the real-time profitability control system to take action. For example, if the environmental risk is close to an unacceptable level, the initial environmental risk control action can send a message to the real-time profitability control function, which can then take action to slow production in order to reduce the real-time environmental risk. If the real-time environmental risk is more significant, the controller can send control messages to the process control system to directly reduce production to mitigate the environmental risk by changing setpoints, or to shut down part or all of the process to avoid a safety incident, or a combination of both.

[0202] In some embodiments, the RT environmental risk controller may simulate the response of environmental experts based on an expert system or automated workflow and send appropriate outputs to real-time profitability control functions or process control functions, or both. In other embodiments, the real-time environmental risk control functions may be implemented manually, for example by environmental professionals.

[0203] In conditional and operational environment event analysis, probability is a function of a specific asset relative to potential environmental events.

[0204] The potential severity of an event can be determined by comparing it to the severity (in terms of cost, damage, injury, and death) of the worst past events of the same type. Since the specific measures of injury and death costs are subjective, the potential severity can be normalized to include these qualitative factors.

[0205] IV. Determination of Real-Time Asset Reliability Risk Constraints

[0206] Figure 16A It's a block diagram. Figure 16B It is a logic flowchart that represents the reliability risk constraints related to assets in a hierarchical asset control application as determined according to various embodiments of this disclosure.

[0207] The overall reliability risk (RT) of a hierarchical asset control application can be defined by the maximum values ​​of the derived reliability risk, operational reliability risk, and conditional reliability risk. The maximum values ​​of reliability risk, operational reliability risk, and conditional reliability risk were chosen because they provide a method for identifying the three most critical risks associated with the operational process at a given time.

[0208] Two real-time metrics provide different aspects of the current reliability of the asset under consideration. The first is "Probability of Asset Failure" (pF), defined as the probability that an asset will fail within time period t. The second is "Asset Sustainability Status" (MS), defined as a measure of the relationship between the maximum asset performance in the current state and the ideal maximum performance. These two definitions provide different but related metrics for measuring asset reliability, and both metrics can be controlled to improve overall asset reliability. Each asset or group of assets must be modeled according to its specific characteristics, but there are common factors among assets and asset groups that can serve as the basis for real-time reliability calculations, as defined by the following relationship:

[0209] MS = Current performance of the asset / Expected performance of the asset

[0210] If an asset is operated properly, its current performance can be measured as a function of its working output. The expected performance of an asset can be determined by analyzing its energy and / or material inputs and by identifying what its output should be if it were designed to operate. For basic equipment assets such as pumps, motors, compressors, and piping, the expected performance should be determined by the equipment manufacturer through performance curves established during the design or testing phase of equipment development. These performance curves can be embedded in the equipment's software model, and the model can be used to determine the asset's actual expected performance during operation.

[0211] Because the performance of any asset may not be linear across its operating range, the asset's maintenance status may differ at different points along its operating range. Furthermore, the performance of an asset can vary depending on the type of material it handles, such as highly corrosive or highly viscous materials. In these cases, different performance curves may exist related to different material properties.

[0212] For more complex asset sets consisting of multiple basic equipment assets (such as processing units or work units), the maintenance status can be determined in the same way as for basic equipment assets. In this case, it may be necessary to utilize the design model used during the plant design phase to determine the expected performance values ​​of the asset set.

[0213] The reliability risk input module 1641 determines the reliability risk and its value is determined by the following relationship:

[0214] Reliability asset failure = MAX(p ms f(t),p c1 f(t),…,p cn f(t))

[0215] Where: pmsf(t) is the probability of failure due to the asset's maintenance status, and pcif(t) is the probability of failure due to the measured condition i. The measured condition includes variables such as temperature, runtime, number of starts, and / or other variables related to the asset.

[0216] The probability of failure is determined by the following relationship:

[0217] pf(t) = 1 - e ut

[0218] Where u is the failure rate over time t.

[0219] For basic equipment assets, the probability of failure is determined based on each measured asset condition (including maintenance status as a special condition) according to equipment manufacturer specifications and testing information. Most equipment manufacturers determine this information during design and testing. However, this information is not often used to determine the likelihood of failure in real time. This information can be loaded into the probability module of the asset failure analysis in the form of models. By analyzing these equipment models and comparing them with current operating conditions, the likelihood of failure due to each condition is determined. The overall failure probability of the asset is then determined as the maximum value of the condition-based failure probabilities.

[0220] In some equipment, there may be additional failure probabilities based on the processes performed within the equipment, which may not be determined by failure probabilities based on individual conditions, but may need to be determined by analyzing combinations of conditions. In these cases, equipment manufacturers may use combined models to determine the likelihood of failure under multivariate equipment process conditions. Alternatively, combined process failures can be predicted by analyzing past events that occurred within this or similar operating assets to identify key indicators of past failures, and then these key indicators can be used to determine whether the current operating conditions in the asset indicate an inherent likelihood of failure.

[0221] For higher-level assets (units, work units, areas, plants, enterprises), by analyzing the failure probability of each equipment asset (or the next lower-level asset) under maintenance, a similar failure analysis probability can be obtained, which is a part of the higher-level asset, and it is determined that the failure probability of the higher-level asset is the maximum of the failure probability of the next lower-level asset.

[0222] There may be process conditional probabilities of failure in advanced assets that behave similarly to those in equipment assets. These can determine the process-based failure probability of basic equipment assets in a very similar manner. Data from process history should help identify process-based failure probabilities by utilizing key indicators of similar past failures.

[0223] Because direct, real-time measurements of reliability of the type described herein are typically not performed for industrial assets, there is currently no history of measured responses and interactions for any particular industrial asset. It is foreseeable that, over time and historical development, more specific equations will be developed for individual assets and asset classes, and these schemes will be widely applicable. Among them:

[0224] The Operational Reliability Risk Input Module 1643 identifies operational reliability risks and is determined by characteristics such as the thoroughness and timeliness of reliability checks. These checks can be defined by industrial application operators according to local standards or relevant regulatory bodies (such as OSHA). Operational reliability risks are also determined by the reliability team on-site or by other responsible parties.

[0225] The Conditional Reliability Risk Input Module 1645 determines the conditional reliability risk. This module determines the value by analyzing past reliability events to identify key indicators of potential reliability events. If potential indicators of a potential reliability event have been identified, the conditional reliability risk value is set based on past experience and the expected timing and severity of the event. The field maintenance team or other responsible party determines the conditional reliability risk.

[0226] To assess overall reliability risk, the process will receive reliability risk information 1650, operational reliability risk information 1651, and conditional reliability risk information 1652. This information can be input into the system by the user, or input in real time from the system itself, such as being transferred from a storage device to the system 1642, or from a repository outside the system (such as the cloud).

[0227] Using these inputs, along with the inputs from the conditional reliability risk workflow monitor 1644, the system will determine the reliability risk (i.e., asset failure risk) 1653, operational reliability risk 1654, and conditional reliability risk 1655 as defined above. Based on the maximum values ​​of the derived reliability risk, operational reliability risk, and conditional reliability risk, the overall RT reliability risk of the 1656-level asset control application is assessed.

[0228] Based on the assessment results of the overall RT reliability risk 1656, the overall reliability risk 1647 will be output, and any action rules 1657 to be assessed will be applied based on the overall RT environmental risk. The notification module 1646 will provide notifications 1659 to the system's users or the system itself, and the workflow triggering and corrective action module 1648 will trigger workflows 1658 that are prohibited from being used by the system to maintain or remedy observed and determined conditions. The resulting overall RT reliability risk analysis measurements will be provided to the application or user 1660.

[0229] It should be recognized that there are multiple ways in which the reliability of industrial assets can be affected in real time. For example, one approach could be to alter the asset's operating level (e.g., slowing down a compressor) to reduce operational degradation over time. A second approach is to perform specific maintenance operations to improve maintenance status and reduce the likelihood of failures (real-time reliability). Controlling real-time reliability may not be as straightforward as controlling real-time profitability, as operational efficiency and profitability are directly influenced by real-time reliability and asset maintenance status. Therefore, controlling real-time reliability may involve operational business decisions. For instance, the optimal operational action might be to slow production by a certain percentage to reduce the likelihood of failures during production, even if instantaneous real-time profitability may temporarily decrease to ensure the completion of overall production.

[0230] In some embodiments, Figure 13The real-time reliability control function 1310 described herein considers real-time reliability metrics and maintenance status metrics for each asset. These two can be used in combination to obtain optimal reliability results. In some embodiments, the real-time reliability control function 1310 can be... Figure 3B The RT reliability controller 355 described in the figure is used to implement this.

[0231] From a manual control perspective, real-time reliability control is achieved by the operator responsible for setting the setpoints of the operation controllers, who then views the real-time reliability and maintenance status metrics of the asset. This operation modifies the setpoints associated with asset operation control to each permissible value, requests maintenance operations, or both. If the operator determines that the maintenance status or real-time reliability degradation exceeds expected levels, he or she can assess the criticality of the production schedule and take appropriate action. One action might be to slow down the process to reduce the degree of degradation. Another action might be to schedule maintenance operations.

[0232] In some embodiments, an automated real-time reliability controller can be used for real-time reliability control. The controller can automate operations using expert system technology, but this can be more complex than an automated real-time profitability controller because appropriate operations are typically associated with business profitability control decisions. Over time and with experience from manual reliability control, appropriate setpoints and maintenance operations can be determined for assets and implemented in an expert system that automatically simulates operator actions in a manner similar to real-time profitability control. However, optimizing operational performance may require implementing a higher-level expert system (automatic or manual) that takes real-time reliability input from the real-time reliability controller and executes control rules to determine the optimal operations that should be applied to the current production or production schedule, the maintenance status degradation curve, or the probability of failure in the business. This is determined by… Figure 13 The diagram illustrates a business profitability control function. A senior expert using this function can determine the most profitable action, such as: continuing current production to complete the current operation and then performing maintenance; slowing production to reduce the decline in real-time reliability so that the current operation can be completed and maintenance performed; or shutting down for maintenance and then continuing production. It should be understood that other methods are included in this disclosure.

[0233] Since the execution of any asset may not be linear within its operating range, the asset's maintenance status may differ at different points along its operating range. Therefore, optionally, the ratio of maximum current efficiency to maximum ideal efficiency can be used to determine the asset's maintenance status.

[0234] In some embodiments, calculations relating to the measurement of asset reliability may be based on the IEC 61508 standard for functional safety of electrical / electronic / programmable electronic safety-related systems, all of which are incorporated herein by reference.

[0235] V. Determination of Real-Time Asset Security Risk Constraints

[0236] Figure 17A It's a block diagram. Figure 17B It is a logic flowchart illustrating the security risk constraints related to assets in a hierarchical asset control application as determined according to various embodiments of this disclosure.

[0237] Figure 6 The real-time security risk control function 630 described in A is used to control cybersecurity risks. Real-time security risk measures the probability of a cybersecurity incident occurring within a controlled environment that controls an asset or set of assets. In some embodiments, the real-time security risk control function may include determining when the probability of a cybersecurity incident occurring is high or severe and taking one or more actions (e.g., taking the affected asset or set of assets offline and notifying personnel). The real-time security risk control function may be... Figure 6 The RT security risk controller 675 shown in Figure B is used to implement this.

[0238] The security risk input module 1761 determines the security risk and its value is determined by the following relationship:

[0239] RT network security risk = f(number of unexpected / unidentified network inputs)Δt

[0240] Since the expected number of anticipated / unidentified network inputs is 0, for multiple unexpected / unidentified network inputs within a specific time period, p (network security incident) can be set to high (>50%) when any unexpected / unidentified event and severe (>90%) event occur. The adjustment parameter can be the time period.

[0241] In some embodiments, internal intrusion detection systems (IDS) and external information from sources such as the Department of Homeland Security, private security companies, or a combination thereof can be used to identify security risks. Internal IDS can provide metrics on the frequency of external attack attempts and any penetration within the protection layer. External data may provide information on escalating threat levels occurring elsewhere.

[0242] Some non-limiting examples of control actions that can be dynamically taken in response to increased threats are: changing authentication from a password to a combination of a password and biometric login parameters; reducing, replacing, and / or deleting authorizations for individuals or roles; disabling and / or enabling encryption of motion and / or still data; changing the length of encryption keys; increasing and / or decreasing the frequency of certificate changes on the system; changing network access permissions and / or opening and / or closing communication ports; disconnecting a part of the plant from the internet or other plant areas; and other measures to improve the robustness of security layers.

[0243] Since there is little industrial experience in using the types of real-time measurements specified in this article, the measurements can become more standardized across assets and asset classes as time goes on and experience is gained in using them.

[0244] It should be noted that controlling these measures (similar to process control used to improve operational efficiency) may involve actually directly controlling specific measures that contribute to higher-level metrics. For example, applying process control to improve operational efficiency typically involves directly controlling logistic measures such as flow, level, temperature, and pressure, compared to directly controlling any operational efficiency measure. For instance, in the case of real-time reliability control, the actual control may depend on the speed of the asset under consideration (e.g., pumps, compressors), which will directly affect the asset's real-time reliability.

[0245] The Operational Safety Risk Input Module 1763 identifies operational safety risks and is determined by characteristics such as the thoroughness and timeliness of safety inspections. These inspections can be defined by industrial application operators according to local standards or relevant regulatory bodies (such as OSHA). Operational safety risks are also identified by the field safety team or other responsible parties.

[0246] The Conditional Safety Risk Input Module 1765 determines the conditional safety risk and its value is determined by analyzing past safety incidents to identify potential indicators of potential safety events. If the main indicators of a potential safety event are identified, the conditional safety risk value is set based on the expected timing and severity of the event based on past experience. The field maintenance team or other responsible party determines the conditional safety risk.

[0247] To assess overall security risks, the process will receive security risk information 1770, operational security risk information 1771, and conditional security risk information 1772. This information can be input into the system by the user, or input in real time from the system itself, from a storage device to the system 1762, or stored from an external repository (such as the cloud).

[0248] Using these inputs and the inputs from the Conditional Security Risk Workflow Monitor 1764, the system will determine the security risks as defined above: asset failure risk 1773, operational security risk 1774, and conditional security risk 1775. The overall RT security risk of the hierarchical asset control application is then assessed by deriving the maximum values ​​of the security risks, operational security risks, and conditional security risks.

[0249] As a result of the overall RT security risk 1776 assessment, the output of the overall security risk 1767 will be analyzed, and any action rules 1777 to be assessed will be applied based on the overall RT environmental risk. The notification module 1766 will provide notifications 1779 to the system's users or the system itself, and the workflow triggering and corrective action module 1768 will trigger workflows 1778 that are prohibited by the system to maintain or remedy observed and determined conditions. The resulting overall RT security risk analysis measurements will be provided to the application or user 1780.

[0250] It should be recognized that there are multiple ways in which the safety of industrial assets can be affected in real time. For example, one approach could be to alter the asset's operational level (e.g., slowing down a compressor) to reduce operational degradation over time. A second approach could be to perform specific maintenance operations to improve maintenance status and reduce the likelihood of failure (real-time safety). Controlling real-time safety may not be as straightforward as controlling real-time profitability, as operational efficiency and profitability are directly influenced by real-time safety and asset maintenance status. Therefore, controlling real-time safety may involve operational business decisions. For instance, the optimal operational action might be to slow production by a certain percentage to reduce the likelihood of failures during production, which might temporarily reduce real-time profitability but ensure the completion of overall production.

[0251] In some embodiments, Figure 13 The real-time security control function 1330 described herein takes into account real-time security measures and maintenance status measures for each asset. Both can be used in combination to drive optimal security outcomes. In some embodiments, the real-time security control function 1310 may be... Figure 3B The RT security controller 360 shown is used to implement this.

[0252] Constraint conditions for normalizing extended control variables

[0253] Figure 18A It's a block diagram. Figure 18B This is a logic flowchart illustrating the objectives related to assets in a hierarchical asset control application as determined by the specification constraints of various embodiments of this disclosure.

[0254] Traditionally, optimization has been applied to industrial operations, regardless of whether the systems are linear or nonlinear, static or dynamic. These optimization methods typically rely on selecting a single objective function and transforming all other objective functions into constraint functions to be added to the actual constraints on the objective. The computational resources required to execute these optimizers are significant, often limiting their execution time to hours or even days. While optimization programs can yield very good results, their efficiency declines as business speeds increase. This is because when the optimizer executes and produces results, the results no longer reflect the business situation being optimized. Therefore, the optimizer fails to optimize the objective. Secondly, as industrial enterprises become increasingly complex, with multiple objectives existing simultaneously, the effectiveness of single-objective optimization has decreased.

[0255] As the speed and complexity of industrial operations continue to increase, the lack of effectiveness of traditional single-objective optimizers may become a problem to be solved in industry. According to this disclosure, a dynamic solution to this situation may include controlling higher-level variables in a balanced manner, which will enable the simultaneous control of multiple objectives and constraints to continuously drive optimal results. Visualization of these objectives can be achieved through… Figure 19A and 19B This is achieved using radar charts. Radar charts allow everyone involved in the operation to see the balance between different dynamic goals and constraints, thus driving each control strategy in a continuously optimal, manually managed manner.

[0256] Figure 19A A visualization of the current and optimal metrics for a hierarchical asset control application with multiple constraints and objectives is shown. In other embodiments, this could be a visualization as part of a human-machine interface, which could also facilitate the verification, simulation, and / or other operator actions. In this example, while the profit objective is at or near optimal, the efficiency objective, as well as the reliability and safety constraints, are suboptimal. Furthermore, the environmental constraints are overoptimal, potentially exhibiting efficiency losses or even exceeding operational boundaries.

[0257] Figure 19B A visualization of the current and optimal metrics for a hierarchical asset control application with multiple constraints and objectives is shown. In other embodiments, this could be a visualization as part of a human-machine interface, which can also facilitate verification, simulation, and / or other operator actions. In this example, the profit objective is close to process limits, similar to environmental risk constraints, but safety and reliability risk constraints are both within process limits. This visualization can provide operators of the hierarchical asset control application with immediate feedback on how the entire system is performing relative to the objectives and constraints.

[0258] In some embodiments, automatic balancing control with extended control variables can be implemented by gaining experience from manual balancing control over time. Automatic balancing control can be achieved using an expert system that simulates an expert operator balancing different control variables. Such an expert system can appropriately weigh each control variable according to the operational objectives and evaluate the error between the actual and desired values ​​in each control domain to determine the actions the control system must take to achieve automatic balancing.

[0259] The real-time measurements defined earlier can provide data points for the current situation. Ideally, these can be statically calculated for assets and used as targets for display, or as a reference. Figure 13 The higher-level business profitability control function module 1300 describes dynamic calculations. In any case, the operating business can be under real-time control, thereby achieving real-time optimization results.

[0260] The standardization of constraints such as safety, reliability, and environment can be represented by the following relationship:

[0261] Standardized constraint = 1 - [(constraint limit - actual value) / constraint limit]

[0262] The standardization of real-time operating profit can be represented by the following relationship:

[0263] Standardized operating profit = 1 - [(optimal operating profit - current operating profit) / optimal operating profit]

[0264] It should be understood that although the above example describes real-time operating profit in detail as the target variable for optimization, any other target variable can be used for the real-time operating optimization unit or parameter.

[0265] Figure 19C The intersection of dynamic constraints according to various embodiments of this disclosure is shown to form the operational boundaries and optimization points of a hierarchical asset control application.

[0266] In order to determine such Figure 19A and 19B The constrained process boundaries, the hierarchical asset control application area 1940, and the optimization point 1950 for real-time operating profit shown are constructed using standardized constraints such as environment 1910, safety 1920, and reliability 1930. It should be understood that these constraints are inherently dynamic, thus causing the hierarchical asset control application 1940 and the optimization point 1950 for real-time operating profit to be inherently dynamic as well.

[0267] Figure 18AThis describes the standardized functional blocks responsible for various constraints and objectives. It evaluates standardized input data 1880, operational profitability input 1883, safety and environmental risk constraint data 1884, and reliability risk constraint data 1885. Any target and / or constraint data 1882 that can be stored or retrieved can be accessed. The notification module 1886 will provide any notifications to the user or system; control analysis 1887 and workflow triggers 1888 will be performed as part of the analysis.

[0268] The standardization process can begin by receiving operational reliability standardization information 1890 and standardization constraint information 1891. Constraints and limitations on the hierarchical asset control application 1892 and the difference from the current operating value to the optimal value 1893 will be determined. The current and future operational setpoints / thresholds and / or states will be evaluated to determine necessary improvements 1894.

[0269] Once determined, in step 1895, the action rules are applied to further improvements from the current state to the optimal state. In step 1896, any necessary workflows are triggered, and in step 1897, notifications are provided to the relevant users or systems. Finally, in step 1898, the new target measurement value is obtained.

[0270] From this standardized analysis of constraints and objectives, we can understand the real-time messages (with some issues) of the hierarchical asset control application, such as: the application's current operation (by... Figure 19B (indicated by the dashed line in the text); related process limitations (by the application, associated with the process). Figure 19B (The solid line in the middle represents the area); the operating area is applied at 1940 and the optimization point for real-time operating profit is 1950.

[0271] Based on the real-time requirements of defining constraints and objectives and realizing changes to potentially large-scale hierarchical asset control applications, a hierarchical approach is described to allow robust monitoring and processing of assets deployed in the application, thereby deriving real-time objectives and constraints. Furthermore, to enable control that improves the application from its current state to its optimal state, asset control is also deployed in a hierarchical manner.

[0272] Figure 20A It's a block diagram. Figure 20B The logical flow of risk constraint communication from asset to collection, and from collection to unit (or other cross-smart asset group communication) in a hierarchical asset control application associated with an integrated smart asset control system, according to various embodiments of this disclosure, is illustrated. Depending on the nature of the implementation, smart assets can be grouped and controlled according to any number of configurations suitable for a particular application. For example, asset groups can be formed by multiple layers across the smart asset hierarchy.

[0273] Risk constraints are defined at the hierarchical intelligent asset layer (as discussed above in this application). Intelligent assets include devices or groups of devices that function according to the hierarchical asset control applications and control hardware that constitute the integrated intelligent asset control system. One example could be a reactor vessel and associated sensors, such as temperature sensors. Figure 20A Multiple smart assets 2020, 2022, 2024, and 2026 are shown. These smart assets will independently determine any constraints associated with them using a smart agent. It should be understood that which constraints are associated with which smart asset is based on the characteristics and application of the smart asset. In step 2030, each of these smart assets 2020, 2022, 2024, and 2026 is processed at the asset level of the hierarchical structure. This determines which constraints apply to each smart asset in step 2032.

[0274] Once the constraints are determined, in step 2034, each asset transmits data to its parent asset at the collection layer. The communication paths 2021, 2023, 2025, and 2027 at the asset level and the parent / child relationships are established beforehand when the integrated smart asset control system is created. In step 2036, verification is performed to ensure that all smart assets are processed at the asset layer.

[0275] Once completed, the smart assets 2010 and 2014 in the collection layer are processed in step 2038 to determine their respective constraints in step 2040. Next, in step 2042, these collection smart assets send data from each collection asset 2010 and 2014 to their parent asset in the unit layer 2000 via their respective communication paths 2011 and 2015. Similar to the asset layer, verification is performed in step 2044 according to one implementation to ensure that all smart assets have been processed in the collection layer. It should be understood that other implementations are possible, and this type of functionality can be used to integrate subsets of the smart asset control system.

[0276] Finally, in step 2046, all smart assets 2000 at the unit layer are processed. Preparation is underway to develop controls to correlate their applications with the already obtained real-time operational revenue points.

[0277] Figure 21A It's a block diagram. Figure 21B This is a logic flowchart illustrating the control communication structure from unit to collection and from collection to asset in a hierarchical asset control application according to various disclosed embodiments.

[0278] To determine the operating or setpoint constraint parameters, all unit-level smart assets 2100 are processed in step 2150 to determine the operating setpoints or constraint parameters of the sub-assets in the aggregation layer in step 2152. Upon completion, in step 2154, these unit-level smart assets 2100 transmit their data via their respective communication paths 2111, 2115 to their respective sub-assets 2110, 2114 in the aggregation layer. Verification is performed in step 2156 to ensure that all smart assets have been processed at the unit level. It should be understood that other implementations are possible and this type of functionality can be used to integrate subsets of a smart asset control system.

[0279] Once completed, step 2158 processes the smart assets located at aggregation layers 2110 and 2114 to determine the operation or setpoint constraint parameters of their respective sub-smart assets in step 2160. Next, in step 2162, these aggregation smart assets send their data from each aggregation asset 2110 and 2114 via their respective communication paths 2121, 2113, 2125, and 2127 to their parent assets at asset layers 2120, 2122, 2124, and 2126. Similar to the aggregation layers, step 2164 performs verification to ensure that all smart assets have been processed at the aggregation layers. Finally, in step 2166, when all smart assets in asset layers 2120, 2122, 2124, and 2126 have been processed, operation or setpoint constraint parameters are set in each layer of the hierarchical asset control application to operate at the real-time operating revenue setpoint.

[0280] Small and large data analytics

[0281] In some embodiments, the asset control system may include an analytics component or engine for information management and analysis to continuously optimize the performance of industrial operations over time. The analytics engine can operate in parallel with the real-time control system, but without real-time limitations. Figure 22This is a diagram depicting an analytical view of an asset control system according to some embodiments of the present disclosure. As shown, each CPS associated with an asset can utilize a network connection to report data (e.g., process history, measurement data, actions taken) generated and / or received by intelligent agents in the CPS directly or indirectly to the big data analytics engine 2205. For example, in some cases, master asset CPSs can report data directly to the big data analytics engine 2205. In other cases, a unit / work unit asset set intelligent agent can collect data from the main intelligent asset it is responsible for and report the data to a higher-level asset set or the big data analytics engine 2205. The big data analytics engine 2205 can collect and process the reported data to extract instructions that can be used to optimize the aforementioned operational efficiency and / or other control functions. In some embodiments, each intelligent agent associated with an intelligent asset or intelligent asset set or other intelligent asset group may include a small data analytics engine (e.g., Figure 3B The small data analytics engine 370 can locally collect and analyze data associated with assets or asset sets, and report the results directly or through a more advanced intelligent agent to the big data analytics engine 2205.

[0282] 4. Example processing

[0283] Figure 23 These are examples of industrial processes according to various embodiments of this disclosure. Exemplary exothermic reactor units are used in patent applications and other design documents.

[0284] The complete reactor unit 2300 includes a reactor vessel 2310 and multiple sensors and valve positioners. A pressure indicator 2310PI, a temperature indicator 2310TI, and a level indicator 2310LI provide measurements within the reactor vessel. A reactor cooling jacket 2311 with an associated flow valve 2311FV controls the flow rate of cooling water entering the reactor cooling jacket 2311. The valve positioner 2311FV controls the flow rate of warm water from the reactor cooling jacket 2311 to the plant effluent, and is monitored by flow indicator sensor 2315FI and temperature indicator 2315TI to provide measurements of the temperature and flow rate of the effluent leaving the reactor unit 2310.

[0285] Reactor stirrer 2312 agitates the reagents to ensure complete reaction. Product C has an associated pump 2313 and flow valve 2313FV. Emergency reaction quench tank 2314 and associated flow valve 2314FV can be used to stop the curing of the reactants and render reaction vessel 2310 unusable.

[0286] Heat exchanger 2320, used to control the temperature of cooling water pumped into reactor jacket 2311, is associated with a series of sensors and valve positioners. Temperature indicator 2320TI, flow indicator 2320FI, and valve positioner 2320FV measure and maintain the cooling water supplied to heat exchanger 2320. Freshwater pump 2340 pumps fresh water into the heat exchanger. This fresh water is pumped into reactor jacket 2311 to remove heat from reactor vessel 2310. The valve positioner associated with freshwater pump 2340 controls the flow rate of cooling water entering heat exchanger 2320.

[0287] Pump 2350, designated "Reagent A," pumps the reagent material into reactor vessel 2310. Associated with the pump are flow sensor 2350FI and valve positioner 2350FV. A similar structure exists for "Reagent B," where pump 2360 pumps the reagent into reactor vessel 2310. Associated with the pump are flow sensor 2360FI and valve positioner 2350FV.

[0288] When the pressure inside reactor vessel 2310 exceeds the limit, pressure relief valve 2370 opens its vent. The pressure is released into the flue gas field, which is open to the external environment.

[0289] In this embodiment, the supply of common water is used from the cooler and reservoir 2830 to cool it to the desired temperature, and pump 2832 pumps it into the reactor cooling jacket 2311. Furthermore, under abnormally high temperature conditions, it can be pumped directly into the reactor cooling jacket 2311 to rapidly slow down or stop the reaction while maintaining the reactor vessel. This cooling water can also be added directly to the effluent to reduce BTU release into the environment.

[0290] Figure 24 These are example diagrams of industrial processes according to various embodiments of this disclosure. Exemplary exothermic reactor units are used in patent applications and other design documents. As one of many examples, the exothermic reactor 2310, reactor jacket 2311, temperature indicators 2310TI and 2310LI include devices comprising a single asset called a reactor asset. Other smart assets such as… Figure 23 The illustrated configuration exists and has been identified and characterized as part of an integrated intelligent asset control system, which, for illustrative purposes, includes, for example, cooler assets 2830, heat exchanger assets 2320, reagents A 2350 and B 2360, product C 2313, emergency quench 2314, stirrer 2312, and effluent assets 2315. For the purposes of the following examples shown in the figures, it should be understood that the features and functions are derived from an operable integrated intelligent asset control system developed from a hierarchical asset control application and for purposes related to… Figure 23 The discussion focuses on the development of corresponding control hardware to implement and execute the underlying device components.

[0291] As part of the ongoing real-time optimization of the entire hierarchical asset control application, constraints on Safety 2410, Reliability 2420, and Environment 2430 are determined for each individual asset. The parameters for each asset constraint are derived by the user or system as described above and are specific to the assets, applications, and devices that comprise real-time and historical data. As one of many possible examples, an environmental constraint for water output emissions might require a temperature range of 35 to 65 degrees Celsius and a maximum flow rate of 5 gallons per minute, according to EPA guidelines. All constraints are derived at the asset level for reactor assets and all other smart assets in the application. Figure 20A and 20B As detailed in the description, each individual asset constraint set is exported and transferred to its parent asset, up to the unit-level asset.

[0292] Input the optimal target point into the system. An example could be producing 10,000 pounds of product C per day using continuous operation. For this example, the objective to be optimized is profit, which can be calculated given various parameters, such as parameters concerning operating costs and the profit per pound of product C.

[0293] In addition to the objective to be optimized, constraints on the optimal value are also input into the system. The current operating point and current constraints have now been derived. For each known constraint and objective in the system, standardization of all constraints and objectives is necessary to perform optimization. Once this standardization is complete, the region of application operation will be known, and the optimization point of the application at the specific time point from which the application is measured will also be known.

[0294] The optimal result can be derived by using the current state and the best state that is known now. Figure 25 The diagram illustrates various examples of optimizations for determining hierarchical industrial asset sets according to various embodiments of the present disclosure.

[0295] Once all constraints have been exported and entered into the hierarchical asset control application 2510 based on real-time monitoring of the entire application, and as... Figure 18A and 18B The standardized 2520 described in the paper means that the current state, the process operating area, and the optimal state are known, and the optimization of 2530 given constraints and objectives can be derived.

[0296] Figure 26This diagram illustrates various examples of adjusting a tiered industrial asset set to achieve an application change to an optimal operating point. In this example, the derived optimizations may include: introducing cooling water from cooler 2830 into reactor jacket 2311 when the temperature in the exothermic reactor becomes too high to meet environmental constraints, based on temperature indicator 2310TI. Furthermore, agitator 2312 may be activated on reactor jacket 2311 via flow valve 2311FV for effluent purification to further ensure the effluent temperature remains within constraints. Finally, to meet gallons per minute requirements, temperature 2311TI and flow rate 2311FI will be monitored, and effluent will be shut off as needed via valve 2322FV.

[0297] The optimized characteristic 2610 has been derived and transferred to smart assets, smart asset groups, or other smart asset groups. Control optimization from the above example to the integrated smart asset control system 2620 could include pumping cooling water 2385 at a rate of 5 gallons per minute while simultaneously opening reactor jacket flow valve 2311FV to allow warmer reaction jacket water to be purified through the associated flow valve 2115FV. This optimization could, for example, be allowed to continue for a period of time and validated 2630 through real-time analysis of the complete integrated smart asset control system.

[0298] In this way, as contemplated in the embodiments of this disclosure, the integrated intelligent asset control system can operate and optimize in real time for any selected set of objectives and constraints, while industrial process operators, enterprises, or evolving industries determine their benefits.

[0299] 5. Example features or aspects of an asset control system

[0300] The following provides various example features or aspects of an asset control system according to some embodiments of this disclosure.

[0301] In some embodiments, the asset control system provides a fully automatic or semi-automatic control system for each asset within the plant.

[0302] In some embodiments, the lower-level equipment asset control system is pre-configured by the equipment supplier.

[0303] In some embodiments, the automatic control system of a system configured as a lower-level avatar or intelligent agent automatically connects to and transforms into a higher-level avatar.

[0304] In some embodiments, the configuration is simplified to each level, which only involves the specific functionality of that level.

[0305] In some embodiments, the scope extends from real-time process control for efficiency improvement to real-time process control for efficiency, reliability, safety risk, environmental risk, security risk, and profitability improvement.

[0306] In some embodiments, the system is enforced at each upper-level avatar or smart agent. In some embodiments, each smart agent provides a complete complement of the functionality associated with an asset or asset set layer, which is associated with the smart agent, including but not limited to: monitoring, context analysis, asset performance control, asset optimization, asset security, and / or asset history (operation, maintenance, performance).

[0307] In some embodiments, dynamic business processes (profitability, etc.) are handled in the same way as dynamic physical processes.

[0308] In some embodiments, the control granularity is reduced to 1, and this single-cycle granularity provides unprecedented scalability.

[0309] In some embodiments, the asset control system has single-loop integrity, and any failure requires only a single-loop backup.

[0310] In some embodiments, the power demand is met by an energy harvester, which may be a component of a smart asset or a cybersecurity system (CPS). The energy harvester can extract energy from the surrounding environment, such as vibrations.

[0311] In some embodiments, the asset control system is self-identifying and configurable. In the asset control system, the equipment vendor may provide a CPS and associated smart agent for each equipment asset. When the smart agent connects to the larger asset control system, it provides a logically sound and unique smart agent identifier for authentication with the system.

[0312] In some embodiments, control and asset management are embedded in the actual assets, rather than being the typical “fixed” component of traditional process control.

[0313] In some embodiments, the physical process equipment or intelligent asset is modeled itself. There is no artificial equipment model required to simulate the intelligent asset. The intelligent asset is modeled itself.

[0314] In some embodiments, the asset becomes generally "controllable" as a process asset. In some embodiments, the necessary sensing / measurement is combined with control, output / actuation, and asset management in the same resources within the asset itself.

[0315] In some embodiments, control and asset management are “clusters” surrounding naturally occurring clusters of process equipment (in the “fog” of public communications).

[0316] In some embodiments, the equipment supplier provides control algorithms and asset performance management for its intelligent equipment assets. Intelligence can be provided to dummy process equipment or intelligent assets to control themselves and monitor their own operational status. This can be applied to dummy process equipment or intelligent assets as simple as the length of a pipe. In some embodiments, this intelligence can be provided to any asset using the techniques disclosed in this disclosure.

[0317] In some embodiments, the "control" is sold with the device itself, rather than as an "add-on".

[0318] In some embodiments, at least the smart agent can be downloaded to the smart asset later. In some embodiments, asset performance intelligence is provided by the vendor.

[0319] In some embodiments, cabling for connection and networking is completely eliminated.

[0320] In some embodiments, the definition of “control” is extended to operational control, just like a control loop, which, in addition to controlling business value performance metrics, can also set and satisfy financial forecast metrics, rather than just being a process parameter.

[0321] In some embodiments, control is assigned to devices or assets and powered by harvested electricity, communicating over a wireless cloud within a combined mesh, and operating within a unified system framework.

[0322] In some embodiments, control is no longer "stuck." It is the origin, part of the process.

[0323] In some embodiments, the factory models itself and its controls represent the actual factory.

[0324] In some embodiments, the asset control system provides maximum reliability and resilience at the lowest possible cost.

[0325] In some embodiments, control extends from the process to the business.

[0326] In some embodiments, the Internet of Things (IoT) is applied to industrial process control because of its functionality, not just connectivity.

[0327] 6. Computerization

[0328] Figure 27 A diagram illustrating an example form of a computer system is shown, within which a set of instructions can be executed to cause the machine to perform any or more of the methods discussed herein. Figure 27In the example, computer system 2700 includes a processor, main memory, non-volatile memory, and interface devices. For simplicity of illustration, various general-purpose components (such as cache memory) are omitted. Computer system 2700 is intended to illustrate a hardware device on which any of the components and methods described in this disclosure can be implemented. For example, respectively in Figure 14A , 15A The processor units 1400, 1500, 1600, 1700, and 1800 depicted in 16A, 17A, and 18A can be hardware devices, such as processors or computer systems capable of performing arithmetic, computation, processing, etc., to perform the tasks described herein. The computer system 2700 can be of any applicable, known, or convenient type. The components of the computer system 2700 can be coupled together via a bus or through some other known or convenient device.

[0329] The processor can be, for example, a conventional microprocessor, such as an Intel Pentium microprocessor or a Motorola Power PC microprocessor. Those skilled in the art will recognize that the terms "machine-readable (storage) medium" or "computer-readable (storage) medium" encompass any type of device accessible to the processor.

[0330] The memory is coupled to the processor via, for example, a bus. The memory may include, but is not limited to, random access memory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). The memory may be local, remote, or distributed.

[0331] The bus also couples the processor to non-volatile memory and drive units. Non-volatile memory is typically a magnetic floppy disk or hard disk, magneto-optical disk, optical disk, read-only memory (ROM) such as CD-ROM, EPROM or EEPROM, magnetic cards or optical cards, or other forms of memory used for large amounts of data. During the execution of software in the computer 2800, some of this data is typically written to memory via direct memory access procedures. Non-volatile memory can be local, remote, or distributed. Non-volatile memory is optional because the system can be created using all applicable data available in the memory. A typical computer system typically includes at least a processor, memory, and devices that couple the memory to the processor (such as a bus).

[0332] Software is typically stored in non-volatile memory and / or drive units. In practice, for large programs, it is not even possible to store the entire program in memory. However, it should be understood that for running software, it is moved to a computer-readable location suitable for processing, and for illustrative purposes, this location is referred to herein as memory. Even when software is moved to memory for execution, the processor will typically utilize hardware registers to store values ​​associated with the software and local caches. Ideally, this helps to speed up execution. As used herein, when a software program is referred to as “implemented in a computer-readable medium,” it is assumed that the software program is stored in any known or convenient location (from non-volatile memory to hardware registers). A processor is considered “configured to execute the program” when at least one value associated with the program is stored in a processor-readable register.

[0333] The bus also couples the processor to network interface devices. Interfaces may include one or more modems or network interfaces. It is understood that a modem or network interface can be considered part of a computer system. Interfaces may include analog modems, ISDN modems, cable modems, token ring interfaces, satellite transmission interfaces (e.g., "direct PC"), or other interfaces used to couple a computer system to other computer systems. Interfaces may include one or more input and / or output devices. By way of example, but not limited to, I / O devices may include keyboards, mice or other pointing devices, disk drives, printers, scanners, and other input and / or output devices, including display devices. Display devices may include, for example, but not limited to, cathode ray tube (CRT), liquid crystal displays (LCDs), or some other known or convenient display devices. For simplicity, it is assumed that the controller of any device not shown resides in its respective interface.

[0334] In operation, the computer system 2700 can be controlled by operating system software, including a file management system (e.g., a disk operating system). An example of operating system software with associated file management system software is the Windows operating system family and its associated file management system from Microsoft Corporation of Redmond, Washington. Another example of operating system software and its associated file management system software is the Linux operating system and its associated file management system. The file management system is typically stored in non-volatile memory and / or drive units and enables the processor to perform various actions required for the operating system to input and output data and store data in memory, including storing files on non-volatile memory and / or drive units.

[0335] Some parts of the detailed description can be presented based on the algorithms and symbolic representations of operations on data bits within computer memory. These algorithmic descriptions and representations are the means by which those skilled in the art of data processing most effectively communicate the essence of their work to others skilled in the art. An algorithm here is generally considered to be a self-consistent sequence of operations that leads to a desired result. These operations are those that require physical manipulation of physical quantities. Typically, but not necessarily, these quantities take the form of electrical or magnetic signals that can be stored, transmitted, combined, compared, and otherwise manipulated. Primarily for common use, it is sometimes convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc.

[0336] However, it should be remembered that all these and similar terms will be associated with appropriate physical quantities and are merely convenient labels applied to those quantities. Unless specifically stated in the discussion below, it should be understood that throughout the description, the use of terms such as “processing” or “operation” or “calculation” or “determining” or “displaying” refers to the actions and processes of a computer system or similar electronic computing device that manipulate and convert data representing physical (electronic) quantities within the registers and memories of the computer system into other data similarly represented as physical quantities within the computer system's memory or registers or other such information storage, transmission, or display devices.

[0337] The algorithms and displays presented herein are not inherently related to any particular computer or other device. Various general-purpose systems can be used with the programs taught herein, or it may prove convenient to construct more specialized devices to perform some of the embodiments. The following description will show the necessary structures for various such systems. Furthermore, these techniques are described without reference to any particular programming language, so various embodiments can be implemented using various programming languages.

[0338] In alternative embodiments, the machine operates as a standalone device or can be connected (e.g., networked) to other machines. In a networked deployment, the machine can operate at the capacity of a server or client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.

[0339] The machine can be a server computer, client computer, personal computer (PC), tablet PC, laptop computer, set-top box (STB), personal digital assistant (PDA), cellular phone, iPhone, Blackberry, processor, telephone, web device, network router, switch or bridge, or any machine capable of executing a set of instructions (sequence or otherwise) that specifies the action to be taken by the machine.

[0340] Although in exemplary embodiments a machine-readable medium or machine-readable storage medium is shown as a single medium, the terms "machine-readable medium" and "machine-readable storage medium" should be understood to include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) that store a set or more sets of instructions. The terms "machine-readable medium" and "machine-readable storage medium" should also be understood to include any medium capable of storing, encoding, or carrying a set of instructions for execution by a machine and causing the machine to perform any one or more methods of currently disclosed technologies and innovations.

[0341] Generally, routines executed to implement embodiments of the present disclosure may be implemented as part of an operating system or a particular application, component, program, object, module, or sequence of instructions referred to as a "computer program." A computer program typically includes one or more instructions set at various times in various memories and storage devices in a computer, and when read and executed by one or more processing units or processors in the computer, causes the computer to perform operations to execute elements relating to various aspects of the present disclosure.

[0342] Furthermore, although embodiments have been described in the context of full-featured computers and computer systems, those skilled in the art will understand that various embodiments can be distributed as program products in various forms, and this disclosure applies equally to any particular type of machine or computer-readable medium used for the actual implementation of the distribution.

[0343] Other examples of machine-readable storage media, machine-readable media, or computer-readable (storage) media include, but are not limited to, recordable media such as volatile and non-volatile storage devices, floppy disks and other removable disks, hard disks, optical disks (e.g., optical disk read-only memories (CD ROMs), digital universal disks (DVDs), etc.), and transmission media such as digital and analog communication links.

[0344] Unless the context explicitly requires otherwise, throughout the specification and claims, the words “comprising,” “including,” etc., shall be understood as inclusive, not exclusive or exhaustive; that is, in the sense of “including but not limited to.” As used herein, the terms “connected,” “coupled,” or any variation thereof mean any direct or indirect connection or coupling between two or more elements; the connection or coupling between elements may be physical, logical, or a combination thereof. Furthermore, when used in this application, the words “here,” “above,” “below,” and similar terms shall be used as a whole and not to refer to any particular part of this application. Where the context permits, the words used in the above detailed description in singular or plural form may also include either the plural or the singular, respectively. The word “or” refers to a list of two or more items, encompassing all of the following interpretations of the word: any item in the list, all items in the list, and any combination of items in the list.

[0345] The above detailed description of embodiments of this disclosure is not intended to be exhaustive or to limit the teachings to the precise forms disclosed herein. While specific embodiments and examples of the invention have been described above for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as will be recognized by those skilled in the art. For example, although processes or blocks are presented in a given order, alternative embodiments may execute routines with steps, or employ a system of blocks with a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and / or modified to provide alternatives or sub-combinations. Each of these processes or blocks may be implemented in a variety of different ways. Furthermore, while processes or blocks are sometimes shown as being executed in series, these processes or blocks may alternatively be executed in parallel, or may be executed at different times. Moreover, any specific figures mentioned herein are merely examples: alternative implementations may employ different values ​​or ranges.

[0346] The teachings provided herein can be applied to other systems, not just those described above. Elements and actions of the various embodiments described above can be combined to provide further embodiments.

[0347] Any patents and applications mentioned above, as well as other references, including any references that may be listed in the accompanying filings, are incorporated herein by reference. If necessary, aspects of this disclosure may be modified to provide yet another embodiment of this disclosure using the systems, functions, and concepts of the various references described above.

[0348] Based on the detailed description above, these and other changes can be made to this disclosure. Although the foregoing description describes certain embodiments of this disclosure and depicts the expected best mode, this teaching can be practiced in a variety of ways, however detailed it may appear in the text. The details of a system can vary considerably in their implementation details, while still remaining covered by the subject matter disclosed herein. As noted above, specific terms used when describing certain features or aspects of this disclosure should not be construed as implying that such terms are hereby redefined as limited to any particular characteristic, feature, or aspect of the disclosure associated with that term. In general, unless such terms are expressly defined in the detailed description section above, the terms used in the following claims should not be construed as limiting the disclosure to the specific embodiments disclosed in the specification. Therefore, the actual scope of this disclosure includes not only the disclosed embodiments but also all equivalent ways of practicing or implementing this disclosure under the claims.

[0349] From the foregoing description, it should be understood that specific embodiments have been described herein for illustrative purposes, but various modifications may be made without departing from the spirit and scope of the embodiments. Therefore, this disclosure is not limited except for the appended claims.

Claims

1. A method for developing a hierarchical asset control application process executed by a controller, comprising: Access the device list; Determine the list of input devices and their associated parameters; Select a smart asset template from the smart asset template library to instantiate a smart agent for the device components contained in the input device list; Instances of intelligent assets are created for the selected device components, which are controlled automatically or semi-automatically by a cyber-physical system (CPS) with the intelligent agent. Fill the selected template with the operational constraints and operational objectives of the selected device components, wherein the operational constraints include dynamic constraints; By connecting each instantiated smart agent based on parent / child information, an associated hierarchical layout of asset control relationships is developed for hierarchical asset control applications. Perform communication / control path verification for the hierarchical asset control application. The corresponding control hardware requirements are based on the developed hierarchical asset control application, and The developed hierarchical asset control application and corresponding control hardware are required to be integrated with the device components to create an integrated intelligent asset control system.

2. The method of claim 1, wherein the selected smart asset template being populated includes smart agent instantiation information.

3. The method of claim 1, wherein a simulation is performed on the hierarchical asset control application to determine anomalies during operation.

4. The method according to claim 1, wherein the integrated intelligent asset control system includes more than one intelligent asset control level.

5. The method of claim 1 further includes integrating one or more smart asset templates to instantiate a smart agent for merging with the smart assets.

6. The method of claim 1, further comprising instantiating a smart asset template for a smart asset group.

7. The method of claim 1, wherein the smart asset template is configured to include application-specific data.

8. The method of claim 1 further includes determining the asset operation library type and industry-specific hierarchical control application default requirements.

9. The method of claim 1, wherein the smart asset application is developed as a smart agent for a specific device component control model.

10. The method of claim 1, wherein the smart asset template includes data parameters, the data parameters including suggested assets interconnected with assets, operational constraints, operational objectives, high availability / critical parameters, or industry-specific industrial applications.

11. The method of claim 10, wherein the smart asset template includes specific model information of the supplier's equipment.

12. The method according to claim 10, wherein, The smart asset template includes operation parameters from the general device type model.

13. The method of claim 1, further comprising determining operational constraint parameters including reliability, environmental or safety.

14. The method of claim 1, further comprising determining operational target parameters including energy costs, material costs, product value, or profitability.

15. The method according to claim 1, further comprising determining an operating efficiency parameter.

16. The method of claim 1, wherein the connection includes grouping related smart assets into smart asset groups, the smart asset groups defining parent / child control relationships between smart assets.

17. The method of claim 3, wherein the simulation involves generating virtualized device component data and executing process control components.

18. A system for developing a hierarchical asset control application process, comprising: Access the device list using the processor; The processor is used to determine the list of input devices and associated parameters; The processor selects a smart asset template from the smart asset template library to instantiate a smart agent for a device element contained in the input device list; An instance of using the processor to create intelligent assets for selected device components, the intelligent assets being automatically or semi-automatically controlled by a cyber-physical system (CPS) with the intelligent agent; Using the processor, a selected template is populated with the operational constraints and operational objectives of the selected device elements, wherein the operational constraints include dynamic constraints; The processor is used to develop an associated hierarchical arrangement of asset control relationships for hierarchical asset control applications by connecting each instantiated smart agent based on parent / child information; The processor is used to perform communication / control path verification for the hierarchical asset control application. The corresponding control hardware requirements are based on the developed hierarchical asset control application, and The developed hierarchical asset control application and corresponding control hardware are required to be integrated with the device components to create an integrated intelligent asset control system.

19. The system of claim 18, wherein the selected smart asset template being populated includes smart agent instantiation information.

20. The system of claim 18, wherein a simulation is performed on the hierarchical asset control application to determine anomalies during operation.

21. The system of claim 18, wherein the integrated intelligent asset control system includes more than one intelligent asset control level.

22. The system of claim 18 further includes integrating one or more smart asset templates to instantiate a smart agent for incorporating smart assets.

23. The system of claim 18, further comprising instantiating smart asset templates for smart asset grouping.

24. The system of claim 18, wherein the smart asset template is configured to include application-specific data.

25. The system of claim 18 further includes determining the asset operation library type and industry-specific hierarchical control application default requirements.

26. The system of claim 18, wherein the intelligent asset application is developed for an intelligent agent of a specific device component control model.

27. The system of claim 18, wherein the smart asset template includes data parameters, the data parameters including suggested assets interconnected with assets, operational constraints, operational objectives, high availability / critical parameters, or industry-specific industrial applications.

28. The system of claim 27, wherein the smart asset template includes supplier equipment-specific model information.

29. The system of claim 27, wherein, The smart asset template includes operation parameters from the general device type model.

30. The system of claim 18, further comprising determining operational constraint parameters including reliability, environmental, or safety.

31. The system of claim 18 further includes determining operational target parameters, including energy costs, material costs, production value, or profitability.

32. The system of claim 18 further includes determining efficiency parameters of the operation.

33. The system of claim 18, wherein the connection includes grouping related smart assets into smart asset groups and defining parent / child control relationships between the smart asset groups.

34. The system of claim 20, wherein the simulation involves generating virtualized device component data and executing process control components.

35. A method for developing a hierarchical asset control application executed by a controller, comprising: Access the device list; Select a smart asset template from the smart asset template library to instantiate an asset application model for the device components contained in the input device list; Fill the selected template with the operational constraints and operational objectives of the selected device components, wherein the operational constraints include dynamic constraints; By connecting each instantiated asset application model based on parent / child information, an associated hierarchical arrangement of asset control relationships is developed for hierarchical asset control applications. Perform communication / control path verification for the hierarchical asset control application. The selected smart asset template that is populated includes smart agent instantiation information. The corresponding control hardware requirements are based on the developed hierarchical asset control application, and The developed hierarchical asset control application and corresponding control hardware are required to be integrated with the device components to create an integrated intelligent asset control system.

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