CN117250915A

CN117250915A - Apparatus and method for computing capital capacity using model predictive control and/or industrial process optimization

Info

Publication number: CN117250915A
Application number: CN202310714172.4A
Authority: CN
Inventors: P·诺德; J·卢; 吴燕玲
Original assignee: Honeywell International Inc
Current assignee: Honeywell International Inc
Priority date: 2022-06-17
Filing date: 2023-06-16
Publication date: 2023-12-19

Abstract

Various embodiments described herein relate to computing capital capacity using model predictive control and/or industrial process optimization. In this regard, an optimization request is received for an industrial process that optimizes production of an industrial process product. Asset modeling data is obtained from one or more asset performance management systems for assets associated with the industrial process in response to the optimization request. One or more real-time operational limits for the industrial process are also adjusted based at least in part on the asset modeling data obtained from the one or more asset performance management systems in response to the optimization request. Further, the control signals configured based at least in part on the one or more real-time operational constraints are transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

Description

Apparatus and method for computing capital capacity using model predictive control and/or industrial process optimization

Cross Reference to Related Applications

The present application claims the benefit of U.S. provisional patent application No. 63/353,144, entitled "APPARATUS AND METHOD FOR CALCULATING ASSET CAPABILITY USING MODEL PREDICTIVE CONTROL AND/OR INDUSTRIAL PROCESS OPTIMIZATION," filed on 6/17 of 2022, the entire contents of which are incorporated herein by reference.

Technical Field

The present disclosure relates generally to industrial process control systems, and more particularly to industrial process control systems for industrial process optimization and/or industrial process simulation.

Background

Industrial facilities are typically managed using industrial process control systems. Industrial processes for industrial assets, such as, for example, oil and gas processes, are typically controlled using fixed operating limits according to fixed equipment specifications and/or fixed operating procedures. However, industrial assets typically operate in a dynamic manner. Furthermore, if fixed operating limits for an industrial asset are not accurately configured, the industrial asset may operate in an inefficient manner. In addition, certain types of industrial processes may be connected to one or more other industrial processes in an industrial facility. As such, certain types of industrial processes may disrupt one or more other industrial processes associated with an industrial facility such that the industrial facility and/or one or more industrial assets in the industrial facility operate in an inefficient and/or undesirable manner.

Disclosure of Invention

The details of some embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.

In one embodiment, a system includes one or more processors, memory, and one or more programs stored in the memory. The one or more programs include instructions configured to receive an optimization request for an industrial process that optimizes production of an industrial process product. In one or more embodiments, in response to the optimization request for the industrial process, the one or more programs additionally or alternatively include instructions configured to obtain asset modeling data from one or more asset performance management systems for assets associated with the industrial process. In one or more embodiments, in response to the optimization request for the industrial process, the one or more programs additionally or alternatively include instructions configured to adjust one or more real-time operational limits for the industrial process based at least in part on the asset modeling data obtained from the one or more asset performance management systems. In one or more embodiments, in response to the optimization request for the industrial process, the one or more programs additionally or alternatively include instructions configured to transmit control signals configured based at least in part on the one or more real-time operational constraints to a controller configured for optimization associated with the industrial process producing the industrial process product.

In another embodiment, a method comprises: at a device having one or more processors and memory, an optimization request is received for an industrial process that optimizes production of an industrial process product. In one or more embodiments, in response to the optimization request for the industrial process, the method additionally or alternatively includes: obtaining asset modeling data from one or more asset performance management systems for assets associated with the industrial process; adjusting one or more real-time operational limits for the industrial process based at least in part on the asset modeling data obtained from the one or more asset performance management systems; and/or transmitting control signals configured based at least in part on the one or more real-time operational constraints to a controller configured for optimization associated with the industrial process producing the industrial process product.

In yet another embodiment, a computer program product includes at least one computer-readable storage medium having program instructions embodied thereon. The program instructions are executable by the processor to cause the processor to receive an optimization request for optimizing an industrial process that produces an industrial process product. In one or more embodiments, in response to the optimization request for the industrial process, the program instructions are additionally or alternatively executable by the processor to cause the processor to obtain asset modeling data from one or more asset performance management systems for assets associated with the industrial process. In one or more embodiments, in response to the optimization request for the industrial process, the program instructions are alternatively or additionally executable by the processor to cause the processor to adjust one or more real-time operational limits for the industrial process based at least in part on the asset modeling data obtained from the one or more asset performance management systems. In one or more embodiments, in response to the optimization request for the industrial process, the program instructions are additionally or alternatively executable by the processor to cause the processor to transmit control signals configured based at least in part on the one or more real-time operational constraints to a controller configured for optimization associated with the industrial process producing the industrial process product.

Drawings

The description of the exemplary embodiments may be read in connection with the accompanying drawings. It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements are exaggerated relative to other elements. Embodiments incorporating teachings of the present disclosure are shown and described with respect to the drawings presented herein, wherein:

FIG. 1 illustrates an exemplary networked computing system environment in accordance with one or more embodiments described herein;

fig. 2 illustrates a schematic block diagram of a framework of an IoT platform of a networked computing system in accordance with one or more embodiments described herein;

FIG. 3A illustrates a planning model in accordance with one or more embodiments described herein;

FIG. 3B illustrates a Model Predictive Control (MPC) model in accordance with one or more embodiments described herein;

FIG. 4 illustrates a system for providing industrial optimization based on data provided by an asset performance management system in accordance with one or more embodiments described herein;

FIG. 5 illustrates a system that provides real-time operational limit calculations based on data provided by an asset performance management system in accordance with one or more embodiments described herein;

FIG. 6 illustrates a system that provides a controller associated with an industrial process according to one or more embodiments described herein;

FIG. 7 illustrates a system providing an exemplary industrial process computer system according to one or more embodiments described herein;

FIG. 8 illustrates a flow diagram for computing capital capacity using model predictive control and/or industrial process optimization, according to one or more embodiments described herein; and is also provided with

Fig. 9 illustrates a functional block diagram of a computer that can be configured to perform the techniques in accordance with one or more embodiments described herein.

Detailed Description

Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be understood by those of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to obscure aspects of the embodiments. The term "or" is used herein in both alternative and combined sense, unless otherwise indicated. The terms "exemplary," "example," and "exemplary" are used for examples without quality level indications. Like numbers refer to like elements throughout.

The phrases "in one embodiment," "according to one embodiment," and the like generally mean that a particular feature, structure, or characteristic that follows the phrase may be included in at least one embodiment, and may be included in more than one embodiment, of the present disclosure (importantly, such phrases are not necessarily referring to the same embodiment).

The word "exemplary" is used herein to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

If the specification states that a component or feature "may", "could", "should", "would", "preferably", "could", "would", "could", "for example", "could" or "could" (or other such words) be included or have a characteristic, a particular component or feature need not be included or possessing that characteristic. Such components or features may optionally be included in some embodiments, or may be excluded.

In one or more embodiments, the present disclosure provides a "internet of things" or "IoT" platform for industrial process control and/or industrial process optimization that uses real-time accurate models and/or real-time control of sustained peak performance for businesses and/or business processes. The IoT platform is an extensible platform that is portable for deployment in any cloud or data center environment for providing enterprise-wide, top-down control of processes and/or assets. Further, the IoT platform of the present disclosure supports end-to-end capabilities to execute models for process data and/or translate output into viable insight and/or real-time control, as detailed in the following description.

Accordingly, to address these and/or other problems, the present disclosure provides for computing capital capacity using model predictive control and/or industrial process optimization. In various embodiments, information from an asset performance management system is integrated into an overall industrial optimization scheme for one or more industrial processes. For example, in various embodiments, an asset management system is coupled to an overall industrial optimization scheme to provide asset constraints and/or capacity constraints based on one or more models integrated within the asset management system. Accordingly, the present disclosure provides for computing capital capability without relying on fixed limits for industrial processes. The asset management system may be, for example, an asset performance management system.

In various embodiments, calculations and/or other data provided by the asset management system are employed to define an optimization scope for the modeled industrial asset. For example, asset operation data and/or model data (e.g., first principles model data, data driven model data, etc.) associated with an asset management system may be employed to define an optimization scope for a modeled industrial asset. The optimization scope may define the viable capabilities of the modeled industrial asset and/or industrial process. In various embodiments, the scope of optimization may include, for example, proxy limits for one or more industrial processes and/or one or more industrial assets. For example, an optimization scope may be employed to optimize current operating conditions for the monitored industrial asset. In various embodiments, an optimization range is employed for model predictive control and/or industrial process optimization. In various embodiments, proxy limits from the secondary multivariable predictive control application and proxy limits from the monitored asset (e.g., proxy limits generated based on data from the asset management system) may be employed to determine an optimization scope for the plant-wide optimizer such that all potential process constraints for the monitored asset are considered without creating large amounts of data (e.g., a data matrix comprising thousands of limits).

Thus, one or more industrial processes can utilize the capacity of the monitored industrial asset in real-time. Error opportunities for asset optimization can also be minimized in real-time. Closed loop optimization with real-time computation of optimization limits for assets and/or industrial processes may also be provided. In addition, plant-wide optimization including detailed asset monitoring information may be provided to ensure feasibility of asset optimization and/or to manage asset capacity.

In various embodiments, the industrial process optimization provides one or more optimal recipes for the industrial process (e.g., optimal batch mixing recipe, optimal batch mixing factory-wide recipe, etc.). In certain embodiments, the industrial process comprises batch product blending for a refinery. In certain embodiments, the industrial process comprises a filter washing process in a chemical refinery (e.g., an alumina refinery, etc.) or a lubricant plant. However, it should be understood that in certain embodiments, the industrial process is a different type of industrial process. It should also be appreciated that the techniques disclosed herein may also be employed for optimizing other types of assets and/or processes.

In various embodiments, damage to industrial processes may be minimized or eliminated by employing one or more of the techniques disclosed herein. In addition, by employing one or more of the techniques disclosed herein, a smooth plant-wide control and/or optimization solution associated with industrial operations may be provided. Further, by employing one or more of the techniques disclosed herein, industrial process performance and/or industrial process efficiency are optimized. In various embodiments, the amount of time and/or throughput associated with an industrial process is reduced. In addition, in one or more embodiments, performance of a processing system (e.g., a control system) associated with an industrial process is improved by employing one or more of the techniques disclosed herein. For example, in one or more embodiments, the number of computing resources, the number of storage requirements, and/or the number of errors associated with a processing system (e.g., a control system) for an industrial process is reduced by employing one or more techniques disclosed herein.

FIG. 1 is an example of an exemplary networked computing system environment 100 according to the present disclosure. As shown in FIG. 1, the networked computing system environment 100 is organized into a plurality of layers, including a cloud 105, a network 110, and an edge 115. In one or more embodiments, cloud 105 is a cloud layer, network 110 is a network layer, and/or edge 115 is an edge layer. As described in further detail below, the components of edge 115 communicate with the components of cloud 105 via network 110.

In various embodiments, network 110 is any suitable network or combination of networks and supports any suitable protocol suitable for transferring data to and from components of cloud 105, as well as transferring data between various other components in networked computing system environment 100 (e.g., components of edge 115). According to various embodiments, network 110 includes a public network (e.g., the internet), a private network (e.g., a network within an organization), or a combination of public and/or private networks. According to various embodiments, network 110 is configured to provide communications between the various components depicted in fig. 1. According to various embodiments, network 110 includes one or more networks that connect devices and/or components in a network topology to allow communication between the devices and/or components. For example, in one or more embodiments, network 110 is implemented as the internet, a wireless network, a wired network (e.g., ethernet), a Local Area Network (LAN), a Wide Area Network (WAN), bluetooth, near Field Communication (NFC), or any other type of network that provides communication between one or more components of a network topology. In some embodiments, network 110 is implemented using a cellular network, a satellite, a licensed radio, or a combination of cellular, satellite, licensed radio, and/or unlicensed radio networks.

The components of the cloud 105 include one or more computer systems 120 that form a so-called "internet of things" or "IoT" platform 125. It should be understood that "IoT platform" is an optional term describing a platform that connects any type of internet-connected device and should not be construed as limiting the types of computing systems available within IoT platform 125. In particular, in various embodiments, computer system 120 includes any type or number of one or more processors for executing applications or software modules of networked computing system environment 100 and one or more data storage devices including memory for storing such applications or software modules. In one embodiment, the processor and the data storage device are embodied in server-like hardware, such as an enterprise-class server. For example, in one embodiment, the processor and data storage device comprise any type of application server, communication server, web server, supercomputer server, database server, file server, mail server, proxy server, and/or virtual server, or combination thereof. Further, the one or more processors are configured to access the memory and execute processor-readable instructions that, when executed by the processors, configure the processors to perform the functions of the networked computing system environment 100.

The computer system 120 also includes one or more software components of the IoT platform 125. For example, in one or more embodiments, the software components of computer system 120 include one or more software modules to communicate with user devices and/or other computing devices over network 110. For example, in one or more embodiments, the software components include one or more modules 141, models 142, engines 143, databases 144, services 145, and/or applications 146, which may be stored in/by computer system 120 (e.g., on memory), as described in detail below with respect to fig. 2. According to various embodiments, the one or more processors are configured to utilize the one or more modules 141, models 142, engines 143, databases 144, services 145, and/or applications 146 when performing the various methods described in the present disclosure.

Thus, in one or more embodiments, the computer system 120 executes a cloud computing platform (e.g., ioT platform 125) with extensible resources for computing and/or data storage, and one or more applications may be run on the cloud computing platform to perform the various computer-implemented methods described in this disclosure. In some embodiments, some of the modules 141, models 142, engines 143, databases 144, services 145, and/or applications 146 are combined to form fewer modules, models, engines, databases, services, and/or applications. In some embodiments, some of the modules 141, models 142, engines 143, databases 144, services 145, and/or applications 146 are separated into separate, more modules, models, engines, databases, services, and/or applications. In some embodiments, some of the modules 141, models 142, engines 143, databases 144, services 145, and/or applications 146 are removed, while other components are added.

Computer system 120 is configured to receive data from other components of networked computing system environment 100 (e.g., components of edge 115) via network 110. Computer system 120 is further configured to utilize the received data to produce a result. According to various embodiments, information indicative of the results is transmitted to the user via the user computing device over the network 110. In some embodiments, computer system 120 is a server system that provides one or more services, including providing information to users indicative of received data and/or results. According to various embodiments, the computer system 120 is part of an entity, including any type of company, organization, or organization that implements one or more IoT services. In some examples, the entity is an IoT platform provider.

The components of edge 115 include one or more enterprises 160a-160n, each enterprise including one or more edge devices 161a-161n and one or more edge gateways 162a-162n. For example, a first enterprise 160a includes a first edge device 161a and a first edge gateway 162a, a second enterprise 160b includes a second edge device 161b and a second edge gateway 162b, and an nth enterprise 160n includes an nth edge device 161n and an nth edge gateway 162n. As used herein, enterprises 160a-160n represent any type of entity, facility, or vehicle, such as, for example, a processing facility, an industrial plant, an oil and gas facility (e.g., refinery), a chemical processing facility (e.g., metal refinery, alumina refinery, etc.), a lubricant industrial plant, a manufacturing plant, a building, a warehouse, a real estate facility, a laboratory, a company, a division, an aircraft, a spacecraft, an automobile, a ship, a boat, a military vehicle, or any other type of entity, facility, and/or vehicle that includes any number of local devices.

According to various embodiments, edge devices 161a-161n represent any of a variety of different types of devices that may be found within enterprises 160a-160 n. Edge devices 161a-161n are any type of device configured to access network 110 or to be accessed by other devices through network 110, such as via edge gateways 162a-162 n. According to various embodiments, edge devices 161a-161n are "IoT devices" that include any type of network-connected (e.g., internet-connected) device. For example, in one or more embodiments, edge devices 161a-161n include sensors, units, tanks, air handler units, fans, actuators, valves, pumps, pipes, processors, computers, vehicle components, cameras, displays, doors, windows, security components, HVAC components, factory equipment, refinery equipment, and/or any other device connected to network 110 for collecting, transmitting, and/or receiving information. Each edge device 161a-161n includes or otherwise communicates with one or more controllers to selectively control the respective edge device 161a-161n and/or to send/receive information between the edge device 161a-161n and the cloud 105 via the network 110. Referring to FIG. 2, in one or more embodiments, edge 115 includes an Operational Technology (OT) system 163a-163n and an Information Technology (IT) application 164a-164n for each enterprise 161a-161 n. The OT systems 163a-163n include hardware and software for detecting and/or causing changes by directly monitoring and/or controlling industrial equipment (e.g., edge devices 161a-161 n), assets, processes, and/or events. The IT applications 164a-164n include networks, storage, and computing resources for generating, managing, storing, and communicating data within an organization or between organizations.

Edge gateways 162a-162n include devices for facilitating communications between edge devices 161a-161n and cloud 105 via network 110. For example, edge gateways 162a-162n include one or more communication interfaces for communicating with edge devices 161a-161n and with cloud 105 via network 110. According to various embodiments, the communication interfaces of edge gateways 162a-162n include one or more cellular radios, bluetooth, wiFi, near field communication radios, ethernet, or other suitable communication devices for transmitting and receiving information. According to various embodiments, a plurality of communication interfaces are included in each gateway 162a-162n for providing various forms of communication between edge devices 161a-161n, gateways 162a-162n, and cloud 105 via network 110. For example, in one or more embodiments, communication with edge devices 161a-161n and/or network 110 is accomplished through wireless communication (e.g., wiFi, radio communication, etc.) and/or a wired data connection (e.g., universal serial bus, on-board diagnostic system, etc.) or other communication mode (such as a Local Area Network (LAN), wide Area Network (WAN) such as the internet, telecommunications network, data network, or any other type of network).

According to various embodiments, edge gateways 162a-162n also include memory for storing program instructions to facilitate data processing and a processor executing these program instructions to facilitate data processing. For example, in one or more embodiments, edge gateways 162a-162n are configured to receive data from edge devices 161a-161n and process the data before sending the data to cloud 105. Thus, in one or more embodiments, edge gateways 162a-162n include one or more software modules or components for providing the data processing services and/or other services or methods of the present disclosure. Referring to FIG. 2, each edge gateway 162a-162n includes edge services 165a-165n and edge connectors 166a-166n. According to various embodiments, edge services 165a-165n include hardware and software components for processing data from edge devices 161a-161 n. According to various embodiments, edge connectors 166a-166n include hardware and software components for facilitating communication between edge gateways 162a-162n and cloud 105 via network 110, as detailed above. In some cases, any of edge devices 161a-n, edge connectors 166a-n, and edge gateways 162a-n combine, omit, or separate their functionality into any combination of devices. In other words, the edge device and its connectors and gateway need not necessarily be separate devices.

Fig. 2 shows a schematic block diagram of a framework 200 of the IoT platform 125 according to the present disclosure. The IoT platform 125 of the present disclosure is a platform for factory-wide optimization that uses real-time accurate models and/or real-time data to deliver intelligently-viable recommendations and/or real-time control of sustained peak performance for the enterprises 160a-160 n. IoT platform 125 is an extensible platform that is portable for deployment in any cloud or data center environment for providing enterprise-wide, top-down views showing the status of processes, assets, personnel, and security. Furthermore, ioT platform 125 supports end-to-end capabilities to execute digital twins for process data using framework 200 and translate output into viable insight, as described in further detail below.

As shown in fig. 2, the framework 200 of the IoT platform 125 includes a plurality of layers including, for example, an IoT layer 205, an enterprise integration layer 210, a data pipe layer 215, a data insight layer 220, an application service layer 225, and an application layer 230.IoT platform 125 also includes a core services layer 235 and an Extensible Object Model (EOM) 250 that includes one or more knowledge maps 251. The layers 205-235 also include various software components that together form each layer 205-235. For example, in one or more embodiments, each layer 205-235 includes one or more of a module 141, a model 142, an engine 143, a database 144, a service 145, an application 146, or a combination thereof. In some embodiments, layers 205-235 are combined to form fewer layers. In some embodiments, some of the layers 205-235 are separated into separate, more layers. In some embodiments, some of the layers 205-235 are removed, while other layers may be added.

IoT platform 125 is a model driven architecture. Thus, in some embodiments, extensible object model 250 communicates with each layer 205-230 to contextualize site data for enterprises 160a-160n using an extensible object model (or "asset model") and knowledge graph 251, where equipment (e.g., edge devices 161a-161 n) and processes for enterprises 160a-160n are modeled. Knowledge graph 251 of EOM 250 is configured to store the model in a central location. Knowledge graph 251 defines a collection of nodes and links that describe the real world connections that implement the intelligent system. As used herein, knowledge-graph 251: (i) Real world entities (e.g., edge devices 161a-161 n) and their interrelationships organized in a graphical interface are described; (ii) defining possible categories and relationships of entities in the diagram; (iii) enabling any entities to correlate with each other; and (iv) encompasses a variety of thematic domains. In other words, the knowledge graph 251 defines a large network of entities (e.g., edge devices 161a-161 n), semantic types of entities, characteristics of entities, and relationships between entities. Thus, the knowledge graph 251 describes a "things" network that is related to a particular domain or business or organization. Knowledge graph 251 is not limited to abstract concepts and relationships, but may also contain instances of objects, such as, for example, documents and datasets. In some embodiments, the knowledge graph 251 includes a Resource Description Framework (RDF) graph. As used herein, an "RDF map" is a map data model formally describing the semantics or meaning of information. RDF graphs also represent metadata (e.g., data describing data). According to various embodiments, knowledge-graph 251 further includes a semantic object model. The semantic object model is a subset of the knowledge graph 251 that defines the semantics of the knowledge graph 251. For example, the semantic object model defines a schema of the knowledge-graph 251.

As used herein, EOM 250 is a collection of Application Programming Interfaces (APIs) that enable extended inoculation semantic object models. For example, the EOM 250 of the present disclosure enables knowledge-graph 251 of a customer to be constructed subject to constraints expressed in the semantic object model of the customer. Thus, knowledge graph 251 is generated by a customer (e.g., an enterprise or organization) to create models of edge devices 161a-161n of enterprises 160a-160n, and knowledge graph 251 is input into EOM 250 for visualizing the models (e.g., nodes and links).

The model describes the assets (e.g., nodes) of the enterprise (e.g., edge devices 161a-161 n) and describes the relationships of the assets to other components (e.g., links). The model also describes the schema (e.g., describes what the data is), and thus the model is self-verifying. For example, in one or more embodiments, the model describes the types of sensors installed on any given asset (e.g., edge devices 161a-161 n) and the type of data sensed by each sensor. According to various embodiments, a Key Performance Indicator (KPI) framework is used to combine characteristics of assets in the extensible object model 250 to inputs of the KPI framework. Thus, ioT platform 125 is an extensible model-driven end-to-end stack that includes: bidirectional model synchronization and secure data exchange between edge 115 and cloud 105, metadata-driven data processing (e.g., rules, computations, and aggregation), and model-driven visualization and application. As used herein, "extensible" refers to the ability to extend a data model to include new properties/columns/fields, new categories/tables, and new relationships. Thus, ioT platform 125 may be extended with respect to edge devices 161a-161n and applications 146 that process those devices 161a-161 n. For example, when a new edge device 161a-161n is added to the enterprise 160a-160n system, the new device 161a-161n will automatically appear in the IoT platform 125 such that the corresponding application 146 knows about and uses data from the new device 161a-161 n.

In some cases, asset templates are used to facilitate configuring instances of edge devices 161a-161n in a model using a common structure. Asset templates define typical characteristics of edge devices 161a-161n of a given enterprise 160a-160n for a particular type of device. For example, asset templates for pumps include modeling pumps having inlet and outlet pressures, speeds, flows, etc. The templates may also include hierarchical or derivative types of edge devices 161a-161n to accommodate variations in the underlying types of devices 161a-161 n. For example, a reciprocating pump is specialization of the base pump type and will include additional features in the template. The instances of edge devices 161a-161n in the model are configured to use templates to match actual physical devices of enterprises 160a-160n to define the expected attributes of the devices 161a-161 n. Each attribute is configured as a static value (e.g., 1000BPH capacity) or a time series tag referencing a provided value. Knowledge graph 251 may automatically map labels to attributes based on naming conventions, parsing, and matching labels to attribute descriptions, and/or by comparing behavior of time series data to expected behavior.

In certain embodiments, the modeling phase includes a plate-up process for synchronizing the model between the edge 115 and the cloud 105. For example, in one or more embodiments, the onstration process includes a simple onstration process, a complex onstration process, and/or a standardized first-display process. A simple panel process involves knowledge-graph 251 receiving raw model data from edge 115 and running a context discovery algorithm to generate a model. The context discovery algorithm reads the context of the edge naming conventions of the edge devices 161a-161n and determines what these naming conventions refer to. For example, in one or more embodiments, knowledge-graph 251 receives "TMP" during the modeling phase and determines that "TMP" relates to "temperature. The generated model is then published. In some embodiments, the complex panel process includes knowledge-graph 251 receiving raw model data, receiving point history data, and receiving site survey data. According to various embodiments, knowledge-graph 251 then uses these inputs to run a context discovery algorithm. According to various embodiments, the generated models are compiled and then released. The standardized first-time presentation process includes manually defining standard models in the cloud 105 and pushing those models to the edges 115.

IoT layer 205 includes one or more components for device management, data ingestion, and/or command/control of edge devices 161a-161 n. The components of IoT layer 205 enable data to be ingested into IoT platform 125 from various sources or otherwise received at the IoT platform. For example, in one or more embodiments, data is ingested from edge devices 161a-161n through a process history database or laboratory information management system. IoT layer 205 communicates with edge connectors 165a-165n disposed on edge gateways 162a-162n over network 110, and edge connectors 165a-165n securely transmit data to IoT platform 205. In some embodiments, only authorization data is sent to IoT platform 125, and IoT platform 125 accepts only data from authorization edge gateways 162a-162n and/or edge devices 161a-161 n. According to various embodiments, data is sent from edge gateways 162a-162n to IoT platform 125 via direct streaming and/or via batch delivery. Further, after any network or system disruption, once communication is reestablished, data transmission will resume and any data lost during the disruption will be backfilled from the source system or IoT platform 125 cache. According to various embodiments, ioT layer 205 further includes means for accessing time series, alerts and events, and transaction data via various protocols.

The enterprise integration layer 210 includes one or more components for event/messaging, file upload, and/or REST/OData. The components of the enterprise integration layer 210 enable the IoT platform 125 to communicate with third party cloud applications 211 (such as any applications operated by the enterprise in relation to its edge devices). For example, the enterprise integration layer 210 is connected with enterprise databases (such as guest databases, customer databases, financial databases, patient databases, etc.). The enterprise integration layer 210 provides a standard Application Programming Interface (API) to third parties for accessing the IoT platform 125. The enterprise integration layer 210 also enables the IoT platform 125 to communicate with the OT systems 163a-163n and IT applications 164a-164n of the enterprises 160a-160 n. Thus, the enterprise syndication layer 210 enables the IoT platform 125 to receive data from the third party applications 211, rather than or in conjunction with directly receiving data from the edge devices 161a-161 n.

The data pipe layer 215 includes one or more components for data cleansing/enrichment, data transformation, data computation/aggregation, and/or APIs for data streaming. Thus, in one or more embodiments, the data pipe layer 215 pre-processes and/or performs an initial analysis on the received data. The data pipeline layer 215 performs advanced data cleaning routines including, for example, data correction, mass balance coordination, data conditioning, component balancing, and modeling to ensure that desired information is used as a basis for further processing. The data pipe layer 215 also provides advanced and fast computation. For example, in one or more embodiments, the cleaned data is run by business-specific digital twins. According to various embodiments, the enterprise-specific digital twins include reliability guides that include process models to determine current operation and fault models, thereby triggering any early detection and determining appropriate solutions. According to various embodiments, the digital twins further include an optimization wizard that integrates real-time economic data with real-time process data, selects the correct feed for the process, and determines the optimal process conditions and product yields.

According to various embodiments, the data pipeline layer 215 employs models and templates to define calculations and analyses. Additionally or alternatively, according to various embodiments, the data pipeline layer 215 employs models and templates to define how computations and analytics relate to assets (e.g., edge devices 161a-161 n). For example, in one embodiment, the fan template defines a fan efficiency calculation such that a standard efficiency calculation is automatically performed for the fan each time the fan is configured. The computation model defines various types of computations, the type of engine on which the computation should be run, input and output parameters, preprocessing requirements and prerequisites, timetables, and the like. According to various embodiments, the actual calculation or analysis logic is defined in the template or may be referenced. Thus, according to various embodiments, a computational model is employed to describe and control the execution of various different process models. According to various embodiments, the computing templates are linked with asset templates such that when an asset (e.g., edge devices 161a-161 n) instance is created, any associated computing instances are also created, with the input and output parameters of these computing instances being linked to the appropriate attributes of the asset (e.g., edge devices 161a-161 n).

According to various embodiments, ioT platform 125 supports a variety of different analytical models including, for example, curve fitting models, regression analytical models, first principles models, empirical models, engineering models, user-defined models, machine learning models, internal functions, and/or any other type of analytical model. The fault model and predictive maintenance model will now be described by way of example, but any type of model may be applicable.

The fault model is used to compare current and predicted enterprise 160a-160n performance to identify problems or opportunities, as well as potential causes or drivers of the problems or opportunities. IoT platform 125 includes a rich hierarchical symptom-fault model to identify abnormal conditions and their potential consequences. For example, in one or more embodiments, the IoT platform 125 analyzes in depth from the high-level conditions to learn contributors, and determines potential impacts that lower-level conditions may have. There may be multiple fault models for a given enterprise 160a-160n that focus on different aspects, such as processes, equipment, control, and/or operations. According to various embodiments, each fault model identifies problems and opportunities in its domain, and may also look at the same core problem from different angles. According to various embodiments, the overall fault model is layered on top to synthesize different perspectives from each fault model into an overall assessment of the situation and to point to the true root cause.

According to various embodiments, when a failure or opportunity is identified, the IoT platform 125 provides recommendations regarding the best corrective action to take. Initially, the recommendation was based on expertise that had been preprogrammed into the system by process and equipment professionals. The recommendation service module presents this information in a consistent manner regardless of source and supports workflows to track, end, and record subsequent recommendations. According to various embodiments, when an existing recommendation is validated (or not validated) or a user and/or analysis learns of new cause and impact relationships, subsequent recommendations are employed to improve the overall knowledge of the system over time.

According to various implementations, the model is used to accurately predict what will happen and to interpret the state of the installed base before it happens. Thus, the IoT platform 125 enables an operator to quickly initiate maintenance measures when an offending action occurs. According to various embodiments, the digital twins architecture of IoT platform 125 employs various modeling techniques. According to various embodiments, modeling techniques include, for example, mechanism models, fault Detection and Diagnosis (FDD), descriptive models, predictive maintenance, normalized maintenance, process optimization, and/or any other modeling techniques.

According to various embodiments, the mechanism model is converted from a process design simulation. In this way, in certain embodiments, the process design is integrated with the feed conditions. Process variations and technical improvements provide business opportunities to achieve more efficient maintenance schedules and resource deployments in the context of production needs. Fault detection and diagnosis includes a generalized rule set that is specified based on industry experience and domain knowledge and that can be easily combined and used when functioning with equipment models. According to various embodiments, the descriptive model identifies problems, and the predictive model determines the extent of possible damage and maintenance options. According to various embodiments, the descriptive model includes a model for defining an operating window of the edge devices 161a-161 n.

Predictive maintenance includes predictive analysis models developed based on mechanism models and statistical models such as, for example, principal Component Analysis (PCA) and least squares (PLS). According to various embodiments, a machine learning method is applied to train a model for fault prediction. According to various embodiments, predictive maintenance utilizes FDD-based algorithms to continuously monitor individual control and equipment performance. Predictive modeling is then applied to the selected condition indicators that deteriorate over time. Normative maintenance includes determining the best maintenance option, and when it should be performed based on actual conditions rather than a time-based maintenance schedule. According to various embodiments, normalization analysis selects the correct solution based on company capital, operating, and/or other requirements. Process optimization determines optimal conditions via adjustment of settings and schedules. The optimized settings and schedules can be transferred directly to the underlying controller, which enables automatic shut-down of the cycle from analysis to control.

The data insight layer 220 includes one or more components for time series databases (TDSBs), relational/document databases, data lakes, blobs, files, images, and videos, and/or APIs for data queries. According to various embodiments, when raw data is received at IoT platform 125, the raw data is stored as a time series tag or event in a warm store (e.g., in a TSDB) to support interactive queries and stored to a cold store for archival purposes. According to various embodiments, data is sent to a data lake for offline analysis development. According to various embodiments, the data pipeline layer 215 accesses data stored in the database of the data insight layer 220 to perform analysis, as detailed above.

The application services layer 225 includes one or more components for rule engines, workflows/notifications, KPI frameworks, insights (e.g., feasible insights), decisions, recommendations, machine learning, and/or APIs for application services. The application services layer 225 enables the creation of applications 146a-d. The application layer 230 includes one or more applications 146a-d of the IoT platform 125. For example, according to various embodiments, the applications 146a-d include a building application 146a, a factory application 146b, an aeronautical application 146c, and other enterprise applications 146d. According to various embodiments, the applications 146 include generic applications 146 for portfolio management, asset management, autonomic control, and/or any other custom applications. According to various embodiments, the combination management includes a KPI framework and a flexible User Interface (UI) generator. According to various embodiments, asset management includes asset performance, asset health, and/or asset predictive maintenance. According to various embodiments, autonomous control includes plant-wide optimization, energy optimization, and/or predictive maintenance. As detailed above, according to various embodiments, the generic applications 146 are extensible such that each application 146 may be configured for different types of enterprises 160a-160n (e.g., building applications 146a, factory applications 146b, aeronautical applications 146c, and other enterprise applications 146 d).

The application layer 230 also enables visualization of the performance of the enterprises 160a-160 n. For example, the dashboard provides in-depth analysis of the high-level overview to support more in-depth surveys. The recommendation summary gives the user preferential action to solve current or potential problems and opportunities. Data analysis tools support ad hoc (ad hoc) data exploration to aid in troubleshooting and process improvement.

The core services layer 235 includes one or more services of the IoT platform 125. According to various embodiments, core services 235 include data visualization, data analysis tools, security, scaling, and monitoring. According to various embodiments, core services 235 also include services for tenant configuration, single-sign-on/public portal, self-service administrator, UI library/UI tile, identification/access/authorization, logging/monitoring, usage metering, API gateway/developer portal, and IoT platform 125 streaming.

Fig. 3A illustrates a planning model 300 in accordance with one or more embodiments of the present disclosure. FIG. 3B illustrates a Model Predictive Controller (MPC) model 350 in accordance with one or more embodiments of the present disclosure. In various embodiments, the MPC model 350 may be a multivariable predictive control model.

The planning model 300 and the MPC model 350 are examples of multi-scale model pairs employed in some embodiments to solve multi-level problems in cascaded MPC architectures. In certain embodiments, the planning model 300 and/or the MPC model 350 are employed to support cascaded MPC methods in industrial process systems (e.g., industrial process control systems, industrial process control and automation systems, etc.). In certain embodiments, planning model 300 is a yield-based planning model.

As shown in fig. 3A, the planning model 300 identifies a plurality of units 302 that generally operate to convert one or more input streams 304 of feed material into one or more output streams 306 of treated material. In this example, unit 302 represents a component of an oil and gas refinery that converts a single input stream 304 (crude oil) into multiple output streams 306 (different refined oil/gas products). Various intermediate components 308 are produced by the unit 302, and one or more reservoirs 310 may be used to store one or more of the intermediate components 308. As shown in FIG. 3B, the MPC model 350 identifies multiple components 352 of a single unit. Various valves and other actuators 354 may be used to adjust operation within the unit, and various APCs and other controllers 356 may be used to control the actuators within the unit.

In general, planning model 300 analyzes the entire plant (or portions of the plant) with a "bird's eye" view, and thus represents the individual cells on a rough scale. In one or more embodiments, the planning model 300 focuses on steady state relationships between units related to unit production, product quality, material and energy balance, and manufacturing activities within the plant. In one or more embodiments, planning model 300 is comprised of a process yield model and product quality attributes. In one or more embodiments, the planning model 300 is constructed from a combination of various sources, such as a planning tool, a scheduling tool, a yield verification tool, and/or historical operating data. However, in one or more embodiments, the MPC model 350 represents at least one cell on a finer scale. In one or more embodiments, the MPC model 350 focuses on intra-cell dynamic relationships between Controlled Variables (CVs), manipulated Variables (MVs), and Disturbance Variables (DV) related to safe, smooth, and efficient operation of the cell. The time scales of the two models 300, 350 are also different. In one or more embodiments, the MPC model 350 ranges from minutes to hours, while the planning model 300 ranges from days to months. It is noted that a "controlled variable" generally means a variable whose value is controlled to be at or near a set value or within a desired range, and a "manipulated variable" generally means a variable that is adjusted to change the value of at least one controlled variable. "disturbance variable" generally represents a variable whose value can be considered but not controlled or adjusted.

In one or more embodiments, the planning model 300 excludes non-production-related or non-economy-related variables such as pressure, temperature, tank level, and valve opening within each cell. For example, in one or more embodiments, the planning model 300 reduces the processing unit to one or several material or energy yield vectors. The MPC model 350, on the other hand, typically includes all of the operating variables for control purposes to help ensure safe and efficient operation of the unit. Thus, in one or more embodiments, the MPC model 350 includes more unit-specific variables than the planning model 300. As a non-limiting example, the MPC model 350 of a Fluid Catalytic Cracking Unit (FCCU) of a refinery includes about 100CV (output) and 40MV (input). In certain embodiments, the planning model 300 of the same unit determines key causal relationships between feed quality and mode of operation (as input) and FCCU product yield and quality (as output). As a non-limiting example, planning model 300 includes three or four inputs and ten outputs.

Fig. 4 illustrates a system 400 of one or more described features in accordance with one or more embodiments of the present disclosure. The system 400 includes an Asset Performance Management (APM) system 402 and an industrial optimization system 404.APM system 402 is configured for real-time monitoring and/or data acquisition with respect to one or more assets. APM system 402 may be one or more APM systems. In various embodiments, the one or more assets are one or more industrial assets. In various embodiments, the one or more assets additionally or alternatively correspond to one or more IoT devices (e.g., one or more industrial IoT devices), one or more connected building assets, one or more sensors, one or more actuators, one or more processors, one or more computers, one or more valves, one or more pumps (e.g., one or more centrifugal pumps, etc.), one or more motors, one or more compressors, one or more turbines, one or more pipes, one or more heaters, one or more cooling devices, one or more coolers, one or more boilers, one or more furnaces, one or more heat exchangers, one or more fans, one or more blowers, one or more conveyor belts, one or more vehicle components, one or more cameras, one or more displays, one or more safety components, one or more air handling units, one or more HVAC equipment, and/or other equipment for connection to the network or equipment 110. In one or more embodiments, one or more assets include, or otherwise communicate with, one or more controllers for selectively controlling respective assets and/or for sending/receiving information between assets. In various embodiments, one or more assets correspond to edge devices 161a-161n. However, it should be understood that in certain embodiments, one or more assets are different types of assets that undergo real-time monitoring and/or data acquisition. In one or more embodiments, APM system 402 employs one or more models to provide real-time monitoring and/or data acquisition with respect to one or more assets. In one or more embodiments, APM system 402 stores data aggregated from one or more assets and/or one or more data sources associated with the one or more assets. The stored data is then analyzed by one or more models.

In one embodiment, APM system 402 is a server system (e.g., a server device) that facilitates a data analysis platform between one or more computing devices, one or more data sources, and/or one or more assets. In one or more embodiments, APM system 402 is a device having one or more processors and memory. In one or more embodiments, APM system 402 is a computer system from computer system 120. For example, in one or more embodiments, APM system 402 is implemented via cloud 105. APM system 402 also relates to one or more technologies such as, for example, enterprise technology, conjoined building technology, industrial technology, internet of things (IoT) technology, data analysis technology, digital conversion technology, cloud computing technology, cloud database technology, server technology, network technology, private enterprise network technology, wireless communication technology, machine learning technology, artificial intelligence technology, digital processing technology, electronic device technology, computer technology, supply chain analysis technology, aircraft technology, industrial technology, network security technology, navigation technology, asset visualization technology, oil and gas technology, petrochemical technology, refining technology, process plant technology, purchasing technology, and/or one or more other technologies. In one implementation, APM system 402 increases the performance of one or more assets. For example, in one or more embodiments, APM system 402 increases the efficiency and/or performance of one or more assets.

In one embodiment, APM system 402 is configured to access asset data provided by edge devices 161a-161 n. Asset data includes, for example, sensor data, real-time data, field attribute value data, historical data, event data, process data, fault data, asset infrastructure data, conjoined building data, location data, and/or other data associated with edge devices 161a-161 n. In one or more embodiments, the APM system 402 determines one or more asset insights for one or more assets. The one or more asset insights include, for example, data that provides a context associated with performance of the one or more assets. In one or more embodiments, the one or more asset insights include information about trends, patterns, and/or relationship contexts associated with the performance of the one or more assets. In one or more embodiments, the one or more asset insights include tags, classifications, insights, inferences, machine learning data, and/or other attributes related to the performance of the one or more assets. In one or more embodiments, APM system 402 employs one or more machine learning models that determine one or more insights related to the performance of one or more assets. For example, in certain embodiments, one or more machine learning models identify, classify, and/or predict one or more contextual characteristics related to the performance of one or more assets. In one or more embodiments, the one or more machine learning models include one or more neural network models, one or more deep neural network models, one or more regression models, one or more first principles models, and/or one or more other machine learning models. In one or more embodiments, the one or more machine learning models employ fuzzy logic, a Bayesian network, a Markov logic network, and/or another type of machine learning technique to determine the one or more asset insights.

The industrial optimization system 404 is configured to optimize one or more industrial processes associated with one or more assets. The industrial optimization system 404 is an industrial process control system. In various embodiments, the industrial optimization system 404 is configured to optimize real-time operational constraints for one or more industrial processes. The industrial optimization system 404 can be an advanced process control system, a plant-wide optimization system, a model predictive control system, a multi-variable predictive control system, and/or another type of industrial optimization system. In various embodiments, the industrial optimization system 404 includes one or more controllers, one or more optimizer controllers, one or more MPC controllers (e.g., one or more multivariable MPC controllers), one or more servers, one or more operator stations, one or more networks, and/or one or more other components providing industrial process control. In various embodiments, the industrial optimization system 404 uses one or more optimization processes to provide plant-wide optimization to control production inventory, manufacturing activities, or product quality within an industrial plant. The phrase "plant-wide optimization" or "plant-wide control" refers to the optimization or control of multiple units in an industrial facility, regardless of whether those multiple units represent each individual unit in the industrial facility.

In one or more embodiments, asset modeling data 406 provided by APM system 402 may be employed to optimize one or more industrial processes associated with industrial optimization system 404. For example, in one or more embodiments, one or more real-time operational limits for one or more industrial processes associated with the industrial optimization system 404 are adjusted based at least in part on asset modeling data 406 obtained from the one or more APM systems 402. In one or more embodiments, the one or more real-time operating limits are a set of limits (e.g., lower limit and upper limit, etc.) within which the one or more industrial processes operate during real-time operation of the one or more industrial processes. In addition, the adjusted one or more real-time operational limits may provide increased (e.g., optimized) processing efficiency, processing performance, and/or product quality for the one or more industrial processes as compared to the one or more industrial processes associated with the one or more real-time operational limits prior to the adjusted one or more real-time operational limits. Asset modeling data 406 includes data employed and/or generated by one or more models of APM system 402. For example, in one or more embodiments, asset modeling data 406 includes asset insight and/or metrics (e.g., KPIs) related to sensor data, real-time data, field attribute value data, historical data, event data, process data, fault data, asset infrastructure data, conjoined building data, location data, and/or other data associated with one or more assets undergoing real-time monitoring and/or data acquisition by APM system 402.

In one or more embodiments, asset modeling data 406 includes asset operation data 408, asset model configuration data 410, asset model output data 412, first principles model data 414, data driven model data 416, and/or other data. Asset operation data 408 includes operation data for one or more assets, such as, for example, sensor data, real-time data, field attribute value data, historical data, event data, process data, fault data, throughput data, capacity data, and/or other data determined by monitoring one or more assets. Asset model configuration data 410 includes configuration data for one or more models employed by APM system 402. For example, asset model configuration data 410 includes model parameters, model hyper-parameters, model variables, model training data, model weights, model coefficients, model bias values, model filter size data, model layer configuration, model function data, activation function data, learning rate data, hidden layer data, convolution layer data, batch size data, pooling size data, and/or other data for configuring or training a model.

Asset model output data 412 includes output data provided by one or more models employed by APM system 402. For example, the asset model output data 412 includes machine learning output data, classification data, inference data, prediction data, asset performance predictions, metric predictions, KPI predictions, tags, attributes, regression values, and/or other asset model output data. The first principles model data 414 includes data provided by one or more first principles models employed by the APM system 402. The one or more first principles models may be associated with physics-based modeling and/or physics-based characteristics related to the behavior of the pressure, temperature, and/or other physics-based attributes of the one or more assets. For example, the first principles model data 414 includes first principles model output data, first principles classification data, first principles inference data, first principles prediction data, first principles asset performance prediction, first principles metric prediction, first principles KPI prediction, first principles labels, first principles attributes, and/or other first principles data. The data-driven model data 416 includes data provided by one or more data-driven models employed by the APM system 402. The one or more data-driven models may be associated with machine learning modeling that is related to physics-based data collected for the one or more assets. For example, the data driven model data 416 includes insight, predictive, and/or informed decisions regarding measured pressures, measured temperatures, and/or other physics-based attributes of one or more assets. In one embodiment, the data driven model data 416 includes linear regression model data.

Fig. 5 illustrates a system 400' in accordance with one or more described features of one or more embodiments of the present disclosure. System 400' is an alternative embodiment of system 400. The system 400' includes an APM system 402 and an industrial optimization system 404. In one or more embodiments, the industrial optimization system 404 includes a real-time operational limit calculation 500. In one or more embodiments, the real-time operational limit calculation 500 determines and/or adjusts one or more real-time operational limits for one or more industrial processes based at least in part on asset modeling data 406 obtained from the APM system 402. For example, in one or more embodiments, the real-time operational limit calculation 500 determines and/or adjusts one or more real-time operational limits for one or more industrial processes based at least in part on the asset operational data 408, the asset model configuration data 410, the asset model output data 412, the first principles model data 414, and/or the data driven model data 416 obtained from the APM system 402. In certain embodiments, the real-time operational limit calculation 500 determines and/or adjusts one or more proxy limits (e.g., high proxy limits and/or low proxy limits) for one or more industrial operations based at least in part on asset modeling data 406 obtained from the APM system 402. In one or more embodiments, the real-time operational limit calculation 500 estimates a feasibility optimization region associated with one or more industrial processes based at least in part on asset modeling data 406 obtained from the APM system 402. In one or more embodiments, the real-time operation limit calculation 500 predicts whether an asset can have additional capabilities and/or whether a particular real-time operation limit will optimize alignment to a plant-wide goal. In other words, the one or more real-time operational limits define a feasibility region in which the asset may be operated in real-time within the asset constraints under each operating condition. Furthermore, given asset conditions and/or related MPC process conditions, one or more real-time operating limits may be communicated to the plant-wide optimization process to ensure that an overall optimization solution based on the one or more real-time operating limits meets the plant-wide optimization objective. In one or more embodiments, one or more real-time operating limits may be determined for each asset that is controlled and/or optimized as part of a plant-wide optimization process.

Fig. 6 illustrates a system 600 of one or more features described in accordance with one or more embodiments of the present disclosure. The system 600 includes an optimizer controller 502 and one or more multi-variable MPC controllers 504a-n. For example, in one or more embodiments, the optimizer controller 502 is a primary controller (e.g., a master MPC controller) and the one or more multivariable MPC controllers 504a-n are one or more secondary controllers. In one or more embodiments, the industrial optimization system 404 includes an optimizer controller 502 and one or more multi-variable MPC controllers 504a-n. In one or more embodiments, the optimizer controller 502 and the one or more multivariable MPC controllers 504a-n represent respective computing devices. For example, in one or more embodiments, the optimizer controller 502 and the one or more multi-variable MPC controllers 504a-n each include one or more processing devices and one or more memories for storing instructions and data used, generated, or collected by the one or more processing devices. In one or more embodiments, the optimizer controller 502 and the one or more multivariable MPC controllers 504a-n each further comprise at least one network interface, such as one or more Ethernet interfaces or one or more wireless transceivers. In one or more embodiments, the industrial process control system associated with the optimizer controller 502 and one or more multivariable MPC controllers 504a-n includes one or more sensors, one or more actuators, one or more other controllers, one or more servers, one or more operator stations, one or more networks, and/or one or more other components.

In one or more embodiments, the optimizer controller 502 and one or more multi-variable MPC controllers 504a-n are configured as a cascaded MPC architecture for plant wide control and optimization to facilitate plant wide optimization as part of an automation control and automation system. In one or more embodiments, optimizer controller 502 is configured to use a planning model (e.g., planning model 300) as a seed model. In one or more embodiments, the optimizer controller 502 uses one or more optimization processes to perform plant-wide economic optimization to control production inventory, manufacturing activities, or product quality within a plant. In one or more embodiments, the optimizer controller 502 is cascaded on top of one or more multi-variable MPC controllers 504 a-n. In certain embodiments, the one or more multi-variable MPC controllers 504a-n represent controllers at a unit level (level 3) of the system, and each multi-variable MPC controller from the one or more multi-variable MPC controllers 504a-n provides one or more respective operating states and/or respective constraints to the optimizer controller 502. As such, in one or more embodiments, the plant-wide optimization solution provided by the optimizer controller 502 takes into account unit-level operating constraints from one or more multi-variable MPC controllers 504 a-n. Jointly, the MPC cascade associated with the optimizer controller 502 and one or more multi-variable MPC controllers 504a-n provides both decentralized control (such as at the unit level) and centralized plant-wide optimization (such as at the plant level) in a single consistent control system.

In one or more embodiments, the MPC cascade solution associated with the optimizer controller 502 and one or more multi-variable MPC controllers 504a-n enables the embedded real-time planning solution to fulfill lower levels of operating constraints. By utilizing both the planning and control models across-line, the MPC cascade solution associated with the optimizer controller 502 and one or more multivariable MPC controllers 504a-n provides a "reduced level" form of planning optimization in real-time within a closed loop control system. For example, in one or more embodiments, the optimizer controller 502 is configured to provide a reduced level of planning optimization. In one or more embodiments, the MPC cascade solution associated with the optimizer controller 502 and one or more multi-variable MPC controllers 504a-n is employed to automatically execute an on-the-fly production plan via the one or more multi-variable MPC controllers 504 a-n.

In one or more embodiments, a planning model (e.g., planning model 300) determines an area for optimization and an MPC cascade solution associated with the optimizer controller 502 and one or more multi-variable MPC controllers 504a-n coordination units, and manages the industrial plant to provide optimal operating conditions for the industrial plant. In one or more embodiments, each of the one or more multivariable MPC controllers 504a-n includes an embedded economic optimizer to facilitate providing optimal operating conditions for an industrial plant. In one or more embodiments, each of the one or more multi-variable MPC controllers 504a-n additionally or alternatively provides multi-variable control functions to facilitate providing optimal operating conditions for an industrial plant. In one or more embodiments, the optimizer controller 502 employs a planning model (e.g., the planning model 300) to provide an initial steady state gain matrix and/or related model dynamics using the operating data of the industrial plant. In one or more embodiments, the optimizer controller 502 is configured to control product inventory, manufacturing activities, and/or product quality within an industrial plant. In one or more embodiments, the optimizer controller 502 provides real-time planning optimization for an industrial plant. In one or more embodiments, the one or more multi-variable MPC controllers 504a-n provide future predictions and/or operating constraints for each unit of the industrial plant to the optimizer controller 502.

Fig. 7 illustrates a system 700 that provides an exemplary environment for one or more described features in accordance with one or more embodiments of the present disclosure. According to one embodiment, the system 700 includes an industrial process computer system 702 to facilitate industrial process control and/or actual application of industrial process simulation to an industrial process. In one or more embodiments, the industrial process computer system 702 provides management of industrial process optimizations related to batch operations. In certain embodiments, the industrial process computer system 702 facilitates the practical application of machine learning techniques to facilitate the management of industrial process optimizations related to batch operations. In one or more embodiments, the industrial process computer system 702 analyzes real-time industrial process data related to an industrial process to provide optimization, cost savings, and/or improved efficiency for the industrial process. In one or more embodiments, the industrial process computer system 702 is implemented via a controller (e.g., the optimizer controller 502 or another controller in communication with the optimizer controller 502).

In one embodiment, industrial process computer system 702 is a server system (e.g., a server device) that facilitates a cloud-based industrial control platform. In one or more embodiments, industrial process computer system 702 is a device having one or more processors and memory. In one or more embodiments, industrial process computer system 702 is a computer system from computer system 120. For example, in one or more embodiments, industrial process computer system 702 is implemented via cloud 105. The industrial process computer system 702 also relates to one or more technologies such as, for example, industrial technologies, process plant technologies, oil and gas technologies, petrochemical technologies, refining technologies, supply chain analysis technologies, sensor technologies, ioT technologies, enterprise technologies, smart building technologies, siamesed building technologies, connected asset technologies, connected edge equipment technologies, HVAC technologies, modeling technologies, energy optimization technologies, predictive maintenance technologies, asset performance management technologies, digital analysis technologies, digital conversion technologies, cloud computing technologies, cloud database technologies, server technologies, network technologies, wireless communication technologies, machine learning technologies, artificial intelligence technologies, digital processing technologies, electronics technologies, computer technologies, aircraft technologies, network security technologies, navigation technologies, asset visualization technologies, purchasing technologies, and/or one or more other technologies.

In addition, the industrial process computer system 702 provides improvements to one or more technologies such as industrial technology, process plant technology, oil and gas technology, petrochemical technology, refining technology, supply chain analysis technology, sensor technology, ioT technology, enterprise technology, smart building technology, siamesed building technology, connected asset technology, connected edge equipment technology, HVAC technology, modeling technology, energy optimization technology, predictive maintenance technology, asset performance management technology, digital analysis technology, digital conversion technology, cloud computing technology, cloud database technology, server technology, network technology, wireless communication technology, machine learning technology, artificial intelligence technology, digital processing technology, electronics technology, computer technology, aircraft technology, network security technology, navigation technology, asset visualization technology, purchasing technology, and/or one or more other technologies. In one implementation, the industrial process computer system 702 improves the performance of an industrial plant. For example, in one or more embodiments, the industrial process computer system 702 optimizes one or more industrial processes for an industrial plant, increases the processing efficiency of one or more controllers of the industrial plant, decreases the power consumption of one or more controllers of the industrial plant, optimizes energy usage associated with one or more controllers of the industrial plant, and the like. Additionally or alternatively, in another implementation, the industrial process computer system 702 improves performance of computing devices (e.g., servers, controllers, processors, etc.). For example, in one or more embodiments, the industrial process computer system 702 increases the processing efficiency of the computing device, reduces the power consumption of the computing device, improves the quality of data provided by the computing device, and the like.

Industrial process computer system 702 includes an asset performance management component 704, an industrial optimization component 706, and/or a control component 708. In addition, in certain embodiments, industrial process computer system 702 includes a processor 710 and/or a memory 712. In certain embodiments, one or more aspects of industrial process computer system 702 (and/or other systems, devices, and/or processes disclosed herein) constitute executable instructions embodied within a computer-readable storage medium (e.g., memory 712). For example, in one embodiment, memory 712 stores computer-executable components and/or executable instructions (e.g., program instructions). Further, processor 710 facilitates execution of computer-executable components and/or executable instructions (e.g., program instructions). In an exemplary embodiment, the processor 710 is configured to execute instructions stored in the memory 712 or otherwise accessible to the processor 710.

Processor 710 is a hardware entity (e.g., physically embodied in circuitry) capable of performing operations in accordance with one or more embodiments of the present disclosure. Alternatively, in embodiments of the executor in which the processor 710 is embodied as software instructions, the software instructions configure the processor 710 to perform one or more algorithms and/or operations described herein in response to the software instructions being executed. In one embodiment, processor 710 is a single-core processor, a multi-core processor, multiple processors within industrial process computer system 702, a remote processor (e.g., a processor implemented on a server), and/or a virtual machine. In certain embodiments, the processor 710 communicates with the memory 712, the asset performance management component 704, the industrial optimization component 706, and/or the control component 708 via a bus to facilitate data transfer between the processor 710, the memory 712, the asset performance management component 704, the industrial optimization component 706, and/or the control component 708, for example. The processor 710 may be embodied in a number of different ways and, in some embodiments, may include one or more processing devices configured to execute independently. Additionally or alternatively, in one or more embodiments, processor 710 includes one or more processors configured in series via a bus to enable independent execution of instructions, pipelining of data, and/or multithreaded execution of instructions.

The memory 712 is non-transitory and includes, for example, one or more volatile memories and/or one or more non-volatile memories. In other words, in one or more embodiments, the memory 712 is an electronic storage device (e.g., a computer-readable storage medium). Memory 712 may be configured to store information, data, content, one or more applications, one or more instructions, or the like, to enable industrial process computer system 702 to perform various functions in accordance with one or more embodiments disclosed herein. As used herein in this disclosure, the terms "component," "system," and the like can be a computer-related entity. For example, the "components," "systems," and the like disclosed herein are hardware, software, or a combination of hardware and software. For example, a component is, but is not limited to being, a process executing on a processor, a circuit, an executable, a thread of instructions, a program, and/or a computer entity.

In one embodiment, an industrial process computer system 702 (e.g., asset performance management component 704 of industrial process computer system 702) receives an optimization request 720. In one or more embodiments, the optimization request 720 is an optimization request for optimizing an industrial process that produces one or more industrial process products. In one or more embodiments, the optimization request 720 is associated with a continuous optimization sub-process (e.g., continuous optimization sub-process 604). Further, in one or more embodiments, the optimization request 720 is generated by a controller (e.g., the optimizer controller 502 and/or one or more of the multivariable MPC controllers 504 a-n). Alternatively, in one or more embodiments, the optimization request 720 is generated by the user device (e.g., in response to user input provided via a user interface of the user device). The user device is a mobile computing device, a smart phone, a tablet computer, a mobile computer, a desktop computer, a laptop computer, a workstation computer, a wearable device, a virtual reality device, an augmented reality device, or another type of user device located remotely from the industrial process computer system 702. In one or more embodiments, the optimization request 720 is received in response to a determination that the time data for the industrial process (e.g., a schedule for the industrial process) meets a defined criteria. In one or more embodiments, the optimization request 720 is received in response to a determination that a condition associated with the industrial process (e.g., an industrial process event) meets a defined criteria. In one or more embodiments, the optimization request 720 is received in real-time with respect to the operation of one or more assets.

In one or more embodiments, the industrial process is associated with a component of the edge 115 (such as, for example, one or more enterprises 160a-160 n). In one or more embodiments, the industrial process is associated with one or more edge devices from one or more edge devices 161a-161 n. In one or more embodiments, the industrial process computer system 702 communicates with one or more portions of the industrial plant (e.g., the optimizer controller 502 and/or one or more multi-variable MPC controllers 504 a-n) via the network 110. For example, in some embodiments, industrial process computer system 702 receives data via network 110 and/or transmits data via network 110. In certain embodiments, the industrial process incorporates encryption capabilities to facilitate encryption of one or more portions of data received via network 110 and/or one or more portions of data transmitted via network 110. In one or more embodiments, the network 110 is Wi-FiA network, a Near Field Communication (NFC) network, a Worldwide Interoperability for Microwave Access (WiMAX) network, a Personal Area Network (PAN), a short range wireless network (e.g.,a network), an infrared wireless (e.g., irDA) network, an Ultra Wideband (UWB) network, an inductive wireless transmission network, and/or another type of network.

In one or more embodiments, the optimization request 720 includes an industrial process identifier that indicates an identity of the industrial process and/or one or more assets associated with the industrial process. The industrial process identifier in the optimization request 720 is a numeric code such as, for example, a machine-readable code, a combination of numbers and/or letters, a bit string, a bar code, a Quick Response (QR) code, or another type of identifier for an industrial process. In addition, the asset identifier in the optimization request 720 facilitates identification of the industrial process.

Asset performance management component 704 employs optimization request 720 and/or information related to optimization request 720 to facilitate optimization of one or more portions of an industrial process. For example, in one or more embodiments, the asset performance management component 704 employs the optimization request 720 and/or information related to the optimization request 720 to facilitate plant-wide optimization based on data provided by one or more asset performance management systems, such as, for example, the APM system 402. In one or more embodiments, in response to the optimization request 720, the asset performance management component 704 obtains asset modeling data 406 from the APM system 402. For example, in response to the optimization request 720, the asset performance management component 704 obtains asset operation data 408, asset model configuration data 410, asset model output data 412, first principles model data 414, and/or data driven model data 416 from the APM system 402. In addition, in response to the optimization request 720, the industrial optimization component 706 adjusts one or more real-time operational limits for the industrial process based at least in part on the asset modeling data 406 obtained from the APM system 402. For example, in response to the optimization request 720, the industrial optimization component 706 adjusts one or more real-time operational limits for the industrial process based at least in part on the asset operation data 408, asset model configuration data 410, asset model output data 412, first principles model data 414, and/or data driven model data 416 from the APM system 402. In addition, in response to the optimization request 720, the control component 708 transmits control signals configured based at least in part on the one or more real-time operational constraints to a controller configured for optimization associated with an industrial process producing an industrial process product. In one or more embodiments, the control component 708 configures the control signal based on a comparison between the adjusted one or more real-time operational limits and a multi-variable control criterion for the industrial process. The multivariable control criteria may be associated with optimization objectives for the industrial process and/or the plant-wide industrial process. In various embodiments, the adjusted one or more real-time operational limits correspond to asset capabilities for one or more assets and/or one or more industrial processes.

In one or more embodiments, control component 708 is configured to generate control data 722 that includes control signals configured based at least in part on one or more real-time operational constraints. For example, in one or more embodiments, control data 722 includes one or more control signals configured based at least in part on one or more real-time operational constraints. In one or more embodiments, the control data 722 includes one or more real-time operational limits. In one or more embodiments, the control data 722 includes one or more proxy limits for one or more industrial processes. In one or more embodiments, the control data 722 includes one or more optimized operational limits for one or more industrial processes. In one or more embodiments, the control component 708 transmits control data 722 (e.g., one or more control signals) to a controller configured for optimization associated with an industrial process. In one or more embodiments, the control data 722 is configured based on one or more communication protocols associated with the controller and/or one or more industrial control system protocols. In one or more embodiments, the controllers correspond to the optimizer controller 502 and/or one or more multivariable MPC controllers 504a-n. In one or more embodiments, the control component 708 alters one or more portions of the optimization process based on the control data 722 (e.g., one or more control signals).

In some embodiments, control component 708 generates a user-interactive electronic interface that renders a visual representation of one or more real-time operational constraints. For example, in one embodiment, control component 708 configures a visualization (e.g., a dashboard visualization for display via a user interface of a computing device). In one embodiment, the user-interactive electronic interface facilitates presentation and/or interaction with one or more portions of one or more real-time operational constraints. In one or more embodiments, a user-interactive electronic interface renders one or more interactive media elements via a set of pixels. In another embodiment, control component 708 transmits one or more notifications associated with one or more real-time operational limits to the computing device. In an exemplary embodiment, the notification suggests that one or more portions of the industrial process are operating inefficiently. In another embodiment, control component 708 performs another type of action associated with application service layer 225, application layer 230, and/or core service layer 235 based on one or more real-time operational constraints.

FIG. 8 illustrates a method 800 for computing capital capacity using model predictive control and/or industrial process optimization, according to one or more embodiments described herein. For example, the method 800 is associated with an industrial process computer system 702. For example, in one or more embodiments, the method 800 is performed at a device (e.g., industrial process computer system 702) having one or more processors and memory. In one or more embodiments, the method 800 facilitates optimization of an industrial process and/or a plant-wide industrial process. In one or more embodiments, the method 800 begins at block 802 that receives (e.g., via the asset performance management component 704) an optimization request for an industrial process that optimizes production of an industrial process product. Optimization requests to optimize an industrial process provide one or more technical improvements, such as, but not limited to, facilitating interactions with a computing device, extending functionality of a computing device, and/or improving accuracy of data provided to a computing device.

At block 804, a determination is made as to whether the optimization request is processed. If not, block 804 is repeated to determine if the optimization request is processed. If so, the method 800 proceeds to block 806. In response to the optimization request, block 806 obtains (e.g., via asset performance management component 704) asset modeling data from one or more asset performance management systems for assets associated with the industrial process. The obtaining provides one or more technical improvements, such as, but not limited to, extending the functionality of the computing device and/or improving the accuracy of the data provided to the computing device.

The method 800 further includes a block 808 of adjusting (e.g., by the industrial optimization component 706) one or more real-time operational limits for the industrial process based at least in part on asset modeling data obtained from the one or more asset performance management systems in response to the optimization request. The adjustment provides one or more technical improvements, such as, but not limited to, extending the functionality of the computing device and/or improving the accuracy of the data provided to the computing device. In one or more embodiments, adjusting the one or more real-time operational limits for the industrial process includes modifying one or more proxy limits for the industrial process based on asset modeling data obtained from the one or more asset performance management systems.

The method 800 further includes a block 810 of transmitting (e.g., via the control component 708) a control signal configured based at least in part on the one or more real-time operational constraints to a controller configured for optimization associated with an industrial process producing an industrial process product in response to the optimization request. Transmitting the control signal provides one or more technical improvements, such as, but not limited to, providing various experiences for the computing device.

In one or more embodiments, the method 800 additionally or alternatively includes configuring the control signal based on a comparison between the adjusted one or more real-time operational limits and an optimization criterion for the industrial process.

In one or more embodiments, the method 800 additionally or alternatively includes configuring the control signal based on a comparison between the adjusted one or more real-time operational limits and a multi-variable control standard for the industrial process.

In one or more embodiments, the method 800 additionally or alternatively includes obtaining asset operation data from one or more asset performance management systems for assets associated with an industrial process. In one or more embodiments, the method 800 additionally or alternatively includes adjusting one or more real-time operational limits for the industrial process based at least in part on asset operation data obtained from one or more asset performance management systems.

In one or more embodiments, the method 800 additionally or alternatively includes obtaining asset model configuration data from one or more asset performance management systems for assets associated with an industrial process. In one or more embodiments, the method 800 additionally or alternatively includes adjusting one or more real-time operational limits for the industrial process based at least in part on asset model configuration data obtained from one or more asset performance management systems.

In one or more embodiments, the method 800 additionally or alternatively includes obtaining asset model output data from one or more asset performance management systems for assets associated with an industrial process. In one or more embodiments, the method 800 additionally or alternatively includes adjusting one or more real-time operational limits for the industrial process based at least in part on asset model output data obtained from one or more asset performance management systems.

In one or more embodiments, the method 800 additionally or alternatively includes obtaining first principles model data from one or more asset performance management systems for assets associated with an industrial process. In one or more embodiments, the method 800 additionally or alternatively includes adjusting one or more real-time operational limits for the industrial process based at least in part on first principles model data obtained from one or more asset performance management systems.

In one or more embodiments, the method 800 additionally or alternatively includes obtaining data-driven model data from a regression model configured for an asset associated with the industrial process. In one or more embodiments, the method 800 additionally or alternatively includes adjusting one or more real-time operational limits for the industrial process based at least in part on data driven model data obtained from one or more asset performance management systems.

In one or more embodiments, the method 800 additionally or alternatively includes obtaining data-driven model data from a neural network model configured for an asset associated with an industrial process. In one or more embodiments, the method 800 additionally or alternatively includes adjusting one or more real-time operational limits for the industrial process based at least in part on data driven model data obtained from one or more asset performance management systems.

Fig. 9 depicts an example system 900 in which the techniques presented herein may be performed. Fig. 9 is a simplified functional block diagram of a computer that may be configured to perform the techniques described herein, according to an example embodiment of the disclosure. In particular, a computer (or "platform" as it may not be a single physical computer infrastructure) may include a data communication interface 960 for packet data communications. The platform may also include a central processing unit ("CPU") 920 in the form of one or more processors for executing program instructions. The platform may include an internal communication bus 910, and may also include program storage and/or data storage for various data files to be processed and/or transferred by the platform, such as ROM 930 and RAM 940, although system 900 may receive programming and data via network communications. The system 900 may also include input and output ports 950 to connect with input and output devices, such as keyboards, mice, touch screens, monitors, displays, and the like. Of course, various system functions may be implemented in a distributed fashion across multiple similar platforms to distribute processing load. Alternatively, the system may be implemented by appropriate programming of a computer hardware platform.

The foregoing method descriptions and the process flow diagrams are provided only as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by those of skill in the art, the order of steps in the above embodiments may be performed in any order. Words such as "after," "then," "next," etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the method. Furthermore, for example, any reference to claim elements in the singular, using the articles "a," "an," or "the," should not be construed as limiting the element to the singular.

It should be understood that "one or more" includes a function performed by one element, a function performed by more than one element, e.g., in a distributed fashion, several functions performed by one element, several functions performed by several elements, or any combination of the above.

Furthermore, it will be further understood that, although the terms "first," "second," etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contacts.

The terminology used in the description of the various illustrated embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the term "if" is optionally interpreted to mean "when..once" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if determined" or "if detected [ the condition or event ]" is optionally interpreted to mean "upon determination" or "in response to determination" or "upon detection of [ the condition or event ]" or "in response to detection of [ the condition or event ]" depending on the context.

The disclosed systems, devices, apparatuses and methods are described in detail by way of example with reference to the accompanying drawings. The examples discussed herein are merely examples and are provided to facilitate the explanation of the apparatuses, devices, systems and methods described herein. Any feature or element shown in the drawings or discussed below should not be construed as mandatory for any particular embodiment of any of the devices, apparatuses, systems, or methods unless explicitly indicated as mandatory. For ease of reading and clarity, certain components, modules or methods may be described in connection with only specific figures. In this disclosure, any designations of particular techniques, arrangements, etc. are either related to the particular examples presented or are merely a general description of such techniques, arrangements, etc. The specification or examples are not intended to or should not be construed as mandatory or limiting unless explicitly so indicated. Any combination or sub-combination of parts not explicitly described should not be construed as an indication that any combination or sub-combination is not possible. It is to be understood that the examples, arrangements, configurations, components, elements, devices, apparatuses, systems, methods, etc., disclosed and described may be modified and may be required for a particular patent application. In addition, for any method described, whether or not the method is described in connection with a flowchart, it should be understood that any explicit or implicit ordering of steps performed by method execution is not meant to imply that the steps must be performed in the order set forth, but may be performed in a different order or in parallel, unless the context indicates otherwise or requires.

Throughout this disclosure, references to components or modules generally refer to articles that can be logically combined together to perform a single function or a related set of functions. Like reference numerals are generally intended to refer to the same or similar parts. The components and modules may be implemented in software, hardware, or a combination of software and hardware. The term "software" is used broadly to include not only executable code such as machine executable or machine interpretable instructions, but also data structures stored in any suitable electronic format, data storage and computing instructions, including firmware and embedded software. The terms "information" and "data" are used broadly and include a wide variety of electronic information, including executable code; content such as text, video data, and audio data, and the like; and various codes or indicia. The terms "information," "data," and "content" are sometimes used interchangeably as the context allows.

The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein may comprise a general purpose processor, a Digital Signal Processor (DSP), a special purpose processor such as an Application Specific Integrated Circuit (ASIC) or Field Programmable Gate Array (FPGA), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively or in addition, some steps or methods may be performed by circuitry specific to a given function.

In one or more exemplary embodiments, the functions described herein may be implemented by dedicated hardware or by a combination of hardware programmed by firmware or other software. In an implementation that relies on firmware or other software, this may be due to storage on one or more non-transitory computer readable media and/or oneOr execution of one or more instructions on a plurality of non-transitory processor-readable media to perform these functions. The instructions may be embodied by one or more processor-executable software modules residing on one or more non-transitory computer-readable or processor-readable storage media. In this regard, a non-transitory computer-readable or processor-readable storage medium may include any storage medium that is accessible by a computer or processor. By way of example, and not limitation, such non-transitory computer-readable or processor-readable media may include Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), FLASH memory, disk memory, magnetic storage devices, and the like. Disk storage, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc ^TM Or other storage device that stores data magnetically or optically with a laser. Combinations of the above are also included within the scope of terms non-transitory computer readable and processor readable media. In addition, any combination of instructions stored on one or more non-transitory processor-readable or computer-readable media may be referred to herein as a computer program product.

Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Although the figures only illustrate certain components of the devices and systems described herein, it should be understood that various other components may be used in conjunction with the supply management system. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Furthermore, the steps in the methods described above may not necessarily occur in the order depicted in the figures, and in some cases one or more of the depicted steps may occur substantially simultaneously, or additional steps may be involved. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A system, the system comprising:

one or more processors;

a memory; and

one or more programs stored in the memory, the one or more programs comprising instructions configured to:

receiving an optimization request for optimizing an industrial process for producing an industrial process product; and

in response to the optimization request for the industrial process:

obtaining asset modeling data from one or more asset performance management systems for assets associated with the industrial process;

adjusting one or more real-time operational limits for the industrial process based at least in part on the asset modeling data obtained from the one or more asset performance management systems; and

transmitting control signals configured based at least in part on the one or more real-time operational constraints to a controller configured for optimization associated with the industrial process producing the industrial process product.

2. The system of claim 1, the one or more programs further comprising instructions configured to:

One or more proxy limits for the industrial process are modified based on the asset modeling data obtained from the one or more asset performance management systems.

3. The system of claim 1, the one or more programs further comprising instructions configured to:

the control signal is configured based on a comparison between the adjusted one or more real-time operational limits and a multi-variable control standard for the industrial process.

4. The system of claim 1, the one or more programs further comprising instructions configured to:

obtaining asset operation data from one or more asset performance management systems for the asset associated with the industrial process; and

the one or more real-time operational limits for the industrial process are adjusted based at least in part on the asset operational data obtained from the one or more asset performance management systems.

5. The system of claim 1, the one or more programs further comprising instructions configured to:

obtaining asset model configuration data from one or more asset performance management systems for the asset associated with the industrial process; and

The one or more real-time operational limits for the industrial process are adjusted based at least in part on the asset model configuration data obtained from the one or more asset performance management systems.

6. The system of claim 1, the one or more programs further comprising instructions configured to:

obtaining asset model output data from one or more asset performance management systems for the asset associated with the industrial process; and

the one or more real-time operational limits for the industrial process are adjusted based at least in part on the asset model output data obtained from the one or more asset performance management systems.

7. The system of claim 1, the one or more programs further comprising instructions configured to:

obtaining first principles model data from one or more asset performance management systems for the asset associated with the industrial process; and

the one or more real-time operational limits for the industrial process are adjusted based at least in part on the first principles model data obtained from the one or more asset performance management systems.

8. The system of claim 1, the one or more programs further comprising instructions configured to:

obtaining data-driven model data from a regression model configured for the asset associated with the industrial process; and

the one or more real-time operational limits for the industrial process are adjusted based at least in part on the data driven model data obtained from the one or more asset performance management systems.

9. The system of claim 1, the one or more programs further comprising instructions configured to:

data-driven model data is obtained from a neural network model configured for the asset associated with the industrial process.

10. The system of claim 9, the one or more programs further comprising instructions configured to: