Method for monitoring and / or controlling one or more chemical plants

Abstract

A method for monitoring and / or controlling a chemical plant (12) having multiple assets via a distributed computing system (10) with three or more deployment layers (14, 16, 30, 32, 34), the deployment layers (14, 16, 30, 32, 34) including at least two of a first processing layer (14), a second processing layer (16, 32, 34), and an external processing layer (30), the method comprising the steps of: providing (60) a containerized application (48, 50) including input data, output data, and an asset or plant template that specifies an asset or plant model; A method is disclosed that includes deploying (62) and executing in at least one of the deployment tiers (14, 16, 30, 32, 34), where the deployment tier (14, 16, 30, 32, 34) is assigned based on input data, load indicators, or system tier tags, executing the containerized application (46, 52, 54) in the assigned deployment tier (14, 16, 30, 32, 34) and generating output data for controlling and / or monitoring the chemical plant (12); and providing (66) the generated output data for controlling and / or monitoring the chemical plant (12).

Description

Technical Field

[0001] Field The present disclosure relates to a method for monitoring and / or controlling a chemical plant having a plurality of assets via a distributed computing system having a plurality of deployment layers.

Background Art

[0002] Background The production of chemical products is a very sensitive production environment, especially with regard to security. Chemical plants typically include a plurality of assets for producing chemical products. A plurality of sensors are distributed throughout such plants for monitoring and control purposes, collecting large amounts of data. Therefore, the production of chemical products is a data-rich environment. However, to date, the benefits from such data for increasing production efficiency in one or more chemical plants have not been fully exploited.

[0003] Therefore, it is very interesting to apply new technologies to cloud computing and big data analysis. However, unlike other manufacturing industries, the processing industry is subject to very high safety standards. For this reason, computing infrastructure is typically siloed with very limited access to monitoring and control systems. Such safety standards conflict with latency and availability considerations for a straightforward migration from legacy control systems, for example, to cloud computing systems. Bridging the gap between highly proprietary industrial manufacturing systems and cloud technology is one of the major challenges.

[0004] WO2016065493 discloses a client device and describes a system for acquiring and pre-processing large volumes of processing-related data from at least one CNC machine or industrial robot, and for transmitting said processing-related data to at least one data receiver, e.g., a cloud-based server. The client device comprises at least one first data communication interface to at least one controller of the CNC machine or industrial robot for continuously recording hard real-time processing-related data via at least one real-time data channel and for recording non-real-time processing-related data via at least one non-real-time data channel. The client device further comprises at least one data processing unit for aggregating a contextualized set of processing-related data by data-mapping at least the recorded non-real-time data to recorded hard real-time data. Furthermore, the client device comprises at least one second data interface for transmitting the contextualized set of processing-related data to the data receiver and for further data communication with the data receiver.

[0005] WO2019138120 discloses a method for improving chemical manufacturing processes. Multiple derivative chemical products are manufactured through derivative chemical manufacturing processes based on at least several derivative manufacturing parameters at each chemical manufacturing facility, each including its own separate facility intranet. At least several respective derivative manufacturing parameters are measured from the derivative chemical manufacturing processes by their respective production sensor computer systems within each facility intranet. Process models for simulating derivative chemical manufacturing processes are recorded in a process model management computer system located outside the facility intranet.

[0006] US20160320768A1 discloses an example of a network environment for monitoring plant processes using a system computer acting as a root cause analyzer. The system computer communicates with a data server to access collected data of measurable process variables from a historical database. The data server is communicatively connected to a distributed control system (DCS) that communicates the collected data to the data server via a communication network.

[0007] The object of the present invention is a highly scalable and flexible method for monitoring and / or controlling chemical plants in the processing industry, which complies with high safety standards and enables enhanced monitoring or control. [Overview of the project]

[0008] overview A method has been proposed for monitoring and / or controlling a chemical plant with multiple assets via a distributed computing system having three or more deployment layers. The deployment layers include at least two of the following: a first processing layer, a second processing layer, and an external processing layer. The method is as follows: - A step of providing a containerized application that includes input data, output data, and asset or plant templates that specify assets or plant models, - A step of deploying a containerized application and running it in at least one of the deployment layers, the deployment layers being assigned based on input data, load indicators, or system layer tags, running the containerized application in each deployment layer and generating output data for controlling and / or monitoring a chemical plant, - The step of providing output data generated for controlling and / or monitoring a chemical plant.

[0009] A system has been proposed for monitoring and / or controlling a chemical plant having multiple assets with three or more deployment layers, wherein the deployment layers include at least two of a first processing layer, a second processing layer, and an external processing layer, and the system is - Provides a containerized application that includes input data, output data, and asset or plant templates that specify assets or plant models. - Deploy the containerized application and run it in at least one of the deployment layers, which are assigned based on input data, load indicators, or system layer tags, and run the containerized application in the assigned deployment layer to generate output data for controlling and / or monitoring the chemical plant. - Provides output data generated for controlling and / or monitoring chemical plants. It is structured in this way.

[0010] The present invention further relates to a distributed computer program or computer program product having computer-readable instructions that, when executed on one or more processors, causes the processors to perform a method for monitoring and / or controlling one or more chemical plants as described herein. The present invention further relates to a computer-readable non-volatile or non-temporary storage medium having computer-readable instructions that, when executed on one or more processors, causes the processors to perform a method for monitoring and / or controlling one or more chemical plants as described herein.

[0011] The proposed method enables highly efficient application processing in distributed computing systems that control and / or monitor chemical plants. By introducing various processing and storage layers, the large-scale data transfer, integration, and execution of applications can be distributed across different layers, enabling flexible application processing. Furthermore, the redundancy of the second processing layer and the external management layer, combined with the three-system-layer concept, allows for highly available and secure monitoring and / or control. In other words, less critical tasks can be assigned to external computing resources, while more critical tasks can be assigned to on-premises computing resources that do not rely on external networks. An additional benefit, depending on the context, is that this method enables the automation of application deployment and the automatic identification of the need for retrofitting additional sensors or IoT sensors.

[0012] Furthermore, the proposed method can accommodate multiple chemical plants via a second or external processing layer. Thus, this method enables highly scalable application integration for more reliable and enhanced monitoring and / or control of chemical plants. In particular, it allows for the organization of containerized application integration across diverse application landscapes to meet the specific needs of the processing industry. For example, the deployment of a containerized application that ingests input data can be streamlined for multiple plants and multiple assets. Moreover, it meets the high availability standards of chemical plants by allowing the selection of the appropriate processing layer depending on the specific data required for the containerized application and the computing resources needed to run such an application. For example, plant-specific data Computationally intensive applications that ingest data may run in the second processing layer, while processing applications that ingest asset or process-specific data and require low latency may run in the first processing layer. Further criteria may be defined regarding which deployment layer to integrate at and when.

[0013] The following description relates to the systems, methods, computer programs, and computer-readable storage media listed above. In particular, the systems, input units, computer programs, and computer-readable storage media are configured to perform the method steps described above and further described below.

[0014] In the context of this invention, a chemical plant refers to any manufacturing facility based on chemical processes, such as using chemical processes to convert raw materials into products. In contrast to individual manufacturing, chemical manufacturing is based on continuous or batch processes. Therefore, monitoring and / or control of a chemical plant is time-dependent and therefore based on large time-series datasets. A chemical plant may include more than 1,000 sensors that generate measurement data points every few seconds. Such a scale results in several terabytes of data to be processed by a system for controlling and / or monitoring the chemical plant. A small chemical plant may include thousands of sensors that generate data points every 1 to 10 seconds. For comparison, a large chemical plant may include tens of thousands, e.g., 10,000 to 30,000 sensors that generate data points every 1 to 10 seconds. Contextualizing such data, hundreds of gigabytes to several terabytes are processed.

[0015] A chemical plant may produce products through one or more chemical processes that convert raw materials into products via one or more intermediate products. Preferably, a chemical plant provides an encapsulated facility that produces products that can be used as raw materials for the next step in the value chain. A chemical plant can be a large-scale plant such as an oil and gas facility, a gas scrubbing plant, a carbon dioxide recovery facility, a liquefied natural gas (LNG) plant, an oil refinery, a petrochemical facility, or a chemical facility. For example, an upstream chemical plant in the production of a petrochemical process includes a steam cracking unit that begins by processing naphtha into ethylene and propylene. These upstream products can then be supplied to a further chemical plant to produce downstream products such as polyethylene or polypropylene, which can again serve as raw materials for a chemical plant that further extracts downstream products. Chemical plants can be used to produce individual products. In one example, one chemical plant may be used to produce a precursor for polyurethane foam. Such a precursor can then be supplied to a second chemical plant to produce an individual product, such as a separation plate containing polyurethane foam.

[0016] Value chain production, from various intermediate products to final products, can be dispersed across different locations or integrated into a Verbund site or e-chemical park. Such Verbund sites or chemical parks constitute a network of interconnected chemical plants, where products manufactured in one plant can be used as raw materials in another.

[0017] A chemical plant can include multiple assets, such as heat exchangers, reactors, pumps, pipes, distillation columns, and absorption columns, to name a few. In a chemical plant, some assets can be critical. Critical assets are those whose failure would have a significant impact on the plant's operation. This could jeopardize the manufacturing process. Product quality could deteriorate, or manufacturing could be halted. In the worst-case scenario, fire, explosion, or release of toxic gases could result from such failure. Therefore, such critical assets may require more rigorous monitoring and / or control than other assets, depending on the chemical process and the chemicals involved. Multiple actors and sensors may be incorporated into a chemical plant to monitor and / or control chemical processes and assets. Such actors or sensors may provide process or asset-specific data related to, for example, the status of individual assets, the status of individual actors, the composition of chemicals, or the status of the chemical process. In particular, process or asset-specific data falls into the following data categories: - Processing operation data such as the composition of raw materials and intermediate products, - Process monitoring data such as flow rate and material temperature, - Asset operation data such as current and voltage, and - Asset monitoring data such as asset temperature, asset pressure, and vibration. Includes one or more of the following.

[0018] In the context of this disclosure, assets may include any component of a chemical plant, such as equipment, instrumentation, machinery, processing, or processing components. Accordingly, the asset model may relate to a machinery, equipment, instrumentation, processing, or processing component model.

[0019] Process or asset-specific data refers to data that is related to a specific asset or process and is contextualized in relation to such specific asset or process. Process or asset-specific data can only be contextualized in relation to individual assets and processes. Process or asset-specific data may include measurements, data quality measurements, time, units of measurement, asset IDs for specific assets, or process IDs for specific process sections or stages. Such process or asset-specific data is collected at the lowest processing tier or the first processing tier and contextualized in relation to a specific asset or process within a single plant. Such contextualization may be related to context available at the first processing tier. Such context may be related to a single plant.

[0020] Plant-specific data refers to process or asset-specific data that is contextualized for one or more plants. Such plant-specific data may be collected in a second processing layer and contextualized for multiple plants. Specifically, contextualization may relate to the context available in the second processing layer. It may be added to process or asset-specific data points through contextualized context such as plant identifiers, plant types, reliability indicators, or plant alarm limits. Further steps may add the technical asset structure of one or more plants, Verbund sites or chemical parks, other asset management structures (such as asset networks), or application context (such as model identifiers or third-party exchanges). Such comprehensive context may arise from functional locations or digital twins, such as digital piping and instrumentation diagrams, 3D models, or scans using the xyz coordinates of plant assets. Alternatively, local scans from mobile devices linked to piping and instrumentation diagrams, for example, may be used for contextualization.

[0021] In particular, plant-specific data related to interfaces between chemical plants within a manufacturing chain can be provided at a second or external processing layer. Therefore, monitoring and / or control can be enhanced, for example, through anomaly detection, setpoint steering, and chain optimization across multiple plants. To monitor and / or control chains across multiple plants, processing applications with online input / output data profiles can be used. Such data and processing applications can be transferred between plants. Combined with a monitorable mass-energy balance, such processing applications can optimize the entire chain of chemical plants, rather than just individual plants within the chain.

[0022] Contextualization processing involves linking data points available in one or more storage units. Such units can be persistent or non-volatile storage. Data points can be related to measurement values or contextual information. The storage units can be part of a first processing layer, a second processing layer, an external processing layer, or distributed across two or more of those layers. Links can be generated dynamically or statically. For example, pre-defined or dynamically generated scripts can generate dynamic or static links between information data points within or between processing layers. Links can be established by creating a new data object that includes the linked data itself and storing such a new data object in a new instance. If a copy is stored elsewhere, any stored data points can be actively deleted. In this way, any data points copied from one storage unit to a new data object within the same or another storage unit can be deleted to reduce storage space. Additionally or alternatively, links can be established by creating a metadata object with embedded links for addressing or accessing each data point within a distributed storage unit. Any data point that can be addressed or accessed via such a metadata object can potentially remain in the original storage unit. Linking such information to form a new data object can still be performed, for example, in the external processing layer. To search for data, access the data object directly or use the metadata object to address or access data distributed across one or more storage units. Operations on such data, such as by an application, can access such data directly or access a non-persistent image of such data from, for example, a cache memory or a persistent copy of the data.

[0023] In this context, a containerized application refers to a processing application that can run in an encapsulated runtime environment independent of the host operating system. Therefore, the application can be considered to be running in a sandbox. A containerized application may be based on a container image containing the application. The container image may contain the software components necessary to run each application in the encapsulated runtime environment, such as a hierarchical tree of software components. Such a containerized application may be stored in or associated with a registry of a second or external processing layer.

[0024] To deploy a containerized application, an integrated application associated with a second or external processing layer may manage the execution of the containerized application. Such management may include common runtime environment configurations such as storage and networking for running the containerized application. Such management may further include host allocations that define the distribution across a central master node or one or more compute nodes for running the application in the first, second, or external management layer. In particular, such computing resource allocations may depend on input data, load indicators, or system layer tags.

[0025] The input data may include real-time data, non-real-time data from sensors such as wireless monitoring devices and IoT devices, or output data of deployed containers and executed applications. Such data may relate to, for example, the type of machine measured with respect to the machine, such as machine type and sensor data, the type of chemical substance measured with respect to the chemical components processed in a chemical plant, such as chemical substance type and sensor data, the type of chemical process measured with respect to the chemical process executed in a chemical plant, such as chemical process type and sensor data, and / or the type of plant measured with respect to a chemical plant, such as plant type or sensor data, for example, environmental measurement data, which may be related to the plant.

[0026] The asset or plant model may include, for example, a data-driven model or a dynamic model that provides a health state, an operation prediction, an event prediction, or an event trigger. The asset or plant model may be based on a mere data-driven model, a hybrid model combining a data-driven model and a dynamic model, or a mere dynamic model. The asset or plant model may further be based on a scenario matrix that maps input data, for example, sensor data, to specific events. The asset model may reflect the physical behavior of a single or multiple assets. The plant model may reflect the physical behavior of a part of one or more plants, the entire plant, or multiple plants.

[0027] The output data may include key performance indicators related to assets, plants, input data, asset model performance, or plant model performance. Asset model performance or plant model performance may be embedded in asset or plant models hosted by containerized applications. Any generated output data from this method can be used as input data in one or more further containerized applications. In this way, a chain of containerized applications can be realized to build a system of system coverage, and the generated output data can be used to control and / or monitor one or more chemical plants. Such chemical plants may be parallel manufacturing plants or plants connected along a value chain.

[0028] A containerized application may include one or more operations for taking in input data, providing that input data to the respective asset or plant model to generate output data, and providing the generated output data to control and / or monitor the chemical plant. Such output data may be passed to persistent instances after the application has run. In particular, such output data may be passed to a control instance, for example, to the first processing layer of the chemical plant. Alternatively, such output data may be passed to a monitoring instance on the first processing layer, a second processing layer, or an external processing layer. The output data may be passed to, for example, a client application for display to an operator, or to a further containerized application for execution.

[0029] In one embodiment, the second processing layer includes larger storage and computing resources than the first processing layer. Alternatively, or even more conveniently, the external processing layer may include larger storage and computing resources than the second processing layer. Such a stacked resource structure helps bridge the gap between the embedded control systems of chemical plants and available cloud technologies. In particular, the embedded control systems of chemical plants have limitations in storage and processing capacity. By expanding such resources, monitoring and / or control can be enhanced.

[0030] The first and second processing layers may be hosted, located, and configured within or inside a secure network. The first processing layer may be communicatively coupled to the second processing layer. The first processing layer may include at least one core processing system associated with a chemical plant or a single chemical plant. Preferably, the first processing layer is configured to control and / or monitor chemical processes and assets at the asset level of individual plants. Thus, the first processing layer monitors and / or controls the chemical plant at the lowest level. A more preferred first processing layer is configured to monitor and control critical assets. Critical assets are those that, if destroyed, would have a significant impact on the operation of the plant. This could jeopardize the manufacturing process. Product quality may be reduced or manufacturing may be halted. In the worst-case scenario, fire, explosion, or release of toxic gases could be the result of such disruption. Therefore, such critical assets may require stricter monitoring and / or control than other assets, depending on the chemical process and the chemicals involved.

[0031] Furthermore, or alternatively, the second processing layer may be configured to provide data to an external network, for example, via an interface to the external network. The second processing layer may be coupled to the external processing layer via the external network in a communicative manner. Adding a stacked processing layer within or inside a secure network allows compliance with the high safety standards of the chemical industry. In particular, such an architecture enables a method that operates completely independently of the external management layer, enabling island mode for one or more chemical plants, where island mode refers to monitoring and / or controlling a chemical plant without access to an external network.

[0032] In a further embodiment, the first processing layer is configured to provide asset or process-specific data, and the second processing layer is configured to provide plant-specific data. The second processing layer may be configured to contextualize assets or process specific data. In this way, the performance of the first processing layer is unaffected. Contextualization is also possible by adding a system with higher performance, especially since a typical core processing system for the first processing layer in older plants may lack the necessary computing power. Furthermore, the second processing layer allows for data contextualization at the plant level rather than the asset level. Contextualizing data in this context involves adding contextual information to asset or process-specific data, or reducing data size by preprocessing asset or process-specific data. Adding context may include adding further informational tags to assets or processing specific data. Preprocessing may include filtering, aggregating, normalizing, averaging, or inferring asset or process-specific data.

[0033] In one embodiment, the first processing layer is associated with one or a single chemical plant. The first processing layer may be a core processing system comprising one or more processing and storage devices. Such a layer may include one or more distributed processing and storage devices that form a programmable logic controller (PLC) system or a decentralized control system (DCS) with a control loop distributed throughout the chemical plant. Preferably, the first processing layer is configured to control and / or monitor chemical processes and assets at the asset level. Thus, the first processing layer monitors and / or controls the chemical plant at the lowest level. Furthermore, the first processing layer may be configured to monitor and control critical assets. Furthermore, or alternatively, the first processing layer is configured to provide process or asset-specific data to the second processing layer. Such data may be provided to the second processing layer directly or indirectly.

[0034] In a further embodiment, the second processing layer is associated with a plurality of chemical plants. The second processing layer may include a processing management system with one or more processing and storage devices. A preferred second processing layer or processing management system is configured to manage data transfer to and / or from the first processing layer. A more preferred second processing layer or processing management system is configured to host and / or organize processing applications. Such processing applications may monitor and / or control one or more chemical plants or one or more assets. The processing management system may be associated with one or more chemical plants. In other words, the processing management system may be communicatively coupled to a plurality of first processing layers associated with one or more chemical plants.

[0035] In further embodiments, the second processing layer may include an intermediate processing system and a processing management system, where the intermediate processing system may be communicatively coupled to the first processing layer, preferably the core processing system, and the processing management system may be communicatively coupled to the intermediate layer. Preferably, the first processing layer and the processing management system are coupled or communicatively coupled via the intermediate processing system. The intermediate processing system may be configured to collect processing or asset-specific data provided by the first processing layer. The processing management system may be configured to provide plant-specific data for one or more chemical plants to an interface to an external network. The intermediate processing system may be associated with one or more chemical plants. In other words, the intermediate processing system may be communicatively coupled to the first processing layer of one chemical plant or to multiple first processing layers of multiple plants. The processing management system may be communicatively coupled to one or more intermediate processing systems. Adding an intermediate processing level to the second processing layer further adds a security layer. The security layer completely removes the toxic first processing layer from any external network access. Furthermore, at the intermediate level, data processing can be further enhanced by reducing the data transfer rate to the external processing layer through preprocessing and improving data quality through contextualization. The intermediate processing system and processing management system may include one or more processing and storage devices.

[0036] In a further embodiment, a secure network is an isolated network that includes three or more security regions separated by firewalls. Such firewalls may be network-based or host-based virtual firewalls or physical firewalls. Firewalls may be hardware-based or software-based to control incoming and outgoing network traffic. Here, predetermined rules in the sense of a whitelist may define the traffic permitted through access management or other configuration settings. Depending on the firewall configuration, security regions may comply with various security standards.

[0037] In a further embodiment, the first processing layer is hosted, located, or configured within a first security area via a first firewall, and the second processing layer is hosted, located, or configured within a second security area via a second firewall. To securely protect the first processing layer, the first security level may comply with a higher security standard than the second security level. The security levels may comply with general industry standards, such as those described in Namur's document IEC62443. The second processing layer may provide further isolation via security areas. For example, an intermediate processing system may be hosted, located, or configured within a third security area via a third firewall, and a processing management system may be configured within a second security area via a second firewall. The third and second security areas may also be staggered in terms of security standards. For example, the third security area may comply with a higher security standard than the second security area. This allows for higher security standards in the lower security area of ​​the first processing layer and lower security standards in the higher security area of ​​the second processing layer. In one embodiment, the first processing layer is located within the first security area, the processing management system is located within the second security area, and the intermediate processing system is located within the third security area.

[0038] A second processing layer may be configured to contextualize processing or asset-specific data. In this way, the performance of the first processing layer is unaffected. Since the typical core processing system in older plants lacks the necessary computing power, adding a higher-performance system also enables contextualization. Furthermore, the second processing layer, particularly the intermediate processing system, enables data contextualization at the plant level rather than the asset level. Contextualizing data in the current context involves adding contextual information to processing or asset-specific data, or reducing data size by preprocessing processing or asset-specific data. Adding context may include adding further informational tags to processing or asset-specific data. Preprocessing may include filtering, aggregating, normalizing, averaging, or inferring from processing or asset-specific data.

[0039] In a further embodiment, unidirectional or bidirectional communication, such as data transfer or data access, may be implemented for data streams between different processing layers. In other words, the system may be configured to enable unidirectional or bidirectional communication, such as data transfer or data access between different processing layers. A single data stream may contain processing or asset-specific data from a first processing layer, which is passed to a second processing layer, contextualized through the second processing layer, and communicated to an external processing layer. Contextualization may be performed by the second processing layer, the external processing layer, or both. In other words, the second processing layer, the external processing layer, or both may be configured to contextualize processing or asset-specific data or plant-specific data. Furthermore, depending on the importance of the processing or asset-specific data or plant-specific data, such data may be assigned to unidirectional or bidirectional communication. In other words, the system may be configured to assign unidirectional or bidirectional communication to processing or asset-specific data or plant-specific data depending on the importance of the processing or asset-specific data or plant-specific data. For example, by implementing a diode-type communication channel, data communication from the second processing layer or the external processing layer to critical assets may be prohibited. In this type of communication, only unidirectional communication from critical assets to the processing layer is possible, but the reverse is not.

[0040] In a further embodiment, data streams may be assigned important or non-important data. In other words, the system may be configured to assign important or non-important data tags. Important data refers to data essential to the operation of a chemical plant, such as short-term data from which the operating points of the chemical plant are derived. Such important data may cover short-term periods, for example, from a few hours or days to a week or more, necessary to operate the plant in an optimal state. Non-important data refers to data that is not important to the operation of a chemical plant, such as medium- to long-term data for monitoring the chemical plant based on medium- to long-term behavior. Such non-important data may cover medium- to long-term periods, for example, from several weeks or months to a year or more, necessary to monitor and / or control an asset or plant over a certain period. Such data may also be called cold, warm, and hot data, with hot data corresponding to important data, warm data corresponding to medium-term non-important data, and cold data corresponding to long-term non-important data.

[0041] In a further embodiment, the contextualization of data is shifted across system layers, processing layers, or processing systems contained within such processing layers, with each layer mapping the contextual information available at its respective layer. In other words, a system may be configured to shift the contextualization of data across system layers, processing layers, or processing systems contained within such processing layers, with each layer mapping the contextual information available at its respective layer. The shift may include the contextualization of asset or process-specific data at various levels, in addition to the addition of contextual information at the single-plant level and / or multi-plant level. In a layered system architecture, contextual information available at one layer may be mapped to data provided by lower layers or processing systems, where lower means closer to the data access of the chemical plant. For example, process or asset-specific data provided by a first processing layer may include asset-level contextual information. In other words, a first processing layer may be configured to provide process or asset-specific data that includes asset-level contextual information. Such contextual information may relate to measurements, measurement quality, product quality, batch-related data, or real-time information such as measurement time. Asset-level contextual information can be further related to asset-specific information such as asset identifiers, intralogistics, or unit of measurement identifiers. Intermediate systems and processing management systems can be configured to add or contextualize further contextual information to such processing or asset-specific data. Such contextual information may be related to the plant level rather than the asset level. For example, contextual information may be related to plant context such as plant identifiers, plant types, reliability indicators, and alarm limits, or to application context such as model identifiers, third-party exchange identifiers, and confidentiality identifiers. In this way, data quality can be improved and context can be maximized, and its data management and the ability to monitor and / or control results through processing applications can be used.

[0042] In a further embodiment, the intermediate processing system is configured to contextualize data by mapping heterogeneous or asset-specific data to homogeneous data formats at the plant level. data "Asset-specific data" refers to data individually related to an asset level or multiple assets, while "homogeneous data" refers to data related to a combination of assets or equivalent types within a plant. An intermediate processing system may be configured to provide such plant-specific data to a processing management system. The processing management system may be further configured to contextualize the plant-specific data provided by the intermediate processing system, preferably at multiple plant levels. Such contextualization may include adding contextual information at multiple plant levels or at site levels, such as multiple plants, or adding site context, such as asset management information, such as the technical asset structure or asset network of one or more plants. Furthermore, or alternatively, such contextualization may include adding application context, such as a model identifier, a third-party exchange identifier, or a confidentiality identifier.

[0043] Furthermore, or alternatively, the first processing layer may be configured to provide assets or process specific data to the second processing layer. Such data may be provided to the second processing layer directly or indirectly. The second processing layer may be associated with one or more plants. The second processing layer may include a processing management system and optionally an intermediate processing layer. The first processing layer may include a plant-specific core processing system. The core processing system may optionally be coupled to the processing management system via an intermediate processing layer in a communicative manner. The second processing layer, in particular the processing management system, and the external processing layer may be configured to contextualize, store, or aggregate data from one or more chemical plants and / or integrate processing models from one or more chemical plants.

[0044] In a further embodiment, the intermediate processing system is configured to contextualize data by mapping heterogeneous or asset-specific data to homogeneous data formats at the plant level. data"Asset-specific data" refers to data individually related to an asset level or multiple assets, while "homogeneous data" refers to data related to a combination of assets or equivalent types within a plant. An intermediate processing system may be configured to provide such plant-specific data to a processing management system. The processing management system may be further configured to contextualize the plant-specific data provided by the intermediate processing system, preferably at multiple plant levels. Such contextualization may include adding contextual information at multiple plant levels or at site levels, such as multiple plants, or adding site context, such as asset management information, such as the technical asset structure or asset network of one or more plants. Furthermore, or alternatively, such contextualization may include adding application context, such as model identifiers, third-party exchange identifiers, or confidentiality identifiers.

[0045] A second processing layer, preferably a processing management system, may be coupled to an external processing layer via an external network in a communicative manner. The second processing layer, preferably a processing management system, may be configured to manage data transfer to and / or from the external processing layer in real time or on demand. The second processing layer, preferably a processing management system, may be configured to provide plant-specific data to the interface to the external network based, for example, on identifiers added by contextualization. Such identifiers may be confidentiality identifiers based on the fact that such data is not provided to the interface to the external network.

[0046] The external processing layer may be a computing or cloud environment that provides virtualized computing resources such as data storage and computing power. The external processing layer may provide private, hybrid, public, community, or multi-cloud environments. Cloud environments are advantageous because they provide on-demand storage and computing power. Furthermore, when monitoring and / or controlling multiple chemical plants operated by different parties, data or processing applications that affect the chemical plants may be shared in such a cloud environment.

[0047] In a further embodiment, a second processing layer, preferably a processing management system, is configured to provide plant-specific data from one or more chemical plants to an external processing layer. The second processing layer, preferably a processing management system, may be further configured to delete at least a portion of the data transferred to the external processing layer. The external processing layer may be configured to store historical data from one or more chemical plants. The external processing layer may be configured to aggregate, store, or contextualize plant-specific data from multiple chemical plants, and / or store historical data from multiple chemical plants. Aggregation here means grouping data through aggregation functions such as sum, mean, mode, etc. Aggregation is therefore associated with the ability to reduce size or storage space. In this way, data storage can be externalized, the required on-premises storage capacity can be reduced, and historical transfers are made redundant. Furthermore, the flexible computing and storage resources of the external processing layer, and the fact that data is available in the external processing layer, allow processing applications to be built, trained, tested, or modified in the external processing layer.

[0048] In a further embodiment, a second processing layer, preferably a processing management system, is configured to manage data transfers to and / or from an external processing layer in real time or on demand. Real-time transfers may be buffered depending on the network and computing load of the interface to the external network. On-demand transfers may be triggered in a predefined or dynamic manner. Preferably, data transfers to the external processing layer are managed in real time, and transfers from the external processing layer are managed on demand.

[0049] In a further embodiment, a second processing layer, preferably a processing management system, is configured to store or manage access to historical data, real-time data, and planning data. In a further embodiment, the second processing layer, preferably a processing management system, is configured to store or manage access to historical data for a first time frame, and the external processing layer is configured to store historical data for a second time frame, the first time frame being shorter than the second time frame. Here, the first time frame may correspond to a critical time frame that allows the system to monitor and / or control the chemical plant in island mode without external network connectivity. The first time frame may be considered a hot window in which historical data is required to safely control and / or monitor the chemical plant. The first time frame or hot window may be determined based on the storage capacity of the second processing layer, preferably a processing management system, or preferably by the processing application and the historical data required to run the processing application on the island mode without external network connectivity. In this way, the availability of the system for monitoring and / or control is always guaranteed.

[0050] In a further embodiment, deployment is managed by an integrated application that manages the deployment of containerized applications based on input data, load indicators, or system layer tags. Furthermore, or alternatively, the integrated application is hosted by a second processing layer and / or an external processing layer. In a further embodiment, an integrated application hosted by the second processing layer at runtime manages critical containerized applications. Furthermore, or alternatively, an integrated application hosted by the external processing layer at runtime manages non-critical containerized applications. Such management at runtime can be assigned statically or dynamically. In a dynamic scenario, if external network connectivity is interrupted, the second processing layer may host backups of critical containerized applications, and the integrated application hosted by the second processing system may access such backups. Here, critical containerized applications refer to containerized applications that monitor and / or control critical assets. Therefore, such applications are necessary if monitoring and / or control needs to be performed in island mode.

[0051] In a further embodiment, the management of critical containerized applications is assigned to a second processing layer based on a historical criterion that reflects the time frame of historical data available in the first or second processing layer. The second processing layer may be configured to store historical aggregate data for the first time frame, and the external processing layer may be configured to store historical aggregate data for the second time frame, where the first time frame is the 2Shorter than the time frame. In a preferred embodiment, the first time frame is selected so that a critical containerized application can run in the first or second processing layer. In a further embodiment, the containerized application is deployed to run in the second processing layer or an external management layer, depending on a historical criterion that reflects the time frame of available historical data. In such embodiments, the application can run at the level at which such data is available. Thus, there is no need to transfer data further between processing layers, reducing the communication and processing load. Combined with the concept of shifted contextualization between layers, processing plant-specific data in the first processing layer introduces redundant data transfers, which are temporarily from the first processing layer to the second processing layer for contextualization and then back to the first processing layer to run the application.

[0052] Deployment may depend on input data. In a further aspect, deployment layer allocation may depend on data availability indicators, severity indicators, or latency indicators.

[0053] Data availability indicators may be associated with input data ingested by a containerized application. Based on such indicators, execution may be assigned to a deployment layer where the data is directly available or stored. For example, a first processing layer may be configured to provide asset or process-specific data, and a second processing layer may be configured to provide plant-specific data. A containerized application that ingests asset or process-specific data may be deployed to the first processing layer. Similarly, a containerized application that ingests plant-specific data may be deployed to the second processing layer. To avoid redundant data transfers and reduce load, applications may run in the processing layer that hosts the data.

[0054] The severity indicator may be a static or dynamic indicator. In the case of a static severity indicator for an asset, the asset group or plant may be predefined. In the case of a dynamic indicator, the severity indicator may be dynamically assigned based on output data from previous application executions or other application executions. For example, the severity indicator may be determined based on key performance parameters such as the health of the asset. If the health of an asset becomes more critical over time, the severity criteria may change, and as a result of such a change, the containerized application may run in a different deployment layer, for example, reducing data transfer latency. In one embodiment, if a containerized application ingests an asset or processes specific data for a particular asset, and the execution is assigned to a second processing layer instead of a first processing layer, the severity indicator may indicate that the severity indicator is met. If the health of a particular asset changes and more rigorous monitoring is required, for example, more frequently, and the execution is assigned to a second processing layer, the severity indicator may be reset to indicate that the severity indicator is not met. In such a case, the application may be assigned to the first processing layer.

[0055] Latency indicators may be static or dynamic. In the case of static indicators, latency requirements for assets, asset groups, parts of a plant, or the plant itself may be predefined. In the case of dynamic indicators, latency requirements may be dynamically assigned based on signatures of input data. Such signatures may relate, for example, to the frequency of changes in real-time measurement data derived from historical real-time measurement data. In one embodiment, a pump monitoring signal may exhibit a higher frequency than a heat exchanger monitoring signal. In such a case, a containerized application monitoring the pump may be deployed directly to the pump controller or at the asset level in the core processing system of the respective plant. For example, with respect to pumps, each containerized application may be deployed in a processing layer as close to the pump as possible to reduce latency. Thus, latency criteria may indicate the temporal importance of the containerized application.

[0056] Load indicators can be based on the processing and / or network load of each deployment layer. Furthermore, or alternatively, the application may run in a deployment layer that provides sufficient computing and storage resources to mitigate the processing load of other processing layers and avoid impacting the execution of critical applications. In such cases, input data can be forwarded to each deployment layer. This is particularly advantageous for applications requiring high computing loads or less time-critical processes, and therefore allowing for lower data transfer latency.

[0057] In a further embodiment, deployment may depend on system-tier tags associated with the containerized application. These system-tier tags may, for example, constitute an integration application that deploys, runs, and monitors the containerized application. In such a case, the containerized application may include an application identifier that the integration application can use for identification during deployment. Thus, deployment may be "hardwired" to ensure that critical applications run at the appropriate tier. Such a deployment scheme may be particularly relevant to containerized applications that monitor and / or control critical assets. In this context, critical assets are those whose failure would have a significant impact on plant operations. Here, plant-specific data refers to contextualized asset or process-specific data.

[0058] In a further embodiment, a containerized application is deployed to multiple assets or plants of the same type. Assets of the same type may relate to assets with similar functionality and / or similar characteristics, such as performance characteristics, from the same supplier. Plants of the same type may relate to plants that produce similar intermediate or final products, have similar physical asset structures, or are based on similar chemical processes. Here, "same" means equivalent in the sense that the behavior of the asset or plant does not deviate beyond a tolerance or exhibits behavior that can be modeled in a single model. Preferably, a containerized application is associated with an asset or plant identifier. Such an identifier may be a configuration setting for an integrated application or a containerized application. The plant or asset identifier may be one-dimensional, representing one plant or asset type on which the containerized application runs, or multi-dimensional, representing multiple plants or assets. In particular, if the first processing layer is different processing unit plants, or asset or process specific, such asset identifiers allow for simultaneous addressing of assets associated with different processing units, thus enabling highly efficient deployment of the containerized application.

[0059] In a further embodiment, the containerized application is modified based on the input and output data of containerized applications run on multiple assets or plants of the same type. This may be run periodically at predefined times or dynamically. By aggregating such data, the accuracy of the containerized application, particularly the plant or asset model, can be verified or improved. In this way, the model can be adapted to reflect the behavior of the physical plant or asset. This is particularly important for maintenance cycles or lifecycles, as the physical plant or asset may shift its behavior depending on the stage of such a cycle. Such shifts can be compensated for by the proposed system through automation and self-optimization.

[0060] In a further embodiment, the containerized application is monitored based on confidence levels of input data, asset models, or plant models. The containerized application may provide confidence levels of output data, etc. Each operation may be incorporated into the containerized application via asset models, plant models, or individual models. Confidence levels of input data may be generated, for example, by analyzing patterns in real-time measurement data. Confidence levels for asset or plant models may be generated as part of the execution of model operations based on input data.

[0061] In a further embodiment, an event signal is triggered if the confidence level falls below a confidence threshold, for example, if the confidence level is less than 70%, 80%, or 90%. Such an event signal may indicate an asset failure or application malfunction. If an abnormal pattern is detected in the input data and the respective confidence levels fall below a threshold, the application execution may be stopped, and a failure signal may be passed to a control instance of the first processing layer of the chemical plant. Alternatively, such a failure signal may be passed to, for example, a monitoring instance of the first or second processing layer, or to a client application for display to, for example, an operator. In this way, sensor failures and the need for retrofitting can be detected.

[0062] In a further embodiment, when the confidence level exceeds a threshold, a change to the asset or plant model is triggered. The change may be triggered automatically. Such changes may include hardware or software changes. For example, the change may include retraining the asset or plant model based on historical data, adapting input data channels, adapting scenario matrices, or triggering event signals to maintain hardware such as Internet of Things (IoT) devices.

[0063] In a further embodiment, changes to the asset or plant model are performed in a second processing layer, particularly a processing management system, or an external processing layer. Since such changes do not interfere with the containerized application in the first place, they can be computed in the external processing layer, reducing the processing load on monitoring and / or control in more critical processing layers such as the first and second processing layers.

[0064] In a further embodiment, external containerized applications from a third-party environment are provided and deployed for execution in the external processing layer. Applications from a third-party environment mean any applications not created in the first processing layer, second processing layer, and external processing layer's own systems. In this way, the risk of third-party containerized applications infecting the chemical plant's monitoring and / or control system is mitigated.

[0065] In a further embodiment, the creation of a new containerized application is performed in an external processing layer. For example, an asset or plant model may be trained in the external processing layer. Preferably, the external processing layer is configured to store aggregated data from multiple chemical plants. A second processing layer may be configured to provide aggregated data from one or more chemical plants to the external processing layer. The second processing layer may be configured to transfer the aggregated data to the external processing layer in real time or on demand. The second processing layer may be configured to delete at least a portion of the data transferred to the external processing layer.

[0066] Exemplary embodiments of this disclosure are shown in the accompanying drawings. However, it should be noted that the accompanying drawings only illustrate specific embodiments of this disclosure and should not be considered to limit its scope. The technical teachings may encompass other equally effective embodiments. [Brief explanation of the drawing]

[0067] [Figure 1]This is a first schematic diagram of a system for monitoring and / or controlling one or more chemical plants. [Figure 2] This is a second schematic diagram of a system for monitoring and / or controlling one or more chemical plants. [Figure 3] This is a third schematic diagram of a system for monitoring and / or controlling one or more chemical plants. [Figure 4] Figures 1 to 3 are schematic diagrams illustrating the concept of data contextualization in a system. [Figure 5] This is a schematic flowchart of a method for monitoring and / or controlling one or more chemical plants. [Figure 6] This is a schematic diagram of a system for monitoring and / or controlling one or more chemical plants via a containerized application. [Figure 7] This is a schematic flowchart illustrating how to monitor and / or control a chemical plant with multiple assets. [Figure 8] This is a schematic diagram of a system configured for data and application transfer, for monitoring and / or controlling multiple chemical plants in different secure networks. [Modes for carrying out the invention]

[0068] Detailed explanation In petrochemical processing, industrial production typically begins with upstream products and is used to extract further downstream products. To date, the production of the value chain from various intermediate products to final products has been severely limited and based on siloed infrastructure. This hinders the adoption of new technologies such as IoT, cloud computing, and big data analytics.

[0069] Unlike other manufacturing industries, the processing industry is subject to extremely high standards, particularly regarding availability and security. For this reason, computing infrastructure is typically unidirectional and siloed, and access to monitoring and control systems for chemical plants is severely restricted.

[0070] Generally, chemical manufacturing plants are integrated into enterprise architectures in a siloed manner at various levels to functionally separate operational technology and information technology solutions.

[0071] Level 0 relates to physical processing and defines the actual physical processing of the plant. Level 1 relates to intelligent devices for detecting and manipulating physical processing, for example, through processing sensors, analyzers, actuators, and associated instrumentation. Level 2 relates to control systems for supervising, monitoring, and controlling physical processing. This includes real-time control and software, i.e., DCS, human-machine interface (HMI), monitoring, and data acquisition (supervisory). control Level 3 relates to manufacturing operations systems for managing production workflows to produce the desired product. Typical components include batch management, manufacturing execution / operations management systems (MES / MOMS), labs, maintenance, plant performance management systems, data historians, and related middleware. Control and monitoring timeframes may be shifts, hours, minutes, or seconds. Level 4 relates to business logistics systems for managing business-related activities of manufacturing operations. ERP is the primary system, establishing basic plant production schedules, material usage, shipments, and inventory levels. Timeframes may be months, weeks, days, or shifts.

[0072] Furthermore, because such structures adhere to strict one-way communication protocols, there is no data flow to Level 2 or lower. Such architectures do not include the enterprise or the external internet. However, this model remains an essential concept within the realm of cybersecurity. In this context, the challenge is to leverage the benefits of cloud computing and big data while ensuring the established advantages of existing architectures—that is, high availability and reliability of low-level systems (Level 1 and Level 2) controlling chemical plants and cybersecurity.

[0073] The technical teachings presented here may systematically modify this framework by enhancing monitoring and / or control, and introduce new capabilities that are compatible with the existing architecture. This disclosure is particularly relevant to highly scalable, flexible, and available computing infrastructure for processing industries, while simultaneously adhering to high security standards.

[0074] Figure 1 shows a first schematic diagram of a system 10 for monitoring and / or controlling a chemical plant 12.

[0075] System 10 comprises two processing layers, including a first processing layer in the form of a core processing system 14 associated with each chemical plant 12, and a second processing layer 16 in the form of a processing management system associated with, for example, two chemical plants 12. The core processing system 14 is communicatively coupled to the second processing layer 16, enabling unidirectional or bidirectional data transfer. The core processing system 14 includes a set of distributed processing units associated with the assets of the chemical plants 12.

[0076] The core processing system 14 and the second processing layer 16 are configured within a secure network 18, 20, which conceptually includes two security areas. The first security area is located at the core processing system 14 level, and the first firewall 18 controls incoming and outgoing network traffic to and from the core processing system 14. The second security area is located on the second processing layer 16, and the second firewall 20 controls incoming and outgoing network traffic to and from the second processing layer 16. Such a decoupled network architecture can protect vulnerable plant operations from cyberattacks.

[0077] The core processing system 14 provides processing or asset or processing-specific data 22 of the chemical plant 12 to the second processing layer 16. The second processing layer 16 is configured to contextualize the processing or asset or processing-specific data of the chemical plant 12. The second processing layer 16 is further configured to provide plant-specific data 24 of the chemical plant 12 to an interface 26 to an external network. Here, the plant-specific data may refer to the contextualized processing or asset or processing-specific data.

[0078] Process or asset, or process-specific data, may include value, quality, time, units of measurement, and asset identifiers. Contextualization may add further context, such as plant identifiers, plant types, reliability indicators, or plant alarm limits. In the next step, application context (such as model identifiers and third-party exchanges) may be added in addition to the technical asset structure and other asset management (such as asset networks) of one or more plants or sites.

[0079] The second processing layer 16 is communicably coupled to the external processing layer 30 via an interface 26 to an external network. The external processing layer 30 may be a computing or cloud environment that provides virtualized computing resources such as data storage and computing power. The second processing layer 16 is configured to provide plant-specific data 24 from one or more chemical plants 12 to the external processing layer 30. Such data may be provided in real time or on demand. The second processing layer 16 is configured to manage data transfer to and / or from the external processing layer in real time or on demand. The second processing layer 16 may provide plant-specific data 24 to the interface 26 to the external network based, for example, on identifiers added by contextualization. Such identifiers may be confidentiality identifiers based on the fact that such data is not provided to the interface 26 to the external network. The second processing layer 16 may be further configured to delete at least a portion of the data transferred to the external processing layer 30.

[0080] The external processing layer 30 is configured to aggregate plant-specific data from multiple chemical plants and / or to store historical data from multiple chemical plants. In this way, data storage can be externalized, the required on-premises storage capacity can be reduced, and historical transfers are made redundant. Furthermore, such a storage concept allows historical data to be stored in a second processing layer 16 of the hot window, which is a critical time frame that enables system 10 to monitor and / or control chemical plants in island mode without external network connectivity. In this way, the availability of system 10 for monitoring and / or control is always guaranteed.

[0081] The second processing layer 16 and the external processing layer 30 are configured to host and / or organize processing applications. In particular, the second processing layer 16 can host and / or organize processing applications related to core plant operations, and the external processing layer 30 may be configured to host and / or organize processing applications related to non-core plant operations.

[0082] Furthermore, the second processing layer 16 and the external processing layer 30 may be configured to organize data visualization, to organize computing workflows, to organize data calculations, to organize APIs to access data, to organize metadata for data storage, transfer, and calculation, to provide users, such as operators, with an interactive plant data working environment, and to verify and improve data quality, for example, by exchanging data with third-party management systems through the integration of third-party external processing layers.

[0083] Figure 2 shows a second schematic diagram of a system 10 for monitoring and / or controlling one or more chemical plants 12.

[0084] The system 10 shown in Figure 2 is similar to the system shown in Figure 1. However, the system in Figure 2 has a second processing layer comprising a processing management system 32 and an intermediate processing system 34. The intermediate processing systems 34.1 and 34.2 are configured in a secure area of ​​the network via a firewall 40.

[0085] Intermediate processing systems 34.1, 34.2 may be configured to take in processing or asset or processing-specific data 22 from individual or multiple chemical plants 12. Such data is contextualized at the plant level in intermediate processing systems 34.1, 34.2, and plant-specific data 38 may be provided to a processing management system 32, for example, Verbund or site-level and plant-level overall contextualization may be performed. In this setup, the contextualization of the data is shifted across different system layers 10, with each layer 14, 34, 32 mapping the contextual information available in its respective layer 14, 34, 32.

[0086] Figure 3 shows a third schematic diagram of a system 10 for monitoring and / or controlling one or more chemical plants 12.

[0087] The system 10 shown in Figure 3 is similar to the systems shown in Figures 1 and 2. However, the system in Figure 3 includes a monitoring device 44 that is communicatively coupled to a processing management system 32 or an external processing layer 30. The monitoring device 36 may be configured to transfer monitoring data to the processing management system 32 or the external processing layer 30. The processing management system 32 or the external processing layer 30 may be configured to manage multiple monitoring devices 44. Since such IoT devices are not considered trustworthy, the monitoring data provided by the monitoring devices 44 may be tagged in one direction, and any control loops associated with the chemical plant 12 may include filters for such tags. Thus, such data is not used for the management of the chemical plant 12.

[0088] Figure 4 shows a schematic diagram of the data contextualization concept of System 10, as shown in Figures 1 to 3.

[0089] The system 10 in Figures 1-3 includes two internal processing layers 14, 16, 32, 34 and an external processing layer 30. The first processing layer 14 may be a distributed control system for supervising, monitoring, and controlling physical processing within the chemical plant 12. The first processing layer 14 may be configured to provide processing or asset or processing-specific data. The second processing layers 16, 32, 34 may include an intermediate processing system 34 and a processing management system 32. The intermediate processing system 34 may be configured as an edge computing layer. Such a layer may be associated with level 3 of an individual plant. The intermediate processing system 34 is, • Collection of processing or asset or processing-specific data, • Interaction with Level 2 basic automation systems. Based on what is known at Level 2 and Level 1, initial contextualization (bottom-up approach) is performed, where context is added within distributed edge devices. It can be configured for this purpose.

[0090] The processing management system 32 can be configured as a centralized edge computing layer. Such a layer can be associated with Level 4 of multiple plants. The processing management system 32 is • Integration of data from various distributed edge devices, including intermediate processing systems 34 or monitoring devices 44. • Further contextualization (bottom-up approach) where additional context is added based on distributed pre-processed context within distributed edge devices. It can be configured for this purpose.

[0091] The external processing layer 30 may be configured as a centralized cloud computing platform. Such a platform may be associated with Level 5 of multiple plants. The external processing layer 30 may be configured as a manufacturing data workspace with complete data integration across multiple plants, including the transfer and streaming of manufacturing data history and the collection of all data from all edge components. In this way, the complete contextualization of all lower-level contexts can be integrated into the external processing layer 30 of multiple plants. Therefore, the external processing layer 30 is • Run cloud-native apps, • Connects to external PaaS and SaaS tenants, • Integrate machine learning with manufacturing data and processing, training-test-deployment, Visualize data, access apps, and organize them. It can be further configured in this way.

[0092] The system architecture can realize the concept of bottom-up contextualization. This concept is shown in Figure 4. In the bottom-up concept, all information available at lower levels may already be added to the data as attributes so that the lower-level context is not lost. Here, the first processing layer 14 as the lowest context level may include contextualized measurements 11 with respect to the items 13 on which measurements were taken. The intermediate processing system 34 can be further contextualized by adding tags 15 related to individual chemical plants 12. The processing management system 32 can be further contextualized by adding tags 17 related to multiple chemical plants 12 and / or business information. The external processing layer 30 can be further contextualized by adding tags 19 related to multiple plants and / or external contextual information, for example, from a third party.

[0093] The concept of contextualization can cover at least two basic types of context. One type may be a functional location within a production environment, including multiple chemical plants. This can cover information about what and where this data point represents within the production environment. Examples include connections to functional locations and attributes related to the physical assets from which the data is collected. This context can be useful in later applications to explain which data is available at which plants and assets.

[0094] Another type may be a classification of confidentiality. Such tags may be added at the lowest possible level, and this information may be propagated to further processing layers. Such tags may be added automatically or manually. For example, technical measures such as filters embedded in a firewall may automatically prevent "strictly confidential" data from being integrated up to the external processing layer 30. When data is shared with an external party, it is automatically notified that "confidential data" is being shared. An automated contract check may be implemented to verify whether this data can be shared with this external party.

[0095] Overall, this contextualization concept enables highly efficient data usage in processing applications deployed at any layer of the system.

[0096] Figure 5 shows a schematic flowchart of a method for monitoring and / or controlling one or more chemical plants.

[0097] Preferably, the method is performed on a distributed computing system shown in Figures 1 to 3, which includes a first processing layer 14 associated with a chemical plant 12 and communicatively coupled to second processing layers 16, 32, and 34. The method may perform all the steps described in the context of Figures 1 to 4, including steps related to contextualization, data processing, processing application management, and monitoring device management.

[0098] In the first step 61, processing or asset or processing-specific data of the chemical plant 12 is provided to the second processing layers 16, 32, and 34 via the first processing layer 14.

[0099] In the second step 63, the processing or asset or processing-specific data is contextualized through the second processing layers 16, 32, and 34 to generate plant-specific data.

[0100] In the third step 65, plant-specific data for one or more chemical plants 12 is provided to the interface 26 to the external network via the second processing layers 16, 32, and 34.

[0101] In the fourth step 67, one or more chemical plants are monitored and / or controlled via the second processing layers 16, 32, 34 or the first processing layer 14 based on processing or asset data or processing-specific or plant-specific data. Monitoring and / or control of one or more chemical plants 12 may be performed via the second processing layers 16, 32, 34 or the external processing layer 30 based on plant-specific data. Furthermore, monitoring and / or control may be performed via the first processing layer 14 based on processing or asset data or processing-specific data. Such monitoring and / or control may be performed via processing applications that take in the respective data and provide monitoring and / or control of the output, as further shown in Figures 6 to 8.

[0102] Figure 6 shows a schematic diagram of a distributed computing system for monitoring and / or controlling one or more chemical plants with multiple assets via a distributed computing system 10 with two or more deployment layers 14, 16, and 30.

[0103] The schematic diagram in Figure 6 represents a containerized application organization across various deployment layers 14, 16, and 30. System 10 includes an external processing system 30, a second processing layer 16, and a first processing layer 14. Here, the second processing layer 16 may contain larger storage and computing resources than the first processing layer 14, and / or the external processing layer 30 may contain larger storage and computing resources than the second processing layer 16. The architecture and functionality of system 10 may conform to the architecture and functionality described with respect to Figures 1 to 3. In particular, the first and second processing layers 14 and 16 may be configured within secure networks 20, 40, and 18. The first processing layer 14 may be communicatively coupled to the second processing layer 16, and the second processing layer 16 may be communicatively coupled to the external processing layer 30 via an external network 24.

[0104] The organizing applications 56 and 58 may be hosted by the external processing layer 30 and the second processing layers 16, 32, and 34, respectively. Thus, the containerized applications or container images 48 and 50 may be stored in the registries of the external processing layer 30 and the second processing layers 16, 32, and 34, respectively. The containerized applications 48 and 50 for execution may include one or more operations for taking in input data, providing input data to each asset or plant model that generates output data, and providing the generated output data for controlling and / or monitoring the chemical plant 12. In this way, the external processing layer 30 and the second processing layers 16, 32, and 34 act as facilitating layers that reduce the computing and storage resources required by the first processing layer 14 at the asset level.

[0105] Figure 7 shows a schematic flowchart of a method for monitoring and / or controlling a chemical plant 12 having multiple assets via a distributed computing system 10, which can be run using the system 10 shown in Figures 1 to 4.

[0106] In the first step 60, containerized applications 48, 50 are provided, which include input data, output data, and asset or plant templates that specify asset or plant models. 、 50 may be created on the external processing layer 30 or modified on the second processing layer 30. External containerized applications from a third-party environment may be provided.

[0107] In the second step 62, the containerized applications 48, 50 are deployed to run on at least one of the deployment layers 30, 32, 16, 34, 14, which are assigned based on input data, load indicators, or system layer tags, and the containerized applications 48, 50 run on the assigned deployment layer 30, 32, 16, 34, 14 to generate output data for controlling and / or monitoring the chemical plant 12. The deployment may be managed by an integration application 56, 50 that manages the deployment of the containerized applications 48, 50 based on input data, load indicators, or system layer tags. The integration application may be hosted by a second processing layer 16, 23, 34 and / or an external processing layer 30. Integration applications 56, 58 hosted by the second processing layers 16, 32, 34 manage critical containerized applications 48, 50, while organization applications 56, 58 hosted by the external processing layer 30 may manage non-critical containerized applications 48, 50. Allocations to deployment layers 30, 32, 34, 16, 14 may be based on input data that depends on data availability indicators, severity indicators, or latency indicators. Containerized applications from third-party environments can be deployed and run in the external processing layer 30.

[0108] Organization applications 56 and 58 may be hosted by an external processing layer 30 and a second processing layer 16, respectively. Organization applications 56 and 58 may deploy containerized applications 48 and 50 to any deployment layer 30, 16, and 14. The containerized applications 48 and 50 may then be executed in their respective deployment layers 30, 16, and 14 by running the processing applications 46, 52, and 54 in a sandbox-type environment. Deployment layers 30, 16, and 14 may be assigned based on input data, load indicators, or system layer tags. For example, the management of a critical containerized application 50 may optionally be assigned to the second processing layer 16 based on historical criteria that reflect the time frame of historical data available on the first or second processing layer 16. Advantageously, containerized applications 48 and 50 may be deployed to multiple assets or plants of the same type. Furthermore, containerized applications 50, 48 can be modified based on input and output data provided by containerized applications 46, 52, 54 that have been run on multiple assets or plants of the same type.

[0109] In the third step 64, the containerized applications 48, 50 may be monitored during or after each execution based on confidence levels of input data, asset models, or plant models. Based on the resulting confidence levels, event signals or changes to the asset or plant model may be triggered. Such triggers may be set to determine whether the confidence level exceeds a threshold. Such thresholds may be predefined or dynamic. If a trigger is set, changes to the asset or plant model may be performed, for example, in the second processing layers 16, 32, 34 or the external processing layer 30.

[0110] In the fourth step 66, the generated output data is provided for controlling and / or monitoring the chemical plant 12. Such output data may be passed to persistent instances after the execution of containerized applications 48, 50. In particular, such output data may be passed to a control instance relating to the first processing layer 14 of the chemical plant 12, for example. Alternatively, such output data may be passed to monitoring instances on the first processing layer 14, second processing layers 16, 32, 34, or external processing layer 30. The output data may be passed to a client application for display to an operator, for example, or to further containerized applications 48, 50 for execution.

[0111] Figure 8 shows a schematic diagram of a system 10.2, 10.2 for monitoring and / or controlling multiple chemical plants 12.1, 12.2 within different secure networks 20.1, 20.2 configured for data and processing application transfer. Figure 8 shows system 10 of Figures 1-3 as an example, including first and second processing layers 14, 16, 32, 34 and an external processing layer 30. Other system architectures may similarly be suitable for processing applications and data transfer. Both systems are associated with separate secure networks 20.1, 20.2 and are communicably coupled to external networks 24.1, 24.2 via interfaces 26.1, 26.2.

[0112] Systems 10.1 and 10.2 are configured to exchange processing or assets or processing-specific data or processing applications based on transfer tags. By adding transfer tags at the earliest possible level—where the data or application is generated or first enters the system—transfer tags become an inherent part of any data point or application as soon as they are added, traversing the data or application along the path through systems 10.1 and 10.2. Such transfer tags enable seamless and secure integration of external data sources or applications, and the transfer of data or applications to external resources.

[0113] In one case shown in Figure 8, application 48 is exchanged between systems 10.1 and 10.2. In this example, the containerized application 48 is transferred via external processing layers 30.1 and 30.2, which are communicatively coupled to the two systems 10.1 and 10.2. Here, external processing layer 30.1 is communicatively coupled to system 10.1, and external processing layer 30.2 is communicatively coupled to system 10.2. The exchange of the containerized application 48 is performed indirectly via external processing layers 30.1 and 30.2. The containerized application is tagged with a transfer tag that includes two transfer settings related to confidentiality settings and / or third-party transfer settings. Thus, for example, if a transfer using a third-party identifier is not associated with a third-party identifier stored in a database of permitted third-party transfers in the processing application 48, the transfer may be prohibited based on a compliance check by external processing layer 30.2. Similarly, processing or assets or processing-specific data may be transferred between systems 10.1 and 10.2. Next, any transfer between systems 10.1 and 10.2 may be followed by further transfers from the external processing layers 30.1 and 30.2 to their respective systems 10.1 and 10.2.

[0114] Furthermore, such transfers based on transfer tags can be performed directly between systems 10.1, 10.2 between processing layers 32, 16 associated with secure networks 20.1, 20.1. Such transfers based on transfer tags can be achieved via secure connections 74 between such layers 16, 23, such as VPN connections. Subsequently, any transfer between systems 10.1, 10.2 may be followed by further transfers between system components within secure networks 20.1, 20.2, or to external processing layers 30.1, 30.2 of each system 10.1, 10.2. By attaching transfer tags to any data point and processing application, third-party transfers between systems 10.1, 10.2 within separate secure networks 20.1, 20.1 can be securely handled, whether or not they are containerized.

[0115] Any component described herein used to implement the methods described herein may take the form of a distributed computer system having one or more processing devices capable of executing computer instructions. Components of a computer system may be connected (e.g., networked) in a communicative manner to other machines in a local area network, secure network, intranet, extranet, or the internet. Components of a computer system may operate as peer machines in a peer-to-peer (or distributed) network environment. Parts of a computer system may be a virtualized cloud computing environment, an edge gateway, a web appliance, a server, a network router, a switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify the actions performed by that machine. Furthermore, it should be understood that terms such as “computer system,” “machine,” and “electronic circuit” are not necessarily limited to a single component and include a collection of machines that individually or collectively execute a set of instructions (or sets of instructions) in order to perform one or more of the methodologies described herein.

[0116] Some or all of the components of such a computer system may be utilized by or illustrated by any of the components of System 10. In some embodiments, one or more of these components may be distributed across multiple devices or integrated into fewer devices than shown. Furthermore, some components may refer to physical components implemented in hardware, while others may refer to virtual components implemented in software on remote hardware.

[0117] Any processing layer may include general-purpose processing devices such as microprocessors, microcontrollers, and central processing units. More specifically, a processing layer may include a CISC (Complex Instruction Set Computing) microprocessor, a RISC (Reduced Instruction Set Computing) microprocessor, a VLIW (Very Long Instruction Word) microprocessor, or a processor implementing a processor or combination of other instruction sets. A processing layer may also include one or more dedicated processing devices such as an ASIC (Application-Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), a CPLD (Complex Programmable Logic Device), a DSP (Digital Signal Processor), and a network processor. The methods, systems, and devices described herein may be implemented as software in a DSP, microcontroller, or any other side processor, or as hardware circuitry within an ASIC, CPLD, or FPGA. The term "processing layer" can refer to one or more processing devices, such as a distributed system of processing devices deployed across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.

[0118] Any processing layer may include a suitable data storage device, such as a computer-readable storage medium, that stores one or more sets of instructions (e.g., software) that embody any one or more of the methods or functions described herein. Instructions may also reside, all or at least partially, in main memory and / or in the processor of a computer system, by processing devices that may constitute main memory and computer-readable storage medium. Instructions may further be transmitted or received over a network via a network interface device.

[0119] Computer programs for implementing one or more embodiments described herein may be stored and / or distributed on suitable media such as optical storage media or solid media supplied together with or as part of other hardware, but may also be distributed in other forms, such as the Internet or other wired or wireless communication systems. However, computer programs may also be presented over a network such as the World Wide Web and can be downloaded from such a network into the working memory of a data processor.

[0120] Terms such as “computer-readable storage medium” and “machine-readable storage medium” should be interpreted to include a single or multiple medium (e.g., a centralized or distributed database, and / or associated caches and servers) that stores one or more sets of instructions. Terms such as “computer-readable storage medium” and “machine-readable storage medium” should also be interpreted to include any temporary or non-temporary medium that is intended for machine execution, thereby enabling a machine to execute one or more of the methodologies of this disclosure by storing, encoding, or carrying a set of instructions. Accordingly, the term “computer-readable storage medium” should be interpreted to include, but is not limited to, solid memory, optical media, and magnetic media.

[0121] Some detailed explanations may be presented in terms of algorithms and symbolic representations of operations on data bits in computer memory. These descriptions and representations of algorithms are means used by those skilled in the art to most effectively communicate the nature of the work to others skilled in the art. An algorithm, as used herein and generally, is considered to be a self-consistent set of steps leading to a desired result. A procedure is one that requires the physical manipulation of physical quantities. These quantities, though not always, take the form of electrical or magnetic signals that can be stored, transferred, combined, compared, and otherwise manipulated. For reasons of common usage, it may be convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc.

[0122] However, it should be noted that all these and similar terms are associated with appropriate physical quantities and are merely convenient labels applied to those quantities. As will be evident from the previous discussion, unless otherwise specifically stated, throughout this explanation, terms such as “receive,” “retrieve,” “transmit,” “calculate,” “generate,” “add,” “subtract,” “multiply,” “divide,” “select,” “optimize,” “calibrate,” “detect,” “store,” “execute,” “analyze,” “decide,” “enable,” “identify,” “modify,” “transform,” “apply,” and “extract” are understood to refer to the operation and processing of a computer system or similar electronic computing device that manipulates and transforms data represented as physical (e.g., electronic) quantities in the registers and memory of a computer system and transforms it into other data, or other such information storage, transmission, or display devices, that are similarly represented as physical quantities in the memory or registers of a computer system.

[0123] It should be noted that embodiments of the present invention are described with reference to different subject matter. In particular, some embodiments are described with reference to method-type claims, and other embodiments are described with reference to system-type claims.

[0124] However, those skilled in the art will gather from the above and below descriptions that, unless otherwise notified, any combination of features relating to different subjects is also disclosed in this application, in addition to any combination of features relating to a certain type of subject matter. However, all features combined may provide more synergistic effects than the simple sum of the features.

[0125] Although the present invention has been illustrated and described in detail in the drawings and the foregoing description, such illustrations and descriptions should be considered illustrative or example and not limiting. In other words, the present invention is not limited to the disclosed embodiments. Other variations of the disclosed embodiments can be understood by those skilled in the art from the study of the drawings, disclosure and the appended claims and the claimed invention can be carried out. In some cases, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring this disclosure.

[0126] In the claims, the word “including” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude plurals. A single processor or controller or other unit may perform the functions of several items described in the claims. The mere fact that certain measures are described in different dependent claims does not imply that combinations of these measures cannot be used advantageously. Reference numerals in the claims should not be construed as limiting the scope.

Claims

1. A method for monitoring and / or controlling a chemical plant having multiple assets via a distributed computing system having three or more deployment layers, wherein the deployment layers include at least two of a first processing layer, a second processing layer, and an external processing layer, and the method is The steps include: providing a containerized application that includes input data, output data, and an asset or plant template specifying an asset or plant model; The distributed computing system includes the steps of deploying the containerized application and executing it in at least one of the deployment layers, the deployment layer being assigned based on the input data, load indicators, or system layer tags, executing the containerized application in the assigned deployment layer, and generating output data for controlling and / or monitoring the chemical plant. The distributed computing system provides the generated output data for controlling and / or monitoring the chemical plant, Includes, A method by which the containerized application is monitored based on the confidence level of the input data, the asset model, or the plant model.

2. The method according to claim 1, wherein the second processing layer includes larger storage and computing resources than the first processing layer, and / or the external processing layer includes larger storage and computing resources than the second processing layer.

3. The method according to claim 1 or 2, wherein the first and second processing layers are configured within a secure network, the first processing layer is communicatively coupled to the second processing layer, and the second processing layer is communicatively coupled to the external processing layer via an external network.

4. The method according to any one of claims 1 to 3, wherein the containerized application for execution includes one or more operations for taking in input data, providing the input data to each asset or plant model that generates output data, and providing the generated output data to control and / or monitor the chemical plant.

5. The method according to any one of claims 1 to 4, wherein the deployment is managed by an integrated application that manages the deployment of a containerized application based on the input data, the load indicator, or the system layer tags.

6. The method according to claim 5, wherein the integrated application is hosted by the second processing layer and / or the external processing layer.

7. The method according to claim 5 or 6, wherein the integrated application hosted by the second processing layer manages critical containerized applications, and the integrated application hosted by the external processing layer manages non-critical containerized applications.

8. The method according to any one of claims 5 to 7, wherein the management of critical containerized applications is assigned to the second processing layer based on historical criteria that reflect the time frame of historical data available in the first or second processing layer.

9. The method according to any one of claims 1 to 8, wherein the assignment of the deployment layer based on input data depends on a data availability indicator, a severity indicator, or a latency indicator.

10. The method according to any one of claims 1 to 9, wherein the containerized application is deployed to multiple assets or plants of the same type.

11. The method according to any one of claims 1 to 10, wherein the containerized application is modified based on the input data and output data provided by containerized applications run on multiple assets or plants of the same type.

12. The method according to any one of claims 1 to 11, wherein an event signal or a change in the asset or plant model is triggered when the confidence level falls below a confidence threshold.

13. The method according to claim 12, wherein the modification of the asset or plant model is performed in the second processing layer or the external processing layer.

14. The method according to any one of claims 1 to 13, wherein an external containerized application from a third-party environment is provided and deployed for execution on the external processing layer.

15. A system for monitoring and / or controlling a chemical plant having multiple assets with three or more deployment layers, wherein the deployment layers include at least two of a first processing layer, a second processing layer, and an external processing layer, and the system is It provides a containerized application that includes input data, output data, and asset or plant templates that specify assets or plant models. The containerized application is deployed and executed in at least one of the deployment layers, which are assigned based on the input data, load indicators, or system layer tags, and the containerized application is executed in the assigned deployment layer to generate output data for controlling and / or monitoring the chemical plant. To provide the generated output data for controlling and / or monitoring the chemical plant, It is configured in such a way, A system in which the containerized application is monitored based on the confidence level of the input data, the asset model, or the plant model.

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