MANUFACTURING SYSTEM FOR MONITORING AND/OR CONTROLLING ONE OR MORE CHEMICAL PLANT(S)
The disclosure relates to a system (10) for monitoring and/or controlling one or more chemical plant(s) (12), wherein the system (10) comprises a first processing layer (14) associated with the chemical plant (12) and communicatively coupled to a second processing layer (16), wherein the first processing layer (14) and the second processing layer (16) are configured in a secure network (18, 20), wherein the first processing layer (14) provides process or asset specific data (22) of the chemical plant (12) to the second processing layer (16), wherein the second processing layer (16) is configured to contextualize process or asset specific data to generate plant specific data and to provide plant specific data (24) of one or more chemical plant(s) (12) to an interface (26) to an external network.
The invention relates to a system and a method for monitoring and/or controlling one or more chemical plant(s) including a first processing layer associated with the chemical plant and communicatively coupled to a second processing layer.
BACKGROUNDChemical production is a highly sensitive production environment particularly with respect to security. Chemical plants typically include multiple assets to produce the chemical product. Multiple sensors are distributed in such plants for monitoring or controlling purposes and collect masses of data. As such chemical production is a data heavy environment. However, to date the gain from such data to increase production efficiency in one or multiple chemical plants has not been fully leveraged.
Applying new technologies in cloud computing and big data analytics is hence of great interest. Unlike other manufacturing industries, however, process industry is subject to very high security standards. For this reason, computing infrastructures are typically siloed with highly restrictive access to monitoring and control systems. Owing to such security standards, latency and availability considerations contravene a simple migration of to date embedded control systems to e.g. a cloud computing system. Bridging the gap between highly proprietary industrial manufacturing systems and cloud technologies is one of the major challenges in process industry.
WO2016065493 discloses a client device and a system for data acquisition and pre-processing of process-related mass data from at least one CNC machine or an industrial robot and for transmitting said process-related data to at least one data recipient, 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-realtime process-related data via at least one realtime data channel, and for recording non-realtime process-related data via at least one non-realtime data channel. The client device further comprises at least one data processing unit data-mapping at least the recorded non-realtime data to the recorded hard-realtime data to aggregate a contextualized set of process-related data. Moreover, the client device comprises at least one second data interface for transmitting the contextualized set of process-related data to the data recipient and for further data communication with the data recipient.
WO2019138120 discloses a method for improving a chemical production process. A plurality of derivative chemical products are produced through a derivative chemical production process based on at least some derivative process parameters at a respective chemical production facility, which chemical production facilities each comprises a separate respective facility intranet. At least some respective derivative process parameters are measured from the derivative chemical production process by a respective production sensor computer system within each facility intranet. A process model for simulating the derivative chemical production process is recorded in a process model management computer system outside the facility intranets.
US20160320768A1 discloses an example network environment for monitoring plant processes with system computers operating as a root-cause analyzer. The system computers communicate with the data server to access collected data for measurable process variables from a historian database. The data server is communicatively coupled to a distributed control system (DCS) in turn communicating collected data to the data server over communications network.
The object of the present invention relates to a highly scalable and flexible computing infrastructure for process industry, which adheres to the high security standards.
SUMMARYA system, preferably a distributed computing system, for monitoring and/or controlling one or more chemical plant(s) is proposed, wherein the system comprises first and a second processing layer, wherein the first processing layer is associated with the chemical plant and communicatively coupled to the second processing layer, optionally wherein the first processing layer and the second processing layer are situated, hosted or configured in or inside a secure network or associated with a secure network, wherein the first processing layer is configured to provide process or asset specific data of the chemical plant to the second processing layer, wherein the second processing layer is configured to contextualize process or asset specific data to generate plant specific data and to provide plant specific data of one or more chemical plant(s) to an interface to an external network.
A method, preferably a computer-implemented method, for monitoring and/or controlling one or more chemical plant(s) with a system, preferably a distributed computing system, comprising a first and a second processing layer, wherein the first processing layer is associated with the chemical plant and communicatively coupled to a second processing layer, optionally wherein the first processing layer and the second processing layer are situated, hosted or configured in or inside a secure network or associated with a secure network, wherein the method comprises the steps of:
-
- providing process or asset specific data of the chemical plant via the first processing layer to the second processing layer,
- contextualizing process or asset specific data via the second processing layer to generate plant specific data,
- providing plant specific data of the one or more chemical plant(s) via the second processing layer to an interface to an external network,
- optionally monitoring and/or controlling one or more chemical plant(s) via the second processing layer or the first processing layer based on the process or asset specific data or the plant specific data, further optionally monitoring and/or controlling one or more chemical plant(s) via the second processing layer based on the plant specific data.
The present invention further relates to a distributed computer program or computer program product with computer-readable instructions that, when executed on one or more processor(s), cause the processor(s) to perform the methods for monitoring and/or controlling one or more chemical plant(s) as described herein. The invention further relates to a computer readable non-volatile or non-transitory storage medium with computer-readable instructions that, when executed on one or more a processor(s), cause the processor(s) to perform the methods for monitoring and/or controlling one or more chemical plant(s) as described herein.
The proposed system and method allow for highly efficient data handling via the second processing layer. In particular, the system allows for seamless data access in different processing layers and cloud connectivity. This way the system enables bridging the gap between operational technology and information technology in the highly advanced process industry environment. By storing or providing contextualized data in a separate layer, the availability and performance of the first layer is not affected. Moreover, the system allows for data exchange with an external processing layer outside the secure network while adhering to high security standards in chemical industry. By introducing different process and storage system layers and communicatively coupling them, the mass data transfer and handling is distributed over different layers allowing for more flexibility in contextualization, storage and access for process applications. In particular, the proposed system can accommodate multiple chemical plants via the second processing layer. Hence the system is highly scalable enabling more reliable and more enhanced monitoring and/or controlling of chemical plant(s). This way new technologies like serverless IaaS/PaaS/SaaS can be integrated into the chemical production environment allowing for continuous application delivery and deployment.
Furthermore, the bottom up data concept of process or asset specific data plus plant specific data allows for more flexible handling of process applications. For instance, the deployment of process applications ingesting such data can be streamlined for multiple assets even in multiple plants. Additionally, depending on the specific data required by the process application and the computing resources required to run such application, the appropriate processing layer may be chosen, thus adhering to high availability standards in chemical plants. For instance, computationally heavy process applications ingesting plant specific may be executed on the second processing layer, while process applications ingesting process or asset specific data and requiring low latency may be executed on the first processing layer.
The following description relates to the system, the method, the computer program, the computer readable storage medium lined out above. In particular the systems, the input units, the computer programs and the computer readable storage media are configured to perform the method steps as set out above and further described below.
In the context of the present invention chemical plant refers to any manufacturing facility based on chemical processes, e.g. transforming a feedstock to a product using chemical processes. In contrast to discrete manufacturing, chemical manufacturing is based on continuous or batch processes. As such monitoring and/or controlling of chemical plants is time dependent and hence based on large time series data sets. A chemical plant may include more than 1.000 sensors producing measurement data points every couple of seconds. Such dimensions result in multiple terabytes of data to be handled in a system for controlling and/or monitoring chemical plants. A small-scale chemical plant may include a couple of thousand sensors producing data points every 1 to 10 s. For comparison a large-scale chemical plant may include a couple of ten-thousand sensors, e.g. 10.000 to 30.000, producing data points every 1 to 10 s. Contextualizing such data results in the handling of multiple hundred gigabytes to multiple terabytes.
Chemical plants may produce a product via one or more chemical processes transforming the feedstock via one or more intermediate products to the product. Preferably a chemical plant provides an encapsulated facility producing a product, that may be used as feedstock for the next steps in the value chain. Chemical plants may be large-scale plants like oil and gas facilities, gas cleaning plants, carbon dioxide capture facilities, liquefied natural gas (LNG) plants, oil refineries, petro-chemical facilities or chemical facilities. Upstream chemical plants in petro-chemicals process production for example include a steamcracker starting with naphtha being processed to ethylene and propylene. These upstream products may then be provided to further chemical plants to derive downstream products such as polyethylene or polypropylene, which may again serve as feedstock for chemical plants deriving further downstream products. Chemical plants may be used to manufacture discrete products. In one example one chemical plant may be used to manufacture precursors for polyurethane foam. Such precursors may be provided to a second chemical plant for the manufacture of discrete products, such as an isolation plate comprising polyurethane foam.
The value chain production via various intermediate products to an end product can be decentralized in various locations or integrated in a Verbund site or e chemical park. Such Verbund sites or chemical parks comprise a network of interconnected chemical plants, where products manufactured in one plant can serve as a feedstock for another plant.
Chemical plants may include multiple assets to run such plants, such as heat exchangers, reactors, pumps, pipes, distillation or absorption columns to name a few of them. In chemical plants some assets may be critical. Critical assets are those, which when disrupted critically impact plant operation. This can lead to manufacturing processes being compromised. Reduced product quality or even manufacturing stops may the result. In the worst-case scenario fire, explosion or toxic gas release may be the result of such disruption. Hence such critical assets may require more rigorous monitoring and/or controlling then other assets depending on the chemical processes and the chemicals involved. To monitor and/or control chemical processes and assets multiple actors and sensors may be embedded in the chemical plant. Such actors or sensors may provide process or asset specific data relating to for instance individual assets or processes e.g. the state of an individual asset, the state of an individual actor, the composition of a chemical, or the state of a chemical process. In particular, process or asset specific data include one or more of the following data categories:
process operation data, such as composition of a feedstock or an intermediate product,
process monitoring data, such as flow, material temperature,
asset operation data, such as current, voltage, and
asset monitoring data, such as asset temperature, asset pressure, vibrations.
Process or asset specific data refers to data relating to a specific asset or process and contextualized with respect to such specific asset or process. Process or asset specific data may be contextualized only with respect to individual assets and processes. Process or asset specific data may include measurement value, data quality measure, time, measurement unit, asset identifier for specific assets or process identifier for a specific process sections or stages. Such process or asset specific data may be collected on the lowest processing layer or the first processing layer and contextualized with respect to specific assets or processes in a single plant. Such contextualization may relate to context available on the first processing layer. Such context may relate to a single plant.
Plant specific data refers to process or asset specific data that is contextualized with respect to one or more plant(s). Such plant specific data may be collected on the second processing layer and contextualized with respect to multiple plants. Specifically, contextualization may relate to context available on the second processing layer. Via contextualization context such as plant identifier, plant type, reliability indicator, or alarm limits for the plant may be added to process or asset specific data points. In a further step technical asset structure of one or more plant(s), a Verbund site, other asset management structure (e.g. asset network), or application context (e.g. model identifier, third party exchange) may be added. Such overarching context can originate from functional locations or digital twins, such as digital piping and instrumentation diagrams, 3D models or scans with xyz coordinates of the plant assets. Additionally or alternatively local scans from mobile devices linked to e.g. piping and instrumentation diagrams may be used for contextualization.
In particular, plant specific data e.g. relating to production interfaces between chemical plants in a manufacturing chain may be provided e.g. between chemical plants across a manufacturing chain via the second processing layer or the external processing layer. Thus, monitoring and/or controlling, e.g. via anomaly detection, setpoint steering and optimization in manufacturing chains across multiple plants, can be enhanced. For monitoring and/or controlling the manufacturing chain across multiple plants process applications with online in/out data profiles may be used. Such data profiles and process applications may be transferred between plants in the manufacturing chain via the second processing layer or the external processing layer. Combined with mass and energy balances that can be monitored, such process applications may optimize the full chain across chemical plants rather than individual plants in the chain.
The process of contextualization refers to linking data points available in one or more storage unit(s). Such unit(s) may be persistent or non-volatile storage. Data points may relate to measurement values or context information. Storage unit(s) may be part of the first processing layer, the second processing layer, the external processing layer or distributed across two or more of those layers. The linking may be generated dynamically or statically. E.g. pre-defined or dynamically generated scripts may generate dynamic or static links between information data points in one processing layer or across processing layers. Links may be established by generating a new data object including the linked data itself and storing such new data object in a new instance. Any data point stored may be actively deleted, if a copy is stored elsewhere. Any data point thus copied from one storage unit to a new data object in the same or another storage unit may be deleted to reduce storage space. Additionally, or alternatively links may be established by generating a meta data object with embedded links to address or access respective data points in distributed storage unit(s). Any data point thus addressable or accessible through the meta data object may remain in its original storage unit. Linking such information to form a new data object may still be performed e.g. on the external processing layer. For the retrieval of data either data objects are accessed directly or meta data objects are used to address or access the data distributed in one or more storage unit(s). Any operations on such data such as applications may either access such data directly, may access a non-persistent image of such data, e.g. from cache memory, or a persistent copy of the data.
In one aspect the first processing layer is associated with one or a single chemical plant. The first processing layer may be a core process system including one or more processing devices and storage devices. Such layer may include one or more distributed processing and storage devices forming a programable logic controller (PLC) system or decentralized control system (DCS) with control loops distributed throughout the chemical plant. Preferably the first processing layer is configured to control and/or monitor chemical processes and assets on the asset level. Hence the first processing layer monitors and/or controls the chemical plant on the lowest level. Furthermore, the first processing layer may be configured to monitor and control critical assets. Additionally 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 directly or indirectly to the second processing layer.
In a further aspect the second processing layer is associated with more than one chemical plant. The second processing layer may include a process management system with one or more processing and storage devices. Preferred the second processing layer or process management system is configured to manage data transfer to and/or from the first processing layer. Further preferred the second processing layer or process management system is configured to host and/or orchestrate process applications. Such process applications may monitor and/or control one or more chemical plant(s) or one or more asset(s). The process management system may be associated with one or more chemical plants. In other words, the process management system may be communicatively coupled to multiple first processing layers associated with one or more chemical plant(s).
In a further aspect the second processing layer may comprise an intermediate processing sys- tem and a process management system. Here the intermediate processing system may be communicatively coupled to the first processing layer, preferably the core process system, and the process management system may be communicatively coupled to the intermediate layer. Preferably the first processing layer and the process management system are coupled or communicatively coupled via the intermediate processing system. The intermediate processing sys- tem may be configured to collect process or asset specific data provided by the first processing layer. The process management system may be configured to provide plant specific data of one or more chemical plant(s) to the interface to the 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 first processing layer of one chemical plant or to multiple first processing layers of multiple plants. The process management system may be communicatively coupled to one or multiple intermediate processing systems. Adding the intermediate processing level to the second processing layer adds a further security layer. It fully detangles the virulent first processing layer from any external network access. Additionally, the intermediate level allows for more enhanced data handling by reducing data transfer rates to the external processing layer via pre-processing and enhancing data quality by contextualization. The intermediate processing system and process management system may comprise one or more processing and storage devices.
In a further aspect the secure network is a segregated network including more than two security zones separated by firewalls. Such firewalls may be network or host-based virtual or physical firewalls. The firewall may be hardware- or software-based to control incoming and outgoing network traffic. Here predetermined rules in the sense of a white listing may define allowed traffic via access management or other configuration settings. Depending on the firewall configuration the security zones may adhere to different security standards.
In a further aspect the first processing layer is configured in or inside or associated with a first security zone e.g. via a first firewall and the second processing layer is configured in or inside or associated with a second security zone e.g. via a second firewall. To securely protect the first processing layer, the first security level may adhere to a higher security standard than the second security level. Security levels may adhere to a common industry standard such as lined out in Namur documentation IEC 62443. The second processing layer may provide further segregation via security zones. For example, the intermediate processing system may be configured in or inside or associated with a third security zone via a third firewall and the process management system may be configured in or inside or associated with the second security zone via the second firewall. The third and second security zones may be staggered in terms of security standard as well. For instance the third may adhere to a security standard higher than the second. This allows for higher security standards on the lower security zone of the first processing layer and lower security standards on higher security zones of the second processing layer. In one embodiment the first processing layer is inside a first security zone, the process management system is inside the second security zone and the intermediate processing system is inside a third security zone.
The second processing layer may be configured to contextualize process or asset specific data. This way the performance of the first processing layer is not affected. Since typical core process systems in older plants do not have the required computing power, adding a further system with higher performance even enables contextualization. Additionally the second processing layer and in particular the intermediate processing system allows for data contextualization on a plant level rather than an asset level. Data contextualization in the present context relates to adding context information to process or asset specific data or to reducing the data size by pre-processing process or asset specific data. Adding context may include adding further information tag(s) to the process or asset specific data. Pre-processing may include filtering, aggregating, normalizing, averaging, or inference of process or asset specific data.
In further aspect unidirectional or bidirectional communication, e.g. data transfer or data access, may be realized for data streams between different processing layers. In other words the system may be configured to allow for unidirectional or bidirectional communication, e.g. data transfer or data access between different processing layers. One data stream may include process or asset specific data from the first processing layer being passed to and contextualized via the second processing layer and communicated to the external processing layer. Contextualization may be performed on 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 process or asset specific data or plant specific data. Furthermore, depending on criticality of the process or asset specific data or the plant specific data such data may be assigned for unidirectional or bidirectional communication. In other words, the system may be configured to assign unidirectional or bidirectional communication to process or asset specific data or the plant specific data depending on criticality of the process or asset specific data or the plant specific data. E.g. data communication from the second or external processing layers to critical assets may be prohibited by realizing a diode type communication channel. Such communication may only allow for unidirectional communication from the critical asset to the processing layers but not vice versa.
In further aspect data streams may be assigned critical or non-critical data. In other words, the system may be configured to assign critical or non-critical data tags. Critical data refers to data that is critical to operate the chemical plant, such as short-term data, from which operation points of the chemical plant are derived. Such critical data may cover a short term horizon of, e.g. hours or days up one or more week(s), which is required to operate the plant in its optimal state. Non-critical data refers to data that is not critical to operate the chemical plant, such as mid- to long-term data for monitoring the chemical plant based on mid- to long-term behavior. Such non-critical data may cover a mid- to long-term time horizon, e.g. multiple weeks or months up to one or more year(s), which is required to monitor and/or control asset(s) or plant(s) e.g. over a time span. Such data may also be referred to as cold, warm and hot data, wherein the hot data corresponds to critical data, the warm data corresponds to mid-term non-critical data and cold data corresponds to long-term non-critical data.
In a further aspect data contextualization is staggered across system layers, processing layers or processing systems included in such processing layers with each layer mapping context information available in the respective layer. In other words, the system may be configured to stagger data contextualization across system layers, processing layers or processing systems included in such processing layers with each layer mapping context information available in the respective layer. Staggering may include contextualization of asset or process specific data on different levels adding context information on single plant level and/or on multi plant level. In the layered system architecture context information available in one layer may be mapped to data provided by the lower layer or processing system. Here lower means closer to the chemical plant data access. For example, the process or asset specific data provided by the first processing layer may include context information on the asset level. In other words, the first processing layer may be configured to provide the process or asset specific data including context information on the asset level. Such context information may relate to real time information such as a measurement value, measurement quality, product quality, batch related data, or measurement time. Context information on asset level may further relate to asset specific information such as an asset identifier, intralogistics or measurement unit identifier. The intermediate system and the process management system may be configured to add further context information to or to contextualize such process or asset specific data. Such context information may relate to the plant level rather than the asset level. Context information for instance relates to plant context such as plant identifier, plant type, reliability indicator, alarm limits, or application context such as model identifier, third party exchange identifier, confidentiality identifier. This way the data quality can be enhanced to maximize context and with that data management and the resulting monitoring and/or controlling capabilities via process applications.
In a further aspect the intermediate processing system is configured to contextualize data by mapping the heterogenous process or asset specific data to a homogeneous data format on plant level. In this context heterogeneous refers to data that relates to the asset level or multiple assets individually, while homogeneous data refers to data that relates to a combination or equal types of assets in a plant. The intermediate processing system may be configured to provide such plant specific data to the process management system. The process management system may further be configured to contextualize the plant specific data provided by the intermediate processing system preferably on multi plant level. Such contextualization may include adding context information on a multiple plant level or on a site level such as multi plant or site context including technical asset structure of one or multiple plant(s) or asset management information such as asset network. Additionally or alternatively such contextualization may include adding application context such as model identifier, third party exchange identifier or a confidentiality identifier.
The second processing layer, preferably the process management system, may be communicatively coupled to an external processing layer via an external network. The second processing layer, preferably the process management system, may be configured to manage data transfer to and/or from the external processing layer e.g. in real time or on demand. The second processing layer, preferably the process management system, may for instance be configured to provide plant specific data to the interface to the external network based on an identifier added by way of contextualization. Such identifier may be a confidentiality identifier based on which such data is not provided to the interface to the external network.
The external processing layer may be a computing or cloud environment providing virtualized computing resources, like data storage and computing power. The external processing layer may provide a private, hybrid, public, community or multi cloud environment. Cloud environments are advantageous, since they provide on demand storage and computing power. Additionally, in cases where multiple chemical plants operated by different parties are to be monitored and/or controlled, data or process applications affecting the chemical plants may be shared in such cloud environment.
In a further aspect the second processing layer, preferably the process management system, is configured to provide plant specific data from one or more chemical plant(s) to the external processing layer. The second processing layer, preferably the process management system, may be further configured to delete at least parts 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 plant(s). The external processing layer may be configured to aggregate, store or contextualize plant specific data from more than one chemical plant and/or to store historical data from more than one chemical plant. Here aggregation means the grouping of data via an aggregate function like a sum, an average or a mode. Aggregation hence relates to a function that reduces dimension or storage space. This way data storage can be externalized, and the required on-premise storage capacities can be reduced plus history transfer is made redundant. Furthermore, owing to the flexible computing and storage resources of the external processing layer and the fact that the data is available in the external processing layer, process applications can be built, trained, tested, or modified in the external processing layer.
In a further aspect the second processing layer, preferably the process management system, is configured to manage data transfer to and/or from the external processing layer in real-time or on demand. Real-time transfer may be buffered depending on network and computing loads on the interface to the external network. On demand transfer may be triggered in a predefined or dynamic manner. Preferred the data transfer to the external processing layer is managed in real-time and the transfer from the external processing layer is managed on demand.
In a further aspect the second processing layer, preferably the process management system, is configured to store or to manage access to historical data, real-time data and planning data. In a further aspect the second processing layer, preferably the process management system, is configured to store or to manage access to historical data for a first time window and the external processing layer is configured to store historical data for a second time window, wherein the first time window is shorter than the second time window. Here the first time window may correspond to a critical time window allowing the system to monitor and/or control the chemical plant in island mode without external network connection. The first time window may be viewed as a hot window, for which historical data is required to safely control and/or monitor the chemical plant. The first time window or hot window may be determined based on storage capacity of the second processing layer, preferably the process management system, or preferably by the process applications and the historical data required to execute on the process applications in island mode without external network connection. This way availability of the system for monitoring and/or controlling is always guaranteed.
In a further aspect the external processing layer is configured to host and/or orchestrate process applications. Such process applications may monitor and/or control one or more chemical plant(s) or one or more asset(s) or chemical process(es). The external processing layer may be configured to host and/or orchestrate process applications that monitor and/or control more than one chemical plant or more than one asset or chemical process. The external processing layer may be configured to host and/or orchestrate process applications for more than one chemical plant.
In a further aspect the second processing layer, preferably the process management system, is configured to host and/or orchestrate process applications relating to core plant operations and the external processing layer is configured to host and/or orchestrate process applications relating to non-core plant operations. Here the core plant operations may correspond to a critical operations allowing the chemical plant to run in island mode without external network connection.
In a further aspect the second processing layer, preferably the process management system, and/or the external processing layer are configured to build and deploy process applications in containers. In a further aspect the second processing layer, preferably the process management system, and/or the external processing layer are configured to orchestrate processing of a container and managing of data input and output of the container. Such processing may be orchestrated by a central master node or distributed among one or more compute node(s). In this context a container is an encapsulated environment running the contained application. A container image forms the basis for running the container and the contained application. It includes all software components like the application code, runtime, system libraries, system libraries, settings. For orchestration of containers available solutions like Docker and Kubernetes may be used. In such an embodiment the second processing layer, preferably the process management system, and/or the external processing layer are configured to monitor the state of a performance indicator of the container, a performance indicator of a process model or a health indicator of an asset or a plant.
In a further aspect the second processing layer, preferably the process management system, and/or the external processing layer are configured to exchange data with 3rd party management systems. This may be realized through a secure connection like VPN or via integration of a 3rd party or shared external processing layer. In a further aspect the second processing layer, preferably the process management system, and/or the external processing layer are configured to orchestrate data visualization, computing process workflows, data calculations, APIs to access data, metadata of data storage, transfer, calculation, provide interactive plant data working environment for user and can verify and improve data quality.
In a further aspect the first processing layer and/or the second processing layer are configured for monitoring and/or controlling one or more chemical plant(s) based on the process or asset specific data or the plant specific data. The second processing layer or the external processing layer may be configured for monitoring and/or controlling one or more chemical plant(s) based on the plant specific data. The first processing layer may be configured for monitoring and/or controlling one or more chemical plant(s) based on the process or asset specific data. Such monitoring and/or controlling may be performed through process applications ingesting respective data and providing monitoring and/or controlling output.
In a further aspect a monitoring device is communicatively coupled to the second processing layer, preferably the process management system, or the external processing layer and configured to transfer monitoring data to the second processing layer, preferably the process management system, or the external processing layer. Such transfer may be in real time or on demand. The monitoring sensor may be any Internet of things (IoT) device. The device may be part of or inside or associated with the secure network or not. Since the monitoring device only allows for unidirectional communication, it may even be directly coupled to the external processing layer without firewall in between. If it is coupled to the second processing layer, preferably the process management system, it may be part of the same security zone.
In a further aspect the second processing layer, preferably the process management system, or the external processing layer are configured to manage monitoring devices. Such management may include managing connectivity protocols of and access to such devices, monitoring the health status of such devices and managing inbound data received from such devices. In a further aspect multiple monitoring device across multiple plants are communicatively coupled to the second processing layer, preferably the process management system, or the external processing layer. Additionally, the monitoring device, the second processing layer or the external processing layer may be configured to tag monitoring data provided by such a monitoring or IoT device unidirectional. A control loop relating to the chemical plant may include a filter for such tag, such that such data is not used for control of the chemical plants, since it is not considered sufficiently reliable.
Example embodiments of the present disclosure are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only particular embodiments of the present disclosure and are therefore not to be considered limiting of its scope. The technical teaching may encompass other equally effective embodiments.
In petrochemicals process industrial production typically starts with upstream products, which are used to derive further downstream products. To date the value chain production via various intermediate products to an end product is highly restrictive and based on siloed infrastructure. This hampers introduction of new technologies such as IoT, cloud computing and big data analytics.
Unlike other manufacturing industries, process industry is subject to very high standards in particular with regard to availability and security. For this reason, computing infrastructures are typically unidirectional and siloed with highly restrictive access to monitoring and control systems of chemical plants.
In general chemical production plants are embedded in an enterprise architecture in a siloed way with different levels to make a functional separation between operational technology and information technology solutions.
Level 0 relates to the physical processes and defines the actual physical processes in the plant. Level 1 relates to intelligent devices for sensing and manipulating the physical processes, e.g. via process sensors, analyzers, actuators and related instrumentation. Level 2 relates to control systems for supervising, monitoring and controlling the physical processes. Real-time controls and software; DCS, human-machine interface (HMI); supervisory and data acquisition (SCADA) software are typical components. Level 3 relates to manufacturing operations systems for managing production work flow to produce the desired products. Batch management; manufacturing execution/operations management systems (MES/MOMS); laboratory, maintenance and plant performance management systems, data historians and related middleware are typical components. Time frames for controlling and monitoring may be shifts, hours, minutes, seconds. Level 4 relates to business logistics systems for managing the business-related activities of the manufacturing operation. ERP is the primary system and establishes the basic plant production schedule, material use, shipping and inventory levels. Time frame may be months, weeks, days, shifts.
Additionally, such structures adhere to strict one-way communication protocols allowing for no data flow into level 2 or below. Not covered in such architectures is the company or enterprise-external internet. The model remains, however, an essential concept within the realm of Cyber Security. Within this context, the challenge is to leverage the benefits of Cloud computing and Big Data, while still guaranteeing the established advantages of existing architectures: i.e. the high availability and reliability of the lower levels system (Level 1 and Level 2), that control the chemical plant, as well as the cyber security.
The technical teaching presented here allows for enhancing monitoring and/or control changing this framework in a systematic way, to introduce new capabilities that are compatible with existing architectures. The present disclosure specifically relates to a highly scalable, flexible and available computing infrastructure for process industry, which at the same time adheres to the high security standards.
The system 10 comprises two processing layers including the first processing layer in the form of a core process system 14 associated with each of the chemical plants 12 and the second processing layer 16, e.g. in the form of a process management system, associated with two chemical plants 12. The core process system 14 is communicatively coupled to the second processing layer 16 allowing for unidirectional or bidirectional data transfer. The core process system 14 comprises a decentralized set of processing units associated with assets of the chemical plant 12.
The core process system 14 and the second processing layer 16 are configured inside the secure network 18, 20, which in the schematic representation includes two security zones. The first security zone is situated on the core process system 14 level, where the first firewall 18 controls incoming and outgoing network traffic to and from the core process system 14. The second security zone is situated on the second processing layer 16, where the second firewall 20 controls incoming and outgoing network traffic to and from the second processing layer 16. Such segregated network architecture allows to shield vulnerable plant operations from cyberattacks.
The core process system 14 provides process or asset specific data 22 of the chemical plant 12 to the second processing layer 16. The second processing layer 16 is configured to contextualize the process or asset specific data of the chemical plants 12. The second processing layer 16 is further configured to provide plant specific data 24 of the chemical plants 12 to the interface 26 to the external network. Here the plant specific data may refer to contextualized process or asset specific data.
Process or asset specific data may include value, quality, time, measurement unit, asset identifier. Via contextualization further context such as plant identifier, plant type, reliability indicator, or alarm limits for the plant may be added. In a further step technical asset structure of one or multiple plant(s) or a site and other asset management (e.g. asset network), plus application context (e.g. model identifier, third party exchange) may be added.
The second processing layer 16 is communicatively coupled to an external processing layer 30 via interface 26 to the external network. The external processing layer 30 may be a computing or cloud environment providing virtualized computing resources, like 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 for instance provide plant specific data 24 to the interface 26 to the external network based on an identifier added by way of contextualization. Such identifier may be a confidentiality identifier based on which 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 parts of the data transferred to the external processing layer 30.
The external processing layer 30 is configured to store, contextualize or aggregate plant specific data from more than one chemical plant and/or to store historical data from more than one chemical plant. This way data storage can be externalized, and the required on-premise storage capacities can be reduced plus history transfer is made redundant. Additionally, such storage concept allows to store historical data on the second processing layer 16 for a hot window, which is a critical time window allowing the system 10 to monitor and/or control the chemical plant in island mode without external network connection. This way availability of the system 10 for monitoring and/or controlling is always guaranteed.
The second processing layer 16 and the external processing layer 30 are configured to host and/or orchestrate process applications. In particular the second processing layer 16 may host and/or orchestrate process applications relating to core plant operations and the external processing layer 30 may be configured to host and/or orchestrate process applications relating to non-core plant operations.
Furthermore, the second processing layer 16 and the external processing layer 30 may be configured to exchange data with 3rd party management systems, e.g. via integration of 3rd party external processing layer, to orchestrate data visualization, to orchestrate computing process workflows, to orchestrate data calculations, to orchestrate APIs to access data, to orchestrate metadata of data storage, transfer and calculation, to provide interactive plant data working environment for users, e.g. operators and to verify and improve data quality.
The system 10 shown in
The intermediate processing systems 34.1, 34.2 may be configured to ingest process or asset specific data 22 from individual or multiple chemical plants 12. Such data is contextualized on a plant level in intermediate processing system 34.1, 34.2 and plant specific data 38 may be provided to the process management system 32, where further contextualization e.g. across plant levels on Verbund or site level may be performed. In this setup the data contextualization is staggered across the different system 10 layers with each layer 14, 34, 32 mapping context information available in the respective layer 14, 34, 32.
The system 10 shown in
The systems 10 of
-
- collecting process or asset specific data,
- interaction with basic automation systems from Level 2,
- initial contextualization (bottom-up approach), wherein context is added based on what is known on Level 2 and Level 1 and within the decentralized edge device,
The process management system 32 may be configured as centralized edge computing layer. Such layer may be associated to Level 4 for multiple plants. The process management system 32 may be configured for:
-
- integration of data from different decentralized edge devices including the intermediate processing system 34 or monitoring devices 44,
- further contextualization (bottom-up approach), wherein additional context is added based on preprocessed context in the decentralized within the decentralized edge devices.
The external processing layer 30 may be configured as centralized cloud computing platform. Such platform may be associated with Level 5 for multiple plants. The external processing layer 30 may be configured as manufacturing data workspace with full data integration across multiple plants including manufacturing data history transport & streaming, collection of all data from all edge components. This way the full contextualization of all lower level context may be integrated in the on the external processing layer 30 for multiple plants. Thus the external processing layer 30 may be further configured to
-
- run cloud-native apps,
- connect with external PaaS and SaaS tenants,
- integrate machine learning with manufacturing data & processes, train-test-deploy,
- visualize data, access apps, orchestrate.
By way of system architecture a bottom-up contextualization concept may be realized. Such concept is shown in
The contextualization concept may cover at least two fundamental types of context. One type may be the functional location within the production environment comprising multiple chemical plants. This may cover information about what and where this data point represents inside the production environment. Examples are the connection with a functional location, an attribute with respect to which physical asset the data is collected, etc. This context may be beneficially used for later applications, since it explains which data is available for which plants and assets.
Another type may be confidentiality categorization. Such tag may be added on the lowest level possible and this information may be propagated to further processing layers. Such tag may be added automatically or manually. With technical measures e.g. via a filter embedded into the firewalls, it may be prohibited automatically, that “strictly confidential” data is integrated all the way up to the external processing layer 30. Sharing of data with externals will lead to an automatic notification that “confidential data” is being shared. An automatic contractual check may be implemented to see whether this data can be shared with this external.
Overall the contextualization concept realized in such way allows for highly efficient data usage in process applications deployed on any layer of the system.
Preferably the method is performed on a distributed computing system as shown in
In a first step, 61, process or asset specific data of the chemical plant 12 is provided via the first processing layer 14 to the second processing layer 16, 32, 34.
In a second step 63, process or asset specific data is contextualized via the second processing layer 16, 32, 34 to generate plant specific data.
In a third step, 65, plant specific data of one or more chemical plant(s) 12 is provided via the second processing layer 16, 32, 34 to the interface 26 to the external network.
In a fourth step, 67, one or more chemical plant(s) are monitored and/or controlled via the second processing layer 16, 32, 34 or the first processing layer 14 based on the process or asset specific data or the plant specific data. Monitoring and/or controlling of the one or more chemical plant(s) 12 may be conducted via the second processing layer 16, 32, 34 or the external processing layer 30 based on the plant specific data. Additionally, monitoring and/or controlling may be conducted via the first processing layer 14 based on the process or asset specific data. Such monitoring and/or controlling may be performed through process applications ingesting respective data and providing monitoring and/or controlling output as further lined out in
The schematic of
The orchestration applications 56, 58 may be hosted by the external processing layer 30 and the second processing layer 16, 32, 34 respectively. Hence containerized applications or container images 48, 50 may be stored in a registry of the external processing layer 30 and the second processing layer 16, 32, 34 respectively. The containerized applications 48, 50 for execution may include one or more operations to ingest input data, to provide the input data to respective asset or plant model(s) generating output data and to provide the generated output data for controlling and/or monitoring the chemical plant 12. This way the external processing layer 30 and the second processing layer 16, 32, 34 act as facilitating layers reducing the computing and storage resources required on the first processing layer 14 on the asset level.
In a first step 60, the containerized application 48, 50 including an asset or plant template specifying input data, output data and an asset or plant model is provided. The containerized application 48. 50 may be created on the external processing layer 30 or may be modified on the second processing layer 30. An external containerized application from a third party environment may be provided.
In a second step 62, the containerized application 48, 50 is deployed to execute on at least one of the deployment layers 30, 32, 16, 34, 14 wherein the deployment layer 30, 32, 16, 34, 14 is assigned based on the input data, a load indicator, or a system layer tag, and the containerized application 48, 50 may be executed on the assigned deployment layer(s) 30, 32, 16, 34, 14 to generate output data for controlling and/or monitoring the chemical plant 12. Deployment may be managed by an orchestration application 56, 50 that manages deployment of containerized applications 48, 50 based on the input data, the load indicator, or the system layer tag. The orchestration application may be hosted by the second processing layer 16, 23, 34 and/or the external processing layer 30. The orchestration application 56, 58 hosted by the second processing layer 16, 32, 34 manages critical containerized applications 48, 50, wherein the orchestration application 56, 58 hosted by the external processing layer 30 may manage non-critical containerized applications 48, 50. The assignment of the deployment layer 30, 32, 34, 16, 14 may be based on input data depends on a data availability indicator, a criticality indicator or a latency indicator. A containerized application from a third party environment may be deployed to execute on the external processing layer 30.
The orchestration applications 56, 58 may be hosted by the external processing layer 30 and the second processing layer 16 respectively. The orchestration applications 56, 58 may deploy containerized applications 48, 50 on any deployment layer 30, 16, 14. The containerized applications 48, 50 may then be executed on respective deployment layer 30, 16, 14 by running the process applications 46, 52, 54 in a sandbox-type environment. The deployment layer 30, 16, 14 may be assigned based on the input data, the load indicator, or the system layer tag. For instance management of critical containerized applications 50 may be assigned to the second processing layer 16 optionally based on a history criterion reflecting a time window of available historical data on the first or second processing layer 16. Advantageously the containerized applications 48, 50 may be deployed to multiple assets or plants of the same type. Furthermore, the containerized applications 50, 48 may be modified based on the input data and the output data provided by containerized applications 46, 52, 54 executed for multiple assets or plants of the same type.
In a third step 64 the containerized application 48, 50 may be monitored based on a confidence level of the input data, the asset model or the plant model during or after each execution. Based on the resulting confidence level an event signal or modification of the asset or plant model may be triggered. Such Trigger may be set, if the confidence level exceeds a threshold. Such threshold may be pre-defined or dynamic. If a trigger is set, the modification of the asset or plant model may be performed e.g. on the second processing layer 16, 32, 34 or the external processing layer 30.
In a 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 a persistent instance after execution of the containerized application 48, 50. In particular such output data may be passed to a controlling instance, e.g. on the first processing layer 14 of the chemical plant 12. Additionally or alternatively such output data may be passed to a monitoring instance on the first processing layer 14, the second processing layer 16, 32, 34 or the external processing layer 30. The output data may be passed to e.g. a client application for display to an operator or a further containerized application 48, 50 for execution.
The systems 10.1, 10.2 are configured to exchange process or asset specific data or the process application based on the transfer tag. By adding the transfer tag on the earliest level possible, i.e. where the data or the application is generated or first enters the system, the transfer tag becomes an inherent part of any data point or application as soon as the tag is added and follows the data or application on its path through the system 10.1, 10.2. Such transfer tag enables seamless, but secure integration of external data sources or external applications as well as transfer of data or application to external resources.
In one case shown in
Additionally, such transfer based on a transfer tag may be conducted directly between the systems 10.1, 10.2 between processing layers 32, 16 associated with the secure networks 20.1, 20.1. Such transfers based on transfer tag may be realized via a secure connection 74 between such layers 16, 23, such as a VPN connection. Any transfer between the systems 10.1, 10.2 may then be followed by further transfers between system components inside the secure networks 20.1, 20.2 or to the external processing layer 30.1, 30.2 of the respective system 10.1, 10.2. By attaching the transfer tag to any data point and process application, containerized or not, allows to securely handle third-party transfers between systems 10.1, 10.2 hosted, configured or situated in or inside or associated with separate secure networks 20.1, 20.1.
Any of the components described herein used for implementing the methods described herein may be in a form of a distributed computer system having one or more processing devices capable of executing computer instructions. Components of the computer system may be communicatively coupled (e.g., networked) to other machines in a local area network, a secure network, an intranet, an extranet, or the Internet. Components of the computer system may operate as a peer machines in a peer-to-peer (or distributed) network environment. Parts of the computer system may be a virtualized cloud computing environment, edge gate ways, web appliances, servers, network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, it is to be understood that the terms “computer system,” “machine,” “electronic circuitry,” and the like are not necessarily limited to a single component, and shall be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
Some or all of the components of such a computer system may be utilized by or illustrative of any of the components of the system 10. In some embodiments, one or more of these components may be distributed among multiple devices or may be consolidated into fewer devices than illustrated. Furthermore, some components may refer to physical components realized in hardware and others may refer to virtual components realized in software on remote hardware.
Any processing layer may include a general-purpose processing device such as a microprocessor, microcontroller, central processing unit, or the like. More particularly, the processing layers may include a CISC (Complex Instruction Set Computing) microprocessor, RISC (Reduced Instruction Set Computing) microprocessor, VLIW (Very Long Instruction Word) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing layer may also include one or more special-purpose 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), a network processor, or the like. The methods, systems and devices described herein may be implemented as software in a DSP, in a micro-controller, or in any other side-processor or as hardware circuit within an ASIC, CPLD, or FPGA. It is to be understood that the term “processing layer” may also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.
Any processing layer may include suitable data storage device like a computer-readable storage medium on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions may also reside, completely or at least partially, within the main memory and/or within the processor during execution thereof by the computer system, main memory, and processing device, which may constitute computer-readable storage media. The instructions may further be transmitted or received over a network via a network interface device.
A computer program for implementing one or more of the embodiments described herein may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems.
However, the computer program may also be presented over a network like the World Wide Web and can be downloaded into the working memory of a data processor from such a network.
The terms “computer-readable storage medium,” “machine-readable storage medium,” and the like should be taken to include a single medium or multiple medium (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The terms “computer-readable storage medium,” “machine-readable storage medium,” and the like shall also be taken to include any transitory or non-transitory medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.
Some portions of the detailed description may have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is herein, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the preceding discussion, it is appreciated that throughout the description, discussions utilizing terms such as “receiving,” “retrieving,” “transmitting,” “computing,” “generating,” “adding,” “subtracting,” “multiplying,” “dividing,” “selecting,” “optimizing,” “calibrating,” “detecting,” “storing,” “performing,” “analyzing,” “determining,” “enabling,” “identifying,” “modifying,” “transforming,” “applying,” “extracting,” and the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
It has to be noted that embodiments of the invention are described with reference to different subject matters. In particular, some embodiments are described with reference to method type claims whereas other embodiments are described with reference to the system type claims.
However, a person skilled in the art will gather from the above and the following description that, unless otherwise notified, in addition to any combination of features belonging to one type of subject matter also any combination between features relating to different subject matters is considered to be disclosed with this application. However, all features can be combined providing synergetic effects that are more than the simple summation of the features.
While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or example and not restrictive; the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art and practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present disclosure.
In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or controller or other unit may fulfil the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.
Claims
1. A system (10) for monitoring and/or controlling one or more chemical plant(s) (12), wherein the system (10) comprises a first processing layer (14) and a second processing layer (16), wherein the first processing layer (14) is associated with the chemical plant (12) and communicatively coupled to the second processing layer (16), wherein the first processing layer (14) and the second processing layer (16) are configured inside a secure network (18, 20), wherein the first processing layer (14) is configured to provide process or asset specific data (22) of the chemical plant (12) to the second processing layer (16), wherein the second processing layer (16) is configured to contextualize process or asset specific data to generate plant specific data and to provide plant specific data (24) of one or more chemical plant(s) (12) to an interface (26) to an external network.
2. The system (10) of claim 1, wherein the second processing layer (16) comprises an intermediate processing system (34) and a process management system (32), wherein the first processing layer (14) and the process management system (32) are communicatively coupled via the intermediate processing system (34).
3. The system (10) of claim 2, wherein the intermediate processing system (34) is configured to collect process or asset specific data provided by the first processing layer (14), wherein the process management system (32) is configured to provide plant specific data of one or more chemical plant(s) (12) to the interface to the external network.
4. The system (10) of claim 2, wherein the intermediate processing system (34) is configured to contextualize data by mapping heterogenous process or asset specific data to a homogeneous data format on plant level, wherein the process management system (32) is configured to contextualize the plant specific data provided by the intermediate processing system (34).
5. The system (10) of claim 1, wherein the second processing layer (16) is associated with more than one chemical plant (12).
6. The system (10) of claim 1, wherein the system is configured to stagger data contextualization across processing layers (14, 16, 32, 34, 30) with each layer mapping context information available in the respective layer.
7. The system (10) of claim 1, wherein the second processing layer (16, 32, 34) is configured to provide plant specific data (24) from one or more chemical plant(s) (12) to an external processing layer (30).
8. The system (10) of claim 7, wherein the plant specific data is provided between chemical plants across a manufacturing chain via the second processing layer (16) or the external processing layer (30).
9. The system (10) of claim 7, wherein the second processing layer (16, 32, 34) is configured to manage data transfer to and/or from the external processing layer (30) in real-time or on demand.
10. The system (10) of claim 7, wherein the second processing layer (16, 32, 34) is configured to store or to manage access to historical data for a first time window, wherein the external processing layer (30) is configured to store historical data for a second time window, wherein the first time window is shorter than the second time window
11. The system (10) of claim 7, wherein the second processing layer (16, 32, 34) is configured to host and/or orchestrate process applications relating to core plant operations, wherein the external processing layer (30) is configured to host and/or orchestrate process applications relating to non-core plant operations.
12. The system (10) of claim 1, wherein a monitoring device (44) is communicatively coupled to the second processing layer (16, 32, 34) or the external processing layer (30), wherein the monitoring device (44) is configured to transfer monitoring data to the second processing layer (16, 32, 34) or the external processing layer (30).
13. The system (10) of claim 12, wherein the second processing layer (16, 32, 34) or the external processing layer (30) is configured to manage monitoring devices (44).
14. The system (10) of claim 11, wherein the monitoring device (44), the second processing layer (16, 32, 34) or the external processing layer (30) is configured to tag monitoring data provided by the monitoring device (44) unidirectional.
15. A method for monitoring and/or controlling one or more chemical plant(s) (12) using a system (10) comprising a first processing layer (14) and a second processing layer (16), wherein the first processing layer (14) is associated with the chemical plant (12) and communicatively coupled to the second processing layer (16), wherein the first processing layer (14) and the second processing layer (16) are configured inside a secure network, wherein the method comprises:
- providing process or asset specific data of the chemical plant from the first processing layer (14) to the second processing layer (16),
- contextualizing process or asset specific data via the second processing layer (16) to generate plant specific data,
- providing plant specific data of one or more chemical plant(s) (12) via the second processing layer (12) to an interface to an external network,
- monitoring and/or controlling one or more chemical plant(s) (12) via the first processing layer (14) or via the second processing layer (16) based on the process or asset specific data or the plant specific data.
Type: Application
Filed: Dec 8, 2020
Publication Date: Feb 16, 2023
Inventors: Bart Van Loon (Ludwigshafen), Jan De Caigny (Ludwigshafen), Sebastian Gau (Ludwigshafen), Daniel Engel (Ludwigshafen)
Application Number: 17/782,212
