Search

EP-4741963-A2 - METHOD FOR MONITORING AND/OR CONTROLLING ONE OR MORE CHEMICAL PLANT(S)

EP4741963A2EP 4741963 A2EP4741963 A2EP 4741963A2EP-4741963-A2

Abstract

Disclosed is a method for monitoring and/or controlling a chemical plant (12) with multiple assets via a distributed computing system (10) with more than two deployment layers (14, 16, 30, 32, 34), wherein the deployment layers (14, 16, 30, 32, 34) comprise 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 an asset or plant template specifying input data, output data and an asset or plant model, - deploying (62) the containerized application (48, 50) to execute on at least one of the deployment layers (14, 16, 30, 32, 34), wherein the deployment layer (14, 16, 30, 32, 34) is assigned based on the input data, a load indicator, or a system layer tag, and executing the containerized application (46, 52, 54) on the assigned deployment layer(s) (14, 16, 30, 32, 34) to generate output data for controlling and/or monitoring the chemical plant (12), - providing (66) the generated output data for controlling and/or monitoring the chemical plant (12).

Inventors

  • WALZENBACH, Alexander
  • JAEGER, MARCO KLAUS
  • Buck, Jan

Assignees

  • BASF SE

Dates

Publication Date
20260513
Application Date
20201208

Claims (16)

  1. A method for monitoring and/or controlling a manufacturing facility based on chemical processes with multiple assets via a distributed computing system (10) with more than two deployment layers (14, 16, 30, 32, 34), wherein the deployment layers (14, 16, 30, 32, 34) comprise 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 an asset or plant template specifying input data, output data and an asset or plant model, - deploying (62) the containerized application (48, 50) to execute on at least one of the deployment layers (14, 16, 30, 32, 34), wherein the deployment layer (14, 16, 30, 32, 34) is assigned based on the input data, a load indicator, or a system layer tag, and executing the containerized application (46, 52, 54) on the assigned deployment layer(s) (14, 16, 30, 32, 34) to generate output data for controlling and/or monitoring the manufacturing facility, - providing (66) the generated output data for controlling and/or monitoring the manufacturing facility.
  2. The method of claim 1, wherein the second processing layer (16, 32, 34) includes larger storage and computing resources than the first processing layer (14), and/or the external processing layer (30) includes larger storage and computing resources than the second processing layer (16, 32, 34).
  3. The method of claims 1 or 2, wherein the first and the second processing layer (14, 16, 32, 34) are configured inside a secure network (20), wherein the first processing layer (14) is communicatively coupled to the second processing layer (16, 32, 34) and the second processing layer (16, 32, 34) is communicatively coupled to the external processing layer (30) via an external network.
  4. The method of any of the preceding claims, wherein the containerized application (48, 50) for execution includes 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 manufacturing facility.
  5. The method of any of the preceding claims, wherein deployment is managed by an orchestration application (56, 58) that manages deployment of containerized applications (48, 50) based on the input data, the load indicator, or the system layer tag.
  6. The method of claim 5, wherein the orchestration application (56, 58) is hosted by the second processing layer (16, 32, 34) and/or the external processing layer (30).
  7. The method of claims 5 or 6, wherein the orchestration application (58) hosted by the second processing layer (16, 32, 34) manages critical containerized applications (48, 50), wherein the orchestration application (56) hosted by the external processing layer (30) manages non-critical containerized applications (48, 50).
  8. The method of any claims 5 to 7, wherein the management of critical containerized applications (56, 58) is assigned to the second processing layer (16, 32, 34) based on a history criterion reflecting a time window of available historical data in the first or second processing layer(14, 16, 32, 34).
  9. The method of any of the preceding claims, wherein the assignment of the deployment layer (14, 16, 30, 32, 34) based on input data depends on a data availability indicator, a criticality indicator or a latency indicator.
  10. The method of any of the preceding claims, wherein the containerized application is deployed to multiple assets or plants of the same type.
  11. The method of any of the preceding claims, wherein the containerized application (48, 50) is modified based on the input data and the output data provided by containerized applications (48, 50) executed for multiple assets or plants (12) of the same type.
  12. The method of any of the preceding claims, wherein the containerized application (48, 50) is monitored based on a confidence level of the input data, the asset model or the plant model.
  13. The method of claim 12, wherein an event signal or a modification of the asset or plant model is triggered, if the confidence level falls below a confidence threshold.
  14. The method of claims 12 or 13, wherein the modification of the asset or plant model is performed on the second processing layer (15, 32, 34) or the external processing layer (30).
  15. The method of any of the preceding claims, wherein an external containerized application from a third-party environment is provided and deployed to execute on the external processing layer (30).
  16. A system (10) for monitoring and/or controlling a manufacturing facility based on chemical processes with multiple assets with more than two deployment layers (14, 16, 30, 32, 34), wherein the deployment layers (14, 16, 30, 32, 34) comprise at least two of a first processing layer (14), a second processing layer (16, 32, 34) and an external processing layer (30), the system (10) being configured to: - provide (60) a containerized application (48, 50) including an asset or plant template specifying input data, output data and an asset or plant model, - deploy (62) the containerized application (48, 50) to execute on at least one of the deployment layers (14, 16, 30, 32, 34), wherein the deployment layer (14, 16, 30, 32, 34) is assigned based on the input data, a load indicator, or a system layer tag, and executing the containerized application (46, 52, 54) on the assigned deployment layer(s) (14, 16, 30, 32, 34) to generate output data for controlling and/or monitoring the manufacturing facility, - provide (66) the generated output data for controlling and/or monitoring the manufacturing facility.

Description

FIELD The disclosure relates to a method for monitoring and/or controlling a chemical plant with multiple assets via a distributed computing system with multiple deployment layers. BACKGROUND Chemical production is a highly sensitive production environment particularly with respect to safety. Chemical plants typically include multiple assets to produce the chemical product. Multiple sensors are distributed in such plants for monitoring and control 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 safety standards. For this reason, computing infrastructures are typically siloed with highly restrictive access to monitoring and control systems. Owing to such safety 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. 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 is described. 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 method for monitoring and/or controlling chemical plants in process industry, which adheres to the high safety standards and allows for enhanced monitoring or controlling. SUMMARY A method for monitoring and/or controlling a chemical plant with multiple assets via a distributed computing system with more than two deployment layers is proposed. The deployment layers comprise at least two of a first processing layer, a second processing layer and an external processing layer. The method comprises the steps of: providing a containerized application including an asset or plant template specifying input data, output data and an asset or plant model,deploying the containerized application to execute on at least one of the deployment layers, wherein the assignment of the deployment layer depends on the input data, a load indicator, or a system layer tag, and executing the containerized application on the respective deployment layer to generate output data for controlling and/or monitoring the chemical plant,providing the generated output data for controlling and/or monitoring the chemical plant. A system for monitoring and/or controlling a chemical plant with multiple assets with more than two deployment layers is proposed, wherein the deployment layers comprise at least two of a first processing layer, a second processing lay