Regional Big Data Nodes for Process Control Systems
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Solution Overview
Problem
Current process control systems face limitations in data management due to limited controller memory, communication bandwidth, and processor capabilities, leading to incomplete data archiving, inaccurate data due to compression, and inefficient data access, which hampers troubleshooting and predictive modeling in process plants.
Innovation Solution
Implementing a regional big data node system that collects, analyzes, and stores real-time data from local nodes across a process plant, using a network interface and big data storage to perform learning analyses and generate knowledge that can adjust process operations, allowing for improved control and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is archived in centralized databases with limited controller memory, then data storage is achieved, but data completeness and accuracy deteriorate due to compression and limited capacity
Solution Approach 1:
The patent divides the centralized data storage system into distributed regional big data nodes across multiple locations in the process control system. Each node stores data locally without compression, eliminating the accuracy loss while maintaining scalable storage capacity through the distributed architecture.
Solution Approach 2:
The patent transitions from a single-dimensional centralized storage model to a multi-dimensional distributed storage architecture. Data is stored across multiple spatial dimensions (different regional nodes) and temporal dimensions (real-time streaming capability), providing both scalability and accuracy simultaneously.
2Quantity of substance
If data is compressed to fit limited controller memory, then storage capacity is improved, but data quality and reliability worsen
Solution Approach 1:
The patent extracts the data storage function from the controller's limited memory and places it in external regional big data nodes. This separation allows the controller to maintain full reliability for control operations while the distributed nodes provide unlimited storage capacity without compression.
Solution Approach 2:
The patent introduces regional big data nodes as intermediary storage components between the controller and the data archive. These intermediaries handle all data storage operations, allowing the controller to focus on control functions while maintaining data reliability through the intermediary's faithful data capture capabilities.
3Ease of operation
If centralized hardware devices are used for data management, then data access is simplified, but communication bandwidth requirements and system complexity increase
Solution Approach 1:
The patent segments the centralized data management function into distributed regional nodes, each independently providing data access to local users. This eliminates the single-point bottleneck of centralized systems while maintaining ease of access through localized data repositories.
Solution Approach 2:
The patent enables regional big data nodes to autonomously manage their own data storage, retrieval, and analysis operations. Each node serves its local region independently, eliminating the need for complex centralized coordination while simplifying data access for local users.
4Loss of information
If real-time data streaming is implemented across the entire plant, then data availability is improved, but communication bandwidth consumption increases
Solution Approach 1:
The patent divides the plant-wide data streaming requirement into regional segments. Each regional big data node streams data only within its local region, dramatically reducing total communication bandwidth requirements while maintaining real-time data availability for all users through the distributed network.
Data Source
AI summary
A regional big data node oversees or services, during real-time operations of a process plant or process control system, a respective region of a plurality of regions of the plant/system, where at least some of the regions each includes one or more process control devices that operate to control a process executed in the plant/system. The regional big data node is configured to receive and store, as big data, streamed data and learned knowledge that is generated, received, or observed by its respective region, and to perform one or more learning analyses on at least some of the stored data. As a result of the learning analyses, the regional big data node creates new learned knowledge which the regional big data node may use to modify operations in its respective region, and/or which the regional big data node may transmit to other big data nodes of the plant/system.