Distributed Big Data in Process Control Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current process control systems face limitations in data management due to limited controller memory, communication bandwidth, and processor capabilities, leading to inefficient data archiving, inaccurate data due to compression, and synchronization issues across different data silos, which hampers troubleshooting and predictive modeling in process plants.
Innovation Solution
Implementing a distributed big data system with embedded big data apparatuses in process control devices that collect, analyze, and store real-time process data locally, perform learning analyses, and transmit learned knowledge to other devices for real-time control and optimization, enabling efficient data management and improved process control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is archived in centralized databases with limited controller memory, then data storage capacity is improved, but data accuracy deteriorates due to compression
Solution Approach 1:
The patent divides the centralized data archiving system into distributed data silos across multiple process control devices. Each device maintains its own data locally without centralized compression, eliminating the trade-off between storage capacity and data accuracy. The segmentation allows each node to store complete, uncompressed data while the system collectively achieves unlimited storage capacity.
2Ease of operation
If data is stored in centralized databases, then data management is simplified, but synchronization issues arise across different data silos
Solution Approach 1:
The patent implements a feedback mechanism where process control devices exchange data silos through published subscriptions. Each device publishes its data silo to a data highway and subscribes to relevant data from other devices, creating automatic feedback loops that ensure real-time synchronization across distributed nodes without centralized coordination.
3Productivity
If distributed big data apparatuses are implemented, then data analysis capability is improved, but device complexity increases
Solution Approach 1:
The patent makes process control devices multi-functional by enabling them to simultaneously perform their primary control functions and big data processing functions. Each process control device with a data highway interface can publish its data silo, subscribe to other data silos, and perform data mining operations, eliminating the need for separate dedicated big data hardware and reducing overall system complexity.
Data Source
AI summary
A distributed big data device in a process plant includes an embedded big data appliance configured to locally stream and store, as big data, data that is generated, received, or observed by the device, and to perform one or more learning analyzes on at least a portion of the stored data. The embedded big data appliance generates or creates learned knowledge based on a result of the learning analysis, which the device may use to modify its operation to control a process in real-time in the process plant, and/or which the device may transmit to other devices in the process plant. The distributed big data device may be a field device, a controller, an input/output device, or other process plant device, and may utilize learned knowledge created by other devices when performing its learning analysis.