Industrial Process Data Center for Scalable DCS Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed industrial process control systems face challenges in scalability and integration with other systems due to fixed networking technologies, making it cumbersome and costly to scale up or down and integrate with systems at different security layers, while also requiring significant data integration for actionable information.
Innovation Solution
An industrial process plant data center is introduced, decoupling process plant data from fixed networking technologies, using a plant information model with a common modeling language to represent physical, control, and network components, and providing a generic framework with APIs for secure access and integration, along with pluggable hardware modules that automatically discover and connect with field devices to populate the plant information model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed networking technologies are used in distributed industrial process control systems, then system stability and reliability are maintained, but scalability and adaptability deteriorate, making it cumbersome and costly to scale up or down and integrate with systems at different security layers
Solution Approach 1:
The system is divided into modular virtualization layers (infrastructure layer, virtualization layer, application layer) that can be independently scaled and integrated. This segmentation allows the control system to maintain stability at each layer while enabling flexible scaling and integration across layers through standardized interfaces and protocols.
2Stability of the object's composition
If fixed networking technologies are used in distributed industrial process control systems, then existing system architecture is maintained, but integration complexity and cost increase when integrating with systems at different security layers
Solution Approach 1:
A virtualization layer acts as an intermediary between the infrastructure layer and application layer, providing standardized interfaces and protocols that simplify integration with systems at different security layers. This intermediary layer maintains architecture stability while reducing integration complexity through abstraction and standardization.
3Loss of information
If significant data integration is performed to obtain actionable information, then data availability and information quality improve, but data processing time and computational resources increase
Solution Approach 1:
Data is pre-processed, validated, and organized into structured formats at the virtualization layer before being made available to applications. This preliminary action reduces the computational burden on application systems and decreases data processing time while maintaining high data availability and quality.
Data Source
AI summary
A distributed control system (DCS) of an industrial process plant includes a data center storing a plant information model that includes a description of physical components, the control framework, and the control network of the plant using a modeling language. A set of exposed APIs provides DCS applications access to the model, and to an optional generic framework of the data center which stores basic structures and functions from which the DCS may automatically generate other structures and functions to populate the model and to automatically create various applications and routines utilized during run-time operations of the DCS and plant. Upon initialization, the DCS may automatically sense the I/O types of its interface ports, detect communicatively connected physical components within the plant, and automatically populate the plant information model accordingly. The DCS may optionally automatically generate related control routines and/or I/O data delivery mechanisms, HMI routines, and the like.