Cloud Virtual Plant Tuning for Faster PID Commissioning
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Solution Overview
Problem
Current industrial plant control systems require extensive manual tuning by experienced engineers, which is time-consuming and inefficient, especially for plants with complex dynamics, and lacks a centralized tuning approach for improved availability and commissioning time.
Innovation Solution
A cloud-based method and system that generates a virtual simulation environment for industrial plants, allowing for the optimization of PID controller settings through virtual machines, enabling automated tuning of process variables without human intervention, and rendering optimized settings to client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning mechanism is used by experienced engineers, then tuning accuracy can be achieved, but commissioning time and maintenance effort increase significantly
Solution Approach 1:
The patent creates a virtual copy of the industrial plant including virtual process controllers, virtual I/O devices, and virtual process models that replicate the actual plant's behavior. This virtual replica allows automated tuning algorithms to optimize PID parameters without requiring physical plant commissioning, thereby reducing commissioning time while maintaining tuning accuracy through the virtual environment's ability to simulate complex dynamics.
Solution Approach 2:
The system implements automated self-tuning capabilities where the virtual plant system automatically adjusts PID controller parameters through computational algorithms without requiring manual intervention by experienced engineers. The system uses historical process data and virtual simulation to autonomously determine optimal tuning parameters, eliminating the time-consuming manual tuning process while preserving accuracy through iterative optimization.
2Loss of time
If auto self-tuning mechanism is used, then commissioning time is reduced, but it is restricted to stable conditions and requires experienced engineer presence
Solution Approach 1:
The patent introduces a virtual plant environment as an intermediary between the actual plant and the tuning process. This virtual environment serves as a safe sandbox where automated tuning algorithms can explore and optimize PID parameters for complex dynamic processes without risking actual plant stability. The virtual model accurately represents the physical plant's behavior, allowing versatile tuning exploration that would be too risky to perform directly on the real system.
Solution Approach 2:
The system performs preliminary tuning actions in the virtual plant environment before applying parameters to the actual plant. By pre-optimizing PID parameters through automated algorithms in the virtual replica, the system prepares optimal tuning settings in advance, reducing commissioning time while ensuring the tuned parameters are validated for complex dynamics before real-world implementation.
3Extent of automation
If traditional auto tuning is performed, then some automation is achieved, but it lacks centralized approach for multiple plants and requires human intervention
Solution Approach 1:
The patent creates a universal virtual plant platform that can host multiple virtual replicas of different industrial plants simultaneously. This multi-functional system allows a single centralized server infrastructure to perform automated tuning for numerous plants across various locations, eliminating the need for separate tuning systems at each site. The virtual environment universally supports different plant types and configurations, enabling scalable automation without proportionally increasing system complexity.
Data Source
AI summary
The present disclosure provides a cloud-based method and a system for optimizing tuning of an industrial plant. The method includes receiving plant engineering data associated with an industrial plant from a plant environment. Further, the method includes generating a cloud-based virtual simulation environment synchronous to the industrial plant based on the plant engineering data. The cloud-based virtual simulation environment includes one or more virtual machines for virtually simulating the plant engineering data. Further, the method includes tuning the raw process variables of the industrial plant in the cloud-based virtual simulation environment to obtain optimized tuned process variables of the industrial plant. Additionally, the method includes rendering the optimized tuned process variables for the industrial plant to a client device.