Control Loop Model Identification for Online Adaptive Tuning
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Solution Overview
Problem
Conventional process control systems face challenges in implementing adaptive control techniques due to the complexity of identifying which control loops benefit from adaptive control and the manual testing required to create process models, which is impractical and often incompatible with online operations.
Innovation Solution
A method for automatically identifying process models for all control loops in a process control system, allowing for on-demand controller tuning, abnormal condition monitoring, and diagnostics, by collecting operating condition data and analyzing it to determine which loops should use adaptive control schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testing is used to create process models for adaptive control, then model accuracy can be improved, but system complexity and operational disruption increase
Solution Approach 1:
The system automatically identifies process models by analyzing operational data from control loops without requiring manual intervention or special testing procedures. The controller autonomously collects data, performs identification calculations, and generates models for multiple control loops, enabling the system to serve itself in the model creation process.
Solution Approach 2:
The patent replaces manual mechanical testing procedures with automated computational analysis of operational data. Instead of physically testing control loops to create models, the system uses algorithms to analyze existing operational data and generate process models automatically.
2Measurement precision
If manual testing is performed to identify process models, then model quality improves, but compatibility with online operations deteriorates
Solution Approach 1:
The system continuously collects operational data from control loops and performs model identification without interrupting normal process operations. The useful action of model identification continues seamlessly alongside regular control operations, eliminating the need to stop or disrupt online processes for testing.
Solution Approach 2:
The controller automatically performs model identification using operational data while the process continues to operate normally, making the system self-sufficient in generating models without requiring external testing interventions that would disrupt online operations.
3Reliability
If adaptive control is implemented for all control loops, then control performance improves, but system complexity increases
Solution Approach 1:
The system divides the plant into multiple control loops and identifies models for each loop individually based on their specific operational characteristics. This segmentation allows adaptive control to be applied selectively to loops that benefit from it, rather than uniformly to all loops, reducing overall system complexity.
Solution Approach 2:
The patent applies different control strategies to different control loops based on their individual characteristics and requirements. By analyzing each loop's specific needs and applying adaptive control only where beneficial, the system optimizes control performance locally without unnecessarily increasing complexity across the entire system.
4Adaptability or versatility
If multiple process models are identified for different operating conditions, then adaptability improves, but system complexity increases
Solution Approach 1:
The system dynamically selects and applies appropriate process models based on current operating conditions. Rather than managing multiple static models for different conditions, the system uses dynamic identification to generate models that adapt to current operating states, simplifying model management while maintaining adaptability.
Data Source
AI summary
A method of controlling and managing a process control system having a plurality of control loops includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively, wherein the control routines may include at least one non-adaptive control routine. The method then collects operating condition data in connection with the operation of each control loop, and identifies a respective process model for each control loop from the respective operating condition data collected for each control loop. The identification of the respective process models may be automatic as a result of a detected process change or may be on-demand as a result of an injected parameter change. The process models are then analyzed to measure or determine the operation of the process control loops.