Gain Adaptive Stepping for Integrating Variable ROC Control
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Solution Overview
Problem
Existing gain adaptive algorithms for Advanced Process Control (APC) systems are limited in their ability to effectively manage integrating control variables, leading to inaccuracies, suboptimal model identification, longer commissioning times, and increased likelihood of constraint violations due to the differing response characteristics of integrating control variables compared to self-regulating control variables.
Innovation Solution
Implementing a gain adaptive stepping algorithm tailored for integrating control variables using Rate of Change (ROC) control theory, which transforms integrating control variables into self-regulating proxies, allowing for precise control and model accuracy by checking constraint violations through ROC limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing gain adaptive algorithms are used for integrating control variables, then the control system can operate, but the model identification accuracy deteriorates and commissioning time increases
Solution Approach 1:
The patent transforms the integrating control variable into a self-regulating proxy variable through parameter transformation (rate of change conversion). This allows the gain adaptive algorithm to operate on the transformed variable with different characteristics, improving model identification accuracy while reducing commissioning time requirements
Solution Approach 2:
The patent introduces a rate of change (ROC) variable as an intermediary between the integrating control variable and the gain adaptive algorithm. This intermediary transforms the problematic integrating variable into a self-regulating form that works better with existing control algorithms, resolving the accuracy and time trade-off
2Reliability
If existing gain adaptive algorithms are used for integrating control variables, then the control system can operate, but constraint violations increase
Solution Approach 1:
By changing the parameter representation from the integrating control variable to its rate of change, the system achieves better constraint compliance. The transformed variable has different dynamic characteristics that reduce the likelihood of constraint violations while maintaining algorithm simplicity
3Productivity
If gain adaptive stepping is performed on integrating control variables, then control performance improves, but the algorithm complexity increases
Solution Approach 1:
The rate of change variable serves as a mediator that enables gain adaptive stepping to work effectively with integrating control variables. The transformation approach maintains algorithmic simplicity while improving control performance, avoiding the need for completely new complex algorithms
Data Source
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AI summary
A system includes processor(s) configured to: receive a rate of change process variable, a rate of change high limit, and a rate of change low limit for an integrating control variable of a process from a process controller used to control the process; determine whether a constraint violation has occurred on integrating control variable of process based on rate of change process variable, rate of change high limit, and rate of change low limit; perform gain adaptive stepping on integrating control variable using rate of change process variable, rate of change high limit, and rate of change low limit; determine at least a first set point for at least a first manipulated variable corresponding to the integrating control variable based on the gain adaptive stepping previously performed; and output the at least the first set point for the at least the first manipulated variable to adjust the process.