Control Valve Friction Tuning Using Pressure Difference Measurement
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Solution Overview
Problem
Existing methods for tuning control valves are time-consuming and prone to errors due to the need for manual input of frictional characteristics, which can lead to improper gain selection and inaccurate control, especially when dealing with varying friction types in valve and actuator assemblies.
Innovation Solution
A tuning controller that automatically determines the frictional characteristics of a valve and actuator assembly by measuring pressure differences and comparing them to the operating range, allowing for the selection of an appropriate control step size and gain value without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning methods are used to determine frictional characteristics of control valves, then operators can adjust control parameters, but the process is time-consuming and prone to errors
Solution Approach 1:
The system performs self-diagnosis by automatically measuring frictional characteristics through hysteresis loops generated during valve stroking operations. The controller independently determines friction coefficients without external intervention, eliminating manual tuning time and reducing human error while maintaining high measurement precision through automated pressure differential measurements and computational analysis.
2Reliability
If manual input of frictional characteristics is required, then control parameters can be set, but improper gain selection and inaccurate control occur due to errors
Solution Approach 1:
The patent replaces manual mechanical tuning procedures with an automated electronic measurement and computation system. The controller automatically generates hysteresis loops by stroking the valve, measures pressure differentials, computationally determines frictional characteristics, and selects optimal control parameters. This substitution eliminates human error in manual input while reducing procedural complexity through integration of measurement and control functions.
3Productivity
If automated measurement of frictional characteristics is implemented, then tuning time is reduced, but measurement and control system complexity increases
Solution Approach 1:
The control valve assembly serves multiple functions: it acts as both the controlled element and the measurement test subject. The existing valve stroking mechanism is utilized to generate hysteresis loops for friction measurement, and the same pressure sensors used for process control are employed for frictional characteristic determination. This multi-functionality increases productivity by eliminating separate tuning equipment while managing system complexity through resource sharing.
4Stability of the object's composition
If frictional characteristics are not accurately determined, then control loops become unstable, but manual tuning methods are error-prone
Solution Approach 1:
The system employs feedback by measuring pressure differentials during valve stroking in both directions, constructing hysteresis loops, and using the measured data to determine frictional characteristics. This feedback mechanism ensures accurate friction measurement by comparing actual valve behavior during stroking operations with expected performance, enabling precise determination of friction coefficients that maintain control loop stability while eliminating manual tuning errors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach eliminates the need for manual tuning, reduces errors, and ensures accurate control by selecting optimal control step sizes and gains, enhancing the stability and responsiveness of control loops in fluid control valves.
Implementation Method 1
measuring first and second pressures corresponding to respective first and second positions of a valve while stroking the valve in a first direction, measuring third and fourth pressures corresponding, respectively, to the second and first positions while stroking the valve in a second direction
Data Source
AI summary
Disclosed examples include sending commands to a positioner to stroke a valve over different ranges of travel; determining a plurality of pressure differences corresponding to the different ranges of travel; selecting a control step size for the valve based on the pressure differences; and selecting a gain value of the positioner based on the control step size.