How to carry out parameter identification of control valves

In industrial automation control systems, control valves, as actuators, play a vital role. They regulate the flow and pressure of fluids by receiving control signals, thus achieving precise control of the process. In order to improve the performance and stability of the control system, it is necessary to identify the parameters of control valves. Parameter identification of control valves refers to obtaining the dynamic or static characteristic model through experiments or data analysis, thus providing a basis for the design and optimization of the control system.

Main parameters of control valves

Key parameters of control valves include flow characteristics, leakage, adjustable ratio, response time, dead zone, gain, and lag, etc. Among them, the flow characteristics (such as linear, equal percentage, quick opening) directly affect the control effect; while the dynamic response characteristics, such as response time and lag, are related to the speed and stability of the system.

Basic methods of parameter identification

1. Static characteristics identification
Static characteristics identification is mainly through measuring the steady-state flow change of the valve at different opening positions, drawing a flow-opening curve, thereby determining its flow characteristics and gain. In the experiment, the fluid pressure is usually kept constant, and the valve opening is gradually changed, with the corresponding flow values recorded and curves drawn for analysis.

2. Dynamic characteristics identification
Dynamic identification usually adopts step signals or pulse inputs to stimulate valve action, recording the output response curve (such as flow or pressure changes). By analyzing the response curve, the delay time, rise time, and stabilization time of the control valve can be determined. Common identification methods include least squares method, recursive identification method, and nonlinear modeling methods based on neural networks.

3. Intelligent identification technology based on data-driven methods
With the development of artificial intelligence and big data, an increasing number of studies adopt data-driven methods for modeling and parameter identification of control valves. For example, support vector machines (SVM), artificial neural networks (ANN), or multivariate regression analysis are used to extract patterns from a large amount of historical operating data and establish high-precision mathematical models.

Points to be aware of in practical applications

When performing parameter identification of control valves, the following points should be noted:

- Control of experimental conditions: Experiments should be conducted under stable process conditions to avoid external interference affecting identification accuracy;
- Accuracy of sensors and acquisition systems: Ensure that the measuring equipment used has sufficient resolution and accuracy;
- Noise processing: The collected data should be filtered to eliminate noise interference;
- Model verification: The identified model should pass cross-validation or simulation testing to ensure its applicability in actual control.

Summary

The parameter identification of control valves is an important means to improve the performance of industrial control systems. Through scientific identification methods, an accurate model of the control valve can be obtained, providing strong support for controller parameter tuning, fault diagnosis, and system optimization. With the development of control theory and information technology, the future parameter identification of control valves will develop towards intelligence, online operation, and high precision, laying a solid foundation for the intelligent upgrade of industrial automation systems.