(374i) Valve Stiction Modeling: Data-Driven Vs. First Principles

Due to adverse impact of valve stiction in the process
industry, both physical models and empirical data-driven models have been
developed in the past decade to investigate the valve stiction behavior. To
simulate valve stiction, both detailed physical models and empirical models
have been developed in recent years. Physical models [1-3] describe the stiction
phenomenon using force balances based on Newton's second law of motion. The
main disadvantage of these models is that they require knowledge of several
parameters such as the mass of the moving parts and different type of friction
forces which cannot be easily measured and depend on the type of fluid and
valve wear. On the other hand, empirical or data-driven models [1,3,4] use
simple empirical relationships between valve input and output to describe valve
stiction, with just a few parameters that can be determined from operating
data. Due to their simplicity and easy implementation, data-driven models have
gained tremendous research interest in recent years.

Compared to physical models, these data-driven models
simplify the simulation of a sticky valve and has been used by several other
researchers for valve stiction simulation. In this work, we compare the
data-driven model that we proposed recently (shorted as He's two-parameter model)
with other two data-driven models, namely Choudhury's model and Kano's model. We examine the fundamental difference between He's two-parameter model and
Choudhury's/Kano's model. We point out that the different valve stiction behaviors
produced by different data-driven models are rooted in the different model
assumptions. To compare the three data-driven models, a well-established
physical model is implemented. The differences among the three different
data-driven models are revealed by comparing to the physical model. It is shown
that He's two-parameter model can best reproduce the signature stick-slip
behavior of a sticky valve which is simulated by the physical model and
observed in actual control valves [5, 6]. Using the physical model, we further
investigate the valve stiction behavior analytically, and derive a
three-parameter model that more accurately reproduces the physical model
response by including a constant gain K.  We show that the three-parameter
model can accurately reproduce the physical model behavior without involving
cumbersome numerical integration. We also show that K is insensitive to valve
parameters and in general very close to 2. Therefore, in the case that valve
parameters are not available, K can be set as a constant (e.g., 1.99) so the
three-parameter model reduces to the modified two-parameter model.

The modified two-parameter model is tested using an
industrial. It is shown that the model satisfactorily simulates the industrial
case and captures the essential characteristics of the stiction. Therefore the
proposed data-driven model should be a useful tool for combating valve stiction
in process industry.

Key words: valve stiction model; control valve; data-driven
modeling; first principles modeling

References:

1.      
He QP,Wang J, PottmannM, Qin SJ
(2007) A curve fitting method for detecting valve stiction in oscillating
control loops. Industrial and Engineering Chemistry Research 46:4549?4560.

2.      
Kayihan A, Doyle III FJ (2000)
Friction compensation for a process control valve. Control Engineering Practice
8:799?812.

3.      
Choudhury MAAS, Thornhill NF, Shah SL (2005) Modeling valve stiction. Control Engineering Practice 13:641?658.

4.      
Kano, M., Maruta, H., Kugemoto, H., Shimizu, K. (2004). Practical model and
detection algorithm for valve stiction. Proc IFAC DYCOPS, Cambridge, USA.

5.      
Gerry J, Ruel M (2001) How to
measure and combat valve stiction online. Proc ISA International Fall
Conference, Houston, TX. http://www.expertune.com/articles/isa2001/StictionMR.htm.

6.      
Ruel M (2000) Stiction: the hidden
menace. Control Magazine 13:69?75. http://www.expertune.com/articles/RuelNov2000/stiction.html.