278218 A Semi-Physical Valve Stiction Model and Its Application for Stiction Quantification

Tuesday, October 30, 2012: 9:55 AM
324 (Convention Center )
Q. Peter He, Chemical Engineering, Tuskegee University, Tuskegee, AL and Jin Wang, Chemical Engineering, Auburn University, Auburn, AL

A semi-physical valve stiction model and its application for stiction quantification

Q. Peter He1 and Jin Wang2

1Department of Chemical Engineering, Tuskegee University, Tuskegee, AL 36088, USA

2Department of Chemical Engineering, Auburn University, Auburn, AL 36849, USA

Valve stiction is one of the most common equipment problems that can cause poor performance in control loops. Consequently, there is a strong need in the process industry for non-invasive methods that can not only detect but also quantify stiction [1].

To simulate valve stiction, both detailed physical models and empirical models have been developed. Physical models [2] 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 [3-5] 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.

In this work, a semi-physical valve stiction model is derived based on the analysis of the dynamic response of a physical model of a pneumatic valve, which is derived based on the Newton's second law of motion [6]. Comparison of the semi-physical model with the three existing data-driven models [3-5] to the physical model [2] shows that only the semi-physical model can reproduce the same valve behavior described by the physical model. One advantage of the semi-physical model is that it contains only two parameters compared to eight parameters of the physical model. Another advantage of the semi-physical model is that its implementation is straightforward without the involvement of cumbersome numerical integration as in the physical model.

In addition, we propose a noninvasive valve stiction quantification method based on the semi-physical model using the routine operating data obtained from the process, i.e., process variable (PV) and controller output (OP). The algorithm is proposed to estimate the stiction parameters, namely static friction fS and dynamic or kinetic friction fD, without requiring the valve position (VP) signal. Identification is accomplished by using linear and nonlinear least-squares methods which are robust and easy to implement. Several simulation and industrial examples, including both self-regulating and integrating processes with different degrees of stiction, are used to demonstrate the effectiveness of the method.

Key words: valve modeling, stiction quantification; control valve; identification and estimation; fault diagnosis

 

  1. Choudhury, M. S.; Shah, S.; Thornhill, N.; Shook, D. Automatic detection and quantification of stiction in control valves. Control Engineering Practice 2006, 14, 1395-1412.
  2. Kayihan A, Doyle III FJ (2000) Friction compensation for a process control valve. Control Engineering Practice 8:799812.
  3. 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 46:45494560.
  4. Choudhury MAAS, Thornhill NF, Shah SL (2005) Modeling valve stiction. Control Engineering Practice 13:641658.
  5. Kano, M., Maruta, H., Kugemoto, H., Shimizu, K. (2004). Practical model and detection algorithm for valve stiction. Proc IFAC DYCOPS, Cambridge, USA.
  6. He, Q.P., Wang, J. & Qin, S.J. (2010), An alternative stiction modeling approach and comparison of different stiction models, in Detection and Diagnosis of Stiction in Control Loops State of the Art and Advanced Methods, Jelali, M. and Huang, B. (Eds.), Springer, pp37-60

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