Sticky Valve Research Articles

Experimental and modeling investigation on dynamic response of sticky control valves

The dynamic response of control valves directly affects the safe and efficient operation of industrial control loops. Valve stiction is a common and persistent issue that causes oscillations in control loops. The stiction behavior of valves has received widespread attention from researchers worldwide in the past two decades. The developed valve stiction models have been widely applied in the detection, quantification, and compensation research of sticky control valves. However, how to more accurately characterize stiction behavior still requires efforts. Most data-driven models do not consider the effects of dynamic response on the stiction behavior. In this paper, the inconsistency of the representative stiction models is discussed during the unidirectional motion of the valve stem, and potential improvements are revealed. An experimental device for valve stiction has been designed. This device can replicate valve stiction caused by tight packing, and measure valve position and friction through smart positioner and force sensor. The second-order dynamic system for the sticky valve is constructed by the physical model, and the response time of the valve is calculated and verified combined with the experiment. The effects of sampling interval on stiction behavior are discussed. On this basis, an improved valve stiction model considering the dynamic response of the valve is proposed, which supplements the situation of stiction during the unidirectional motion of the valve stem. The proposed model can be applied in control systems with fixed sampling intervals that involve sticky valves. It can also be extended to control systems with variable sampling intervals. This work contributes to evaluating the performance of control systems with sticky valves and providing reference value for the detection, quantification, and compensation of valve stiction.

Static Friction Detection Based on Artificial Neural Networks Method

Introduction: Poor product quality and high energy consumption ofmany control loops is due to the presence of static friction. This phenomenon is monitored by human in many industrials. The decision ismade based on human’s brain which is not effective and reliable. Methods: A model-based method of stiction detection based on an artificialneural network (ANN) is proposed. The ANN which is run in parallel tothe process predicts a dynamic model of the process using data obtainedfrom control signal and process output. Results: It can be seen that theproposed method based on ANN can be replaced with human monitoring method. Conclusions: Capability of the proposed method of staticfriction detection for the process with the sticky valve is confirmed bydata obtained from the simulation in a control loop with sticky valve.

Enhancing MPC formulations by identification and estimation of valve stiction

A common source of poor control performance in industrial processes is represented by stiction in control valves, which often induces offset, oscillating behavior, and even loss of stability. Recent studies have investigated the effectiveness of embedding stiction models into model predictive controller (MPC) schemes, moving from stiction unaware to different stiction aware formulations, which help to remove fluctuations and may guarantee higher set-point tracking ability. To this aim, along with the process model the controller needs to use a dynamic model of sticky valves. This paper proposes an efficient, computational approach to obtain both valve and process dynamics, under the framework of Hammerstein system identification, which is based on nonlinear, gradient-based, numerical optimization. In order to improve the computational behavior and effectiveness of the methodology, a recently proposed smoothed model of stiction is deployed. The proposed methodology is validated in several (single-input single-output, and multivariable) examples, where the effectiveness of the obtained stiction aware MPC regulator is also evaluated against a stiction unaware counterpart.

Control Valve Stiction Compensation - Part I: A New Method for Compensating Control Valve Stiction

Valve stiction is a hidden menace in process control loops. The presence of stiction in control valves limits the control loop performance. Compensation of its effect is beneficial before the sticky valve can be sent for maintenance. This work is the first of a two part study on control valve stiction compensation. This part proposes a novel stiction compensation method while the second part compares the performance of this proposed stiction compensation method with some of other compensation methods appeared in literature. The proposed compensator is developed based on reduction of control action and addition of an extra pulse of finite energy as required. A method for estimating an appropriate parameter for reducing controller action has been developed. The proposed stiction compensator has been extensively evaluated using MATLAB Simulink environment. The compensator has also been implemented in a pilot plant experimental set-up. It has been found to be successful in removing valve stiction induced osci...

Adaptive Valve Stiction Compensation Using Differential Evolution

This paper proposes a new stiction compensation method that combines adaptive inverse model techniques and intelligent control theories to compensate for a process containing a sticky valve. This new method reduces the cycling caused by the nonlinear dynamic associated with stiction, while avoiding aggressive movements of the valve stem. The parameters of the compensator depend on both the type and degree of stiction, and their estimates are optimized using differential evolution. Both simulation and experimental results validate the approach. The compensation method demonstrates excellent performance in overcoming the effect of stiction and in reducing the oscillations with smooth valve stem movements.

Silique valves as sails in anemochory of Lunaria (Brassicaceae).

The generally held opinion that seeds of Lunaria remain at the replum after detachment of the two valves and then wind causes a shaking or rattling of the replum with its diaphragm, thus launching the seeds, is challenged. In a sparse forest in the Swabian Alb, the first author noticed flying valves of Lunaria rediviva to which the narrow-winged flat seeds are attached. Investigations with SEM and histology have shown that the valves secrete a glue only at those sites where the seeds rest on the valves before valve tissues die. Further analysis has shown (using the periodic acid-Schiff reaction) that the glue consists of polysaccharides. After detachment and dispersal of the valves, the adhesive strength continuously decreases. This is the first report for a sticky valve exudate in the Brassicaceae. Because of the adhesion of Lunaria seeds to their valves for some time, the 1st order diaspore is a mericarp, in a broad sense, and can be interpreted as an adaptation to long-distance dispersal by stronger winds. In this context, the 'flying carpets' of Lunaria are more effective and transport more than one seed. Molecular studies assigned Lunaria to the tribe Biscutelleae, which now contains the angustiseptate genera Biscutella and Megadenia as well as the latiseptate genera Lunaria and Ricotia. The valves in Ricotia can easily be detached (studied in herbarium material and a living plant), but, in contrast to Lunaria, the ripe seeds remain at the replum and its diaphragm, respectively.

Data-Based Method To Diagnose Valve Stiction with Variable Reference Signal

Stiction is a well-known villain in industry because of the limit-cycle imposed on the controller. Several methodologies are reported in the literature to automatically detect this problem using only normal operating data. However, this becomes more difficult when the loop with stiction is affected by disturbances or the sticky valve is inside a cascade loop. This study proposes two methods to automatically diagnose valve stiction when the reference signal is variable and centers primarily on recognizing triangular or sinusoidal patterns. The first method is based on the slope of the signal peaks and the second on data segmentation. These techniques were compared to a curve-fitting method, providing similar results when the reference is fixed. However, for processes significantly affected by disturbances or when the sticky valve was inside a cascade loop, stiction detection was better for both methods proposed. These results are corroborated by simulation and industrial data.

System identification applied to stiction quantification in industrial control loops: A comparative study

A comparative study of different models and identification techniques applied to the quantification of valve stiction in industrial control loops is presented in this paper, with the objective of taking into account for the presence of external disturbances. A Hammerstein system is used to model the controlled process (linear block) and the sticky valve (nonlinear block): five different candidates for the linear block and two different candidates for the nonlinear block are evaluated and compared. Two of the five linear models include a nonstationary disturbance term that is estimated along with the input-to-output model, and these extended models are meant to cope with situations in which significant nonzero mean disturbances affect the collected data. The comparison of the different models and identification methods is carried out thoroughly in three steps: simulation, application to pilot plant data and application to industrial loops. In the first two cases (simulation and pilot plant) the specific source of fault (stiction with/without external disturbances) is known and hence a validation of each candidate can be carried out more easily. Nonetheless, each fault case considered in the previous two steps has been found in the application to a large number of datasets collected from industrial loops, and hence the merits and limitations of each candidate have been confirmed. As a result of this study, extended models are proved to be effective when large, time varying disturbances affect the system, whereas conventional (stationary) noise models are more effective elsewhere.

Actuator stiction compensation via model predictive control for nonlinear processes

The problem of valve stiction is addressed, which is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. A model predictive control (MPC) stiction compensation formulation is developed including detailed dynamics for a sticky valve and additional constraints on the input rate of change and actuation magnitude to reduce control loop performance degradation and to prevent the MPC from requesting physically unrealistic control actions due to stiction. Although developed with a focus on stiction, the MPC‐based compensation method presented is general and has potential to compensate for other nonlinear valve dynamics which have some similarities to those caused by stiction. Feasibility and closed‐loop stability of the proposed MPC formulation are proven for a sufficiently small sampling period when Lyapunov‐based constraints are incorporated. Using a chemical process example with an economic model predictive controller (EMPC), the selection of appropriate constraints for the proposed method is demonstrated. The example verified the incorporation of the stiction dynamics and actuation magnitude constraints in the EMPC causes it to select set‐points that the valve output can reach and causes the operating constraints to be met. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2004–2023, 2016

Empirical Modeling of Control Valve Layer with Application to Model Predictive Control-Based Stiction Compensation

Abstract: In this work, we develop an empirical modeling procedure for feedback loops comprised of sticky valves and linear controllers for the valves and incorporate the models in a model predictive control (MPC)-based stiction compensation strategy. The empirical models are developed from data on the measured values of the valve outlet flowrates and the set-points for these flowrates. They utilize standard empirical model structures but are defined in a piecewise fashion, with different branches identified for set-point changes that correspond to sticking of the valve and to sliding of the valve, and modifications to account for the effect of the linear controller on the response of the valve outlet flowrate to a set-point change. Through a chemical process example, it is shown that the use of the empirical models in the MPC-based stiction compensation strategy can decrease the computation time of the method without significantly jeopardizing the constraint satisfaction of the closed-loop process, but preventing the need for a valve layer model with parameters and/or details about the valve that are difficult to obtain.

Topological preservation techniques for nonlinear process monitoring

This work proposes a novel approach for the offline development and online implementation of data-driven process monitoring (PM) using topological preservation techniques, specifically self-organizing maps (SOM). Previous topological preservation PM applications have been restricted due to the lack of monitoring and diagnosis tools. In the proposed approach, the capabilities of SOM are further extended so that all aspects of PM can be performed in a single environment. First for fault detection, using SOM's vector quantization abilities, an SOM-based Gaussian mixture model (GMM) is proposed to define the normal region. For identification, an SOM-based contribution plot is proposed to identify the variables most responsible for the fault. This is done by analyzing the residual of the faulty point and an SOM model of the normal region used in fault detection. The data points are projected on the model by locating the best matching unit (BMU) of the point. Finally, for fault diagnosis a procedure is formulated involving the concept of multiple self-organizing maps (MSOM), creating a map for each fault. This allows the ability to include new faults without directly affecting previously characterized faults. A Tennessee Eastman Process (TEP) application is performed on dynamic faults such as random variations, sticky valves and a slow drift in kinetics. Previous studies of the TEP have considered particular feed-step-change faults. Results indicate an excellent performance when compared to linear and nonlinear distance preservation techniques and standard nonlinear SOM approaches in fault diagnosis and identification.

Identification techniques for stiction quantification in the presence of nonstationary disturbances

The paper presents a detailed comparison of different identification techniques applied to valve stiction quantification, possibly in the presence of nonstationary unknown disturbances. The control loop with sticky valve is modeled as a Hammerstein system, in which the nonlinearity is identified using enumeration of the parameters’ space. Five different techniques for identification of the linear model are compared in terms of achievable performance. In particular, the capability to cope with the presence of nonstationary disturbances is analyzed. The techniques allow one to estimate the unknown actual valve position (MV), without requiring any process knowledge, being based only on data which are usually recorded in industrial plants: controller output (OP) and controlled variable (PV). Simulations show that external perturbations can be tolerated, thus ensuring a reliable evaluation of stiction in practical situations where external disturbances are usually present. Models which incorporate a time varying additive nonstationary disturbance grant a better process identification and a more accurate stiction estimation in the case of disturbance acting simultaneously with valve stiction. However, simpler models are the best choice when stiction happens to be the only source of loop oscillation. Results are confirmed by application to real data: pilot plant data are used to corroborate the effectiveness of the techniques.

Valve Stiction Quantification Method Based on a Semiphysical Valve Stiction Model

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 noninvasive methods that can not only detect but also quantify stiction. In this work, on the basis of a physical and a semiphysical model, a new valve stiction signature is proposed. Industrial evidence is provided to validate the new valve stiction signature. Although valve stiction is a stochastic phenomenon that can not be exactly described by any deterministic model, the revised valve signature provides a better description of sticky valve behavior, particularly when valve stiction is severe. On the basis of the revised valve stiction signature, a noninvasive, simple, and robust valve stiction quantification method is proposed using the routine operation data and limited process knowledge. The proposed quantification method estimates the stiction parameters, namely, static friction and dynamic or kinetic friction, without requiring the valve position signal. Quantification is accomplished by using linear and nonlinear least-squares methods which are robust and easy to implement. The properties of the proposed algorithm are investigated using simulated case studies of first order plus time delay processes, and the performance of the method is compared to other stiction quantification methods using 20 industrial cases.

Identification of a process with control valve stiction using a fuzzy system: A data-driven approach

Many researchers focus on detecting and modelling the valve stiction because it has undesirable effects on the control loop performance, which consequently results in poor product quality and increased energy consumption. It is difficult to model a process with a sticky valve using the mathematical definition because of its nonlinear properties such as stiction, hysteresis, dead band and dead zone. This work aims to develop and determine the appropriate model of a process with stiction, which can be used in controller design to mitigate the undesirable effect of the stiction. To achieve this goal by mapping the process with valve stiction to a fuzzy system, a dynamic fuzzy model of the plant is derived through an iterative well-developed fuzzy clustering algorithm, which generates suitable antecedent parameters from a set of input–output measurements that are obtained from the control output (OP) and the process output (PV). To determine the consequent parameters, the least square (LS) estimation is applied. The results reveal that the obtained data-driven Takagi–Sugeno-type (TS) fuzzy rule-based model can effectively represent an appropriate model of the process with stiction for different amounts of stiction that are obtained from the simulation and different industrial loops.

Modified PI Controller for Stiction Compensation

Stiction is a well known problem of the process performance. Compensate its effect is essential, because most of sticky valves cannot be replaced and they remain working for months or years. The scope of this work is to propose a novel methodology to compensate stiction effects, through the modification of the controller (PI) block in the control loop. The proposed approach is based on the two moves method, however it allows to specify closed loop performances faster than open loop and reject load disturbances efficiently. We assume here that a small offset between process variable and setpoint is accepted, what decreases significantly the valve traveling, comparing with available approaches. The method is described for both setpoint tracking and disturbance rejection. The efficacy is corroborated by a case study, considering setpoint changes and disturbance rejection.

Detection and quantification of valve stiction by the method of unknown input estimation

Valve stiction is one of the most common causes of oscillations in industrial process control loops. Such oscillations can degrade the overall performance of the loop and eventually the final product quality. The detection and quantification of valve stiction in industrial process control loops is thus important. From previous studies in the literature, a sticky valve has been shown to have a distinct signature of the stiction phenomena in its valve positioner data. However, the position of the modulating control valves is seldom available. We consider the problem of estimating the valve position as an unknown input estimation problem. In this work, we propose a novel application of the unknown input estimator in order to estimate the valve position given the process model and the data of the process variable and controller output. Using the estimated valve position, we can detect and also quantify the amount of stiction. We demonstrate the efficacy of the method through simulation examples where a sticky valve is deliberately introduced in the closed loop using a two-parameter stiction model available in the literature. Application of the proposed methodology to a laboratory scale flow control loop is presented. An industrial case study is also presented in which the algorithm accurately detects and quantifies stiction in the level control loop of a power plant.

Confirmation of control valve stiction in interacting systems

AbstractStiction in a control valve generates limit cycles which can propagate to the connecting units, resulting in plant‐wide oscillations and impacting the overall process performance. For stiction confirmation, Choudhury et al. [Choudhury et al., CJChE 2007;85:208–219] introduced the controller gain change method, which is based on the change in the oscillation frequency due to changes in the controller gain. In this paper, we show that this method can fail to accurately isolate and confirm the presence of the sticky valve in interacting multi‐input multi‐output systems. We propose a strategy based on the magnitude of relative change in oscillation frequency due to changes in controller gain to overcome the limitations of the existing method. The effectiveness of the proposed method is demonstrated using simulation studies.

Detection and quantification of valve stiction by the method of unknown input estimation

Valve stiction is one of the most common causes of oscillations in industrial process control loops. Such oscillations can degrade the overall performance of the loop and eventually the final product quality. The detection and quantification of valve stiction in industrial process control loops is thus important. From previous studies in the literature, a sticky valve has been shown to have a distinct signature of the stiction phenomena in its valve positioner data. However, the position of the modulating control valves is seldom available. We consider the problem of estimating the valve position as an unknown input estimation problem. In this work, we propose a novel application of the unknown input estimator in order to estimate the valve position given a linear model of the process and closed loop input-output data. Using the estimated valve position data, we can detect and also quantify the amount of stiction. We demonstrate the efficacy of the method through simulation examples where a sticky valve is deliberately introduced in the closed loop using a two-parameter stiction model available in the literature. An industrial case study is also presented in which the algorithm accurately detects and quantifies stiction in a level control loop of a power plant.

Limit Cycles with Imperfect Valves: Implications for Controllability of Processes with Large Gains

There is some disagreement in the literature in regard to whether or not large plant gains are a problem when input-output controllability issues are involved. In this paper, controllability requirements are derived for two types of input errors, namely, restricted (low) input resolution (e.g., caused by a sticky valve) and input disturbances. In both cases, the controllability is limited if the plant gain is large at high frequencies. Limited input resolution causes limit cycle behavior (oscillations) similar to that observed with relay feedback. The magnitude of the output variations is dependent on the plant gain at high frequency but is independent of the controller tuning. Provided frequent input (valve) movements are acceptable, one may reduce the output magnitude by forcing the system to oscillate at a higher frequency, for example by introducing a faster local feedback (e.g., a valve positioner) or by pulse modulating the input signal.

Control Loop Performance Assessment. 2. Hammerstein Model Approach for Stiction Diagnosis

In part 2 of this two-part series, an approach for diagnosis and quantification of stiction using a simple single-parameter model is proposed. The stiction model, in conjunction with an identified process model from routine operating data, is shown to successfully facilitate stiction diagnosis. An optimization approach is used to jointly identify the process model and the stiction parameter. This approach is based on the identification of a Hammerstein model of the system comprising the sticky valve and the process. In this work, a new identification procedure for Hammerstein systems that supports stiction diagnosis is proposed. Industrial and simulation case studies are shown to demonstrate the application of the proposed approach for diagnosing stiction.