Valve stiction is one of the most common problems on industrial process control loops. The detection and quantification of valve stiction in control loops is therefore important to ensure the high quality of the products and maintain the reliable performance of control loops. This paper presents an algorithm for quantifying valve stiction in control loop based on linear decrease inertia weight particle swarm optimization to obtain more accurate estimates of stiction parameters. The amount of stiction present in the valve is estimated by identifying parameters of Kano model which is a two-parameter data-driven stiction modelling based on the parallelogram of MVPV phase plot. Simulation results have demonstrated the efficacy of this algorithm in valve stiction quantification and also its robustness to oscillations due to inappropriate controller tuning and external disturbances. Results are confirmed by application to real process industrial data.

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