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In the process industries, stiction is the most common performance-limiting valve problem and over the last decade numerous different techniques for overcoming it have been proposed. This book represents a comprehensive presentation of these methods, including their principles, assumptions, strengths and drawbacks. Guidelines and working procedures are provided for the implementation of each method and MATLAB®-based software can be downloaded from www.ualberta.ca/~bhuang/stiction-book enabling readers to apply the methods to their own data. Methods for the limitation of stiction effects are proposed within the general context of: oscillation detection in control loops, stiction detection, diagnosis and stiction quantification and diagnosis of multiple faults. The state-of-the-art algorithms presented in this book are demonstrated and compared in industrial case studies of diverse origin – chemicals, building, mining, pulp and paper, mineral and metal processing.

A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.