Practical tools for the configuration of control structures

Abstract: Process industries have to operate in a very competitive and globalized environment, requiring efficient and sustainable production processes. Production targets need to be translated into control objectives and are usually formulated as performance specifications of the process. The controller design is a difficult task which involves assumptions and simplifications because of the process complexity. Complexity arises often due to the large scale character of a process, i.e. a pulp and paper mill which can be composed by thousands of control loops. A critical step is the choice of the control configuration, which involves choosing a set of measurements to be used to calculate the control action for each actuator.Current methods for Control Configuration Selection (CCS) include Interaction Measures (IMs). The probably most widely used IM dates back to 1966 when the Relative Gain Array (RGA) was introduced by Bristol. However, these methods rarely become applied in industry, where control structures are often designed based on previous experience or common sense in interpreting process knowledge, but without the support of theoretical and systematic tools.The work in this thesis is oriented towards the development of these tools for industry application. Several topics on CCS are addressed to deal with this lack of practical use, including the robustness to model uncertainty, the need of parametric process models of the complex process, the lack of tools which present the information in connection to the process layout, and the delay from research to education and finally industry application.The main contribution of this thesis is on the consideration of model uncertainty in the CCS problem. Since uncertainty is an intrinsic property of all process models, the validity of the control configuration suggested by the IMs cannot be assessed by only analyzing the nominal model. This thesis introduces methods for the computation of the uncertainty bounds of two gramian-based IMs, which can be used to design robust control configurations.The requirement of process models is an important limitation for the use of the IMs, and the complexity of modeling increases with the number of process variables. This thesis presents novel results in the estimation of IMs, which aim to remove the need of parametric process models for the design of control configurations.CCS using IMs is a heuristic approach, being interpretation needed to select the process interconnections on which control will be based. The traditional IMs present information as an array of real numbers which is disjoint from the process layout. This thesis describes new methods for the interaction analysis of complex processes using weighted graphs, allowing integrating the analysis with process visualization for an increased process understanding.As final contribution, this thesis describes the development of the software tool ProMoVis (Process Modeling and Visualization), which is a platform in which state-of-the-art research in CCS is implemented for facilitating its use in industry applications.