Local self-optimizing control of constrained processes

The available methods for selection of controlled variables (CVs) using the concept of self-optimizing control have been developed under the restrictive assumption that the set of active constraints remains unchanged for all the allowable disturbances and implementation errors. To track the changes in active constraints, the use of split-range controllers and parametric programming has been suggested in the literature. An alternate heuristic approach to maintain the variables within their allowable bounds involves the use of cascade controllers. In this work, we propose a different strategy, where CVs are selected as linear combinations of measurements to minimize the local average loss, while ensuring that all the constraints are satisfied over the allowable set of disturbances and implementation errors. This result is extended to select a subset of the available measurements, whose combinations can be used as CVs. In comparison with the available methods, the proposed approach offers simpler implementation of operational policy for processes with tight constraints. We use the case study of forced-circulation evaporator to illustrate the usefulness of the proposed method.

► Selection of controlled variables is critical during design of control systems.
► Most of available methods for controlled variable selection ignore process constraints.
► Self-optimizing control based method proposed for local analysis.
► Requires solving mixed integer convex program.