Development and application of an automatic tool for the selection of control variables based on the selfoptimizing control methodology

Abstract

Rules for control structure design for industrial processes have been extensively proposed in the literature. Some model-based methodologies have a sound mathematical basis, such as the self-optimizing control technology. The procedure can be applied with the aid of available commercial simulators, e.g., PRO/IITM and AspenPlus®, from which the converging results are obtained more suitably for industrial applications, lessening the effort needed to build an appropriate mathematical model of the plant. Motivated by this context, this work explores the development and application of a tool designed to automatically generate near-optimal controlled structures for process plants based on the self-optimizing control technology. The goal is to provide a means to facilitate the way possible arrangements of controlled variables are generated. Using the local minimum singular value rule supported by a modified version of a branch-and-bound algorithm, the best sets of candidate controlled variables can be identified that minimize the loss between real optimal operation and operation under constant set-point policy. A case study consisting of a deethanizer is considered to show the main features of the proposed tool. The conclusion indicates the feasibility of merging complex theoretical contents within the framework of a user-friendly interface simple enough to generate control structures suitable for real world implementation.