-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/II TM 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.

Estimating kinetic parameters in heterogeneous solid catalytic reaction networks is known to being a difficult task. This work aims at proposing a down-to-earth methodology to obtain kinetic parameters from numerical experiments. We present three techniques: a multivariable linear regression model, a stochastic metamodeling, and an optimized Kriging interpolator connected to a least-squares method. We consolidate the methodology in two different applications. The first one is a process with few components in two reactions from where it was possible to acquire the reaction rate equations that fitted literature data. The second one is a complex industrial reaction network. The results showed that even if the candidate proposed reaction rate equations do not fit the experiments, it is possible to construct a mathematical metamodel that conforms to the behavior of the components. Statistical tests showed that in both cases the proposed models successfully fit the experimental data.