Heleno Bispo

Auto Tuning Applied to Distillation Column Based on Armax Model Parameter Estimation

An auto-tuning procedure for PID controllers based on model ARMAX is the main subject of this paper. Such a model has been used in the on-line controller parameters estimation through the relay method, resulting in a smaller deviation between the model and process variables when utilized a normalized regression matrix. The procedure was applied to an Aspen Plus Dynamics® model of Naphtha Treatment Unit. The Aspen model takes into account the main control structures and the principal observed disturbances. The procedure has been developed in Visual Basic for Applications programming language, making possible a real-time connection with the Aspen model. The on-line identification of the ARMAX model was made using the RELS (PR), thereby allowing the estimation of the controllers parameters from classical tuning procedures in closed loop. With the use of this strategy, the updating of the controllers parameters has been possible, promoting a superior performance of the control structure.

On the Role of Mechanical Work Transfer in Optimization Procedures

Abstract This paper is concerned with the analysis of the role of the mechanical work transferred by the stirrer, applied to reactive and non-reactive systems. Classical methods for analysis and optimization take into account mass and enthalpy balances, emphasizing that the term referring to the energy in the form of work added to the system by stirring has usually been neglected. Since the entropy balance has also been considered in the optimization strategy, then the inclusion of work from stirring in such balances has permitted the degree of freedom for such a strategy to be increased. The analysis shows that the use of work in balance equations can play a pivotal role for a more realistic approach, besides providing a better understanding of the optimization mechanism, and when compared with the classical analysis, the results are much more consistent.

Joint Confidence Region for the Tuning Parameters of the PID Controller

Abstract The aim of this paper is to estimate the joint confidence region for the tuning parameters of the PID controller. Considerations on the statistical independence and the linear relationship between the tuning parameters for a more realistic scenario are taken into account. In order to capture the data necessary for the simulation, an appropriate structure was implemented, consisting of a process-model-based controller. By using the data resulting from the simulations, it was also possible to find the probability density function for each tuning parameter, as well the region of joint confidence of such parameters, which indicates a contraction of the region when compared with the standard level of significance. What is also shown is how to restore the joint confidence region by means of the Principal Components Technique.

Entropic-Model-Based PI Controller

Abstract Contributions of entropic modelling to the performance of reactive process control have been investigated. The modelling has been developed based on mass, energy and entropy balances and thermodynamics relations, resulting in a model for calculating the entropy production rate. Using the conventional optimization technique, a minimum for the entropy production rate has been found when a given relationship between the temperatures of the inlet stream and of the reaction is satisfied for a particular residence time in the reactor. A new class of nonlinear controller has been developed by means of introducing entropic models into classical PI algorithms designed by reference system synthesis. The results indicate that such a controller yields a superior performance when compared to classical feedback control strategies.