Advanced search
Start date
Betweenand
(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Robust predictive control of a gasoline debutanizer column

Full text
Author(s):
E. Almeida Neto ; M.A. Rodrigues ; D. Odloak
Total Authors: 3
Document type: Journal article
Source: Brazilian Journal of Chemical Engineering; v. 17, n. 4-7, p. -, Dez. 2000.
Abstract

This paper studies the application of Model Predictive Control to moderately nonlinear processes. The system used in this work is an industrial gasoline debutanizer column. The paper presents two new formulations of MPC: MMPC (Multi-Model Predictive Controller) and RSMPC (Robust Stable MPC). The approach is based on the concepts of Linear Matrix Inequalities (LMI), which have been recently introduced in the MPC field. Model uncertainty is considered by assuming that the true process model belongs to a convex set (polytope) of possible plants. The controller has guaranteed stability when a Lyapunov type inequality constraint is included in the MPC problem. In the debutanizer column, several nonlinearities are present in the advanced control level when the manipulated inputs are the reflux flow and the reboiler heat duty. In most cases the controlled outputs are the contents of C5+ (pentane and heavier hydrocarbons) in the LPG (Liquefied Petroleum Gas) and the gasoline vapor pressure (P VR). In this case the QDMC algorithm which is usually applied to the debutanizer column has a poor performance and stability problems reflected in an oscillatory behavior of the process. The new approach considers several process models representing different operating conditions where linear models are identified. The results presented here show that the multimodel controller is capable of controlling the process in the entire operating window while the conventional MPC has a limited operating range. (AU)

FAPESP's process: 98/14384-3 - Integration of chemical processes in real time
Grantee:Rubens Maciel Filho
Support type: Research Projects - Thematic Grants