Open AccessRobust predictive control of a gasoline debutanizer column
Author(s) -
Euclides Almeida Neto,
Magali A. Rodrigues,
Darci Odloak
Publication year - 2000
Publication title -
brazilian journal of chemical engineering/brazilian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.313
H-Index - 52
eISSN - 1678-4383
pISSN - 0104-6632
DOI - 10.1590/s0104-66322000000400061
Subject(s) - model predictive control , control theory (sociology) , reboiler , controller (irrigation) , fractionating column , gasoline , convex optimization , engineering , computer science , mathematical optimization , mathematics , regular polygon , distillation , control (management) , chemistry , mechanical engineering , agronomy , geometry , heat exchanger , organic chemistry , artificial intelligence , biology , waste management
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