Open AccessAdaptive control using a hybrid-neural model: application to a polymerisation reactor
Author(s) -
Francisco Cubillos,
H. Callejas,
Enrique Luis Lima,
Márcia Peixoto Vega
Publication year - 2001
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/s0104-66322001000100010
Subject(s) - control theory (sociology) , continuous stirred tank reactor , setpoint , model predictive control , artificial neural network , plug flow reactor model , robustness (evolution) , computer science , engineering , control engineering , chemistry , control (management) , chemical engineering , artificial intelligence , biochemistry , gene
This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM) is based on fundamental conservation laws associated with a neural network (NN) used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN) used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor