Produção Nacional
Revisado por pares
Adaptive control using a hybrid-neural model: application to a polymerisation reactor
2001; Springer Science+Business Media; Volume: 18; Issue: 1 Linguagem: Inglês
10.1590/s0104-66322001000100010
ISSN1678-4383
AutoresFrancisco Cubillos, H. Callejas, Enrique Luis Lima, Márcia Peixoto Vega,
Tópico(s)Innovative Microfluidic and Catalytic Techniques Innovation
ResumoThis 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.
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