Neural Networks-Based Control Strategies Applied to a Fed-Batch Crystallization Process

• Tipo: Artigo em Revista Científica Internacional
• Data: 2006

Autores

P. Georgieva (Autor) FEUP; S. Feyo de Azevedo (Autor) FEUP

Revista

Título: International Journal of Computational Intelligence

Páginas: 224-233

ISSN: 1304-2386

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Outras ciências da engenharia e tecnologias

CORDIS: Ciências Tecnológicas

Outras Informações

Abstract (EN): This paper is focused on issues of process modeling and two model based control strategies of a fed-batch sugar crystallization process applying the concept of artificial neural networks (ANNs). The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. Two control alternatives are considered – model predictive control (MPC) and feedback linearizing control (FLC). Adequate ANN process models are first built as part of the controller structures. MPC algorithm outperforms the FLC approach with respect to satisfactory reference tracking and smooth control action. However, the MPC is computationally much more involved since it requires an online numerical optimization, while for the FLC an analytical control solution was determined.

Idioma: Inglês

Tipo (Avaliação Docente): Científica