Dynamic Optimisation of Industrial Sugar Crystallization Process based on a Hybrid (mechanistic+ANN) Model

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2006

Autores

Vytautas Galvanauskas (Autor) FEUP; Petia Georgieva (Autor) FEUP; Sebastião Feyo de Azevedo (Autor) FEUP

Ata de Conferência Internacional

Título: IEEE World Congress on Computational Intelligence

Páginas: 2728-2735

2006 International Joint Conference on Neural Networks

Vancouver, Canada, 16-21 July 2006

Indexação

ISI Proceedings

Scopus

INSPEC

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia química

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia química

Outras Informações

Abstract (EN): A model-based optimization of an industrial fedbatch sugar crystallisation process is considered in this paper. The objective is to define the optimal profiles of the manipulated process inputs, the feeding rate of liquor/syrup and the steam supply rate, such that the crystal content and the crystal size distribution (CSD) measures at the end of the batch cycle reach the reference values. A knowledge-based hybrid model is implemented, which combines a partial first principles model reflecting the mass, energy and population balances with an artificial neural network (ANN) to estimate the kinetics parameters - particle growth rate, nucleation rate and the agglomeration kernel. The simulation results demonstrate that the very tight and conflicting end-point objectives are simultaneously feasible in the presence of hard process constrains.

Idioma: Inglês

Tipo (Avaliação Docente): Científica