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

• Type: Article in International Conference Proceedings Book
• Date: 2006

Authors

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

Conference proceedings International

Title: IEEE World Congress on Computational Intelligence

Pages: 2728-2735

2006 International Joint Conference on Neural Networks

Vancouver, Canada, 16-21 July 2006

Indexing

ISI Proceedings

Scopus

INSPEC

Scientific classification

FOS: Engineering and technology > Chemical engineering

CORDIS: Technological sciences > Engineering > Chemical engineering

Other information

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.

Language: English

Type (Professor's evaluation): Scientific