Modelling of the batch sucrose crystallization kinetics using artificial neural networks: Comparison with conventional regression analysis

• Tipo: Artigo em Revista Científica Internacional
• Data: 2008

Autores

Vasanth V Kumar (Autor) Outra; Martins, P (Autor) FEUP; Rocha, F (Autor) FEUP

Revista

Título: Industrial & Engineering Chemistry Research

Vol. 47

Páginas: 4917-4923

ISSN: 0888-5885

Editora: American Chemical Society

Indexação

ISI Web of Knowledge - 8 Citações

ISI Web of Science

Scopus - 14 Citações

COMPENDEX

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia química

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia química

Outras Informações

ID Authenticus: P-003-XR2

DOI: 10.1021/ie701706v

Abstract (EN): A three-layer feed-forward artificial neural network (ANN) was constructed and tested to analyze the crystal growth rate of sucrose under different operating conditions. The operating variables studied were used as inputs to predict the corresponding crystal growth rate. The operating variables studied include the supersaturation, temperature, agitation speed, and seed crystal diameter. The constructed ANN was determined to be precise in modeling the crystal growth rate for any operating conditions. The constructed network was also found to be precise in predicting the crystal growth rate for the new input data, which are kept unaware of the trained neural network, showing its applicability to determine the growth rate for any operating conditions of interest. The ANN-predicted crystal growth rates were compared to those from the conventional nonlinear regression analysis. The ANN was observed to be more accurate in predicting the crystal growth rate, irrespective of the operating conditions studied. The correlation coefficients between the experimentally determined crystal growth rate and the crystal growth rates determined by the ANN and multiple nonlinear regression (MNLR) were determined to be 0.999 and 0.748, respectively. The correlation coefficient between the experimentally determined crystal growth rates and the crystal growth rates determined by the ANN for new inputs was observed to be >0.98.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7