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

• Type: Article in International Scientific Journal
• Date: 2008

Authors

Vasanth V Kumar (Author) Other; Martins, P (Author) FEUP; Rocha, F (Author) FEUP

Journal

Title: Industrial & Engineering Chemistry Research

Vol. 47

Pages: 4917-4923

ISSN: 0888-5885

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 8 Citations

ISI Web of Science

Scopus - 14 Citations

COMPENDEX

Scientific classification

FOS: Engineering and technology > Chemical engineering

CORDIS: Technological sciences > Engineering > Chemical engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7