Artificial neural networks for modeling in reaction process systems

• Type: Article in International Scientific Journal
• Date: 2009

Authors

Cristina Oliveira (Author) Other; Petia Georgieva (Author) Other; Fernando Rocha (Author) FEUP; Sebastiao Feyo de Azevedo (Author) FEUP

Journal

Title: Neural Computing and Applications

Vol. 18 No. 1

Pages: 15-24

ISSN: 0941-0643

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 8 Citations

Scopus - 10 Citations

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Technological sciences > Engineering > Chemical engineering

Other information

Authenticus ID: P-003-REN

DOI: 10.1007/s00521-008-0200-8

Abstract (EN): This work is focused on developing a more efficient computational scheme for estimation of process reaction rates based on NN models. Two scenarios are considered: (1) the kinetics coefficients of the process are completely known and the process states are partly known (measured); (2) the kinetics coefficients and the states of the process are partly known. The contribution of the paper is twofold. From one side we formulate a hybrid (ANN and mechanistic) model that outperforms the traditional reaction rate estimation approaches. From other side, a new procedure for NN supervised training is proposed when target outputs are not available. The two scenarios are successfully tested for two benchmark problems, estimation of the precipitation rate of calcium phosphate and estimation of sugar crystallization growth rate.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10