Neural network-based estimation of reaction rates with partly unknown states and completely known kinetics coefficients

• Type: Article in International Conference Proceedings Book
• Date: 2008

Authors

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

Conference proceedings International

Title: IEEE International Conference on Intelligent Systems

Pages: 7-8-7-13

4th International IEEE Conference "Intelligent Systems"

Varna, Bulgária, 6 a 8 de Setembro de 2008

Indexing

Scopus

Scientific classification

FOS: Engineering and technology > Chemical engineering

CORDIS: Technological sciences > Engineering > Chemical engineering

Other information

Abstract (EN): This work is focused on developing a more efficient computational scheme for estimation of process reaction rates based on NN models. In contrast to the traditional way of process reaction rates estimation by exhaustive and expensive search for the most appropriate parameterized structure, a neural network (NN) based procedure is proposed here to identify the reaction rates in the framework of an analytical process model. The reaction rates are not measured, therefore a special hybrid NN training structure and adaptation algorithm are proposed to make possible the supervised NN learning. The present contribution is focused on the general modelling of a class of nonlinear systems representing several industrial processes including crystallization and precipitation, polymerization reactors, distillation columns, biochemical fermentation and biological systems. The proposed algorithm is further applied for estimation of the sugar crystallization growth rate and compared with alternative solution.

Language: English

Type (Professor's evaluation): Scientific