Hybrid modelling of a PHA production process using modular neural networks

• Type: Article in International Conference Proceedings Book
• Date: 2004

Authors

Joana Peres (Author) FEUP; R. Oliveira (Author) Other; L. S. Serafim (Author) Other; P. Lemos (Author) Other; M. A. Reis (Author) Other; Sebastião Feyo de Azevedo (Author) FEUP

Conference proceedings International

Title: EUROPEAN SYMPOSIUM ON COMPUTER-AIDED PROCESS ENGINEERING - 14

Pages: 733-738

14th European Symposium on Computer Aided Process Engineering (ESCAPE-14)

Lisbon, PORTUGAL, MAY 16-19, 2004

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Other information

Authenticus ID: P-000-CPR

DOI: 10.1016/s1570-7946(04)80188-3

Abstract (EN): A novel method for bioreactor hybrid modeling is presented that combines first principles models and modular artificial neural networks trained with the Expectation Maximization (EM) algorithm. The use of modular networks was motivated by the nature of the 'cells system' that may be viewed as a highly complex network of metabolic reactions organised in modular pathways. The proposed hybrid modelling technique is validated experimentally with a laboratory scale Polyhydroxyalkanoates (PHAs) production process. The main results show that the embedded modular network. if trained with the EM algorithm, is able to organise itself in modules that have correspondence to the underlying biological pathways. In the particular case of the PHA process discussed, the network learned to discriminate between acetate and internal reserves respiration, with the smaller network modules developing expertise in describing the reaction kinetics of the one or other metabolic state.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6