Adaptive Recurrent Neural Network Control of Biological Wastewater Treatment

• Type: Article in International Scientific Journal
• Date: 2005

Authors

Ieroham S. Baruch (Author) FEUP; Petia Georgieva (Author) FEUP; Josefina Barrera-Cortes (Author) Other; Sebastiao Feyo de Azevedo (Author) FEUP

Journal

Title: International Journal of Intelligent Systems

Vol. 20 No. 2

Pages: 173-193

ISSN: 0884-8173

Publisher: Wiley-Blackwell

Indexing

ISI Web of Science

Scopus

INSPEC

COMPENDEX

Scientific classification

FOS: Engineering and technology > Chemical engineering

CORDIS: Technological sciences > Engineering > Chemical engineering

Other information

Abstract (EN): Three adaptive neural network control structures to regulate a biological wastewater treatment process are introduced: indirect, inverse model, and direct adaptive neural control. The objective is to keep the concentration of the recycled biomass proportional to the influent flow rate in the presence of periodically acting disturbances, process parameter variations, and measurement noise. This is achieved by the so-called Jordan Canonical Recurrent Trainable Neural Network, which is a completely parallel and parametric neural structure, permitting the use of the obtained parameters, during the learning phase, directly for control system design. Comparative simulation results confirmed the applicability of the proposed control schemes.

Language: English

Type (Professor's evaluation): Scientific