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Adaptive recurrent neural network control of biological wastewater treatment

✍ Scribed by Ieroham S. Baruch; Petia Georgieva; Josefina Barrera-Cortes; Sebastiao Feyo de Azevedo

Publisher
John Wiley and Sons
Year
2004
Tongue
English
Weight
595 KB
Volume
20
Category
Article
ISSN
0884-8173

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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