Centralized indirect control of an anaerobic digestion bioprocess using recurrent neural identifier

Ieroham S. Baruch, Rosalba Galvan-Guerra, Boyka Nenkova

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

7 Citas (Scopus)

Resumen

The paper proposed to use a Recurrent Neural Network Model (RNNM) and a dynamic Backpropagation learning for centralized identification of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The anaerobic digestion bioprocess represented a distributed parameter system, described by partial differential equations. The analytical model is simplified to a lumped ordinary system using the orthogonal collocation method, applied in three collocation points, generating data for the neural identification. The obtained neural state and parameter estimations are used to design an indirect sliding mode control of the plant. The graphical simulation results of the digestion wastewater treatment indirect control exhibited a good convergence and precise reference tracking.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence
Subtítulo de la publicación alojadaMethodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings
Páginas297-310
Número de páginas14
DOI
EstadoPublicada - 2008
Publicado de forma externa
Evento13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008 - Varna, Bulgaria
Duración: 4 sep. 20086 sep. 2008

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5253 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008
País/TerritorioBulgaria
CiudadVarna
Período4/09/086/09/08

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