Centralized neural identification and control of an anaerobic digestion bioprocess

Ieroham Baruch; Rosalba Galvan Guerra

doi:10.23919/ecc.2009.7074749

Centralized neural identification and control of an anaerobic digestion bioprocess

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The paper proposed to use a Recurrent Neural Network Model (RNNM), and a dynamic backpropagation algorithm of its learning for centralized modeling, identification and direct adaptive control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess, used as process data generator represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points plus the recirculation tank. The graphical simulation results of the digestion wastewater treatment system direct adaptive neural control, exhibited a good convergence and precise reference tracking, outperforming the optimal control.

Original language	English
Title of host publication	2009 European Control Conference, ECC 2009
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2307-2312
Number of pages	6
ISBN (Electronic)	9783952417393
DOIs	https://doi.org/10.23919/ecc.2009.7074749
State	Published - 26 Mar 2014
Externally published	Yes
Event	2009 10th European Control Conference, ECC 2009 - Budapest, Hungary Duration: 23 Aug 2009 → 26 Aug 2009

Publication series

Name	2009 European Control Conference, ECC 2009

Conference

Conference	2009 10th European Control Conference, ECC 2009
Country/Territory	Hungary
City	Budapest
Period	23/08/09 → 26/08/09

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10.23919/ecc.2009.7074749

Cite this

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title = "Centralized neural identification and control of an anaerobic digestion bioprocess",

abstract = "The paper proposed to use a Recurrent Neural Network Model (RNNM), and a dynamic backpropagation algorithm of its learning for centralized modeling, identification and direct adaptive control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess, used as process data generator represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points plus the recirculation tank. The graphical simulation results of the digestion wastewater treatment system direct adaptive neural control, exhibited a good convergence and precise reference tracking, outperforming the optimal control.",

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year = "2014",

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Baruch, I & Guerra, RG 2014, Centralized neural identification and control of an anaerobic digestion bioprocess. in 2009 European Control Conference, ECC 2009., 7074749, 2009 European Control Conference, ECC 2009, Institute of Electrical and Electronics Engineers Inc., pp. 2307-2312, 2009 10th European Control Conference, ECC 2009, Budapest, Hungary, 23/08/09. https://doi.org/10.23919/ecc.2009.7074749

Centralized neural identification and control of an anaerobic digestion bioprocess. / Baruch, Ieroham; Guerra, Rosalba Galvan.
2009 European Control Conference, ECC 2009. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2307-2312 7074749 (2009 European Control Conference, ECC 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - The paper proposed to use a Recurrent Neural Network Model (RNNM), and a dynamic backpropagation algorithm of its learning for centralized modeling, identification and direct adaptive control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess, used as process data generator represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points plus the recirculation tank. The graphical simulation results of the digestion wastewater treatment system direct adaptive neural control, exhibited a good convergence and precise reference tracking, outperforming the optimal control.

AB - The paper proposed to use a Recurrent Neural Network Model (RNNM), and a dynamic backpropagation algorithm of its learning for centralized modeling, identification and direct adaptive control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess, used as process data generator represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points plus the recirculation tank. The graphical simulation results of the digestion wastewater treatment system direct adaptive neural control, exhibited a good convergence and precise reference tracking, outperforming the optimal control.

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ER -

Centralized neural identification and control of an anaerobic digestion bioprocess

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