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

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

doi:10.1007/978-3-540-85776-1_25

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

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

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

7 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Artificial Intelligence
Subtitle of host publication	Methodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings
Pages	297-310
Number of pages	14
DOIs	https://doi.org/10.1007/978-3-540-85776-1_25
State	Published - 2008
Externally published	Yes
Event	13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008 - Varna, Bulgaria Duration: 4 Sep 2008 → 6 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5253 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008
Country/Territory	Bulgaria
City	Varna
Period	4/09/08 → 6/09/08

Keywords

Anaerobic digestion bioprocess
Backpropagation learning
Distributed parameter system
Recurrent neural network model
Sliding mode control
Systems identification
Wastewater treatment bioprocess

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10.1007/978-3-540-85776-1_25

Cite this

Baruch, I. S., Galvan-Guerra, R., & Nenkova, B. (2008). Centralized indirect control of an anaerobic digestion bioprocess using recurrent neural identifier. In Artificial Intelligence: Methodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings (pp. 297-310). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5253 LNAI). https://doi.org/10.1007/978-3-540-85776-1_25

Baruch, Ieroham S. ; Galvan-Guerra, Rosalba ; Nenkova, Boyka. / Centralized indirect control of an anaerobic digestion bioprocess using recurrent neural identifier. Artificial Intelligence: Methodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings. 2008. pp. 297-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Centralized indirect control of an anaerobic digestion bioprocess using recurrent neural identifier",

abstract = "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.",

keywords = "Anaerobic digestion bioprocess, Backpropagation learning, Distributed parameter system, Recurrent neural network model, Sliding mode control, Systems identification, Wastewater treatment bioprocess",

author = "Baruch, {Ieroham S.} and Rosalba Galvan-Guerra and Boyka Nenkova",

year = "2008",

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Baruch, IS, Galvan-Guerra, R & Nenkova, B 2008, Centralized indirect control of an anaerobic digestion bioprocess using recurrent neural identifier. in Artificial Intelligence: Methodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5253 LNAI, pp. 297-310, 13th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2008, Varna, Bulgaria, 4/09/08. https://doi.org/10.1007/978-3-540-85776-1_25

Centralized indirect control of an anaerobic digestion bioprocess using recurrent neural identifier. / Baruch, Ieroham S.; Galvan-Guerra, Rosalba; Nenkova, Boyka.
Artificial Intelligence: Methodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings. 2008. p. 297-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5253 LNAI).

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

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AU - Baruch, Ieroham S.

AU - Galvan-Guerra, Rosalba

AU - Nenkova, Boyka

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Anaerobic digestion bioprocess

KW - Backpropagation learning

KW - Distributed parameter system

KW - Recurrent neural network model

KW - Sliding mode control

KW - Systems identification

KW - Wastewater treatment bioprocess

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SN - 9783540857754

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Artificial Intelligence

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Baruch IS, Galvan-Guerra R, Nenkova B. Centralized indirect control of an anaerobic digestion bioprocess using recurrent neural identifier. In Artificial Intelligence: Methodology, Systems, and Applications - 13th International Conference, AIMSA 2008, Proceedings. 2008. p. 297-310. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-85776-1_25

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

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