Modelling and Analysis of the Anaerobic Digestion Process

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006

2006

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A new methodology to design fuzzy observers for anaerobic wastewater treatment plants is introduced in this article. First, a general explanation of the anaerobic digestion in a completely stirred tank reactor is presented. A model of the system is explained and studied using the principal components analysis in view to choose variables and local models which characterize the bioprocess. These variables and local models will be used to design the fuzzy observer. The observer performances are validated via realistic simulations and experimentally.

Section: B Methodology For Fuzzy Variables Choicementioning

confidence: 99%

“…One hundred ninety two input scenarios are possible from these intervals. In [19], the authors present a classic stability analysis for the linearized models. They are stable for the input intervals considered; all poles keep real negatives for great variations in the substrate input.…”

Section: A Model Analysismentioning

confidence: 99%

Design and real-time implementation of a TS fuzzy observer for anaerobic wastewater treatment plants

Carlos-Hernández¹,

Béteau²,

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006

2006

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“…The observability property of this anaerobic digestion process was analyzed in a previous work [2]. It was concluded that substrates (S 1 and S 2 ), biomasses (X 1 and X 2 ) and inorganic carbon (IC) are observable.…”

Section: Discrete Time Rhonomentioning

confidence: 98%

“…More details are presented in [19,20,21]. The used EKF-based training algorithm is described by (2).…”

Section: Discrete Time Rhonomentioning

confidence: 99%

Dynamic learning rate (η<inf>D</inf>) for recurrent high order neural observer (RHONO): Anaerobic process application

Gurubel

The 2011 International Joint Conference on Neural Networks

Carlos-Hernández

2011

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In this paper, a dynamic learning rate, for recurrent high order neural observer (RHONO), is proposed. The dynamic learning rate depends on the pH on-line measurement. The main objective is to improve learning of the neuronal network in presence of disturbances, which is obtained by increasing the performance of the neuronal observer by means of the dynamic learning rate. The learning algorithm is based on an extended Kalman filter. The applicability of the proposed dynamic rate is illustrated via simulation, as applied to a RHONO for an anaerobic process.

“…In biological processes there exist hardly measurable or immeasurable variables which are necessary for process control. 2 Furthermore, the last two decades have seen an increasing interest to improve the operation of bioprocesses by applying advanced control schemes. [3][4][5] Hence, observer design is a major problem to be solved in addition to adequate sensors selection.…”

Section: Introductionmentioning

confidence: 99%

A New Neural Observer for an Anaerobic Bioreactor

Belmonte-Izquierdo

Carlos-Hernández

2010

Int. J. Neur. Syst.

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In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter (EKF). The applicability of the proposed scheme is illustrated via simulation. A validation using real data from a lab scale process is included. Thus, this observer can be successfully implemented for control purposes.