DocumentCode :
671396
Title :
Neuro-fuzzy control strategy for methane production in an anaerobic process
Author :
Gurubel, K.J. ; Sanchez, Edgar N. ; Carlos-Hernandez, S.
Author_Institution :
Centro de Investig. y Estudios Av., Inst. Politec. Nac., Guadalajara, Mexico
fYear :
2013
fDate :
4-9 Aug. 2013
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, a neuro-fuzzy control strategy composed by a neural observer and fuzzy supervisors for an anaerobic digestion process is proposed in order to maximize methane production. A nonlinear discrete-time recurrent high order neural observer (RHONO) is used to estimate biomass concentration and substrate degradation in a continuous stirred tank reactor. A Takagi-Sugeno supervisor controller based on the estimation of biomass, selects and applies the most adequate control action, allowing a smooth switching between open loop and closed loop. The control law calculates dilution rate and bicarbonate rate based on speed-gradient inverse optimal neural control. Finally, Takagi-Sugeno supervisors calculate reference trajectories for the system states, and gain scheduling for the dilution rate control law at different operating points of the process. The applicability of the proposed scheme is illustrated via simulations.
Keywords :
bioreactors; discrete time systems; fuzzy control; fuzzy neural nets; neurocontrollers; nonlinear control systems; optimal control; recurrent neural nets; RHONO; Takagi-Sugeno supervisor controller; anaerobic digestion process; bicarbonate rate; biomass concentration estimate; biomass estimation; continuous stirred tank reactor; dilution rate control; fuzzy supervisors; gain scheduling; methane production; neural observer; neuro-fuzzy control strategy; nonlinear discrete-time recurrent high order neural observer; optimal neural control; reference trajectories; speed-gradient inverse optimal neural control; substrate degradation; Biomass; Observers; Optimal control; Process control; Production; Substrates; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
ISSN :
2161-4393
Print_ISBN :
978-1-4673-6128-6
Type :
conf
DOI :
10.1109/IJCNN.2013.6706735
Filename :
6706735
Link To Document :
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