A NEW NEURAL OBSERVER FOR AN ANAEROBIC BIOREACTOR
2010 ◽
Vol 20
(01)
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pp. 75-86
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Keyword(s):
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.
2015 ◽
Vol 178
◽
pp. 285-296
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2014 ◽
Vol 192
◽
pp. 59-61
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2005 ◽
Vol 40
(5)
◽
pp. 1679-1691
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Keyword(s):
Keyword(s):
2005 ◽
Vol 40
(2)
◽
pp. 895-902
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2009 ◽
Vol 168
(1)
◽
pp. 390-399
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Keyword(s):
2005 ◽
Vol 40
(11)
◽
pp. 3419-3428
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2007 ◽
Vol 40
(5)
◽
pp. 1026-1034
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Keyword(s):