A New Neural Observer for an Anaerobic Wastewater Treatment Process

被引：0

作者：

Belmonte-Izquierdo, Ruben ^{[1
]}

Carlos-Hernandez, Salvador ^{[2
]}

Nelson Sanchez, Edgar ^{[1
]}

机构：

[1] CINVESTAV, IPN, Dept Automat Control, Unidad Gdl, Av Cient 1145, Zapopan 45015, Jalisco, Mexico

[2] CINVESTAV, IPN, Dept Energy & Nat Resource, Unidad Saltillo, Saltillo 25900, Coahuila, Mexico

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE | 2009年 / 61卷

关键词：

Recurrent high order neural observer; anaerobic digestion; extended Kalman filter; TREATMENT PLANTS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate biomass and substrate in a completely stirred tank reactor. The recurrent high order neural network (RHONN) structure is based on hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter. The applicability of the proposed scheme is illustrated via simulation. Thus, this observer can be Successfully implemented for control purposes.

引用

页码：123 / +

页数：3

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