Open AccessSoft sensors with white- and black-box approaches for a wastewater treatment process
Author(s) -
Danielle Zyngier,
O.Q.F. Araújo,
Enrique Luis Lima
Publication year - 2000
Publication title -
brazilian journal of chemical engineering/brazilian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.313
H-Index - 52
eISSN - 1678-4383
pISSN - 0104-6632
DOI - 10.1590/s0104-66322000000400008
Subject(s) - soft sensor , robustness (evolution) , matlab , software , artificial neural network , feed forward , feedforward neural network , process (computing) , computer science , wastewater , process engineering , engineering , control engineering , waste management , chemistry , artificial intelligence , biochemistry , gene , operating system , programming language
The increasing degradation of water resources makes it necessary to monitor and control process variables that may disturb the environment, but which may be very difficult to measure directly, either because there are no physical sensors available, or because these are too expensive. In this work, two soft sensors are proposed for monitoring concentrations of nitrate (NO) and ammonium (NH) ions, and of carbonaceous matter (CM) during nitrification of wastewater. One of them is based on reintegration of a process model to estimate NO and NH and on a feedforward neural network to estimate CM. The other estimator is based on Stacked Neural Networks (SNN), an approach that provides the predictor with robustness. After simulation, both soft sensors were implemented in an experimental unit using FIX MMI (Intellution, Inc) automation software as an interface between the process and MATLAB 5.1 (The Mathworks Inc.) software