Soft sensors with white- and black-box approaches for a wastewater treatment process

Brazilian Journal of Chemical Engineering. 2000;17(4-7):433-440 DOI 10.1590/S0104-66322000000400008

 

Journal Homepage

Journal Title: Brazilian Journal of Chemical Engineering

ISSN: 0104-6632 (Print); 1678-4383 (Online)

Publisher: Brazilian Society of Chemical Engineering

LCC Subject Category: Technology: Chemical technology: Chemical engineering

Country of publisher: Brazil

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

D. Zyngier
O.Q.F. Araújo
E.L. Lima

EDITORIAL INFORMATION

Peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 12 weeks

 

Abstract | Full Text

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.