Dynamic modelling of the activated sludge process: Improving prediction using neural networks

Dynamic modelling of the activated sludge process: Improving prediction using neural networks A procedure has been developed to improve the accuracy of an existing mechanistic model of the activated sludge process, previously described by Lessard and Beck ( Wat. Res. 27 , 963–978 (1993)). As a first step, optimization of the numerous model parameters has been investigated using the downhill simplex method in order to minimize the sum of the squares of the errors between predicted and experimental values of appropriate variables. Optimization of various sets of parameters has shown that the accuracy of the mechanistic model, especially on the prediction of the dissolved oxygen (DO) in the mixed liquor, can be easily improved by adjusting only the values of the overall oxygen transfer coefficients, K L a . Then, in a second step, neural network models have been used successfully to predict the remaining errors of the optimized mechanistic model. The coupling of the mechanistic model with neural network models resulted in a hybrid model yielding accurate simulations of the five key variables of the activate sludge process. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Water Research Elsevier

Dynamic modelling of the activated sludge process: Improving prediction using neural networks

Loading next page...
 
/lp/elsevier/dynamic-modelling-of-the-activated-sludge-process-improving-prediction-uAS00augoA
Publisher
Elsevier
Copyright
Copyright © 1995 Elsevier Ltd
ISSN
0043-1354
D.O.I.
10.1016/0043-1354(95)93250-W
Publisher site
See Article on Publisher Site

Abstract

A procedure has been developed to improve the accuracy of an existing mechanistic model of the activated sludge process, previously described by Lessard and Beck ( Wat. Res. 27 , 963–978 (1993)). As a first step, optimization of the numerous model parameters has been investigated using the downhill simplex method in order to minimize the sum of the squares of the errors between predicted and experimental values of appropriate variables. Optimization of various sets of parameters has shown that the accuracy of the mechanistic model, especially on the prediction of the dissolved oxygen (DO) in the mixed liquor, can be easily improved by adjusting only the values of the overall oxygen transfer coefficients, K L a . Then, in a second step, neural network models have been used successfully to predict the remaining errors of the optimized mechanistic model. The coupling of the mechanistic model with neural network models resulted in a hybrid model yielding accurate simulations of the five key variables of the activate sludge process.

Journal

Water ResearchElsevier

Published: Apr 1, 1995

References

  • Fault diagnosis in complex chemical plants using artificial neural networks
    Hoskins, J.C.; Kaliyur, K.M.; Hemmelblau, D.M.
  • Dynamics process modelling with recurrent neural networks
    You, Y.; Nikolaou, M.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off