Application of artificial neural network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT

Mahmoud Nasr, Medhat A E Moustafa, Hamdy A E Seif, Galal El Kobrosy

Research output: Contribution to journalArticle

57 Citations (Scopus)

Abstract

A reliable model for any Wastewater Treatment Plant WWTP is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This paper focuses on applying an Artificial Neural Network (ANN) approach with a Feed-Forward Back-Propagation to predict the performance of EL-AGAMY WWTP-Alexandria in terms of Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSSs) data gathered during a research over a 1-year period. The study signifies that the ANN can predict the plant performance with correlation coefficient (R) between the observed and predicted output variables reached up to 0.90. Moreover, ANN provides an effective analyzing and diagnosing tool to understand and simulate the non-linear behavior of the plant, and is used as a valuable performance assessment tool for plant operators and decision makers.

Original languageEnglish
Pages (from-to)37-43
Number of pages7
JournalAlexandria Engineering Journal
Volume51
Issue number1
DOIs
Publication statusPublished - Mar 2012
Externally publishedYes

Fingerprint

Wastewater treatment
Neural networks
Biochemical oxygen demand
Chemical oxygen demand
Backpropagation
Costs

Keywords

  • Artificial neural networks
  • MATLAB
  • Modeling
  • Statistical analysis
  • Wastewater plant

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Application of artificial neural network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT. / Nasr, Mahmoud; Moustafa, Medhat A E; Seif, Hamdy A E; El Kobrosy, Galal.

In: Alexandria Engineering Journal, Vol. 51, No. 1, 03.2012, p. 37-43.

Research output: Contribution to journalArticle

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