Artificial neural network simulation of combined humic substance coagulation and membrane filtration

Mohammed Al-Abri, Nidal Hilal

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Backpropagation artificial neural network (BPNN) was utilized to predict membrane performance. The network was used to predict and compare humic substance (HS) retention and membrane fouling with previously obtained experimental data. BPNN simulation results show high network reliability, if the network is implemented correctly. The difference between the predicted and experimental data was lower than 5%. Low number of training data input has been shown to hinder the learning process. A high number of training data input has lead to over-fitting or memorization of the training data set, reducing the networks predictability. The number of neurons in the hidden layers needs to be chosen carefully to obtain a reliable network. This paper shows that a lower number of neurons result in low reliability, while a higher number of neurons leads to data over-fitting. The best performance was obtained with 2-10 neurons for HS and heavy metals agglomeration and 5-15 neurons for HS coagulation with and without heavy metals.

Original languageEnglish
Pages (from-to)27-34
Number of pages8
JournalChemical Engineering Journal
Volume141
Issue number1-3
DOIs
Publication statusPublished - 15 Jul 2008
Externally publishedYes

Fingerprint

Humic Substances
humic substance
Coagulation
coagulation
artificial neural network
Neurons
membrane
Neural networks
Membranes
simulation
Heavy Metals
Backpropagation
Heavy metals
heavy metal
Membrane fouling
agglomeration
fouling
Agglomeration
learning

Keywords

  • Artificial neural network
  • Membrane separation
  • Prediction

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Environmental Engineering

Cite this

Artificial neural network simulation of combined humic substance coagulation and membrane filtration. / Al-Abri, Mohammed; Hilal, Nidal.

In: Chemical Engineering Journal, Vol. 141, No. 1-3, 15.07.2008, p. 27-34.

Research output: Contribution to journalArticle

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