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

Mohammed Al-Abri, Nidal Hilal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)


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
Issue number1-3
Publication statusPublished - Jul 15 2008
Externally publishedYes


  • Artificial neural network
  • Membrane separation
  • Prediction

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering


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