Humic substance coagulation: Artificial neural network simulation

Mohammed Al-Abri*, Khalid Al Anezi, Akram Dakheel, Nidal Hilal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


This paper investigates the use of backpropagation neural network (BPNN) to predict humic substance (HS) UV absorbance experimental results. The studied experimental sets include HS and heavy metal agglomeration, HS coagulation using polyelectrolytes and HS and heavy metal coagulation using polyelectrolytes. BPNN simulation showed high prediction accuracy where regression coefficient (R) was > 0.95 for all simulations. Lower and higher than optimum training data input reduces BPNN reliability due to under training or over-fitting. The number of neurons study showed that a lower number of neurons led to under training, while a higher number of neurons resulted in the network memorizing the input dataset.

Original languageEnglish
Pages (from-to)153-157
Number of pages5
Issue number1-3
Publication statusPublished - Apr 2010


  • ANN
  • Humic acid
  • Polymer coagulation
  • Prediction

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Materials Science(all)
  • Water Science and Technology
  • Mechanical Engineering


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