Humic substance coagulation: Artificial neural network simulation

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

Research output: Contribution to journalArticlepeer-review


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 (US)
Pages (from-to)153-157
Number of pages5
Issue number1-3
StatePublished - Apr 2010


  • ANN
  • Humic acid
  • Polymer coagulation
  • Prediction

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • General Materials Science
  • Water Science and Technology
  • Mechanical Engineering


Dive into the research topics of 'Humic substance coagulation: Artificial neural network simulation'. Together they form a unique fingerprint.

Cite this