Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks

Mohamed Sherif Zaghloul, Rania Ahmed Hamza, Oliver Terna Iorhemen, Joo Hwa Tay

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


Aerobic granulation is a complex process that, while proven to be more effective than conventional treatment methods, has been a challenge to control and maintain stable operation. This work presents a static data-driven model to predict the key performance indicators of the aerobic granulation process. The first sub-model receives influent characteristics and granular sludge properties. These predicted parameters then become the input for the second sub-model, predicting the effluent characteristics. The model was developed with a dataset of 2600 observations and evaluated with an unseen dataset of 286 observations. The prediction R2 and RMSE were >99% and <5% respectively for all predicted parameters. The results of this paper show the effectiveness of data-driven models for simulating the complex aerobic granulation process, providing a great tool to help in predicting the behaviour, and anticipating failures in aerobic granular reactors.

Original languageEnglish
Pages (from-to)449-459
Number of pages11
JournalScience of the Total Environment
Publication statusPublished - Dec 15 2018
Externally publishedYes


  • Aerobic granulation
  • Data-driven modelling
  • Modelling
  • Neural networks
  • Wastewater treatment

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution


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