Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors

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

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


Machine learning models provide an adaptive tool to predict the performance of treatment reactors under varying operational and influent conditions. Aerobic granular sludge (AGS) is still an emerging technology and does not have a long history of full-scale application. There is, therefore, a scarcity of long-term data in this field, which impacted the development of data-driven models. In this study, a machine learning model was developed for simulating the AGS process using 475 days of data collected from three lab-based reactors. Inputs were selected based on RReliefF ranking after multicollinearity reduction. A five-stage model structure was adopted in which each parameter was predicted using separate models for the preceding parameters as inputs. An ensemble of artificial neural networks, support vector regression and adaptive neuro-fuzzy inference systems was used to improve the models’ performance. The developed model was able to predict the MLSS, MLVSS, SVI5, SVI30, granule size, and effluent COD, NH4-N, and PO43− with average R2, nRMSE and sMAPE of 95.7%, 0.032 and 3.7% respectively.

Original languageEnglish
Article number116657
JournalWater Research
Publication statusPublished - Feb 1 2021
Externally publishedYes


  • Adaptive Neuro-Fuzzy Inference Systems
  • Aerobic granular sludge
  • Artificial neural networks
  • Machine Learning
  • Sequencing Batch Reactors
  • Support Vector Regression

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Ecological Modelling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution


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