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ANN and GPR Modeling of Multipollutant Removal in Rotating Biological Contactor.

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Title: ANN and GPR Modeling of Multipollutant Removal in Rotating Biological Contactor.
Authors: Alkhattabi, Loai1 (AUTHOR), Waqas, Sharjeel2 (AUTHOR) sharjeel.waqas@kfupm.edu.sa, Imran, Muhammad3 (AUTHOR), Alsaady, Mustafa4 (AUTHOR), Hanbazazah, Abdulkader5 (AUTHOR), Alahmadi, Eeyad5 (AUTHOR), Angiulli, Giovanni (AUTHOR) giovanni.angiulli@unirc.it
Source: Journal of Engineering (2314-4912). 5/22/2026, Vol. 2026, p1-18. 18p.
Subjects: Artificial neural networks, Gaussian processes, Wastewater treatment, Machine learning, Chemical oxygen demand, Organic compounds removal (Sewage purification)
Abstract: Effective treatment of domestic wastewater requires biological processes capable of maintaining stable performance under varying operational conditions. This study applies artificial neural network (ANN) and Gaussian process regression (GPR) to model and predict multipollutant removal performance in a lab‐scale rotating biological contactor (RBC). The RBC performance was evaluated for the removal of chemical oxygen demand (COD), ammonium‐N, and turbidity under different hydraulic retention time (HRT), sludge retention time (SRT), and disk rotational speed. ANN model demonstrated strong predictive agreement with experimental observations, effectively capturing nonlinear relationships between operational parameters and removal efficiencies. GPR provided accurate predictions together with quantitative uncertainty estimates, offering additional insight into model confidence and sensitivity across operating parameters. The combined ANN–GPR framework highlights the trade‐off between predictive accuracy and uncertainty‐aware interpretation in data‐driven modeling of the RBC bioreactor. The findings enhance understanding of parameter interactions in RBC and support the use of machine learning–based tools for performance analysis and decision support for wastewater treatment. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 193980932
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  Data: ANN and GPR Modeling of Multipollutant Removal in Rotating Biological Contactor.
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  Data: <searchLink fieldCode="AR" term="%22Alkhattabi%2C+Loai%22">Alkhattabi, Loai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Waqas%2C+Sharjeel%22">Waqas, Sharjeel</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sharjeel.waqas@kfupm.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Imran%2C+Muhammad%22">Imran, Muhammad</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alsaady%2C+Mustafa%22">Alsaady, Mustafa</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hanbazazah%2C+Abdulkader%22">Hanbazazah, Abdulkader</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alahmadi%2C+Eeyad%22">Alahmadi, Eeyad</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Angiulli%2C+Giovanni%22">Angiulli, Giovanni</searchLink> (AUTHOR)<i> giovanni.angiulli@unirc.it</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+%282314-4912%29%22">Journal of Engineering (2314-4912)</searchLink>. 5/22/2026, Vol. 2026, p1-18. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Wastewater+treatment%22">Wastewater treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+oxygen+demand%22">Chemical oxygen demand</searchLink><br /><searchLink fieldCode="DE" term="%22Organic+compounds+removal+%28Sewage+purification%29%22">Organic compounds removal (Sewage purification)</searchLink>
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  Label: Abstract
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  Data: Effective treatment of domestic wastewater requires biological processes capable of maintaining stable performance under varying operational conditions. This study applies artificial neural network (ANN) and Gaussian process regression (GPR) to model and predict multipollutant removal performance in a lab‐scale rotating biological contactor (RBC). The RBC performance was evaluated for the removal of chemical oxygen demand (COD), ammonium‐N, and turbidity under different hydraulic retention time (HRT), sludge retention time (SRT), and disk rotational speed. ANN model demonstrated strong predictive agreement with experimental observations, effectively capturing nonlinear relationships between operational parameters and removal efficiencies. GPR provided accurate predictions together with quantitative uncertainty estimates, offering additional insight into model confidence and sensitivity across operating parameters. The combined ANN–GPR framework highlights the trade‐off between predictive accuracy and uncertainty‐aware interpretation in data‐driven modeling of the RBC bioreactor. The findings enhance understanding of parameter interactions in RBC and support the use of machine learning–based tools for performance analysis and decision support for wastewater treatment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/je/7707098
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      – Code: eng
        Text: English
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        PageCount: 18
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      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Gaussian processes
        Type: general
      – SubjectFull: Wastewater treatment
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Chemical oxygen demand
        Type: general
      – SubjectFull: Organic compounds removal (Sewage purification)
        Type: general
    Titles:
      – TitleFull: ANN and GPR Modeling of Multipollutant Removal in Rotating Biological Contactor.
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            NameFull: Alkhattabi, Loai
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              M: 05
              Text: 5/22/2026
              Type: published
              Y: 2026
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