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Computational artificial intelligence modeling and optimization of nutrient removal in microalgae membrane bioreactors using ANN and RSM

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Abstract

Biological based methods for wastewater treatment are preferred because of they are more economical and environmentally friendly. This work sought to predict microalgae membrane bioreactor (MBR) levels of ammonia nitrogen, phosphate concentration, and removal efficiency. Forecasting the values in the study using artificial neural network (ANN) modeling and response surface methodology (RSM). According to the data, the RSM model accurately estimates the effectiveness of phosphate, nitrogen, and ammonia removal. The R2 values of 0.9733 and 0.9608 help to justify the significant correlation between the predictions of the model and the removal efficiencies. The model uses time to predict the removal effectiveness of these contaminants, along with TOC content. Moreover, the performance metrics for ANN designs are R2 coefficient of determination of 0.99791, mean squared error (MSE) of 3.57E-02 for ammonia nitrogen, and R2 coefficient of determination of 0.99722 and MSE of 2.75E-02 for phosphate concentration. Nonetheless, variations between the actual data and the ANN’s predictions suggest possible limits in the model or experimental design. Furthermore, using RSM and NSGA-II in the optimization process demonstrated the model’s ability to accurately pinpoint the ideal circumstances for removing nutrients from the bioreactor.

Data availability

The authors declare that the data will be made available on reasonable request.

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Funding

This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2603).

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Authors and Affiliations

  1. Department of Software Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il city, Saudi Arabia

    Nadia Ghezaiel Hammouda

  2. College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Karim Kriaa & Khalil Hajlaoui

  3. Refrigeration and Air-condition Department, Technical Engineering College, The Islamic University, Najaf, Iraq

    Ahmed Mohsin Alsayah

  4. College of Engineering, Department of Mechanical Engineering, Najran University, King Abdulaziz Road, P.O Box 1988, Najran, Saudi Arabia

    Husam Rajab

  5. Department of Mechanical Engineering, College of Engineering, University of Ha’il, Ha’il City, 81451, Saudi Arabia

    Walid Aich

  6. Islamic Azad University, Ayatollah Amoli Branch, Amol, Iran

    Mohammad Jadidi

  7. Faculty of Data Science and Information Technology, INTI International University, Persiaran Perdana BBN, Putra Nilai, Nilai 71800, Malaysia

    Narinderjit Singh Sawaran Singh

Authors

  1. Nadia Ghezaiel Hammouda
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  2. Karim Kriaa
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  6. Khalil Hajlaoui
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  8. Mohammad Jadidi
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Contributions

Nadia Ghezaiel Hammouda; Study conception and design. Karim Kriaa; Data collection. Ahmed Mohsin Alsayah; Software. Husam Rajab; Validation. Narinderjit Singh Sawaran Singh; Visualization. Khalil Hajlaoui; Formal analysis. Walid Aich; Investigation. Mohammad Jadidi; Writing original draft.

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Correspondence to
Mohammad Jadidi.

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Hammouda, N.G., Kriaa, K., Alsayah, A.M. et al. Computational artificial intelligence modeling and optimization of nutrient removal in microalgae membrane bioreactors using ANN and RSM.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34775-w

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  • DOI: https://doi.org/10.1038/s41598-025-34775-w

Keywords

  • Membrane bioreactor
  • Phosphate
  • Wastewater and treatment
  • ANN

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