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<p>Forward osmosis (FO) is a promising membrane-based technology for removing heavy metals, such as zinc, from wastewater. This study explores the potential of FO for zinc removal using data-driven models, including RSM, ANN, and ANFIS to model and optimize the FO system. The economic feasibility of implementing this system on a large scale is also assessed. The findings reveal that ANN outperformed the other models in terms of prediction accuracy, achieving R<sup>2</sup> values of 0.9841 for water flux and 0.9499 for zinc removal efficiency. The 3D surface plots generated by ANN and ANFIS demonstrated that feed velocity was the most significant factor influencing water flux, followed by draw velocity, while feed concentration had a minimal impact. Under optimized conditions, the FO system achieved a maximum water flux of 7.17 LMH and zinc removal efficiency of 92.80%. The economic feasibility analysis indicates that the FO process is financially viable for large-scale applications, with a water production cost of 0.623 $/m<sup>3</sup>. These results highlight the potential of advanced modeling techniques to enhance the efficiency and cost-effectiveness of FO systems for wastewater treatment. Insights from this study could support industries in adopting cleaner technologies for wastewater management, thereby contributing to environmental sustainability.</p>

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Assessing the Feasibility of Forward Osmosis for Sustainable Zinc Removal from Wastewater: Statistical and Neural Intelligence Modeling, Optimization, and Economic Evaluation

  • 期刊论文

  • 发表日期：

    2025年10月4日

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  • Ali Boubakri,
  • Sarra Elgharbi,
  • Salah Bouguecha,
  • Sondes Chemingui,
  • Haessah D. Alanazy,
  • Amor Hafiane

摘要

Forward osmosis (FO) is a promising membrane-based technology for removing heavy metals, such as zinc, from wastewater. This study explores the potential of FO for zinc removal using data-driven models, including RSM, ANN, and ANFIS to model and optimize the FO system. The economic feasibility of implementing this system on a large scale is also assessed. The findings reveal that ANN outperformed the other models in terms of prediction accuracy, achieving R2 values of 0.9841 for water flux and 0.9499 for zinc removal efficiency. The 3D surface plots generated by ANN and ANFIS demonstrated that feed velocity was the most significant factor influencing water flux, followed by draw velocity, while feed concentration had a minimal impact. Under optimized conditions, the FO system achieved a maximum water flux of 7.17 LMH and zinc removal efficiency of 92.80%. The economic feasibility analysis indicates that the FO process is financially viable for large-scale applications, with a water production cost of 0.623 $/m3. These results highlight the potential of advanced modeling techniques to enhance the efficiency and cost-effectiveness of FO systems for wastewater treatment. Insights from this study could support industries in adopting cleaner technologies for wastewater management, thereby contributing to environmental sustainability.