Advancing Wastewater Treatment via Next‐Generation CFD: A Critical Review of Multiphase Physics, Scale‐Up Challenges, and AI Integration
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Wiley
2026
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| _version_ | 1867016055913709568 |
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| author | Egemen Aras Damla Yilmaz Çelik |
| author_facet | Egemen Aras Damla Yilmaz Çelik Egemen Aras Damla Yilmaz Çelik |
| collection | Wiley Open Access |
| contents | Advancing Wastewater Treatment via Next‐Generation CFD: A Critical Review of Multiphase Physics, Scale‐Up Challenges, and AI Integration Egemen Aras Damla Yilmaz Çelik Water Environment Research ABSTRACT Wastewater treatment plants (WWTPs) are facing dual pressures of increasing influent variability and the urgent need for decarbonization. Aeration processes, accounting for 50%–70% of total energy consumption, represent the most critical target for optimization. Specifically, transitioning from conventional empirical methods to AI–CFD integrated frameworks can quantitatively reduce aeration energy consumption from 0.30 to 0.70 kWh/m 3 down to 0.15–0.25 kWh/m 3 . This review provides a comprehensive analysis of the evolution from classical hydrodynamic assessments to advanced computational fluid dynamics (CFD) and artificial intelligence (AI) integrated frameworks, identifying the multiphase Euler–Euler approach coupled with population balance models (PBM) as the most suitable framework for capturing complex WWTP hydrodynamics. Unlike conventional reviews, this study critically evaluates the numerical robustness of turbulence closures, highlighting how advanced formulations such as shear stress transport (SST) k–ω can enhance predictive reliability in shear‐dominated and rotational flow regions when appropriately validated against experimental data (typically yielding R 2 > 0.90 and RMSE < 10%). Furthermore, the role of population balance modeling (PBM) in capturing complex bubble dynamics—such as coalescence and breakup—is analyzed as a prerequisite for accurate oxygen transfer efficiency (OTE) estimations. A significant focus is placed on the “scale‐up” challenge, identifying the mathematical discrepancies between pilot‐scale validations and full‐scale plant performance. To bridge these gaps, the review explores emerging approaches such as physics‐informed neural networks (PINNs) and digital twins. Specifically, AI‐driven surrogate models drastically reduce computational times to enable real‐time control, whereas PINNs ensure predictions remain physically robust even with sparse operational data. These integrations offer substantial potential for reducing energy use and associated carbon emissions when integrated with renewable energy systems. By synthesizing validated physical models with AI‐driven surrogates, this review demonstrates that actionable CFD strategies can yield absolute aeration energy reductions of 15%–30% while paving the way for carbon‐conscious wastewater treatment. 10.1002/wer.70421 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/wer.70421 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_wer_70421 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Advancing Wastewater Treatment via Next‐Generation CFD: A Critical Review of Multiphase Physics, Scale‐Up Challenges, and AI Integration Egemen Aras Damla Yilmaz Çelik Water Environment Research Advancing Wastewater Treatment via Next‐Generation CFD: A Critical Review of Multiphase Physics, Scale‐Up Challenges, and AI Integration Egemen Aras Damla Yilmaz Çelik Water Environment Research ABSTRACT Wastewater treatment plants (WWTPs) are facing dual pressures of increasing influent variability and the urgent need for decarbonization. Aeration processes, accounting for 50%–70% of total energy consumption, represent the most critical target for optimization. Specifically, transitioning from conventional empirical methods to AI–CFD integrated frameworks can quantitatively reduce aeration energy consumption from 0.30 to 0.70 kWh/m 3 down to 0.15–0.25 kWh/m 3 . This review provides a comprehensive analysis of the evolution from classical hydrodynamic assessments to advanced computational fluid dynamics (CFD) and artificial intelligence (AI) integrated frameworks, identifying the multiphase Euler–Euler approach coupled with population balance models (PBM) as the most suitable framework for capturing complex WWTP hydrodynamics. Unlike conventional reviews, this study critically evaluates the numerical robustness of turbulence closures, highlighting how advanced formulations such as shear stress transport (SST) k–ω can enhance predictive reliability in shear‐dominated and rotational flow regions when appropriately validated against experimental data (typically yielding R 2 > 0.90 and RMSE < 10%). Furthermore, the role of population balance modeling (PBM) in capturing complex bubble dynamics—such as coalescence and breakup—is analyzed as a prerequisite for accurate oxygen transfer efficiency (OTE) estimations. A significant focus is placed on the “scale‐up” challenge, identifying the mathematical discrepancies between pilot‐scale validations and full‐scale plant performance. To bridge these gaps, the review explores emerging approaches such as physics‐informed neural networks (PINNs) and digital twins. Specifically, AI‐driven surrogate models drastically reduce computational times to enable real‐time control, whereas PINNs ensure predictions remain physically robust even with sparse operational data. These integrations offer substantial potential for reducing energy use and associated carbon emissions when integrated with renewable energy systems. By synthesizing validated physical models with AI‐driven surrogates, this review demonstrates that actionable CFD strategies can yield absolute aeration energy reductions of 15%–30% while paving the way for carbon‐conscious wastewater treatment. 10.1002/wer.70421 http://creativecommons.org/licenses/by/4.0/ |
| title | Advancing Wastewater Treatment via Next‐Generation CFD: A Critical Review of Multiphase Physics, Scale‐Up Challenges, and AI Integration |
| topic | Water Environment Research |
| url | https://onlinelibrary.wiley.com/doi/10.1002/wer.70421 |