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<p>The performance of sewer network is associated with both clean water infiltration and in-sewer pollutant degradation. Quantifying their contributions in large-scale sewer network remains challenging due to the infeasibility of numerous on-site measurements of water flows and water quality concentrations in the whole system. This study developed a physically inverse problem approach to address this challenge, which was tested in an actual sewer network system (25.66 km<sup>2</sup>) with gridding-based in-sewer flow rate and water quality measurements. Bayesian optimization framework was integrated into sewer hydrodynamic and water quality models to inversely estimate source parameters including source flow rates and source discharge concentrations. Employing simulated annealing algorithm can demonstrate 20.6%–54.2% higher accuracy compared with the other methods, due to its progressive instead of fast and steep convergence toward the true solutions. With the developed approach, the infiltrated clean water infiltration and mass loss of chemical oxygen demand (COD) within the sewer network were quantified synchronously. Further, the condition of sewer structural defects was assessed, and a reference value for allowable in-sewer COD degradation was also presented, which was 4%–5% COD mass per hour of sewage hydraulic retention. Therefore, this methodology can provide cost-effective solution for comprehensive assessment of sewer network conditions.</p>

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Quantifying the performance of urban sewer network using inverse-problem models: An approach for synchronous determination of in-sewer groundwater infiltration and pollutant degradation

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Hui-jin Zhang,
  • Zu-xin Xu,
  • Wan-qiong Wang,
  • Shou-hai Peng,
  • Chong Li,
  • Shuai Fang,
  • Danlu Guo,
  • Hai-long Yin

摘要

The performance of sewer network is associated with both clean water infiltration and in-sewer pollutant degradation. Quantifying their contributions in large-scale sewer network remains challenging due to the infeasibility of numerous on-site measurements of water flows and water quality concentrations in the whole system. This study developed a physically inverse problem approach to address this challenge, which was tested in an actual sewer network system (25.66 km2) with gridding-based in-sewer flow rate and water quality measurements. Bayesian optimization framework was integrated into sewer hydrodynamic and water quality models to inversely estimate source parameters including source flow rates and source discharge concentrations. Employing simulated annealing algorithm can demonstrate 20.6%–54.2% higher accuracy compared with the other methods, due to its progressive instead of fast and steep convergence toward the true solutions. With the developed approach, the infiltrated clean water infiltration and mass loss of chemical oxygen demand (COD) within the sewer network were quantified synchronously. Further, the condition of sewer structural defects was assessed, and a reference value for allowable in-sewer COD degradation was also presented, which was 4%–5% COD mass per hour of sewage hydraulic retention. Therefore, this methodology can provide cost-effective solution for comprehensive assessment of sewer network conditions.