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<p>The operation of wastewater treatment plants (WWTPs) is frequently characterized by complexity, largely attributable to the properties of the influent and the nonlinear fluctuations that occur throughout the wastewater treatment process. Accurate modeling of wastewater quality within WWTPs is essential for informed decision-making. In this research, we utilized a stacking model to amalgamate five foundational models, thereby enhancing the precision of the total nitrogen (TN) prediction model for effluent. This methodology mitigates the inherent risk of overfitting associated with individual base models while preserving robust predictive capabilities in relation to feature inputs and intricate influent conditions. Following the integration of the models, the coefficient of determination (<i>R</i><sup>2</sup>) for the stacking model achieved a value of 0.90. Furthermore, through SHAP analysis, we elucidated the model and identified the parameters that exert the most significant influence on the prediction of effluent TN in WWTPs, notably electricity, Inf_BOD5, Inf_TN, and MLSS. To further augment the model’s applicability in optimizing effluent TN, we performed simulations by adjusting the controllable parameter MLSS to forecast effluent TN. The findings indicate a correlation between increased MLSS concentration and reduced effluent TN, with the predicted trends facilitating the analysis of scenarios involving elevated effluent TN concentrations. This, in turn, offers valuable engineering insights for the reduction of effluent TN in wastewater treatment facilities.</p>

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Sensitivity-driven control strategy and analysis of operating parameter MLSS in the stacking total nitrogen prediction model

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Huining Zhang,
  • Wenrui Cai,
  • Yang Cao,
  • Baixiang Wang,
  • Yi Zhao

摘要

The operation of wastewater treatment plants (WWTPs) is frequently characterized by complexity, largely attributable to the properties of the influent and the nonlinear fluctuations that occur throughout the wastewater treatment process. Accurate modeling of wastewater quality within WWTPs is essential for informed decision-making. In this research, we utilized a stacking model to amalgamate five foundational models, thereby enhancing the precision of the total nitrogen (TN) prediction model for effluent. This methodology mitigates the inherent risk of overfitting associated with individual base models while preserving robust predictive capabilities in relation to feature inputs and intricate influent conditions. Following the integration of the models, the coefficient of determination (R2) for the stacking model achieved a value of 0.90. Furthermore, through SHAP analysis, we elucidated the model and identified the parameters that exert the most significant influence on the prediction of effluent TN in WWTPs, notably electricity, Inf_BOD5, Inf_TN, and MLSS. To further augment the model’s applicability in optimizing effluent TN, we performed simulations by adjusting the controllable parameter MLSS to forecast effluent TN. The findings indicate a correlation between increased MLSS concentration and reduced effluent TN, with the predicted trends facilitating the analysis of scenarios involving elevated effluent TN concentrations. This, in turn, offers valuable engineering insights for the reduction of effluent TN in wastewater treatment facilities.