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Carbon source dosage intelligent determination using a multi-feature sensitive back propagation neural network model.

Ziqi Zhou1, Xiaohui Wu1, Xin Dong2

  • 1School of Environment Science & Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.

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|February 11, 2025
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Summary

This study introduces a novel deep learning model for optimizing wastewater treatment. The MFS-BPNN-SSA model efficiently predicts external carbon source dosage, reducing costs and improving effluent quality in wastewater treatment plants (WWTPs).

Keywords:
Back propagation neural networkIntelligent carbon source dosageSensitivity analysisShapley additive explanations

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Area of Science:

  • Environmental Engineering
  • Artificial Intelligence in Water Treatment
  • Sustainable Wastewater Management

Background:

  • Wastewater treatment plants (WWTPs) face challenges in optimizing external carbon source addition for denitrification, leading to high costs and unstable effluent quality.
  • Current manual methods rely on experience, often resulting in uneconomical dosage and fluctuating total nitrogen (TN) concentrations.
  • Existing deep learning methods require substantial data, posing limitations for real-world applications with short-term or limited data.

Purpose of the Study:

  • To develop an accurate and data-efficient model for predicting optimal external carbon source dosage in WWTPs.
  • To address the limitations of traditional methods and data-intensive deep learning approaches in managing denitrification processes.
  • To enhance the sustainability and cost-effectiveness of WWTP operations through intelligent carbon source management.

Main Methods:

  • Development of a multi-feature sensitive back propagation neural network (MFS-BPNN-SSA) model.
  • Integration of Shapley additive explanations (SHAP) and sensitivity analysis (SSA) for feature importance and model interpretability.
  • Incorporation of theoretical formulas and feedback regulation to improve prediction accuracy and handle anomalous data, particularly for limited datasets.

Main Results:

  • The MFS-BPNN-SSA model demonstrated superior prediction performance compared to traditional machine learning and deep learning models.
  • Achieved R and R² values 1.75% and 3.48% higher, respectively, than the best-performing traditional model, with R and R² reaching 0.9999.
  • Successfully operated in a real WWTP for over two years, resulting in a 9% improvement in effluent TN concentration and a 14% reduction in carbon source dosage.

Conclusions:

  • The MFS-BPNN-SSA model offers a novel and effective strategy for optimizing carbon source dosage in WWTPs, even with limited data.
  • This approach contributes to pollution reduction and carbon mitigation goals in wastewater treatment.
  • The model's successful long-term operation validates its practical applicability and efficiency in sustainable WWTP management.