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Modeling Method for A2O Anoxic Zone Based on PSO-SCN.

Wenxia Lu1,2, Xueyong Tian2, Yinyan Guan2

  • 1School of Materials Science and Engineering, Shenyang University of Technology, Shenyang, China.

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This study introduces a Particle Swarm Optimization-optimized Stochastic Configuration Network (PSO-SCN) for advanced wastewater treatment. The PSO-SCN model accurately predicts effluent quality in the anoxic zone, improving intelligent control.

Keywords:
A2O processPSOSCNmodeling of anoxic zonestaged modeling

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Area of Science:

  • Environmental Engineering
  • Artificial Intelligence in Water Treatment
  • Wastewater Treatment Process Modeling

Background:

  • The A2O process is crucial for wastewater treatment, but optimizing its anoxic zone is complex.
  • Traditional models struggle with the dynamic and nonlinear nature of wastewater treatment processes.
  • Integrating data-driven and mechanistic approaches offers a promising path for improved control.

Purpose of the Study:

  • To develop and validate a Particle Swarm Optimization-optimized Stochastic Configuration Network (PSO-SCN) model.
  • To enhance the prediction accuracy of key effluent parameters (COD, NH4+-N, NO3--N) in the anoxic zone of A2O systems.
  • To improve the interpretability and computational efficiency of wastewater treatment models.

Main Methods:

  • Data preprocessing included isolation forest for outlier handling, KNN for missing values, and Kernel Principal Component Analysis (KPCA) for dimensionality reduction.
  • A Stochastic Configuration Network (SCN) was optimized using Particle Swarm Optimization (PSO).
  • Model performance was validated using data from both a simulator and an actual wastewater treatment plant, with SHAP analysis for interpretability.

Main Results:

  • The PSO-SCN model significantly outperformed the unoptimized SCN, ASM1, PSO-BP, and PSO-RBF models in predicting effluent COD, NH4+-N, and NO3--N concentrations.
  • The model demonstrated superior accuracy, evidenced by improved Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) values.
  • SHAP analysis confirmed the model's enhanced interpretability.

Conclusions:

  • The PSO-SCN framework effectively balances prediction accuracy, computational efficiency, and mechanistic interpretability for wastewater treatment.
  • This integrated approach provides a valuable tool for intelligent control of the A2O process.
  • The study highlights the potential of hybrid AI-mechanistic models in environmental engineering applications.