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Efficient model calibration method based on phase experiments for anaerobic-anoxic/nitrifying (A2N) two-sludge

Hongliang Dai1,2, Wenliang Chen1,2,3, Zheqin Dai1,2

  • 1School of Energy and Environment, Southeast University, No. 2 Sipailou Road, Nanjing, 210096, China.

Environmental Science and Pollution Research International
|July 1, 2017
PubMed
Summary

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This summary is machine-generated.

This study presents an efficient method for calibrating complex anaerobic-anoxic/nitrifying (A2N) models using phase experiments and genetic algorithms. The validated model accurately predicts effluent quality in A2N systems.

Area of Science:

  • Environmental Engineering
  • Wastewater Treatment Technologies
  • Biochemical Process Modeling

Background:

  • Mechanistic modeling of anaerobic-anoxic/nitrifying (A2N) two-sludge systems requires systematic calibration and validation.
  • Existing methods may lack efficiency or accuracy in reflecting complex process conditions.

Purpose of the Study:

  • To propose an efficient method for calibrating A2N mechanistic models.
  • To validate the calibrated model for predicting effluent quality in A2N systems.

Main Methods:

  • Utilized phase experiments (anaerobic phosphorus release, aerobic nitrification, anoxic denitrifying phosphate accumulation) in an A2N sequencing batch reactor (SBR).
  • Employed sensitivity analysis and a genetic algorithm for efficient model calibration.
  • Validated the model using batch and continuous flow (CF) experiments.
Keywords:
Activated sludge models (ASMs)Anaerobic–anoxic/nitrifying (A2N) two-sludge systemGenetic algorithmModel calibrationSensitivity analysis

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Main Results:

  • The calibrated model accurately predicted effluent chemical oxygen demand (COD), ammonia nitrogen (NH4+-N), total nitrogen (TN), and total phosphorus (TP).
  • Model validation demonstrated high accuracy across both A2N-SBR and CF-A2N processes.
  • Statistical criteria including ARD, MAE, RMSE, and Janus coefficient confirmed prediction accuracy.

Conclusions:

  • The proposed method offers an efficient and accurate approach for A2N model calibration.
  • The validated model provides reliable predictions for key effluent parameters in A2N wastewater treatment.
  • This systematic procedure enhances the utility of mechanistic models in optimizing A2N processes.