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Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent

Bin Xie1, Yong-Wen Ma1, Jin-Quan Wan1

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Environmental Science and Pollution Research International
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Summary
This summary is machine-generated.

This study introduces a multi-objective control strategy mixed soft-sensing model (MCSSM) for optimizing anaerobic ammonium oxidation (ANAMMOX) in wastewater treatment. The model accurately predicts nitrogen removal and suggests optimal operating conditions for efficient treatment.

Keywords:
ANAMMOXLeast square support vector machine∙modelingMulti-objective optimizationNon-dominated sorting genetic algorithm-IIWastewater treatment

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Area of Science:

  • Environmental Engineering
  • Biotechnology
  • Wastewater Treatment Technologies

Background:

  • Anaerobic ammonium oxidation (ANAMMOX) is an efficient nitrogen removal process for wastewater.
  • Understanding ANAMMOX reaction mechanisms, simulation, and control remains a challenge.

Purpose of the Study:

  • Develop a multi-objective control strategy mixed soft-sensing model (MCSSM) for ANAMMOX processes.
  • Integrate PCA-LSSVM and NSGA-II for systematic design of operating variations.
  • Enhance prediction accuracy and provide optimal operational guidance.

Main Methods:

  • Developed a multi-objective control strategy mixed soft-sensing model (MCSSM).
  • Utilized principal component analysis-optimized least square support vector machine (PCA-LSSVM) for prediction.
  • Employed non-dominated sorting genetic algorithm-II (NSGA-II) for optimization.

Main Results:

  • PCA-LSSVM achieved high prediction accuracy for effluent ammonia nitrogen (R²=0.997) and total nitrogen removal (R²=0.989).
  • NSGA-II provided reasonable influent operating conditions.
  • Optimal conditions include pH 7.50-7.52, COD/TN ratio of 0.15, and NH₄⁺-N/NO₂⁻-N ratio of 0.61.

Conclusions:

  • The MCSSM approach offers a robust framework for ANAMMOX bioprocess optimization.
  • The developed model demonstrates high applicability for predicting and optimizing wastewater treatment.
  • Further research can involve numerical modeling and experimental validation for data acquisition.