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Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive

Zemin Li1, Yulun Wu2, Tao Chen1

  • 1School of Environment, South China Normal University, Guangzhou, Guangdong 510006, PR China; SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, PR China.

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Organic matter type and concentration significantly impact anaerobic ammonium oxidation (Anammox) nitrogen removal. Machine learning models reveal organic type is crucial for biorefractory systems, while concentration dominates biodegradable ones.

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Area of Science:

  • Environmental Microbiology
  • Wastewater Treatment Engineering
  • Biotechnology

Background:

  • Anaerobic ammonium oxidation (Anammox) is vital for nitrogen removal.
  • Organic compounds influence Anammox performance, but synergistic effects are debated.
  • Inconsistencies in operational conditions and microbial environments complicate understanding.

Purpose of the Study:

  • Investigate critical factors governing nitrogen removal efficiency in Anammox processes.
  • Emphasize the combined effects of influent organic concentration and organic matter characteristics.
  • Develop a predictive model for Anammox performance under varying organic loads.

Main Methods:

  • Constructed three datasets based on organic compound types: biodegradable, biorefractory, and combined.
  • Employed two machine learning models, selecting Random Forest (RF) as optimal.
  • Validated the RF model using real coking industry wastewater treatment data.
  • Utilized SHapley Additive exPlanations (SHAP) for factor importance analysis.

Main Results:

  • Organic concentration and ammonium nitrogen (NH4+-N) were primary factors in biodegradable systems.
  • Organic type was the most critical factor in biorefractory and combined systems.
  • Evaluating organic impacts requires considering both type and concentration, not just concentration alone.
  • The BOD/COD ratio was validated as a comprehensive indicator of carbon source effects.

Conclusions:

  • Machine learning effectively integrates material stoichiometry, environmental parameters, and microbial functionality.
  • This approach advances energy-efficient nitrogen removal technologies.
  • Enhances the evaluation system for wastewater treatment processes, considering complex organic influences.