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Related Experiment Video

Updated: Jun 7, 2025

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors
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Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological

Shiqi Liu1, Zeqing Long2, Jinsong Liang1

  • 1School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.

Bioresource Technology
|November 10, 2024
PubMed
Summary

Interpretable causal machine learning accurately predicted denitrification performance. Key factors like HRT and C/N ratio were identified to optimize nitrogen removal in wastewater treatment.

Keywords:
C/NICML frameworkNitrogen removalSludge disintegrationSludge supernatant

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Area of Science:

  • Environmental Science
  • Water Treatment Engineering
  • Machine Learning Applications

Background:

  • Denitrification is crucial for removing nitrogen from wastewater.
  • Optimizing denitrification requires understanding complex relationships between operational parameters and performance.
  • Existing models may lack interpretability in identifying key influencing factors.

Purpose of the Study:

  • To predict denitrification performance using interpretable causal machine learning (ICML).
  • To identify and clarify the relationships between key factors and denitrification efficiency.
  • To determine optimal operating ranges for enhanced nitrogen removal.

Main Methods:

  • Application of interpretable causal machine learning (ICML) framework.
  • Evaluation of multiple machine learning models, including XG-Boost.
  • Utilizing tapping point and partial dependence analyses for factor regulation.

Main Results:

  • The XG-Boost model achieved the highest prediction accuracy (R² = 0.8743).
  • Hydraulic retention time (HRT), COD/TN ratio (C/N), COD concentration, and pretreatment technology were identified as critical factors.
  • Optimal ranges were determined: HRT (6-10.5 h), C/N (6-12), and COD (300-600 mg L⁻¹), yielding 73-77% TN removal.

Conclusions:

  • ICML provides an effective framework for predicting and understanding denitrification processes.
  • Optimized operating conditions significantly improve total nitrogen removal efficiency.
  • Findings support using excess sludge as an internal carbon source for denitrification enhancement.