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Multi-objective decision model for wastewater treatment technology selection based on machine learning.

Yanbo Liu1, Zhaohan Zhang1, Xinyi Chen1

  • 1State Key Laboratory of Urban-Rural Water Resources and Environment, School of Environment, Harbin Institute of Technology, No73, Huanghe Road, Nangang District, Harbin 150090, China.

Bioresource Technology
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

This study optimized wastewater treatment technology selection in the Yellow River Basin using life cycle assessment (LCA), machine learning (ML), and analytic hierarchy process (AHP). The integrated AHP-ML model identified AAO+SBR and AAO+MBR as optimal for ecological sustainability.

Keywords:
Extreme Gradient Boosting (XGBoost)Life cycle assessment (LCA)Monte Carlo simulation (MCS)Random Forest (RF)Support vector machine (SVM)

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Sustainable Engineering

Background:

  • The upper Yellow River Basin faces ecological fragility and limited carrying capacity, necessitating optimized wastewater treatment.
  • Traditional wastewater treatment selection methods may not adequately address complex environmental and economic constraints.

Purpose of the Study:

  • To develop and apply an integrated framework combining life cycle assessment (LCA), machine learning (ML), and analytic hierarchy process (AHP) for wastewater treatment technology selection.
  • To identify optimal wastewater treatment technologies for the upper Yellow River Basin that balance treatment efficiency with ecological sustainability.

Main Methods:

  • Life Cycle Assessment (LCA) was used to evaluate the environmental footprint of different wastewater treatment configurations.
  • Machine learning algorithms (XGBoost, Random Forest, SVM) were employed for predictive modeling, with XGBoost showing superior performance.
  • Analytic Hierarchy Process (AHP) was integrated with ML to rank and select the most suitable technologies.
  • Monte Carlo simulations were utilized to enhance dataset reliability.

Main Results:

  • The anaerobic-anoxic-oxic combined with sequencing batch reactor (AAO+SBR) configuration exhibited the lowest environmental footprint in LCA.
  • XGBoost machine learning model demonstrated improved accuracy over RF and SVM, reducing Mean Squared Error (MSE) by 1.4-3.1%.
  • The integrated AHP-ML model identified AAO+SBR and AAO with membrane bioreactor (AAO+MBR) as the optimal wastewater treatment technologies.

Conclusions:

  • The data-driven intelligent model provides precise guidance for low-carbon wastewater governance in ecologically fragile regions.
  • The study successfully reconciled treatment efficiency with ecological sustainability in technology selection for the Yellow River Basin.
  • This integrated approach offers a valuable tool for sustainable wastewater management in similar regions globally.