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Microbial Wastewater Treatment01:30

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Machine learning-integrated multi-objective application and optimization framework for sulfur-based reactive filler

Jia-Min Xu1, Jia-Qiang Lv2, Wen-Ke He3

  • 1State Key Laboratory of Urban-rural Water Resources and Environment, School of Eco-Environment, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China; UNSW Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.

Water Research
|June 27, 2026
PubMed
Summary
This summary is machine-generated.

Sulfur-siderite composite reactive fillers (SSCReFs) offer advanced wastewater treatment. A machine learning framework optimizes SSCReF design for efficient nutrient removal and cost reduction.

Keywords:
Consumption-guided compositingMachine learningMulti-objective optimizationNitrogen and phosphorus removalSulfur-siderite composite reactive filler

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Environmental Engineering
  • Geochemistry
  • Biotechnology

Background:

  • Sulfur-siderite composite reactive fillers (SSCReFs) show promise for nutrient removal from wastewater.
  • Nonlinear interactions between sulfur, siderite, and microbial processes complicate SSCReF performance and optimization.
  • Empirical tuning of SSCReFs is unreliable due to complex, coupled reactions.

Purpose of the Study:

  • To develop an interpretable, machine learning (ML)-integrated framework (SSCReF-MAOF) for optimizing SSCReFs.
  • To capture complex sulfur-iron-microbe-mineral interactions in wastewater treatment.
  • To enable multi-objective optimization of effluent quality, material consumption, and treatment cost.

Main Methods:

  • Developed an ML-integrated framework (SSCReF-MAOF) incorporating five optimized ML models.
  • Integrated ML predictions with stoichiometric constraints for cost-minimized formulation.
  • Utilized experimental data to train and validate ML models, achieving high predictive accuracy (R² = 0.930-0.981).

Main Results:

  • Higher sulfur-to-siderite ratios and alkalinity enhanced denitrification.
  • Siderite-rich formulations improved dephosphorization and consumption-guided compositing (CGC).
  • Identified key features linking sulfur oxidation, iron dissociation, and nutrient removal dynamics.

Conclusions:

  • SSCReF-MAOF provides accurate predictions and cost-effective SSCReF formulations for nutrient removal compliance.
  • Dissociated iron plays a crucial role in coordinating nitrogen and phosphorus removal.
  • The framework offers a practical decision-support tool for wastewater treatment plants, enhancing SSCReF design and nutrient polishing.